Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Cognitive functioning in bipolar disorder : the influence of sex Popuri, Swetha 2012

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2012_fall_popuri_swetha.pdf [ 1.58MB ]
Metadata
JSON: 24-1.0072759.json
JSON-LD: 24-1.0072759-ld.json
RDF/XML (Pretty): 24-1.0072759-rdf.xml
RDF/JSON: 24-1.0072759-rdf.json
Turtle: 24-1.0072759-turtle.txt
N-Triples: 24-1.0072759-rdf-ntriples.txt
Original Record: 24-1.0072759-source.json
Full Text
24-1.0072759-fulltext.txt
Citation
24-1.0072759.ris

Full Text

COGNITIVE
FUNCTIONING
IN
BIPOLAR
DISORDER:
 THE
INFLUENCE
OF
SEX
 
 
 by
 
 
 Swetha
Popuri
 
 
 B.Sc.
(Honours)
Queen’s
University,
2009
 
 
 
 A
THESIS
SUBMITTED
IN
PARTIAL
FULFILLMENT
OF
 THE
REQUIREMENTS
FOR
THE
DEGREE
OF
 
 
 MASTER
OF
SCIENCE
 
 in
 
 The
Faculty
of
Graduate
Studies
 
 (Neuroscience)
 
 
 THE
UNIVERSITY
OF
BRITISH
COLUMBIA
 (Vancouver)
 
 
 April,
2012
 
 
 
 ©
Swetha
Popuri,
2012
 
 
  Abstract
 
  Following
a
wave
of
similar
research
conducted
in
samples
with
schizophrenia,
there
has
 been
a
recent
surge
of
studies
investigating
sex
differences
in
the
phenomenology
of
 bipolar
disorder
(BD).
These
studies
have
almost
exclusively
focused
on
sex
differences
 in
course
and
clinical
presentation.
As
compared
with
male
BD
patients,
women
with
BD
 have
increased
likelihood
of
experiencing
rapid
cycling,
mixed
mania,
suicidal
ideation,
 and
a
medical
or
psychiatric
comorbidity.
However,
in
addition
to
its
characteristic
 affective
disturbance,
the
phenomenology
of
BD
is
associated
with
significant
and
 persistent
cognitive
impairment.
There
is
evidence
to
support
that
sexual
dimorphisms,
 the
basis
of
sex
differences
in
cognitive
functioning,
are
altered
in
BD.
Additionally,
it
 has
been
found
that
healthy
patterns
of
cognitive
sex
differences
are
disrupted
in
 schizophrenia,
a
closely
related
illness
to
BD.
Despite
this
evidence,
there
have
been
few
 studies
that
have
investigated
the
influence
of
sex
on
cognitive
functioning
in
BD;
the
 results
that
are
available
are
both
scant
and
contradictory.

 
  
  In
order
to
clarify
whether
sex
influences
cognitive
functioning
in
BD,
66
patients
with
 BD‐I
disorder
and
105
matched
healthy
controls
were
tested
on
a
broad
battery
of
 neuropychological
tests.
As
patients
used
in
this
sample
were
tested
immediately
 proceeding
symptomatic
remission
from
their
first‐manic
episode,
this
experimental
 design
is
poised
to
assess
sex
differences
in
cognitive
functioning
early
in
the
course
of
 BD.
Overall,
unlike
in
schizophrenia,
healthy
patterns
of
cognitive
sex
differences
are
 intact
early
in
the
course
of
BD.

 
 To
supplement
and
contextualize
the
study
presented
above,
a
large
portion
of
this
 thesis
is
dedicated
to
providing
literature
reviews
of
the
following
topics:
sex
differences
 in
the
clinical
phenomenology
of
BD,
cognitive
impairment
in
BD,
sex
differences
in
 cognitive
impairment
and
their
neurobiological
underpinnings
in
healthy
samples.

  
  ii
  Preface

 
 All
research
used
in
this
thesis
was
approved
by
the
Clinical
Research
Ethics
Board
 (certificate
#:
H0470169)
and
the
Vancouver
Coastal
Health
Research
Institute
Ethics
 Board
(certificate
#:
V04‐0109).
 
 Dr.
Bill
Honer
and
his
research
group
provided
some
of
the
control
data
that
was
pooled
 and
used
for
analysis
in
Chapter
4.
 
  
 
  
  iii
  
  Table
of
Contents
 Abstract ........................................................................................................................... ii
 Preface............................................................................................................................ iii
 Table
of
contents ............................................................................................................ iv
 List
of
tables..................................................................................................................... v
 Acknowledgments........................................................................................................... vi
 Dedication...................................................................................................................... vii
 1.
Introduction:
literature
overview
and
thesis
objectives ................................................1
 1.1
Clinical
description
of
BD........................................................................................................... 1
 1.2
Sex
differences
in
the
clinical
phenomenology
of
BD ............................................................... 7
 1.2.1
Epidemiological
considerations................................................................................. 7
 1.2.2
Sex
differences
in
the
clinical
presentation
of
BD ..................................................... 9
 1.2.3
Sex
differences
in
the
course
of
BD......................................................................... 16
 1.3
Thesis
Objectives ..................................................................................................................... 22
  2.
Cognitive
impairment
in
BD ........................................................................................ 25
 2.1
Cognitive
research
in
BD:
Relevance
to
researcher,
clinicians,
and
patients .......................... 25
 2.1.1
Cognitive
domains
impacted
in
BD.......................................................................... 29
 2.2
Cognitive
functioning
across
illness
phases
in
BD ................................................................... 33
 2.2.1
Cognitive
dysfunction
associated
with
symptomatic
states.................................... 33
 2.2.2
Cognitive
dysfunction
during
euthymia .................................................................. 34
 2.2.3
Cognitive
dysfunction
associated
with
comorbid
conditions .................................. 35
 2.3
Neurobiological
basis
of
cognitive
impairment
in
BD
............................................................. 37
 2.4
Cognitive
impact
of
medications
used
in
BD
and
future
directions ........................................ 39
 2.4.1
Pharmacological
therapy
for
cognitive
dysfunction ................................................ 45
  3.
Sex
differences
in
cognitive
domains
impaired
in
BD .................................................. 47
 3.1
Sexual
dimorphisms
of
the
human
brain
in
healthy
populations............................................ 47
 3.2
Sex
differences
in
cognitive
domains
impaired
in
BD.............................................................. 51
 3.2.1
Attention/Processing
speed .................................................................................... 53
 3.2.2
Verbal
learning
and
memory................................................................................... 56
 3.2.3
Executive
function ................................................................................................... 58
  4.
The
influence
of
sex
on
cognitive
functioning
in
first‐episode
BD‐I
patients ................ 62
 4.1
Introduction............................................................................................................................. 64
 4.2
Materials
and
methods ........................................................................................................... 66
 4.3
Results ..................................................................................................................................... 72
 4.4
Discussion................................................................................................................................ 78
  References ..................................................................................................................... 86
 Appendices .................................................................................................................. 100
 Appendix
A:
Demographic
and
clinical
variable
histograms ....................................................... 100
 Appendix
B:
Cognitive
variable
histograms ................................................................................. 102
 
  
  iv
  List
of
Tables
 
 Table
1

 Table
2

 Table
3

 
  
  Demographic
characteristics
of
study
participants
………………………………
73
 Clinical
characteristics
of
study
participants
………………………………………..
75
 Descriptive
statistics
for
neurocognitive
variables
……………………………….
77
  v
  Acknowledgments
 
 First
and
foremost,
I
would
like
to
offer
my
sincerest
gratitude
to
Dr.
L.
N.
Yatham
and
 Dr.
I.
J.
Torres
for
their
guidance
and
their
patience.
Under
their
combined
supervision,
I
 was
afforded
continual
access
to
wide‐ranging
expertise.
Without
their
thoughtful
and
 coherent
answers
to
my
questions,
I
could
not
have
completed
this
thesis.


 
 I
offer
my
thanks
to
the
staff
and
fellow
graduate
students
of
the
UBC
Department
of
 Psychiatry.
My
ever‐colorful
conversations
with
the
very
dedicated
Jan‐Marie
Kozicky
 and
Dr.
D.
Bond
helped
shape
the
scientific
framework
with
which
I
researched
and
 wrote
the
chapters
to
follow.

 
 Special
thanks
are
owed
to
my
parents,
whose
loving
support
helped
me
field
the
many
 academic
and
personal
hurdles
of
these
past
two
years.

  
  vi
  
 
 
 
 
 
 
 
 
 
  To my parents, Padma and Murli Krishna  
  vii
  1.
Introduction:
literature
overview
and
thesis
objectives
 
 
  The
goal
of
this
thesis
is
to
better
understand
how
sex
influences
cognitive
  impairment
in
bipolar
disorder;
this
chapter
serves
as
an
introduction
to
the
thesis
 materials
to
follow.
A
clinical
description
of
bipolar
disorder
will
first
be
provided
 followed
by
a
literature
review
regarding
sex
differences
found
in
the
clinical
 phenomenology
of
this
illness.
These
data
will
contextualize
and
justify
the
thesis
 objectives
that
will
be
delineated
in
the
final
section
of
this
chapter.
 1.1
Clinical
description
of
bipolar
disorder
 
  Acute
affective
episodes:
manic,
depressive
and
mixed
states.
Bipolar
disorder
  (BD)
is
a
chronic,
recurrent
affective
disorder
characterized
by
cyclic
episodes
of
 mania/hypomania
and
depression,
with
intervening
periods
of
clinical
remission
or
 euthymia.
In
addition
to
this
affective
disturbance,
the
phenomenology
of
BD
commonly
 involves
cognitive
deficits,
disturbances
to
the
sleep/wake
cycle,
and
high
rates
of
 medical
and
psychiatric
comorbidity
(Balanza‐Martinez
et
al.,
2010).
Despite
the
 prevalence
of
these
associated
impairments,
the
diagnostic
criteria
for
BD
in
the
 American
and
international
diagnostic
systems
primarily
center
on
recognizing
and
 delineating
symptoms
associated
with
acute
mood
episodes
and
their
course
of
 presentation
(Goodwin
and
Lieberman,
2010).
Mania
is
seen
as
the
defining
feature
of
 BD.
According
to
the
Diagnostic
and
Statistical
Manual
of
Mental
Disorders
(DSM‐IV‐TR),
 a
manic
episode
involves
an
abnormally
and
persistently
elevated,
expansive,
or
irritable
 mood
lasting
for
at
least
one
week
or
until
hospitalization
is
required.
In
addition
to
 these
core
features,
several
other
symptoms
are
commonly
present
during
a
manic
  
  episode;
these
include:
inflated
self‐esteem
or
grandiosity,
decreased
need
for
sleep,
 pressure
of
speech,
distractibility,
flight
of
ideas,
and
psychomotor
agitation.
However,
 this
list
of
symptoms
is
not
exhaustive
and
the
expression
of
mania
is
highly
 heterogeneous
across
patients
(Goodwin
and
Lieberman,
2010).
Though
each
individual
 patient
may
experience
some
degree
of
consistency
across
their
manic
episodes,
this
 consistency
is
not
a
given,
and
the
symptom
profile
may
change
considerably
over
the
 course
of
this
illness;
treatment
factors
such
as
medication
may
increase
the
chance
of
 this
variance
(Cassidy
et
al.,
2002).
Patients
may
also
present
with
psychotic
symptoms
 during
mania
and
several
studies
have
associated
the
presence
of
these
symptoms
with
 a
less
favorable
long‐term
course
(MacQueen
et
al.,
1997;
Tohen
et
al.,
2003).
 Withstanding
the
considerable
variability
that
can
be
expressed
during
mania,
these
 episodes
frequently
impose
marked
social
or
occupational
impairment
for
individuals
 (Goodwin
and
Lieberman,
2010).

 
  Significant
functional
impairment
is
also
associated
with
depressive
states.
In
  fact,
the
functional
impact
of
syndromal
and
subsyndromal
depression
is
higher
than
 mania
both
to
the
individual
and,
from
an
economic
standpoint,
to
society
at
large
 (Gitlin
et
al.,
1995;
Bryant‐Comstock
et
al.,
2002).
To
be
characterized
as
a
depressive
 episode,
a
patient
must
exhibit
a
depressed
mood
or
a
loss
of
interest
or
pleasure
in
 most
activities
for
at
least
two
weeks.
Again,
depressive
episodes
are
marked
by
 significant
heterogeneity,
and
a
constellation
of
other
symptoms
may
accompany
these
 core
features:
changes
in
appetite,
weight,
or
sleep;
decreased
energy;
difficulty
 thinking
and
making
decisions;
feelings
of
worthlessness
or
guilt
(Goodwin
and
  
  2
  Lieberman,
2010).
Some
symptoms,
such
as
irritability
and
psychomotor
speed,
are
 expressed
commonly
during
both
mania
and
depression,
and
these
overlaps
may
work
 to
blur
the
distinction
between
the
two
polarities
(Deckersbach
et
al.,
2004;
Goodwin
 and
Lieberman,
2010).


 
  Indeed,
many
symptoms
associated
with
depression
and
mania
can
be
  simultaneously
expressed.
During
a
mixed
episode,
a
patient
expresses
a
range
of
 symptoms
so
as
to
qualify
for
the
DSM‐IV‐TR
criteria
of
both
a
manic
and
depressive
 episode
(Goodwin
and
Lieberman,
2010).
Mixed
episodes
occur
frequently
in
BD,
with
 one
estimate
citing
that
40%
of
BD‐I
hospital
admissions
were
for
mixed
states
(Kruger
 et
al.,
2005).
Mixed
symptoms
may
emerge
simultaneously,
or
manic
symptoms
may
 build
on
preexisting
depressive
symptoms.
It
is
common
for
a
mixed
episode
to
evolve
 into
a
major
depressive
episode
(Goodwin
and
Lieberman,
2010).
More
so
than
in
manic
 episodes,
mixed
episodes
accompany
comorbid
obsessive
compulsive
disorder,
feelings
 of
helplessness,
and
suicidal
ideation
(Dilsaver
et
al.,
1994;
McElroy
et
al.,
1995).
 Patients
who
frequently
experience
mixed
episodes
are
often
excluded
from
research
 studies,
and
for
this
reason
relatively
little
is
known
about
this
subset
of
individuals
 (Goodwin
and
Lieberman,
2010).
Given
their
prevalence
and
contribution
to
the
 mortality
rate
associated
with
BD,
BD
samples
with
mixed
states
warrant
further
 research.
 

  Bipolar
subtypes.
BD
is
often
construed
as
a
spectrum
with
three
major
  subgroups:
bipolar
I
(BD‐I),
bipolar
II
(BD‐II),
and
cyclothymia
(Goodwin
and
Lieberman,
 2010).
BD‐I
is
characterized
by
the
presence
of
recurrent
manic
or
mixed
episodes;
  
  3
  psychotic
symptoms
are
experienced
by
75%
of
BD‐I
patients
(Tohen
et
al.,
1990).
BD‐II
 is
characterized
by
hypomanic
rather
than
manic
or
mixed
episodes.
Although
 hypomania
has
a
similar
symptom
profile
to
mania,
these
symptoms
are
experienced
at
 a
decreased
severity
(Goodwin
and
Lieberman,
2010).
However,
due
to
the
increased
 chronicity
and
time
spent
in
depression
that
is
associated
with
BD‐II,
both
subtypes
have
 an
equally
severe
impact
on
functioning
(Suppes
and
Dennehy,
2002;
Vieta
and
Suppes,
 2008).
In
addition,
the
lifetime
prevalence
of
BD‐I
and
BD‐II
are
a
similar
1%
and
1.1%,
 respectively
(Merikangas
et
al.,
2007).
Cyclothymia
is
considered
more
benign
than
 either
BD‐I
or
BD‐II
and
is
associated
with
hypomanic
features
and
symptoms
that
fall
 short
of
qualifying
for
a
major
depressive
disorder.
Milder
affective,
motoric,
and
 cognitive
symptoms
are
seen
in
this
sample
(Goodwin
and
Lieberman,
2010).
The
 taxonomy
of
a
psychiatric
illness
profoundly
affects
both
researchers
and
clinicians
as
it
 determines
research
samples
and
treatment
strategies.
The
diagnostic
definition
of
BD
 and
the
subtype
classification
system
described
above
is
continually
evolving
as
more
is
 understood
about
the
genetic
and
neurobiological
abnormalities
that
underlie
this
 illness.

 
  Cognitive
impairment.
Beyond
the
affective
disturbance
that
classifies
patients
as
  having
BD,
this
illness
accompanies
significant
cognitive
deficits
in
the
domains
of
verbal
 learning
and
memory,
attention/processing,
and
executive
dysfunction
(Robinson
et
al.,
 2006;
Torres
et
al.,
2007).
Presumably,
these
cognitive
aberrations
are
the
product
of
 gross
anatomical
and
neurochemical
irregularities;
indeed,
evidence
of
such
pathology
is
 continually
amassing
(Post
and
Kaur‐Sant’Anna,
2010).
Many
of
these
cognitive
  
  4
  impairments
are
present
in
BD
patients
after
their
very
first
manic
episode,
although
the
 severity
of
these
impairments
is
less
than
that
observed
later
in
the
course
(Nehra
et
al.
 2006).
As
such,
cognitive
impairment
in
BD
is
most
likely
generated
by
both
 neurodevelopmental
and
neurodegenerative
factors
(Goodwin
et
al.,
2008).
Clinical
 management
of
cognitive
symptoms
is
made
difficult
as
the
pharmacological
agents
 used
to
treat
affective
symptoms
sometimes
adversely
affect
cognition
(Balanza‐ Martinez
et
al.,
2010).
Currently
no
established
cognitive
remediation
programs
address
 these
impairments
from
a
nonpharmacological
angle.
In
part,
this
delay
in
generating
 cognitive
behavioural
intervention
strategies
is
caused
by
the
complicated
nature
of
 discerning
iatrogenic
from
illness‐related
cognitive
impairment
in
BD
(Goldberg
and
 Chengappa,
2009);
polypharmacy
treatment
regimes
and
comorbid
conditions,
both
of
 which
are
highly
common
in
BD,
may
also
contribute
to
overall
cognitive
impairment
 (Balanza‐Martinez
et
al.,
2010).
 
  Cognitive
deficits
represent
an
important
target
for
future
therapeutic
  intervention
as
these
impairments
carry
a
significant
functional
burden.
Psychosocial
 dysfunction
in
BD
was
once
thought
to
be
completely
resolved
by
periods
of
euthymia;
 it
is
now
known
that
there
is
a
gap
between
the
syndromal
recovery
associated
with
this
 phase
and
functional
recovery
(Bonnin
et
al.,
2010).
A
two‐year
longitudinal
study
 following
patients
after
their
first
manic
episode
reported
that
73%
of
their
sample
 failed
to
achieve
functional
recovery
even
after
their
affective
symptoms
had
remitted
 (Tohen
et
al.,
2000).
Many
factors
are
associated
with
poor
functional
outcome
such
as
 history
of
psychosis,
greater
chronicity
of
illness,
and
comorbid
substance
abuse;
  
  5
  cognitive
impairment
is
an
especially
impactful
member
of
this
list
(Bonnin
et
al.,
2010).
 In
fact,
cognitive
impairment
is
thought
to
be
a
better
indicator
of
functional
outcome
 than
many
clinical
indices
(Wingo
et
al.
2009).
In
part,
the
strength
of
this
association
 with
functional
outcome
can
be
explained
by
the
pervasive
nature
of
cognitive
 impairment
throughout
the
course
of
BD.
While
clinical
symptoms
completely
or
 partially
resolve
during
euthymia,
cognitive
dysfunction
in
many
domains
persists
during
 this
phase
(Torres
and
Malhi,
2010).

 
  As
full
affective
and
functional
remission
is
not
yet
possible
with
the
treatment
  strategies
available,
neurobiologists
and
psychiatrists
alike
are
searching
for
ways
in
 which
to
minimize
heterogeneity
and
gain
a
clearer
picture
of
the
etiology
of
this
 complex
disorder.
For
example,
there
has
been
a
recent
push
to
consider
BD‐I
patients
 with
a
history
of
psychotic
symptoms
as
a
separate
subtype
(Goodwin
and
Lieberman,
 2010).
Affective
and
functional
disturbance
is
more
severe
in
these
individuals
and
it
is
 thought
that
“psychotic
BD”
is
more
closely
related
to
schizophrenia
(SZ)
than
other
 subtypes
(Tohen
et
al.,
2003).
Considering
bipolar
patients
with
a
history
of
psychosis
as
 a
separate
subtype
may
be
an
etiologically
revealing
avenue
of
thought
in
that
it
a)
may
 construct
a
subgroup
with
reduced
heterogeneity
and
b)
comparing
differences
across
 subgroups
may
lead
to
insight
as
to
their
mechanisms
of
pathogenesis.
Additionally,
 identifying
a
subgroup
with
less
heterogeneity
can
lead
to
the
development
of
more
 targeted
therapeutic
interventions
that
may
be
needed
to
maximize
recovery
in
these
 individuals.
An
analogous
approach
is
currently
being
considered
with
sex
and
its
 influence
on
BD.
Following
a
similar
wave
of
research
conducted
in
SZ
samples,
many
  
  6
  studies
have
revealed
that
that
there
are
distinct
differences
in
the
clinical
 phenomenology
of
men
and
women
with
BD
(Abel
et
al.,
2010;
Diflorio
and
Jones,
 2010).
As
this
is
a
nascent
area
of
research,
several
questions
remain
to
be
answered:
 What
is
the
neurobiological
contribution
to
these
sex
differences?
Are
the
 pathophysiological
mechanisms
that
produce
the
symptoms
of
BD
the
same
in
both
 men
and
women?
Are
these
sex
differences
a
reflection
of
the
disease
itself,
or
do
 treatment
factors
play
a
role?
Of
primary
concern
to
this
thesis
are
the
following
two
 questions:

 1. Are
sex
differences
in
the
cognitive
impairment
observed
in
BD?
 2. If
so,
are
these
sex
differences
in
cognitive
impairment
present
at
the
onset


 



 of
the
illness
or
do
they
emerge
over
the
course
of
several
episodes?

 In
beginning
to
answer
these
two
questions,
it
would
be
prudent
to
first
review
the
 literature
regarding
phenomenological
sex
differences
observed
in
BD.
This
review
is
 presented
in
the
next
section.

 1.2
Sex
differences
in
the
clinical
phenomenology
bipolar
disorder
 
  There
are
numerous
sex
differences
found
in
the
course
and
clinical
presentation
  of
BD.
Both
epidemiological
and
phenomenological
considerations
will
be
reviewed.
 
  1.2.1
Epidemiological
considerations
  
  The
lifetime
prevalence
rate
for
bipolar
disorder
I
(BD‐I)
has
traditionally
been
  quoted
as
1‐1.3%
(Diflorio
and
Jones,
2010).
More
recent
large‐scale
epidemiological
 surveys
have
resulted
in
a
wider
range
of
reported
statistics.
The
National
 Epidemiological
Survey
on
Alcohol
and
Related
Conditions
(N
=
43,093)
conducted
out
of
  
  7
  the
United
States
received
a
lifetime
prevalence
rate
for
DSM‐IV
BD‐I
of
3.3%
(Morgan
 et
al.,
2010).
As
a
follow‐up,
the
National
Comorbidity
Survey
Replication,
again
 conducted
out
of
the
United
States
(N
=
9,282),
found
the
lifetime
prevalence
rate
of
1%
 for
BD‐I
using
DSM‐IV
criteria
(Nierenberg
et
al.,
2010).
Furthermore,
the
Psychosis
in
 Finland
Study
(PIF)
found
a
DSM‐IV
BD‐I
lifetime
prevalence
rate
of
only
0.24%
using
a
 representative
sample
of
8,028
persons
(Jonna
et
al.,
2008).

 
  Multiple
factors
seem
to
underlie
the
variance
found
in
these
figures,
including
  geographic
location,
choice
of
diagnostic
instrument,
and
differing
assessment
 guidelines.
Age
inclusion
criteria
seem
to
vary
drastically
among
national
surveys,
with
 some
including
persons
15
years
or
older
and
others
using
participants
30
years
or
 older.
This
may
play
an
especially
important
role
in
determining
the
lifetime
prevalence
 rate
of
bipolar
disorder
as
this
condition
is
known
to
have
an
age
of
onset
during
the
 adolescent
years
(Kawa
et
al.,
2005).
Another
major
concern
is
that
many
national
 surveys
fail
to
discriminate
between
the
various
subtypes
of
BD
and
statistically
group
 BD‐I,
bipolar
disorder
II
(BD‐II),
and
occasionally
subsyndromal
versions
of
bipolar
 disorder
all
into
one
category.
A
recent
Canadian
survey
(N
=
36,984)
reported
a
lifetime
 prevalence
rate
of
2.2%
using
this
unstratified
definition
of
bipolar
disorder
(Schaffer
et
 al.,
2006).
Two
other
national
surveys
from
Germany
and
the
U.S.
have
utilized
a
 grouped
BD‐I
and
BD‐II
definition
of
BD
and
have
received
a
lifetime
prevalence
rate
of
 1%
and
3.4%,
respectively
(Kawa
et
al.,
2005).
It
should
be
noted,
however,
that
this
 definitional
ambiguity
is
intrinsic
to
the
study
of
BD
itself
as
there
continues
to
be
a
lack
 of
consensus
regarding
the
boundaries
between
the
various
subtypes
of
BD.

  
  8
  
  Though
the
figures
reported
by
these
studies
have
considerably
varied,
they
  have
been
consistent
in
reporting
no
significant
sex
differences
in
the
lifetime
 prevalence
of
BD‐I
(Diflorio
and
Jones,
2010).
However,
several
papers
have
found
that
 BD‐II
is
more
common
in
women.
BDII
has
a
reported
lifetime
prevalence
rate
of
1.1%
 (Merikangas
et
al.,
2007)
and
as
its
diagnosis
does
not
require
symptoms
of
full
mania,
 BD‐II
seems
to
be
more
consistent
with
“depressive
diathesis”
exhibited
by
women
who
 show
signs
of
BD
(Diflorio
and
Jones,
2010).
Despite
the
multitude
of
papers
that
claim
 that
BD‐II
is
more
common
amongst
women,
this
finding
is
weakened
by
the
fact
that
 BD‐II
is
difficult
to
diagnose
and
easy
to
misdiagnose.
In
addition
to
being
consistently
 underdiagnosed,
attention
deficit
disorder
and
major
depressive
disorder
are
both
 commonly
misdiagnosed
as
BD‐II
(Baldassano
et
al.
2005).
Potential
sex
differences
in
 these
misdiagnosed
groups
would
be
an
interesting
line
of
inquiry
that
has
yet
to
be
 addressed
by
the
literature.
Moreover,
Benazzi
et
al.,
a
research
group
that
produced
 two
seminal
papers
that
first
proposed
the
increased
preponderance
of
BD‐II
amongst
 women,
have
recently
published
a
paper
that
states
that
when
age
is
accounted
for
in
 the
analysis,
women
are
no
more
likely
to
be
diagnosed
with
BD‐II
than
men
(Benazzi
 1999,
2001,
2004).
As
such,
the
heightened
prevalence
of
BD‐II
amongst
women
is
still
a
 disputed
issue
in
the
literature.

 
  1.2.2
Sex
differences
in
the
clinical
presentation
of
bipolar
disorder
  
  The
clinical
presentation
of
bipolar
disorder
in
women
seems
to
be
distinct
from
  that
of
men.
Previous
reviews
regarding
sex
differences
in
BD
have
described
the
unique
 clinical
presentation
displayed
by
women
with
BD
as
having
a
‘depressive
diathesis’;
it
  
  9
  was
traditionally
held
that
women
tend
towards
more
depressive
episodes
than
men
 who
tend
towards
more
manic
episodes
(Diflorio
and
Jones,
2010).
However,
the
 current
survey
of
the
literature
presented
much
more
balanced
findings.
This
review
will
 also
present
findings
regarding
mixed
mania,
suicidality,
and
co‐morbidity.

 
  Depression.
Depression
is
characterized
by
a
minimum
of
two
weeks
of
low
  mood,
diminished
enthusiasm,
or
anhedonia
with
accompanying
neurovegetative
 symptoms
and
cognitive
changes.
A
robust
finding
within
the
literature
is
that
women
 are
more
likely
to
present
with
and
initial
episode
of
depression
and
are
more
likely
to
 have
a
depressive/mixed
episode
precede
a
manic/hypomanic
episode
(Kawa
et
al.
 2005;
Kessing,
2004).

Women
are
also
considered
unipolar
depressed
longer
than
men.
 Older
studies
utilizing
inpatient
samples
have
reported
that
women
tend
to
experience
 more
depressive
episodes
than
manic
episodes.

Studies
using
hospital
admission
rates
 as
an
index
have
corroborated
this
finding
(Angst,
1978;
Roy‐Byrne
et
al.,
1985).


 
  However,
there
is
still
much
debate
regarding
whether
women
experience
more
  depressive
episodes
than
men.
Though
older
studies
have
reported
that,
in
comparison
 with
men,
women
experience
depressive
episodes
that
are
lengthier
and
occur
more
 frequently,
newer
studies
have
largely
overturned
these
findings.
In
a
study
that
 investigated
sex
differences
in
BD
using
a
48‐week
prospective
design
that
is
free
from
a
 recall
bias,
no
sex
differences
in
total
number
of
any
mood
episode
using
both
BD‐I
and
 BD‐II
samples
was
found
(Benadetti,
2007).
Recent
studies
have
also
found
no
sex
 differences
in
percentage
of
time
spent
in
any
mood
episode
(Baldassano
et
al.,
2005;
 Morgan
et
al.
2005).
Reasons
for
the
discrepancy
between
older
and
newer
studies
may
  
  10
  be
due
to
several
methodological
differences
and
variations
in
inclusion/exclusion
 criteria.

 
  However,
there
is
evidence
to
suggest
that
the
depressive
episodes
experienced
  by
women
exhibit
sex
specific
characteristics.
As
compared
to
men,
women
tend
to
 experience
depressive
episodes
marked
by
more
atypical
symptoms,
which
include:
 weight
gain,
hypersomnia,
and
leaden
paralysis
(Kawa
et
al.,
2005).
A
limited
number
of
 studies
have
also
reported
that
women
experience
depression
in
a
more
seasonal
 pattern
than
men.
In
terms
of
hospitalizations
for
depression,
women
with
BD
displayed
 a
bimodal
peak
in
admissions
during
the
spring
and
fall
(Diflorio
and
Jones,
2010).
 Finally,
there
is
some
evidence
that
suggests
that
women
suffer
from
depressive
 episodes
that
are
more
treatment
refractory
than
men
(Kawa
et
al.,
2005).
Whether
this
 is
due
to
the
unique
etiology
of
BD
in
women
or
simply
an
artifact
of
the
delay
in
 properly
diagnosing
women
with
BD
is
an
issue
unexplored
by
the
literature.
 
  Mania.
Mania
is
defined
as
a
consistent
abnormally
elevated
or
irritable
mood
  for
at
least
1
week.
Overall,
an
episode
of
mania
often
accompanies
marked
increased
 energy
level,
poor
judgment,
and
inappropriate
social
behavior.
Older
studies
often
 implicated
men
as
having
more
manic
episodes
of
greater
severity
than
women
(Kessing
 et
al.,
2004).
Again,
newer
literature
has
found
that
in
terms
of
these
clinical
variables,
 men
and
women
are
equal.
Studies
have
found
that
men
and
women
have
the
same
 time
to
remission
from
a
manic
episode
and
time
spent
with
any
subsyndromal
manic
 symptoms
(Diflorio
and
Jones,
2010).

  
  11
  
  Some
studies
have
also
claimed
that
men
and
women
show
difference
in
their
  symptomatological
presentation
of
mania.
Men
are
more
likely
to
present
with
mania
 that
includes
hyperactivity,
risk‐taking
behavior,
and
grandiosity,
while
women
more
 often
present
with
symptoms
of
racing
thoughts
and
distractibility
(Taylor
and
Abrams,
 1981;
Young
et
al.,
2007).
Some
studies
have
found
that
women
experience
less
 psychotic
symptoms
than
men
(Diflorio
and
Jones,
2010).
Additionally,
there
is
evidence
 to
suggest
that
there
are
sex
differences
in
clinical
features
of
psychosis
during
a
manic
 episode.
In
one
recent
study
involving
137
women
and
109
men
admitted
to
the
 hospital
with
mania,
found
that
as
compared
to
men,
women
experience
more
 hallucinations,
delusions
of
reference,
and
paranoid
delusions
(Kruger,
2009).
The
 source
of
these
sex
differences
in
the
clinical
presentation
of
mania
is
currently
 uncharacterized.
 
  Mixed
episodes
/
mixed
mania.
The
current
diagnostic
criteria
for
mixed
episode,
  or
mixed
mania,
is
the
co‐occurrence
of
a
full
depressive
episode
and
a
full
manic
 episode.
However,
this
strict
definition
is
often
replaced
by
‘intermediate’
and
‘broad’
 definitions
that
seem
to
be
more
useful
in
both
research
and
clinical
settings.
When
 using
the
‘intermediate’
definition,
the
diagnosis
of
mixed
mania
can
be
made
when
 acute
mania
is
accompanied
by
several
depressive
symptoms
whereas
the
‘broad’
 definition
requires
only
the
presence
of
symptoms
that
seem
to
oppose
the
manic
state
 (Goodwin
and
Lieberman,
2010).
Despite
the
definition
for
mixed
mania
that
is
used,
an
 overwhelming
amount
of
evidence
suggests
that
mixed
mania
occurs
more
often
in
 women
(Arnold
et
al.,
2000;
Kessing,
2004).
Unfortunately,
the
wide
array
of
  
  12
  employable
definitions
for
mixed
mania
make
it
difficult
to
compare
studies
from
a
 quantitative
perspective.
Although
ratio
of
diagnosed
women
to
men
varies
according
 to
the
definition
employed
during
research,
one
study,
which
pooled
13
others,
found
 an
average
female
to
male
ratio
of
1.9:
1
(Diflorio
and
Jones,
2010).
The
mechanism
 underlying
women’s
increased
succeptibility
to
mixed
mania
is
still
not
fully
established.
 The
majority
of
studies
point
to
a
mechanism
that
involves
the
hypothalamic‐pituitary‐ axis
(Diflorio
and
Jones,
2010).
The
propensity
of
women
towards
mixed
mania
remains
 an
important
clinical
issue
as
this
mixed
state
is
associated
with
increased
suicidality
and
 greater
chronicity
(Goodwin
and
Lieberman,
2010).
 
  Suicidality.
The
suicide
rate
within
BD
samples
is
15
times
higher
than
the
  general
population
with
10‐19%
of
BD
patients
completing
the
act
(Rihmer
and
Fawcett,
 2010).
However,
when
type
I
and
type
II
of
BD
are
statistically
considered
together,
the
 rate
may
be
even
higher
with
one
study
placing
the
lifetime
risk
of
suicide
at
26%
(Kawa
 et
al.,
2005).
In
fact,
the
suicide
risk
is
greater
in
BD
than
in
unipolar
depression
(Rihmer
 and
Fawcett,
2010).
Studies
have
found
that
women
are
more
likely
to
report
a
history
 of
suicidal
gestures
and
attempts
(Morgan
et
al.,
2005).
Yet,
this
finding
has
not
been
 completely
consistent.
One
recent
longitudinal
study
reported
that
men
are
at
greater
 risk
(Marangell
et
al.,
2008).
In
terms
of
completed
suicides,
however,
there
seem
to
be
 no
sex
differences.
This
latter
finding
is
interesting
as
the
women
in
the
general
 population
are
more
likely
than
men
to
complete
suicide
(Kawa
et
al.,
2005).

 Though
the
rate
of
completed
acts
is
equivocal,
there
are
specific
concerns
regarding
 suicidality
that
may
be
especially
important
for
women.
There
has
been
some
literature
  
  13
  to
suggest
that
the
female
sex
is
at
increased
risk
for
mixed
mania,
rapid
cycling,
and
 BDII
(Diflorio
and
Jones,
2010).
Rapid
cycling
and
BDII
are
both
associated
for
increased
 risk
of
suicidality
(Goodwin
and
Lieberman,
2010).
Although
there
is
a
lack
of
consensus
 on
the
issue,
there
is
some
literature
to
suggest
that
mixed
mania
is
also
associated
with
 greater
risk
of
suicide
(Kessing,
2004).
Particularly
pertinent
to
women
is
the
finding
that
 female
patients
with
BD
are
more
likely
to
have
suffered
from
childhood
sexual
abuse
 than
men.
Additionally,
female
psychiatric
patients
are
more
likely
to
be
sexually
and
 physically
abused
than
male
psychiatric
patients.
Early
stressors
such
as
childhood
 sexual
abuse
has
been
strongly
linked
to
increased
risk
of
suicide
(Bonnin
et
al.,
2010).
 
 
  Comorbidity.
BD
is
often
accompanied
by
a
constellation
of
comorbidities
with
  65%
of
patients
suffering
from
other
psychiatric
or
physical
ailments
(Belanza‐Martinez
 et
al.,
2009).
Comorbidity
is
an
exceedingly
important
clinical
consideration
as
it
is
 connected
with
poorer
outcomes.
Several
studies
have
reported
that
medical
and
 psychiatric
comorbidity
is
more
common
amongst
women
(Diflorio
and
Jones,
2010).
 One
study
that
analyzed
comorbidity
rates
in
hospitalizations
for
a
first
episode
of
 mania
found
that
women
were
2.7
times
more
likely
than
men
to
present
with
a
 comorbid
diagnosis
(Tuhen
et
al.,
2003).
The
medical
illnesses
that
have
been
found
to
 be
more
prevalent
amongst
women
with
BD
are:
thyroid
disease,
migraine
headaches,
 and
obesity
(Kawa
et
al.,
2005).
However,
there
seems
to
be
some
dispute
amongst
the
 literature
regarding
this
last
factor
with
some
studies
reporting
null
or
opposite
findings
 (Suominen
et
al.,
2009).
These
variations
may
be
due
to
treatment
effects
as
various
 first‐line
medications
are
known
to
affect
weight
gain
and
may
do
this
differentially
  
  14
  between
the
sexes.
In
general,
women
with
BD
seem
to
incur
greater
impairments
to
 physical
health
and
more
pain
disorders
than
men
(Kawa
et
al.,
2005).

 There
is
a
lengthy
list
of
axis
I
psychiatric
comorbidities
that
have
found
to
be
 common
in
BD
including:
agoraphobia,
post
traumatic
stress
disorder,
panic
disorder,
 social
phobia,
alcohol
abuse,
substance
abuse,
and
bulimia
nervosa.
Several
of
these
 illnesses
have
been
reported
to
be
more
common
in
women
including:
post
traumatic
 stress
disorder,
panic
disorder,
social
phobia,
and
bulimia
(Suominen
et
al.,
2009).
One
 study
placed
women
with
BD
at
a
ten
times
greater
risk
of
developing
eating
disorders
 than
men
(Kawa
et
al.,
2005).
Men
with
BD
have
been
associated
with
greater
risk
of
 developing:
alcohol
abuse,
substance
abuse,
obsessive‐compulsive
disorder,
and
 gambling
problems.
Studies
have
placed
conduct
disorder
as
being
four
times
as
 common
in
male
than
female
patients
with
BD
(Hendrick
et
al.,
2000).
One
study
also
 placed
alcohol
and
substance
abuse/dependence
at
two
times
greater
in
men
than
in
 women.
Several
other
studies
have
corroborated
this
finding
of
a
greater
incidence
of
 alcoholism
and
substance
abuse
amongst
men.
However,
studies
have
also
shown
that
 women
with
BD
have
a
four
times
greater
risk
of
developing
alcoholism
than
men
and
 that
this
relative
risk
is
greater
than
in
men
with
BD.
This
increased
risk
of
alcoholism
 may
be
especially
problematic
for
women
as
first‐pass
metabolism
and
alcohol
 dehydrogenase
activity
is
lower
in
women
than
in
men,
thus
lending
to
a
greater
degree
 of
alcohol
toxicity
(Diflorio
and
Jones,
2010).
 
  
  
  
  15
  
  1.2.3
Sex
differences
in
the
course
of
bipolar
disorder
  
  This
review
will
attempt
to
summarize
known
data
regarding
sex
differences
in
  age
of
onset,
diagnosis,
rapid
cycling,
and
prognosis
and
outcome.
The
discussion
of
sex
 differences
in
course
will
not
be
assessed
from
a
lifetime
perspective
in
this
review.
As
 such,
the
exploration
of
special
considerations
needed
in
treatment
and
management
of
 BD
women
in
pregnancy,
perimenopause,
and
menopause
is
beyond
the
scope
of
this
 review.

 
  Age
of
onset.
The
average
age
of
onset
for
bipolar
disorder
is
21
years.
The
last
  three
decades
have
witnessed
papers
consistently
reporting
a
lower
age
of
onset
for
BDI
 versus
BDII
(Goodwin
and
Lieberman,
2010).
However,
there
is
much
less
consensus
 regarding
potential
sex
differences
in
age
of
onset.
Many
papers
have
reported
that
 women
have
an
earlier
age
of
onset
of
bipolar
disorder
than
men
(Kawa
et
al.,
2005).
 Several
more
studies
have
indicated
that
women
with
BDII
tend
to
have
lower
age
of
 onset
than
men
with
the
same
diagnosis
(Diflorio
and
Jones,
2010).
However,
this
last
 decade
has
produced
studies
that
have
been
inconsistent
in
corroborating
this
claim.
 One
study,
which
included
360
outpatients
diagnosed
with
bipolar
disorder,
found
that
 women
had
an
age
of
onset
of
illness
3.2
years
later
than
men
(Kennedy
et
al.
2005).
 However,
another
recent
study
having
211
outpatients
found
no
such
difference
 (Nagash
et
al.,
2005).
A
general
criticism
has
been
cast
on
studies
that
attempt
to
 evaluate
age
of
onset
through
assessing
a
clinical
sample
asserting
that
such
a
sample
 may
include
an
overrepresentation
of
the
very
ill.
This
issue
has
been
addressed
by
a
 recent
Canadian
community
survey
having
36,984
participants.
This
study
also
failed
to
  
  16
  observe
any
significant
differences
in
age
of
onset
between
men
and
women
(Schaffer
 et
al.,
2006).

 
  Diagnosis.
Several
studies
have
pointed
to
a
greater
delay
in
diagnosis
for
  women
with
bipolar
disorder
versus
men.
One
study
placed
this
delay
at
11
years
for
 women
versus
an
average
of
6
years
for
men
(Kennedy
et
al.,
2005).
Clinical
practice
 biases
towards
diagnosing
women
with
unipolar
depression
may
account
for
some
of
 this
delay.
However,
the
more
likely
source
of
this
diagnostic
lag
may
be
the
unique
 clinical
presentation
of
bipolar
disorder
in
women.
Women
are
more
likely
to
 experience
their
first
bipolar
episode
in
the
depressive
polarity.
Studies
have
also
shown
 that,
when
compared
to
men,
women
experience
a
longer
interim
between
this
first
 episode
and
their
first
manic
episode
(Kawa
et
al.,
2005).
As
a
first
manic
episode
must
 precede
a
diagnosis
of
BD,
this
extended
interim
would
certainly
be
one
factor
 accounting
for
the
delay
in
an
accurate
diagnosis
for
women.
As
several
papers
 demonstrate,
diagnosis
of
women
with
BDII
also
shows
this
same
pattern
of
delayed
 diagnosis
(Kawa
et
al.,
2005).

 These
delays
in
diagnosis
result
in
delays
to
treatment
that
may
adversely
affect
 functional
outcome.
Failure
to
diagnose
BD
has
been
associated
with
a
more
persistent
 and
treatment
resistant
course
of
illness
(Diflorio
and
Jones,
2010).
These
findings
 certainly
highlight
the
importance
of
early
intervention
for
women
and
stress
the
 importance
of
continually
refining
diagnostic
tools.
Two
recent
papers
have
suggested
 using
early
age
of
onset
as
a
characteristic
to
distinguish
bipolar
disorder
from
unipolar
 disorder
(Alba
et
al.,
2006;
Benazzi,
2003).
Unipolar
disorder
has
a
later
average
age
of
  
  17
  onset
than
both
BDI
and
BDII.
In
one
study,
an
analysis
of
3,014
Sardinian
adults
 diagnosed
with
BDI,
BDII,
or
major
depressive
disorder
found
an
average
age
of
onset
of
 24,
29,
32,
respectively.
Further
research
needs
to
be
conducted
in
a
clinical
setting
to
 determine
the
utility
of
age
of
onset
as
a
discriminating
diagnostic
feature
of
BD.
As
 there
are
only
poor
biological
markers
and
no
laboratory
tests
to
diagnose
BD,
the
 search
for
unique
and
readily
identifiable
clinical
features
remains
a
pressing
goal
for
BD
 research.
 
  Rapid
cycling.
Rapid
cycling
is
defined
by
the
DSM‐IV
as
the
occurrence
of
a
  minimum
of
four
mood
episodes
during
a
12‐month
period.
The
major
depressive,
 manic,
hypomanic,
or
mixed
episodes
must
also
be
separated
by
full
or
partial
remission
 that
is
maintained
for
at
least
two
months
or
must
switch
in
affective
polarity.
This
 affective
lability
is
associated
with
much
social
and
functional
impairment.
Indeed,
rapid
 cycling
has
been
linked
to
increased
rates
of
depression,
suicidality,
substance
abuse,
 and
anxiety
(Goldberg
and
Berk,
2010).

Several
studies
have
found
rapid
cycling
to
be
 significantly
more
common
amongst
women
than
men
with
some
studies
quoting
a
2:1
 female
to
male
ratio
(Kupka
et
al.,
2003;
Tondo
and
Baldessarnini,
1998;
Coryell
et
al.,
 1992).
One
study
involved
456
individuals
diagnosed
with
either
BDI
or
BDII;
out
of
their
 sample,
91
participants
in
their
study
met
the
criteria
for
rapid
cycling
and
61
of
these
 participants
were
women.
From
these
data,
they
concluded
that
rapid
cyclers
are
more
 likely
to
be
women
(Coryell
et
al.,
1992).

 However,
there
are
several
recent
studies
that
have
found
no
sex
differences
in
 rapid
cycling.
Retrospective
data
from
481
patients
enrolled
in
the
STEP‐BD
project
  
  18
  found
that
men
and
women
had
equal
rates
of
rapid
cycling
(Baldassano
et
al.,
2005).
 The
same
result
was
concluded
from
the
18‐month
prospective
Jorvi
Bipolar
Study
of
 160
patients
diagnosed
with
bipolar
disorder
(Suominen
et
al.
2009).
Another
study
 conducted
from
the
STEP‐BD
data
stated
that
although
BDII
and
the
female
sex
are
both
 linked
to
rapid
cycling,
they
could
neither
be
used
as
statistical
predictors
nor
are
of
 much
clinical
utility
in
predicting
future
mood
episodes
(Schneck
et
al.,
2008).
Clearly
 the
literature
is
mixed
regarding
rapid
cycling
and
its
particular
frequency
amongst
 women.
This
may
be
partially
attributable
to
the
difficulty
in
recruiting
a
large
sample
of
 rapid
cyclers.
Additionally,
the
preponderance
of
substance
abuse
and
psychiatric
 comorbidities
amongst
samples
of
rapid
cycling
patients
make
it
difficult
to
conduct
 statistical
analysis
that
is
free
of
confounds
(Goldberg
and
Berk,
2010).

 Of
greater
concern
than
the
prevalence
of
rapid
cycling
amongst
women,
is
the
 growing
body
of
evidence
implicating
antidepressants
in
triggering
an
increase
rate
of
 mood
cycling
(Suominen
et
al.,
2009).
These
data
have
a
two‐fold
consequence
for
 women.
First,
some
studies
have
shown
that
women
are
more
susceptible
to
this
 antidepressant‐induced
rapid
cycling.
Second,
studies
have
shown
that
there
is
a
longer
 delay
in
accurate
diagnosis
for
women
as
compared
to
men.
Initially,
women
with
 bipolar
symptoms
are
often
misdiagnosed
with
unipolar
depression
for
which
 antidepressants
are
usually
prescribed
(Kawa
et
al.,
2005).
Some
studies
even
go
so
far
 as
to
imply
that
the
increased
risk
of
women
towards
rapid
cycling
is
simply
an
artifact
 of
women’s
tendency
to
be
prescribed
anitdepressants
(Schneck
et
al.,
2008)
 
  
  19
  
  Prognosis
and
outcome.
Many
factors
need
to
be
considered
when
discussing
  functional
outcome
for
bipolar
disorder
patients
including:
the
natural
course,
the
 impact
of
the
first
episode,
the
impact
of
the
depressive
phase,
cycle
length,
age
of
 onset,
age,
sex,
type
of
illness,
personality
traits
and
temperament,
co‐morbidity,
family
 history,
and
life
events
(Baur
et
al.,
2001).
This
list
of
variables
is
not
exhaustive
by
any
 means
and
though
they
all
seem
intuitively
related
to
functional
recovery,
one
review
of
 15
studies
found
that
not
all
of
these
variables
impact
functional
outcomes
consistently
 across
studies
(Martinez‐Aran
et
al.,
2007).
Predominant
methods
for
assessing
 functional
impairment
include
the
Global
Assessment
of
Functioning
scale
(GAF)
as
well
 as
the
consideration
of
socially
relevant
variables
such
as
employment
status,
marriages
 status,
and
ability
to
live
independently
(Tabares‐Seisdedos
et
al.,
2008).
Assessments
 using
the
latter
variables
have
recently
led
many
to
believe
that
functional
impairment
 in
BD
is
much
greater
than
previously
considered.
Independent
living,
personal
 relationships,
and
vocational
success
are
all
greatly
stunted
in
BD
patients.
While
6%
of
 the
general
population
is
unemployed,
50‐65%
of
BD
patients
are
jobless;
19‐23%
of
BP‐ I
Patients
were
married
(versus
60%
of
adults
in
the
general
population);
and
as
 compared
to
6%
of
the
general
population,
19‐58%
of
BD
patients
were
not
living
 independently
(Martinez‐Aran
et
al.,
2007).

In
the
McLean‐Harvard
First
Episode
Mania
 Study,
it
was
found
that
two
years
after
an
initial
hospitalization
for
mania
or
mixed‐ mania
depression,
only
43%
of
BPI
patients
regained
their
premorbid
occupational
and
 residential
status
(Tuhen
et
al.,
2003).



  
  20
  

  Although
there
is
a
growing
interest
in
investigating
functional
recovery
of
BD
  patients
within
the
literature,
there
are
no
studies
that
specifically
consider
sex
in
the
 context
of
assessing
outcomes.
It
is
known
that
both
rapid
cycling
and
mixed
mania
are
 associated
with
poorer
outcomes
(Suominen
et
al.,
2005).
As
was
presented,
there
is
 some
research
to
suggest
that
both
states
are
more
frequent
amongst
women
(Arnold
 et
al.,
2000).
Men
and
women
also
display
a
unique
profile
of
comorbidities
(Diflorio
and
 Jones,
2010).
The
health
hazards
associated
with
each
comorbidity
also
drastically
 affects
functional
outcome.
The
psychosocial
outcome
for
women
is
additionally
 affected
by
their
tendency
towards
a
depressive
affective
polarity
in
BD
(Kawa
et
al.
 2005).
Women
with
bipolar
disorder
report
a
lower
perception
of
their
overall
health
 and
well‐being
when
compared
to
men
(Bonnin
et
al.,
2010).
The
efficacy
of
treatment
 protocols
may
also
be
attenuated
by
the
delay
in
diagnosis
for
women
and
any
 conflicting
therapies
provided
when
women
are
misdiagnosed.
Although
there
are
some
 sex
differences
reported
in
the
literature,
there
are
also
studies
that
have
shown
no
 significant
differences
between
men
and
women
in
terms
of
time
spent
in
remission
and
 time
spent
in
any
syndromal
or
sub‐syndromal
episode
(Diflorio
and
Jones,
2010).
 However,
the
McLean‐Harvard
First
Episode
Mania
Study
found
that
the
female
sex
is
 associated
with
shorter
time
to
syndromal
recovery
(Tuhen
et
al.,
2003).
Unfortunately,
 a
confound
that
is
present
in
these
multitudinous
studies
which
investigate
clinical
 variables
is
the
presence
of
differing
treatment
strategies.

 
 
  
  21
  
  Summary.
Although
there
is
considerable
heterogeneity
in
the
literature,
the
  overall
picture
nevertheless
conveys
that
there
are
sex
differences
in
the
 phenomenology
of
BD.
The
most
consistent
findings
include
the
increased
prevalence
of
 rapid
cycling,
mixed
episodes,
suicidality,
and
a
psychiatric
or
medical
comorbidity
in
 women
with
BD.
These
differences
are
most
likely
the
product
of
a
complex
 combination
of
neurobiological
and
psychosocial
factors;
the
relative
contribution
of
 neurobiology
versus
psychosocial
characteristics
to
these
sex
differences
in
the
 phenomenology
of
BD
is
still
unknown.
Understanding
the
basis
of
these
sex
differences
 in
the
course
and
clinical
presentation
of
BD
may
lead
to
increased
etiological
 understanding
and
more
targeted
and
effective
treatment
strategies.

 1.3
Thesis
objectives
 

  The
body
of
literature
presented
in
the
previous
section
focused
exclusively
on
  sex
differences
found
in
the
sectors
of
BD
phenomenology
that
concern
affective
 disturbance.
Beyond
affective
irregularities,
the
pathology
of
BD
includes
significant
 deficits
to
cognitive
functioning.
Despite
the
functional
impact
of
these
cognitive
 deficits,
sex
differences
in
the
cognitive
impairment
associated
with
BD
is
a
topic
that
 has
been
minimally
broached.
This
thesis
intends
to
explore
how
sex
influences
 cognition
in
BD
by
assessing
whether
there
are
sex
differences
in
the
cognitive
 impairment
profiles
of
men
and
women
with
BD
early
in
their
course
of
illness.

 
  The
crux
of
the
thesis
is
presented
in
Chapter
4,
which
contains
original
work
  that
investigates
cognitive
impairment
in
the
domains
of
verbal
learning
/memory,
 sustained
attention/processing
speed,
and
executive
function
in
a
first‐episode
sample
  
  22
  of
BD
patients.
Chapters
2
and
3
encompass
relevant
literature
reviews
that
will
enable
 a
better
understanding
of
the
material
that
is
presented
in
Chapter
4.
Chapter
2
 summarized
what
is
known
about
cognitive
impairment
across
both
symptomatic
and
 euthymic
mood
states
in
BD.
The
cognitive
impact
of
various
comorbidities
and
 medications
used
to
treat
BD
will
also
be
presented.
Overall,
the
goal
of
this
chapter
will
 be
to
underscore
the
importance
of
cognitive
impairment
in
BD
and
to
present
relevant
 research
that
identifies
the
cognitive
domains
that
are
the
most
impacted
by
the
 disease.
This
research
was
used
to
inform
the
decision
making
process
that
chose
the
 cognitive
domains
to
be
assessed
for
sex
differences
in
Chapter
4.
Chapter
3
similarly
 informed
the
experimental
design
of
the
work
presented
in
Chapter
4.
Chapter
3
 concerns
morphological,
physiological,
and
cognitive
sex
differences
found
in
healthy
 populations.
A
better
understanding
of
how
sex
influences
cognition
in
healthy
samples
 is
an
informative
step
towards
understanding
how
sex
influences
cognition
in
abnormal
 sample
such
as
BD.
Together,
Chapters
2
and
3
prepare
the
reader
for
the
arguments
 presented
in
Chapter
4.

 
  Again,
the
ultimate
goal
of
this
thesis
is
to
better
understand
whether
sex
  influences
cognitive
impairment
in
BD
early
in
its
course.
To
this
end,
the
content
of
this
 thesis
explores
the
following
concepts:
 •  Cognitive
impairment
in
BD:

 o Dysfunction
associated
with
mood
states
and
euthymia
 o Confounding
effects
of
common
comorbidities
and
treatment
strategies

 o The
neurobiological
basis
of
cognitive
impairment
in
BD
  
  23
  o The
functional
impact
of
cognitive
impairment
in
BD
 •  Sex
differences
in
cognition
in
healthy
samples:
 o The
neurobiological
origin
of
sex
differences
in
cognition
 o Morphological
and
physiological
sex
dimorphisms
 o Sex
differences
in
cognition











































































  In
exploring
these
concepts,
it
is
hoped
that
the
necessity
of
understanding
how
sex
 impacts
psychiatric
conditions
‐‐
including
BD
‐‐
is
conveyed.

  
  24
  2.
Cognitive
Impairment
in
bipolar
disorder
 
  This
chapter
will
begin
by
underscoring
the
importance
of
cognitive
research
in
  BD
to
researchers,
clinicians,
and
patients.
With
this
discussion
in
place,
the
next
section
 of
the
chapter
will
summarize
the
cognitive
dysfunction
that
is
associated
with
BD
 during
manic
and
depressive
acute
mood
states
as
well
as
during
euthymia.
What
is
 currently
known
about
the
neurobiology
of
the
cognitive
dysfunction
in
BD
and
the
 steps
that
are
being
taken
to
better
understand
the
etiology
of
this
impairment
is
then
 considered.
Finally,
the
cognitive
impact
of
medications
used
to
treat
affective
 symptoms
in
BD
will
be
presented
followed
by
a
brief
overview
of
the
novel
 pharmacological
agents
that
may
be
used
to
treat
cognitive
impairment
in
BD
in
the
 future.


 2.1
Cognitive
research
in
bipolar
disorder:
relevance
for
researchers,
clinicians,
and
 patients
 
  
  Research
investigating
cognitive
functioning
in
BD
has
grown
extensively
over
  the
past
decade.
This
illuminating
body
of
work
has
significantly
contributed
to
a
drastic
 reconceptualization
of
the
illness.
BD
was
originally
characterized
as
a
chronic,
recurrent
 affective
disorder
with
patients
experiencing
cyclic
episodes
of
mood
instability
–
 mania/hypomania
and
depression
–
followed
by
periods
of
complete
clinical
remission
 or
euthymia.
Initially,
neurocognitive
dysfunction
in
these
samples
was
thought
to
be
 mild,
transient,
and
restricted
to
acute
symptomatic
episodes
(Goodwin
and
Lieberman,
 2010).
However,
an
abundance
of
evidence
has
now
established
that
lasting
and
stable
 cognitive
impairment
is
present
in
all
phases
of
bipolar
disorder
including
the
remission
  
  25
  phase
(Torres
and
Malhi,
2010).

Recent
meta‐analyses
and
reviews
of
studies
 investigating
euthymic
BD
patients,
have
found
marked
impairment
across
the
cognitive
 domains
of
attention/processing
speed,
verbal
learning/memory,
and
executive
 functioning
(Torres
et
al.,
2007).
As
cognitive
dysfunction
persists
during
euthymia,
this
 phase
is
no
longer
considered
a
period
of
complete
functional
recovery.
Indeed,
there
is
 now
broad
consensus
that
cognitive
impairment
is
state‐independent
and
represents
a
 core
clinical
feature
of
BD
(Torres
and
Malhi,
2010);
this
addition
to
the
clinical
picture
is
 of
potentially
great
etiological
and
therapeutic
importance.

 
  The
finding
that
full
cognitive
functionality
is
not
recovered
even
when
affective
  symptoms
have
remitted,
has
led
to
a
conceptual
reframing
of
BD.
It
was
largely
on
the
 basis
of
these
intermittent
phases
of
complete
recovery
that
the
traditional
Kraepelinian
 model
distinguished
BD
from
schizophrenia
(SZ;
Jamrozinski,
2010).
Evidence
regarding
 cognitive
impairment
during
euthymia
has
contributed
to
challenging
the
notion
of
BD
 and
SZ
as
separate
clinical
illnesses
with
distinct
pathophysiologies
(Hill
et
al.,
2008).
 Converging
evidence
from
several
fields,
most
notably
genetic
psychiatry
and
cognitive
 neuropsychology,
has
prompted
some
researchers
to
suggest
that
BD
and
SZ
are
better
 characterized
along
a
continuum
rather
than
as
separate
disorders
(Latalova
et
al.,
 2011).
Researchers
adopting
this
perspective
began
searching
for
neurocognitive
allied
 phenotypes,
or
endophenocognitypes,
shared
by
BD
and
SZ
in
order
to
better
 understand
the
etiopathophysiology
of
both
disorders
(Hill
et
al.,
2008).
These
efforts
 have
led
to
some
promising
results
with
deficits
in
verbal
memory
and
some
aspects
of
 executive
functioning
being
targeted
as
putative
endophenotypes
(Balanza‐Martinez
et
  
  26
  al.,
2008).
With
further
research,
these
same
cognitive
deficits
may
prove
to
be
of
 considerable
diagnostic
utility
to
clinicians
by
serving
as
illness‐trait
markers.


 
  In
addition
to
its
relevance
for
researchers
and
clinicians,
studies
investigating
  cognitive
impairment
in
BD
may
have
salient
implications
for
patients.
Although
 affective
and
psychotic
symptom
management
are
currently
the
primary
targets
of
 psychiatric
intervention,
cognitive
impairment
in
BD
is
far
from
benign.
Persistent
 cognitive
disability,
especially
in
the
domains
of
verbal/learning
and
memory
and
 executive
function,
has
been
associated
with
poor
functional
outcome
and
psychosocial
 adjustment
in
BD
patients
as
measured
by
both
subjective
reports
and
objective,
 performance‐based
indices
such
as
vocational,
education,
and
marital
status
(Wingo
et
 al.,
2009).
In
fact,
a
global
index
of
cognition
was
found
to
predict
functional
outcome
 better
than
clinical
factors
in
both
BD
and
SZ
(Bonnin
et
al.,
2010).
Subjectively,
nearly
 two‐thirds
of
BD
patients
complain
of
awareness
of
cognitive
dysfunction
even
during
 periods
of
affective
regularity
(Martinez‐Aran
et
al.,
2005).
Overall,
cognitive
 impairment
seems
to
adversely
affect
the
quality
of
life
of
patients
by
establishing
 significant
social
and
occupational
barriers
to
their
successful
reintegration
into
 community
settings.
As
BD
affects
nearly
4.4.%
of
the
population,
suboptimal
functional
 recovery
in
these
patients
carries

with
it
a
significant
societal
burden
(Merikangas
et
al.,
 2007).


 
  Given
its
functional
impact,
cognitive
impairment
is
a
likely
therapeutic
target.
  However,
it
is
often
difficult
for
a
clinician
to
assess
whether
these
cognitive
 disturbances
arise
from
the
pathophysiology
of
BD
or
are
secondary
due
to
the
adverse
  
  27
  effects
of
the
treatment
medication
(Goldberg
and
Chengappa,
2009).
The
picture
is
 further
complicated
by
the
frequent
use
of
polypharmacy
and
the
presence
of
comorbid
 conditions
such
as
ADHD
and
substance
abuse
that
are
themselves
associated
with
 cognitive
dysfunction
(Balanza‐Martinez
et
al.,
2010).
These
difficulties
have
contributed
 to
the
lag
in
generating
effective
behavioral
and
pharmacological
intervention
strategies
 for
cognitive
dysfunction
in
BD.
Nevertheless,
the
field
is
moving
towards
this
direction
 and
knowledge
of
the
cognitive
impact
of
medications
and
comorbidities
associated
 with
BD
is
continually
increasing.
There
have
also
been
some
preliminary
attempts
to
 generate
pharmacological
cognitive
enhancement
agents
to
be
used
as
adjunctive
 therapy
in
BD
(Balanza‐Martinez
et
al.,
2010).
 
  To
summarize,
research
investigating
cognitive
impairment
in
BD
is
of
crucial
  relevance
to
researchers,
clinicians,
and
patients
alike.
For
researchers,
this
field
has
 contributed
significantly
to
understanding
the
etiology
of
BD
by
clarifying
the
 relationship
between
BD
and
SZ
and
by
identifying
potential
endophenotypes
for
both
 illnesses.
By
recognizing
that
cognitive
impairment
is
a
core
clinical
feature
of
BD
that
 persists
across
moods
states
and
considerably
contributes
to
patient
disability,
this
body
 of
research
has
better
equipped
clinicians
to
generate
long‐term
pharmacological
and
 behavioral
treatment
strategies
for
their
patients.
There
is
also
the
potential
that
the
 identification
of
cognitive
disruption
in
specific
domains
will
serve
as
illness‐trait
 markers
that
may
aid
clinicians
in
diagnosing
patients
earlier
and
with
more
prognostic
 power
and
accuracy.
These
improvements
to
clinical
practice
may
ultimately
result
 better
illness
management
and
increased
quality
of
life
for
patients
with
BD.
Given
the
  
  28
  importance
of
this
vein
of
research,
this
chapter
will
continue
by
reviewing
what
is
 currently
known
about
cognitive
impairment
in
BD.
Before
discussing
the
dysfunction
 that
is
involved
in
both
symptomatic
and
remitted
states
of
the
illness,
cognitive
 domains
consistently
impacted
by
BD
and
their
psychiatric
assessment
will
be
discussed.

 
  2.1.1
Cognitive
domains
impacted
in
bipolar
disorder

 
  As
cognitive
impairment
is
a
core
clinical
feature
of
BD,
evaluation
of
  neuropsychological
functioning
is
an
important
part
of
the
clinical
examination.
 Neuropsychological
assessment
usually
involves
the
administration
of
a
battery
of
 psychometrically
validated
cognitive
tests
(Torres
and
Malhi,
2010).
These
tests
will
 have
been
previously
administered
to
large
numbers
of
healthy
individuals
that
vary
in
 demographic
characteristics
such
as
age
and
educational
attainment.
The
patient’s
 performance
on
any
given
test
can
then
be
compared
to
the
normative
scores
achieved
 by
healthy
individuals
that
share
their
demographic
profile
(Torres
and
Malhi,
2010).
 The
choice
to
administer
any
particular
test
should
rely
on
that
test’s
reliability,
validity,
 specificity,
and
sensitivity.
An
ideal
neuropsychological
test
consistently
assesses
 functioning
in
one
cognitive
construct
or
domain;
in
psychiatric
settings,
this
test
must
 be
sufficiently
sensitive
to
detect
the
cognitive
impairment
imparted
by
that
specific
 illness
pathology.
A
patient’s
performance
on
such
a
test
would
be
highly
informative
to
 the
clinician
as
cognitive
domains
may
be
associated
with
specific
neural
regions
or
 brain
systems
(Latlova
et
al.,
2011);
poor
performance
on
a
cognitive
task
would
allow
a
 clinician
to
infer
that
the
task’s
associated
neural
substrate
may
be
impacted
by
that
 illness.
However,
such
inferences
should
be
made
with
caution
as
the
cognitive
tasks
  
  29
  currently
employed
in
research
all
fall
short
of
the
ideal
especially
in
the
areas
of
test
 validity
and
specificity
(Torres
and
Malhi,
2010).
In
reality,
cognitive
tests
often
tap
into
 multiple
domains
and
it
still
remains
unclear
how
most
of
these
cognitive
domains
(e.g.
 attention)
are
neurally
generated
and
organized
(Burdick
et
al.,
2007).
In
response
to
 this
dilemma,
newer
experimental
paradigms
borrowing
techniques
from
both
cognitive
 neuropsychology
and
neuroscience
have
been
generated.
For
example,
there
has
been
a
 recent
wave
of
studies
employing
experimental
designs
in
which
a
participant’s
brain
is
 imaged
using
structural
or
functional
magnetic
resonance
imaging
while
they
are
 simultaneously
engaged
in
a
cognitive
task
(Cahill,
2006).

These
studies
have
been
 particularly
revealing
in
the
effort
to
understand
the
biological
underpinnings
of
 cognitive
constructs.
Deciphering
the
biological
processes
that
underlie
healthy
 cognitive
functioning
is
an
important
step
towards
understanding
the
 pathophysiological
mechanisms
that
result
in
cognitive
dysfunction
in
BD
and
other
 mental
illnesses.

 
  The
first
step
in
this
process,
however,
must
be
to
characterize
the
cognitive
  domains
that
are
most
consistently
impaired
in
patients
with
BD.
Only
a
subset
of
all
 existing
cognitive
domains
is
adversely
impacted
in
BD
and
general
intellectual
 functioning
is
largely
preserved
in
these
patients
(Goldberg
and
Chegappa,
2009;
 McDonough‐Ryan
et
al.,
2002).
Recently,
the
International
Society
for
Bipolar
Disorder
 (ISBD)
identified
the
domains
that
have
repeatedly
been
found
to
be
impaired
in
BD
 (Yatham
et
al.,
2009).
Broadly,
these
domains
are
attention/vigilance,
verbal
  
  30
  learning/memory,
and
executive
function.
These
domains
and
their
respective
cognitive
 tests
will
be
discussed
in
detail
bellow.
 
  Attention/processing
speed.
Attention
is
a
multidimensional
cognitive
process;
in
  its
active
form,
attention
allows
the
individual
to
focus
on
important
stimuli
by
filtering
 out
irrelevant
information
and
inhibiting
competing
actions
or
thoughts.
Vigilance,
or
 sustained
attention,
is
the
ability
to
maintain
this
attentional
focus
over
time.
Failure
to
 maintain
attention
to
the
relevant
stimuli
will
negatively
impact
task
processing
speed
 (Filley
and
Cullum,
1994).
Attentional
impairment
can
be
especially
impactful
as
an
 intact
attentional
capacity
is
essential
to
all
higher
cognitive
skills
(Burdick
et
al.,
2007).
 Neuropsychologists
have
yet
to
fully
understand
attention
in
a
mechanistic
fashion.
 However,
the
frontal
and
parietal
lobes
are
thought
to
be
essential
to
this
cognitive
 process
(Balanza‐Martinez
et
al.,
2008).
Attention
and
sustained
attention
have
been
 assessed
in
BD
using
many
varying
cognitive
tests:
Continuous
Performance
Task,
Trail
 Making
Test
–
Part
A,
and
WAIS
Digit
Symbol
task.
In
each
task,
the
participant’s
ability
 to
focus
on
relevant
stimuli
and
ignore
competing
stimuli
is
challenged;
several
 attentional
tasks
test
psychomotor
processing
speed,
which
is
defined
as
the
time
it
 takes
to
process
a
signal,
prepare
a
response,
and
execute
that
response
(Sobin
and
 Sackeim,
1997).
Both
attention
and
vigilance
have
been
found
to
be
in
impaired
in
BD
 patients
regardless
of
mood
state
(Najt
et
al.,
2005;
Clark
et
al.,
2002).
There
are
also
 reports
of
impaired
selective
attention,
psychomotor
speed,
and
sustained
attention
in
 relatives
and
first‐degree
relatives
of
patients
with
BD
(Antila
et
al.,
2007;
Kilmes‐ Dougan,
2006).



  
  31
  
  Verbal
learning/memory.
Learning
and
memory
refer
to
the
cognitive
processes
  of
acquiring
and
retaining
symbolically
represented
information.
In
verbal
learning
and
 memory,
the
symbolic
system
used
to
represent
information
is
language
(Ditmann
and
 Abel,
2010).
Items
to
be
acquired
and
recalled
in
this
research
domain
include:
letters,
 letter
combinations,
digits,
numbers,
sentences,
etc.
The
participant
may
be
told
to
 either
pay
attention
to,
or
disregard
the
spatio‐temporal
relationship
between
items
 (Healy
and
McNamara,
1996).
These
cognitive
processes
have
been
broadly
associated
 with
the
left‐hemisphere
and
the
Peri‐Sylvian
region
(Ojemann,
2002).
Verbal
learning
 and
memory
has
been
assessed
in
BD
samples
using
specific
subtests
of
the
California
 Verbal
Learning
Test
(CVLT)
and
Rey
Auditory
Verbal
Learning
Test
(RAVLT)
(Andreano
 and
Cahill,
2009).
In
these
tasks,
participants
are
read
a
list
of
words
and
are
asked
to
 remember
and
repeat
them
immediately
and
after
a
brief
delay.
Verbal
learning
and
 memory
is
impaired
in
BD
patients
during
both
symptomatic
and
remission
phases
 (Burdick
et
al.,
2007).
Verbal
learning
and
memory
was
also
impaired
in
first‐,
and
 second‐degree
relatives
with
BD
(Balanza‐Martinez
et
al.,
2008).

 
  Executive
function.
Executive
function
is
an
overarching
term
relating
to
several
  cognitive
processes
including:
working
memory,
planning,
task‐monitoring,
response
 inhibition,
attentional
set‐shifting,
and
preservation
(Alfredo,
2008).
These
diverse
 cognitive
processes
come
into
play
during:
novel
or
unfamiliar
situations,
activities
that
 involve
planning
and
decision
making,
and
tasks
that
involve
error
monitoring
and
 correction.
The
neural
regions
thought
to
be
involved
in
executive
function
include
the
 prefrontal
and
parietal
cortices,
as
well
as
several
subcortical
structures
(Friedman,
  
  32
  2008).
Many
cognitive
tests
have
been
used
to
assess
executive
function
in
BD
samples:
 Trail
Making
Test‐part
B,
Stroop
Test,
Wisconsin
Card
Sorting
Test,
Digit
Span
 Backwards,
and
Tower
of
London
(Yatham,
2009).
Executive
dysfunction
is
experienced
 across
mood
states
with
recent
studies
indicating
that
impairment
in
this
domain
is
the
 most
commonly
reported
cognitive
deficit
in
patients
with
BD
during
euthymia
(Burdick
 et
al.,
2007).
Impairment
in
executive
function,
specifically
abstraction,
cognitive
 flexibility,
planning,
and
working
memory,
has
been
found
in
first‐degree
relatives
in
BD
 (Balanza‐Martinez
et
al.,
2008).




 2.2.
Cognitive
functioning
across
illness
phases
in
bipolar
disorder

 
  Bipolar
disorder
is
characterized
by
both
symptomatic
and
euthymic
phases.
The
  affective
irregularity
during
the
symptomatic
phase
can
be
of
a
depressive
or
manic
 polarity
and
can
vary
in
severity
(e.g.
mania
vs.
hypomania).
Although
cognitive
 impairment
in
BD
is
present
during
both
symptomatic
and
remitted
phases,
the
research
 indicates
that
the
extent
of
cognitive
impairment
experienced
may
be
influenced
to
 some
degree
by
state
(Torres
and
Malhi,
2010).
The
section
will
proceed
with
a
 discussion
of
the
cognitive
impairment
that
is
associated
with
the
various
states
of
BD:
 depression,
mania
and
euthymia.
Finally,
as
BD
patients
commonly
present
with
one
or
 more
comorbid
illnesses,
the
impact
of
these
comorbidities
to
overall
cognitive
 impairment
will
be
discussed
(Balanza‐Martinez
et
al.,
2009).

 
  2.2.1
Cognitive
dysfunction
associated
with
symptomatic
states
  
  There
is
a
general
worsening
of
cognitive
dysfunction
during
a
manic
or
  depressive
mood
state
as
compared
to
an
affectively
stabilized
state
(Torres
and
Malhi,
  
  33
  2010).
Mania
is
associated
with
globalized
neuropsychological
impairment
with
deficits
 being
seen
in
the
domains
of
sustained
attention,
verbal
and
visual
learning/memory,
 and
executive
function
(Latalova
et
al.,
2011).
A
depressive
state
is
also
associated
with
 deficits
in
these
cognitive
domains
in
addition
to
impairment
being
seen
in
psychomotor
 speed
(Torres
and
Malhi,
2010).
In
cross‐sectional
studies
comparing
manic,
hypomanic
 and
depressed
BD
patients
to
healthy
controls,
all
patient
groups
show
deficits
in
verbal
 memory
and
executive
function
when
compared
to
healthy
controls;
however,
few
 significant
differences
are
seen
in
cognitive
performance
across
patient
groups
 (Martinez‐Aran
et
al.,
2004).
Collectively,
these
studies
indicate
that
there
are
no
 substantial
differences
to
the
severity
of
cognitive
dysfunction
experienced
across
 depressive,
manic,
and
hypomanic
mood
states.
After
the
resolution
of
a
symptomatic
 mood
state,
some
cognitive
impairments
improve
while
other
deficits
remain
(Latalova
 et
al.,
2011).
The
etiology
of
cognitive
impairment
during
states
of
affective
disturbance
 remains
unclear
and
is
most
likely
multifaceted.
It
is
possible
that
during
symptomatic
 remission,
patients
put
in
more
effort
into
achieving
the
highest
possible
score
when
 completing
cognitive
tasks;
the
poorer
cognitive
performance
seen
in
hypomanic,
 manic,
and
depressed
patients
could
partially
be
a
function
of
willingness
rather
than
 exacerbated
pathology
per
se
(Torres
and
Malhi,
2010).



 
 
  2.2.2
Cognitive
dysfunction
during
euthymia
  
  Research
regarding
cognitive
dysfunction
during
the
euthymic
phase
in
BD
is
  growing
rapidly;
a
recent
review
found
that
45
original
articles,
nine
review
articles,
and
 4
meta‐analyses
have
been
written
on
the
subject
between
2008
and
2009
alone
  
  34
  (Jamrozinski,
2010).
The
most
consistently
reported
cognitive
domains
that
are
impaired
 during
euthymia
are
verbal
learning/memory,
attention
(including
sustained
attention
 and
psychomotor
speed),
and
executive
function
(including
preservation
and
response
 inhibition)
(Torres
et
al.,
2007).
Beyond
qualifying
for
statistical
relevance,
one
recent
 study
concluded
that
43%
of
euthymic
bipolar
patients
show
clinically
significant
 cognitive
impairment,
with
clinically
significant
impairment
being
defined
as
scoring
two
 standard
deviations
below
the
normative
mean
in
at
least
one
cognitive
domain
 (Gualtieri
and
Morgan,
2008).
However,
there
is
a
significant
degree
of
heterogeneity
in
 this
literature
(Jamrozkinski,
2011).
There
are
several
reasons
that
account
for
this
 heterogeneity:
the
definition
for
euthymia
varies
considerably
between
studies;
data
 that
describe
the
type
of
psychotic
symptoms
experienced
by
the
sample
are
usually
 lacking,
various
neuropsychological
tests
are
used
to
assess
the
same
cognitive
domain,
 and
neuropsychological
tests
are
sometimes
assigned
to
different
cognitive
domains.
 Finally,
mediating
and
moderating
variables
may
further
obscure
the
picture;
these
 include:
residual
symptoms,
medication
and
polypharmacy,
alcohol/substance
abuse,
 and
smoking
(Torres
and
Malhi,
2010).
Some
heterogeneity
may
also
result
from
studies
 accounting
for
comorbidities
differently.
The
neurocognitive
impact
of
common
 comorbidities
is
described
below.

 
  2.2.3
Cognitive
dysfunction
associated
with
comorbid
conditions
  
  Most
patients
with
BD
present
with
a
medical
or
psychiatric
comorbidity
  (Krishnan,
2005).
Many
of
these
comorbid
diseases
have
been
associated
with
cognitive
 dysfunction
and
it
is
difficult
to
understand
the
cognitive
impairment
that
is
due
to
BD
  
  35
  disease
processes
versus
the
pahtophysiology
of
these
other
illnesses
(Goldberg
and
 Chengappa,
2009).
The
medical
comorbidities
that
are
commonly
seen
in
BD
patients
 include:
cardiovascular/cerebrovascular
diseases,
neurological
disorders
(e.g.
migraine,
 epilepsy),
and
metabolic
abnormalities
(e.g.
obesity,
diabetes
mellitus
type‐II).
These
 disorders
all
accompany
cognitive
impairment;
pharmacological
agents
used
to
manage
 these
illnesses
may
also
have
a
cognitive
impact
(Balanza‐Martinez,
2010).
Studies
that
 investigate
the
contribution
of
cognitive
impairment
imparted
by
these
medical
 comorbidities
in
patients
with
BD
are
currently
unavailable.

 The
most
well
researched
psychiatric
comorbidites
in
BD
samples
are
substance
abuse
 and
attention
deficit
hyperactivity
disorder
(ADHD);
several
studies
have
investigated
 neurocognitive
functioning
in
BD
patients
with
either
substance
abuse
or
ADHD.
Among
 all
Axis
I
disorders,
BD
has
the
highest
lifetime
prevalence
of
alcohol
and
substance
 abuse
(Sbrana
et
al.,
2005).
In
a
recent
study
that
compared
euthymic
BD
patients
with
 and
without
alcohol
dependence
and
abuse,
to
control
groups
found
that
the
patient
 group
with
comorbid
substance
abuse
had
increased
dysfunction
than
both
the
control
 and
substance
abuse‐free
patient
group
in
certain
measures
of
executive
control
(Levy
 et
al.,
2008).
Although
there
is
some
contention
as
to
the
degree
to
which
comorbid
 alcohol
abuse
affects
neurocognitive
function
in
BD
patients,
collectively
the
literature
 suggests
that
alcohol
abuse
either
adds
to
the
neurocognitive
dysfunction
by
the
way
of
 its
own
independent
mechanisms
or
that
it
lends
to
an
exacerbation
of
BD‐associated
 pathological
processes
that
impact
cognition
(Balanza‐Martinez
et
al.,
2010;
Levy
et
al.,
 2008).
There
is
also
limited
evidence
to
support
that
other
addictive
substances
(e.g.
  
  36
  cocaine)
also
contribute
to
cognitive
dysfunction
in
BD
(Cahill
et
al.,
2006).
With
regards
 to
ADHD,
this
comorbidity
is
seen
more
often
in
children
and
adolescents
with
BD
 (Balanza‐Martinez
et
al.,
2010).
Consequently,
much
of
the
literature
that
investigates
 the
cognitive
effects
of
ADHD
in
patients
with
BD
are
conducted
in
younger
samples.
 There
is
evidence
to
support
that
young
BD
patients
with
comorbid
ADHD
perform
 worse
on
verbal
memory
than
young
BD
patients
without
ADHD
(McClure
et
al.,
2005;
 Pavuluri
et
al.,
2006);
although,
whether
ADHD
confers
additional
cognitive
impairment
 is
still
disputed
in
the
literature
(Balanza‐Martinez
et
al.,
2010).

 2.3
Neurobiological
basis
of
cognitive
impairment
in
bipolar
disorder
 
  The
exact
etiology
of
cognitive
dysfunction
in
BD
is
currently
unknown.
BD
is
  extremely
heterogeneous
and
it
is
likely
that
multiple
factors
feed
into
the
cognitive
 pathogenesis
from
which
cognitive
impairments
arise.
Efforts
to
understand
the
 contribution
of
genetics,
environmental
factors,
and
clinical
factors
will
be
discussed.

 
  With
regard
to
genetics,
the
specific
susceptibility
genes
for
developing
BD
are
  still
unknown
as
this
illness
is
complex
and
multidimensional.
However,
there
have
been
 several
studies
in
recent
years
that
have
attempted
to
find
endophenotypes
for
BD;
 endophenotypes
have
the
potential
to
be
etiologically
revealing
as
they
provide
 intermediate
disease
related
phenotypes
for
which
it
is
easier
to
susceptibility
genes
 (Balanza‐Martinez
et
al.,
2008).
The
criteria
for
a
characteristic
to
qualify
as
an
 endophenotype
state
that
the
characteristic
is:
a)
associated
with
the
illness,
b)
is
 hereditable,
c)
state‐independent,
d)
co‐segregated
with
illness
within
families,
and
e)
 also
found
in
unaffected
relatives
at
a
higher
rate
than
in
the
general
population
(Hasler
  
  37
  et
al.,
2006).
Neurocognitive
studies
investigating
cognitive
impairment
in
the
relatives
 of
BD
patients
–
discordant
twins,
first‐,
and
second‐degree
relatives
–
have
identified
 measures
of
verbal
learning
and
memory,
attention,
and
some
aspects
of
executive
 function
as
suitable
endophenotypes
(Balanza‐Martinez,
2010).

 
  The
field
is
still
waiting
to
see
whether
these
putative
characteristics
will
help
  find
susceptibility
genes
that
explain
the
etiopathogenic
mechanism
that
leads
to
 cognitive
impairment
in
BD.
However,
some
genetic
abnormalities
have
already
been
 suggested
as
contributing
to
cognitive
impairment
in
BD.
Brain‐derived
neurotrophic
 factor
(BDNF)
and
cathecol
O‐methlytransferase
(COMT)
polymorphisms
have
been
 associated
with
impaired
performance
on
executive
function
tasks
in
BD
samples
 (Rybakowski
et
al.,
2006;
Burdick
et
al.,
2007).
Mutations
in
genes
governing
the
 neuronal
migration
process
have
been
found
to
predict
prefrontal
cognitive
deficits
in
a
 mixed
sample
of
BD‐I
and
SZ
patients
(Tabares‐Seisdedos
et
al.,
2008).
Overall,
there
is
 evidence
to
suggest
that
genetics
contribute
to
cognitive
disability
in
BD
patients.

 
  Recent
studies
have
investigated
the
potential
of
environmental
factors
to
  influence
cognitive
dysfunction
in
BD
patients.
There
is
limited
evidence
to
support
that
 obstetric
complications
and
early
traumatic
adversity
(e.g.
sexual
and
emotional
abuse)
 is
associated
with
poorer
performance
on
verbal
and
executive
tasks
in
BD
samples
 (Martino
et
al.,
2008;
Strejilivich
and
Martino,
2008).
However,
it
is
understood
that
 these
factors
do
not
wholly
account
for
the
cognitive
deficits
seen
in
BD
and
that
other
 factors
must
contribute
to
produce
the
entire
pathological
phenotype
as
it
relates
to
 cognition
(Balanza‐Martinez
et
al.,
2009).

  
  38
  
  Clinical
factors
have
also
been
associated
with
worsening
cognitive
dysfunction
  in
BD;
these
include:
lifetime
number
of
acute
episodes,
illness
duration,
and
number
of
 hospital
admissions
(Robinson
and
Ferrier,
2006).
Increases
in
these
factors
have
all
 been
associated
with
greater
cognitive
impairment.
However,
the
direction
of
causality
 between
a
more
aggressive
course
of
illness
and
greater
cognitive
impairment
cannot
 yet
be
concluded.
Several
reports
have
also
found
that
a
history
of
psychosis
is
also
 associated
with
more
severe
cognitive
impairment
especially
in
the
domains
of
verbal
 memory
and
executive
function
(Bora
et
al.,
2007),
although
some
have
failed
to
 replicate
this
finding
(Lahera
et
al.,
2008).
A
confound
in
any
study
that
attempts
to
 associate
clinical
factors
to
cognitive
impairment
is
medication
effects.
For
example,
it
is
 unclear
whether
a
history
of
psychosis
or
the
use
of
antipsychotics
is
the
main
 underlying
factor
contributing
to
cognitive
dysfunction.
It
is
clear
that
certain
 medications
add
to
the
cognitive
burden
found
in
BD,
but
the
exact
mechanism
by
 which
this
exacerbation
is
produced
is
still
largely
unknown
(Balanza‐Martinez
et
al.,
 2009).
What
is
known
about
the
cognitive
impact
of
major
pharmacological
agents
used
 to
treat
BD
is
summarized
below.


 2.4
Cognitive
impact
of
medication
used
in
BD
and
future
directions
 
  It
is
now
understood
that
several
medications
that
are
commonly
used
to
treat
  BD
have
a
significant
cognitive
impact.
The
cognitive
profile
of
a
medication
should
be
 carefully
considered
when
constructing
long‐term
prophylactic
treatment
strategies.
In
 many
cases,
optimally
managing
affective
and
psychotic
symptoms
with
medication
 leads
to
a
trade‐off
in
cognitive
impact.
Knowledge
of
how
a
drug
positively
or
  
  39
  negatively
impacts
cognition
can
aid
clinicians
in
teasing
out
iatrogenic
from
illness‐ related
cognitive
complaints
and
help
them
in
creating
pharmacological
strategies
with
 the
most
favorable
functional
outcome.
However,
understanding
how
medications
 contribute
to
cognitive
dysfunction
remains
a
complex
issue
(Goldberg
and
Chegappa,
 2009).
As
there
are
ethical
concerns
to
keeping
patients
medication‐naïve,
very
few
 studies
have
investigated
cognitive
impairment
in
unmedicated
patients.
In
patients
 who
are
medicated,
given
the
heterogeneity
of
the
illness,
there
is
great
variability
in
 the
type
of
medication
and
dose
that
are
prescribed
(Balanza‐Martinez
et
al.,
2010).
 Additionally,
polypharmacy
is
very
commonly
practiced
in
BD
treatment
management.
It
 can
be
difficult
to
predict
the
contribution
of
each
drug
to
overall
cognitive
dysfunction
 when
a
patient
is
managed
using
polypharmacy
as
the
cognitive
impact
is
not
simply
an
 additive
function
of
the
impairment
experienced
by
patients
receiving
monotherapy
 (Goldberg
and
Chengappa,
2009).
Nevertheless,
understanding
how
commonly
 prescribed
drugs
affect
cognition
is
a
useful
first
step.
What
is
known
about
the
 cognitive
impact
of
lithium,
anticonvulsants,
antipsychotics,
and
antidepressants
used
to
 treat
BD
will
be
summarized
below.

 
  Lithium.
Lithium
remains
the
first‐line
treatment
option
for
long‐term
  prophylaxis
of
BD.
Overall,
the
cognitive
impact
of
lithium
is
weak
(Pachet
and
 Wisniewski,
2003).
Studies
investigating
cognitive
dysfunction
in
euthymic
BD
patients
 found
that
cognitive
impairments
are
similar
across
patients
who
are
treated
with
 lithium
and
those
treated
without
lithium
(Clark
et
al.,
2002).
In
another
study,
plasma
 lithium
levels
were
not
related
to
performance
on
a
broad
neuropsychological
battery
  
  40
  (van
Gorp
et
al.,
1998).
However,
there
is
some
contention
as
to
the
domains
that
are
 affected
and
those
that
are
spared.
Two
early
reviews
of
the
literature
concluded
that
 lithium
exerts
mild
negative
effects
in
tasks
of
verbal
memory
and
psychomotor
speed,
 while
sparing
visuo‐spatial,
attentional,
and
executive
function
capability
(Honig
et
al.,
 1999;
Pachet
and
Wisniewski,
2003).
While
more
recently
a
two‐year
longitudinal
study
 found
that
euthymic
BD
patients
on
lithium
monotherapy
display
stable
deficits
in
 attention/processing
speed
and
executive
function,
but
not
verbal
memory
(Mur
et
al.,
 2008).
Further
studies
are
needed
to
resolve
these
discrepancies.
With
regards
to
the
 reversibility
of
the
cognitive
effects
of
lithium,
most
studies
point
to
lithium‐associated
 deficits
reverting
upon
discontinuation
(Kocsis
et
al.,
1993).
However,
there
is
a
study
 that
reported
lasting
negative
effects
after
lithium
use
has
ceased
as
well
(Tsaltas
et
al.,
 2009).

 
  There
have
also
been
several
neuroimaging
studies
investigating
the
effect
of
  lithium
on
the
brain
of
patients
with
BD.
Overall,
lithium
has
been
associated
with
 increases
in
total
grey
matter,
and
grey
matter
of
the
hippocampus
and
prefrontal
 cortex
in
these
patients
(Bearden
et
al.,
2007;
Yucel
et
al.,
2007).
In
one
report,
lithium
 was
found
to
counteract
the
long‐term
gray
matter
deterioration
associated
with
BD
 (van
der
Schot
et
al.,
2009).
Taken
together,
these
findings
seem
to
suggest
a
 neurotrophic/neuroprotective
nature
to
lithium.
This
viewpoint
meshes
well
with
 preclinical
data
that
has
also
shown
the
neurotrophic/neuroprotective
effects
of
lithium
 in
animal
models
of
Alzheimer’s,
Huntington’s,
and
Parkinson’s
diseases
(Balanza‐ Martinez
et
al.,
2010).
On
a
clinical
level,
however,
there
is
question
as
to
how
this
grey
  
  41
  matter
growth
translates
to
cognitive
improvement.
There
are
currently
no‐reports
of
 lithium‐associated
neurocognitive
improvement
in
BD.
The
discrepancy
between
what
is
 clinically
observed
with
lithium
use
–
mild
neurocognitive
deficits
–
and
what
is
 observed
in
neuroimaging
and
preclinical
studies
–
neurotrophic/neuroprotective
 effects
‐
needs
to
be
resolved
with
further
investigation.

 
  Anticonvulsants.
Several
anticonvulsants,
or
antiepileptic
drugs,
have
been
found
  to
be
beneficial
to
symptom
management
in
BD;
these
include
valproic
acid
(valproate),
 carbamazepine,
and
lamotragine.
Topiramate
is
also
an
anticonvulsant
that
is
used
to
 treat
BD
but
it
is
not
a
front‐line
treatment
and
is
often
used
in
conjunction
with
mood
 stabilizers
(Balanza‐Martinez
et
al.,
2010).
The
cognitive
effects
of
anticonvulsants
have
 been
minimally
investigated
in
BD.
However,
the
literature
investigating
the
cognitive
 impact
of
these
drugs
in
samples
with
epilepsy
is
abundant.
In
epileptic
samples,
 valproate
and
carbamazapine
has
been
associated
with
attention
and
memory
tasks
 (Senturk
et
al.,
2007).
Lamotrigine,
a
newer
generation
of
anticonvulsant,
is
thought
to
 have
a
better
cognitive
profile
than
either
valproate
or
carbamazapine
in
samples
with
 epilepsy
(Gualtieri
and
Johnson,
2006).
In
the
few
studies
that
have
been
conducted
in
 patients
with
BD,
valproate
was
found
to
have
a
similar
cognitive
profile
to
lithium
 (Senturk
et
al.,
2007);
patients
prescribed
monotherapy
with
either
valproate
or
lithium
 experience
mild
impairments
to
verbal
learning
and
memory.
Promisingly,
lamotrigine
 was
found
to
have
some
cognitive
benefits
in
samples
with
BD.
Two
studies
 investigating
cognition
in
BD‐I
patients
reported
that
lamotrigine
therapy
was
associated
 with
significant
improvement
in
self‐reported
cognitive
ability
(Kaye
et
al.,
2007;
Khan
et
  
  42
  al.,
2004).
In
another
study
investigating
euthymic
BD
patients
using
lamotrigine
found
 that
these
patients
perform
better
on
cognitive
tasks
assessing
verbal
learning
and
 memory
than
do
patients
receiving
carbamazapine
or
valproate
(Daban
et
al.,
2006).
In
 a
recent
study
that
compared
the
cognitive
profile
of
several
anticonvulsants
in
samples
 with
BD
found
that
valproate,
topiramate,
and
carbamazapine
all
have
poorer
cognitive
 profiles
that
lamotrigine
(Gualtieri
and
Johnson,
2006).

 
  Antipsychotics.
Antipsychotics
are
prescribed
to
the
subset
of
BD
patients
that
  experience
psychotic
symptoms
during
a
manic
episode.
There
are
several
reports
of
the
 adverse
cognitive
impact
of
antipsychotics
(Balanza‐Martinez
et
al.,
2009).
Compared
to
 euthymic
patients
not
taking
antipsychotics,
euthymic
BD
patients
taking
antipsychotics
 complete
significantly
fewer
categories
of
the
Wisconsin
Card
Sorting
Task
(WCST),
an
 indication
of
impaired
abstraction
and
concept
formation;
duration
of
therapy
with
 antipsychotic
medication
has
been
negatively
correlated
with
completed
categories
on
 the
WCST
(Zubieta
et
al.,
2001).
Atypical
antipsychotic
use
has
also
been
linked
to
 deficits
in
psychomotor
speed
and
verbal
learning
and
memory
measures
in
pediatric
 and
adult
BD
samples
(Bearden
et
al.,
2007;
Savitz
et
al.,
2008).
However,
it
is
not
often
 easy
to
interpret
these
findings.
A
history
of
psychosis
in
BD
is
itself
associated
with
a
 more
severe
cognitive
impairment
profile
as
compared
to
BD
patients
without
psychosis
 (Goodwin
and
Lieberman,
2010).
As
patients
with
a
history
of
psychosis
are
those
who
 are
prescribed
antipsychotics,
it
is
difficult
to
know
whether
the
additional
cognitive
 impairments
are
due
to
the
disease
process
or
are
secondary
due
medication
use.
Of
all
 the
cognitive
impairments
associated
with
antipsychotic
use
in
BD
samples,
deficits
in
  
  43
  psychomotor
speed
seem
to
be
the
most
likely
to
be
iatrogenically
induced
(Bora
et
al.,
 2009).
Overall,
there
is
a
need
for
further
investigation
as
to
detrimental
cognitive
 effects
of
antipsychotics.
It
would
be
beneficial
to
compare
groups
of
BD
patients
using
 different
atypical
antipsychotics
with
and
without
concomitant
medication
use.
 
  Antidepressants.Antidepressants
are
frequently
used
to
manage
depression
in
  BD
patients
in
addition
to
therapy
with
mood
stabilizers.
The
cognitive
effects
of
 antidepressants
in
BD
are
not
currently
known.
However,
in
unipolar
depression,
 tricyclic
antidepressants
have
been
linked
to
impairments
in
verbal
learning
and
 memory
(Amado‐Boccara
et
al.,
1995).
Conversely,
selective
serotonin
reuptake
 inhibitors
(SSRIs)
and
other
non‐tricyclic
agents
are
know
to
spare
cognition
in
unipolar
 samples.
In
fact,
some
animal
studies
have
found
that
some
antidepressants
have
 neuroprotective
effects
(Zobel
et
al.
2004).
Given
the
frequency
of
prescribing
 antidepressants
in
BD,
studies
investigating
the
cognitive
effects
of
antidepressant
use
 in
these
samples
are
urgently
needed.

 
  
Studies
investigating
drug
naïve‐patients
are
both
few
in
number
and
  contradictory
in
results.
Some
reports
conclude
that
cognitive
impairment
in
is
 independent
of
medication
use
(Taylor‐Tavares
et
al.,
2007)
and
other
report
that
some
 medications
exacerbate
impairment
in
certain
cognitive
domains
(Holmes
et
al.,
2008).
 These
reports
usually
compare
drug
naïve
groups
to
their
medicated
counterparts.
 Several
confounds
have
been
attributed
to
the
type
of
experimental
design
that
has
 been
employed
in
these
types
of
studies;
drug‐naïve
patients
are
usually
symptomatic
 to
some
degree
and
severity
of
symptoms
across
medicated
and
unmedicated
groups
  
  44
  may
be
difficult
to
match;
the
unmedicated
group
contains
a
disproportionately
large
 number
of
BD‐II
patients,
a
group
that
is
thought
to
have
more
cognitive
domains
 spared
than
BD‐I
patients;
comorbid
illnesses
are
not
well
matched
across
medicated
 and
medication‐free
groups
(Balanza‐Martinez
et
al.,
2009).
Due
to
these
confounds,
the
 proportion
of
cognitive
impairment
conferred
by
medications
is
still
unknown.

 
  2.4.1
Pharmacological
therapy
for
cognitive
dysfunction
  
  In
addition
to
investigating
the
cognitive
impact
of
medications
currently
  indicated
to
treat
BD,
researchers
have
attempted
to
find
pharmacological
agents
that
 enhance
cognitive
function
and
can
eventually
be
used
as
adjunctive
therapy
in
these
 samples.
However,
this
is
a
nascent
field
and
many
of
the
putative
agents
have
yet
to
be
 rigorously
tested
using
randomized
and
placebo‐controlled
trials
with
BD
samples.
The
 agents
that
have
been
considered
can
be
grouped
into
the
procholinergics,
 antiglutamatergics,
and
stimulants.
Procholinergic
agents,
such
as
donepezil
and
 galantamine,
have
been
hypothesized
to
correct
the
muscarinic
and
nicotinic
cholinergic
 receptor
function
that
is
aberrant
in
BD
and
is
thought
to
be
associated
with
deficits
in
 attention
and
working
memory.
These
agents
have
met
with
limited
success
in
small,
 uncontrolled,
trials.
Antiglutamatergic
agents
have
been
proposed
to
combat
cognitive
 dysfunction
through
their
action
on
the
N‐methyl‐D‐aspartate
(NMDA)
glutamate
 receptor;
hypofunction
of
NMDA
receptors
is
associated
with
pathogenesis
of
psychosis
 in
schizophrenia
(Goldberg
and
Chengappa,
2009).
However,
there
are
no
trials
that
 investigate
the
efficacy
of
antiglutamatergic
agents
in
BD
samples
and
those
that
have
 been
investigated
in
SZ
samples
have
failed
to
improve
cognitive
scores
(Lieberman
et
  
  45
  al.,
2009).
Stimulants
such
as
amphetamine
and
mixed
amphetamine
salts,
 methylphenidate,
and
modafinil,
have
been
suggested
to
improve
cognition
in
BD
but
 have
not
been
tested
in
these
samples
yet;
drug
trials
with
these
agents
have
shown
 improvement
in
executive
function
in
ADHD
and
SZ
samples
(Goldberg
and
Chengappa,
 2009).
While
novel
approaches
for
improving
cognition
in
BD
samples
are
continually
 emerging
(e.g.
agents
that
enhance
dopaminergic
activity),
there
needs
to
be
more
 effort
to
systematically
test
the
agents
that
have
been
mentioned
above
in
a
clinical
trial
 format
(Burdick
et
al.,
2007).

 
  There
is
a
great
paucity
of
research
investigating
nonpharmacological,
behavioral
  interventions
for
cognitive
improvement
in
BD
samples
as
compared
to
similar
research
 in
SZ.
In
SZ,
cognitive
remediation
strategies
have
been
shown
to
be
effective
in
 improving
neurocognitive
functioning
on
specific
cognitive
measures
(Penades
et
al.,
 2006).
Given
that
psychosocial
approaches
have
been
shown
to
be
effective
in
BD
for
 affective
symptom
management,
there
is
great
potential
for
cognitive
remediation
to
be
 beneficial
to
these
patients
(Balanza‐Martinez
et
al.,
2009).
Although
such
programs
are
 currently
unavailable,
several
groups
are
working
towards
developing
such
cognitive
 remediation
strategies
for
BD.

 
 
  
  
 
 
  
  46
  3.
Sex
differences
in
Cognitive
Domains
Impaired
in
bipolar
disorder

 
  This
chapter
centers
on
summarizing
sex
differences
found
in
the
cognitive
  domains
of
attention/processing
speed,
verbal
learning/memory,
and
executive
 function
in
healthy
samples.
As
discussed
in
Chapter
2,
these
are
the
cognitive
domains
 that
are
the
most
consistently
impaired
in
BD.
Before
these
impairments
are
delineated,
 however,
the
neurobiological
origin
of
sex
differences
in
cognitive
functioning
will
be
 explained;
studies
concerning
the
morphological
and
physiological
sexual
dimorphisms
 of
the
brain
in
healthy
populations
will
be
presented.
 3.1
Sexual
dimorphisms
of
the
whole
brain
in
healthy
populations

 The
diverse
and
impactful
actions
of
gonadal
hormones
during
brain
 development,
along
with
contributions
from
sex
chromosomes
and
experience,
 accurately
predict
the
existence
of
structural
and
functional
brain
differences
between
 sexes.
Male
and
female
tissues
differ
at
every
level
of
organization
from
histological
to
 morphological
and
physiological
(Cahill,
2006).
In
humans,
the
liver
and
kidneys
display
 noted
sex
differences
that
ultimately
influence
organ
functionality
in
healthy
and
 disease
states
(Lenroot
and
Giedd,
2010).
But
without
doubt,
the
most
influential
 sexually
dimorphic
organ
in
humans
is
the
brain.
Again
the
reverberations
of
these
 sexual
dimorphisms
can
be
seen
in
the
healthy
(cognitive
sex
differences)
and
the
 diseased
(phenomenological
differences
on
mental
disorders).
Literature
in
area
of
 human
sexual
dimorphisms
of
the
brain
is
vast.
Here,
a
review
of
sex
differences
in
gross
 brain
morphology
and
physiology
will
be
presented
that
is
restricted
to
features
of
the
  
  47
  whole
brain
and
individual
lobes.
These
data
were
derived
from
humans
in
healthy
 populations.





 
  Morphology.
Brain
structure
was
historically
investigated
with
postmortem
  studies
and
then
later
in
vivo
with
computerized
tomography
and
magnetic
resonance
 imaging
(MRI).
Converging
evidence
from
studies
conducted
with
various
protocols
in
 children
and
adults
has
reliably
shown
that
males
have
larger
brain
volumes
than
 females,
even
after
accounting
for
body
size
(Witelson,
2006;
Allen
et
al.,
2003;
 Nopoulos,
2000).
It
has
been
estimated
that
male
brain
volumes
exceeding
female
brain
 volumes
by
approximately
9‐12%
with
the
average
male
brain
measureing
1260cc
and
 the
average
female
brain
measuring
1130cc
when
excluding
CSF
and
non‐neural
tissues
 (Witelson
et
al.,
2006).
Males
also
show
greater
ventricular
and
CSF
volumes
than
 females.
These
robust
data
regarding
total
brain
volume
(TBV),
though
interesting
when
 considered
alone,
become
increasingly
important
when
investigating
volumetric
sex
 differences
in
any
subsection
of
the
brain
(Lenroot
and
Giedd,
2010).
TBV
must
be
 accounted
for
when
measuring
sex
differences
in
grey
matter
(GM)
content,
white
 matter
(WM)
content,
and
volumes
of
individual
lobes
and
substructures.
The
 sometimes‐discrepant
findings
in
literature
investigating
volumetric
sex
differences
 arise,
in
part,
due
to
different
statistical
strategies
taken
in
accounting
for
TBV.
The
two
 major
statistical
strategies
employed
are
normalization,
where
the
target
volumes
are
 each
divided
by
the
TBV
measure,
and
covariance,
where
the
TBV
measure
is
used
as
a
 covariate
in
all
parametric
analysis.
In
addition,
the
measure
of
TBV
can
include
or
 discount
the
skull.

  
  48
  At
times,
employing
different
strategies
in
statically
accounting
for
TBV
can
 nullify,
moderate,
or
reverse
findings
of
volumetric
sex
differences.
This
is
well
 demonstrated
in
literature
investigating
sex
differences
in
GM
content.
In
a
study
where
 data
were
covaried
for
intracranial
volume,
height,
and
weight,
total
GM
percentage
 was
found
to
be
higher
in
females
(Luders
et
al.
2004).
In
another
study
that
 investigated
GM
as
a
percentage
of
total
intracranial
volume,
men
were
found
to
have
a
 higher
percentage
of
GM
(Good
et
al.,
2001).
Many
more
studies
have
investigated
 GM/WM
ratios
rather
than
GM
or
WM
volumes
alone.
In
these
studies,
many
but
not
all
 reported
higher
GM/WM
ratios
in
females
in
the
whole
brain
as
well
as
several
brain
 regions
(Peters
et
al.,
1998;
Gur
et
al.,
1999).
In
addition
to
sex
differences
in
GM
and
 WM
content,
studies
have
found
differing
developmental
trajectories
of
GM
and
WM
 volumes
in
males
versus
females.
For
example,
multiple
studies
have
found
that
WM
 volumes
increase
more
rapidly
in
males
(Lenroot
and
Giedd,
2010;
Perrin
et
al.,
2008).
 These
changes
are,
in
part,
attributable
to
gonadal
steroids.
There
is
evidence
to
 support
that
total
GM
volume
and
GM
volumes
of
specific
regions
positively
correlate
 with
estradiol
in
developing
females.
Volumetric
changes
in
GM
and
WM
can
also
be
 induced
by
age
in
a
sex‐dependent
manner
(Sowell
et
al.,
2004).
Studies
in
the
realm
of
 sex
differences
in
GM/WM
content
continue
to
be
clarified.

 There
have
also
been
consistent
reports
of
sex
differences
in
cortical
 morphometry.
Post‐mortem
studies
in
adults
have
found
higher
neuronal
densities
in
 granular
layers
of
the
cortex
in
females,
while
higher
overall
neuronal
density
and
 number
is
observed
in
males,
regardless
of
body
size.
Higher
synaptic
density
  
  49
  throughout
the
cortex
is
also
found
in
males.
Some
studies
have
found
greater
overall
 surface
area,
cortical
gyrification,
and
cortical
complexity
in
females
(Luders
et
al.,
 2004).
Post‐mortem
and
neuroimaging
studies
have
been
inconclusive
in
determining
 sex‐differences
in
cortical
thickness.
Some
studies
have
found
greater
cortical
thickness
 in
females
and
other
studies
have
found
greater
cortical
thickness
in
males,
with
TBV
 being
accounted
for
in
all
cases
(Lenroot
and
Giedd,
2010).
The
discrepancies
found
in
 these
results
have
been
attributed
to
the
compounding
effects
of
varying
imaging
and
 statistical
techniques.



 
  Physiology.
In
addition
to
sex
differences
found
in
brain
morphology,
sex
  differences
in
brain
function
have
also
been
reported.
These
data
were
acquired
from
 neuroimaging
studies
employing
functional
magnetic
resonance
imaging
(fMRI),
 positron
emission
tomography
(PET),
and
single‐photon
emission
computed
 tomography
(SPECT).
Some
imaging
studies
have
been
conducted
while
the
participant
 is
at
rest
(Devous
et
al.,
1986);
however,
many
are
conducted
while
the
participant
is
 engaged
in
some
cognitive
activity
(Jones,
et
al.
1998;
Esposito
et
al.,
1996).
A
majority
 of
studies
have
found
that
females
exhibit
greater
total
cerebral
blood
flow
(CBF)
than
 males
during
both
rest
and
cognitive
activity.
In
alignment
with
these
finding,
several
 studies
have
found
higher
cerebral
metabolic
rate
of
glucose
utilization
(CMRglu)
in
 females,
though
not
all
studies
have
replicated
this
claim.
It
has
also
been
suggested
 that
the
CMRglu
found
in
females
is
an
artifact
of
their
own
average
smaller
brain
size
as
 compared
to
men
as
CMRglu
inversely
correlates
to
brain
size
(Hatazawa
et
al.,
1987).

 CMRglu
is
known
to
vary
across
the
menstrual
cycle
suggesting
that
brain
metabolism
  
  50
  may
be
modified
by
hormonal
action
(Reiman
et
al.,
1998).
Brain
activation
levels
have
 also
reported
sex
differences,
even
in
studies
controlling
for
cognitive
ability.
One
study
 reported
increased
bilateral
activation,
as
opposed
to
regional
activation,
in
females
 versus
males
in
response
to
a
cognitive
task
(Cosgrove
et
al.,
2007);
although,
the
 concept
of
increased
bilaterality
in
females
versus
males
remains
a
highly
disputed
 issue.
Like
CMRglu,
brain
activation
during
cognitive
tasks
fluctuates
across
the
 menstrual
cycle.
The
sex
steroid
mechanisms
that
allow
for
this
modulation
in
activation
 are
still
under
question
(Cosgrove
et
al.,
2007).
Nevertheless,
the
acknowledgment
that
 sex
steroids
impact
brain
physiology
during
the
menstrual
cycle
should
be
accounted
for
 in
the
methodology
of
future
studies
in
this
area.
Widespread
sex
differences
are
also
 abundant
in
the
serotonergic
and
glutamatergic
function
(Cosgrove
et
al.,
2007).
These
 differences
may
have
profound
affects
in
both
healthy
and
abnormal
(psychiatric)
 states.

 3.2
Sex
differences
in
cognitive
domains
impaired
in
bipolar
disorder
 
  With
the
ubiquitous
influence
of
sex
at
the
neural
level,
it
is
unsurprising
that
  these
are
sex
differences
at
the
behavioral
level.
With
regards
to
sex
differences
in
 cognition,
the
classic
dichotomy
holds
that
men
are
better
at
spatial
abilities
while
 women
are
better
at
verbal
abilities;
however,
even
as
an
overarching
summary
 describing
patterns
of
cognitive
strengths,
this
may
be
an
oversimplification
(Andreano
 and
Cahill,
2009).
Men
are
not
better
at
all
spatial
abilities,
and
the
cognitive
tactics
that
 lend
to
the
female
advantage
in
the
verbal
domain
confer
similar
advantage
into
realms
 that
are
not
explicitly
verbal.
In
addition,
the
notion
of
a
performance‐based
advantage
  
  51
  as
the
key
metric
for
assessing
sex
differences
in
cognition
is
now
being
widely
 challenged.
With
the
advent
of
experimental
paradigms
that
marry
human‐brain
 imaging
techniques
with
cognitive
testing,
it
is
now
being
understood
that
on
many
 cognitive
tasks,
males
and
females
use
differing
neural
strategies
to
perform
at
a
similar
 level
(De
Vries,
2004).
Rather
than
searching
for
performance‐based
differences
on
 psychometric
tasks,
many
researchers
investigating
the
influence
of
sex
on
cognition
 have
now
shifted
their
focus
to
unraveling
these
distinctions
in
cognitive
strategy.


 
  Over
four‐decades
of
literature
concerning
cognitive
sex
differences
has
  amassed.
Given
the
sheer
volume
of
literature
that
is
available,
a
thorough
review
is
 inappropriate
for
this
thesis.
Briefly,
the
most
robust
and
widely
reported
cognitive
sex
 difference
is
the
male
advantage
seen
in
mental
spatial
rotation
(Andreano
and
Cahill,
 2009;
Silverman
et
al.,
2007).
However,
it
is
thought
that
this
advantage
is
restricted
to
 the
visuo‐spatial
component
of
this
task.
Studies
investigating
sex
differences
in
spatial
 working
memory,
a
cognitive
domain
also
tested
by
mental
rotation,
have
found
 equivocal
results
(Andreano
and
Cahill,
2009).
A
large
and
consistent
male
advantage
is
 also
seen
in
tests
of
navigation,
with
men
completing
tasks
more
quickly
and
with
 greater
accuracy
(Galea
and
Kimura,
1993;
Silverman,
2000).
Interestingly,
a
significant
 body
of
research
has
established
that
men
and
women
use
distinct
cognitive
strategies
 when
partaking
in
visuo‐spatial
tasks;
women
have
increased
neural
activation
in
right
 frontal
regions,
while
significant
activation
is
restricted
to
parietal
regions
in
men
 (Andreano
and
Cahill,
2009).
Several
more
studies
indicate
that
in
tasks
involving
 remembering
objects
in
an
array,
women
have
an
advantage;
again,
men
and
women
  
  52
  are
thought
to
employ
different
cognitive
strategies
when
completing
object
location
 memory
task
(Levy
et
al.,
2005;
Silverman
et
al.,
2007).
A
female
advantage
is
also
 observed
in
tasks
of
verbal
memory,
episodic
and
autobiographical
memory,
some
tasks
 involve
processing
speed,
and
emotional
memory
(Andreano
and
Cahill,
2009).
 However,
effect
sizes
for
these
differences
are
less
large
than
those
consistently
 observed
in
mental
rotation
and
there
is
much
heterogeneity
in
the
literature.
 
  For
the
purposes
of
this
thesis,
literature
regarding
sex
differences
in
the
  cognitive
domains
that
are
consistently
found
to
be
impaired
in
BD
will
be
discussed.
 The
domains
of
focus
include
attention/processing
speed,
verbal
learning
and
memory,
 and
executive
function:

 
  3.2.1
Attention/processing
speed
 Attention
is
complex
cognitive
domain
that
has
not
been
traditionally
associated
  with
sex
differences
(Maccoby
and
Jacklin,
1974).
However,
there
have
been
several
 standardized
tests
of
attention
and
processing
speed
that
have
found
sex
differences
in
 their
normative
data.
Studies
have
found
that
women
perform
more
poorly
than
men
 on
the
Continuous
Performance
Test
(CPT)
(Chen
et
al.,
1998).
The
CPT
is
a
strenuous
 and
sometimes
lengthy
test
of
sustained
attention.
In
these
types
of
tasks,
the
 participant
must
pay
attention
to
a
continuous
stream
of
stimuli
while
responding
to
a
 sporadically
presented
preset
target
stimulus.
In
one
study
with
816
participants,
 women
were
found
to
have
longer
reaction
times
to
responding
the
targets
stimulus
 and
were
found
to
have
decreased
accuracy
(Conners
et
al.,
2003).

  
  53
  As
the
CPT
requires
a
motor
reaction
(e.g.
such
as
clicking
a
mouse)
in
response
 to
target
sequences,
an
important
aspect
to
consider
on
these
tests
is
psychomotor
 speed.
Several
studies
have
shown
that
psychomotor
speed
is
faster
in
men.
In
one
 study
of
7979
individuals
30
years
or
older,
reaction
time
was
shown
to
be
shorter
in
 men
across
all
age
groups,
where
an
age
group
width
was
10
years
(Era
et
al.,
2011).
 This
male
advantage
is
seen
throughout
the
lifetime;
a
study
of
1799
older
adults
found
 poorer
attention
and
psychomotor
performance
in
women
(Mazaux
et
al.,
1995).
 However,
it
has
been
suggested
that
the
male
advantage
in
psychomotor
speed
is
 conferred
solely
by
way
of
the
motor
component.
In
processing
speed,
women
may
 have
a
slight
advantage.
In
a
task
where
participants
were
asked
to
simply
name
colors
 and
forms,
rather
than
press
a
button
in
response
to
the
forms,
females
performed
 better
(Kimura
et
al.,
1996).
Conversely,
men
were
found
to
have
better
reaction
times
 on
a
Stroop
Test;
the
Stroop
test
is
a
test
of
selective
attention
and
processing
speed
 (Alansari,
2006).

 Several
other
cognitive
tests
of
attention
have
shown
sex
differences.
The
Trail
 Making
Test
(Part
A
and
Part
B;
TMT)
is
another
test
of
attention
in
which
sex
 differences
have
been
found.
In
Part
A
of
this
paper
and
pencil
test,
the
participant
is
 presented
with
an
array
of
digits
from
1
to
24
that
are
placed
randomly
throughout
the
 test
sheet.
The
participant
is
requested
to
connect
the
numbers
in
sequential
order
by
 drawing
lines
between
them.
In
Part
B,
the
test
sheet
contains
a
random
array
of
letters
 and
numbers.
The
participant
is
asked
to
draw
lines
between
the
numbers
and
letters
 such
that
letter
and
number
is
alternated
with
the
letter
in
alphabetical
order
and
the
  
  54
  numbers
in
numerical
order
(e.g.
A‐1‐B‐2,
etc.).
Studies
with
larger
sample
sizes
have
 found
a
sex
difference
in
the
TMT
favoring
men
(Wiederhold
et
al.,
1993);
some
studies
 have
reported
separate
data
for
men
and
women
(Elias
et
al.,
1993).
However,
studies
 with
smaller
samples
sizes
stratified
by
age
group
have
failed
to
consistently
replicate
 this
result
(Soukup
et
al.,
1998).
The
Paced
Auditory
Serial
Addition
Test
(PASAT)
is
a
test
 of
information
processing
requiring
the
participant
to
add
digits
as
they
are
presented
in
 a
continuous
manner.
This
task
requires
the
participant
to
attend
to
the
next
stimulus
 while
maintain
the
current
total
in
short
term
memory.
Normative
data
for
the
PASAT
 report
sex
differences
in
performance.
Interestingly,
the
direction
of
the
difference
is
 dependent
on
ethnicity,
with
male
advantage
seen
in
some
ethnicities
and
a
female
 advantage
being
seen
in
others
(Diehr
et
al.,
1998).


 While
the
sex
differences
reported
in
adult
samples
have
been
highly
 heterogeneous,
there
seems
to
be
a
consistent
female
advantage
in
attention
in
 children
and
adolescents.
In
a
study
that
tested
1,100
girls
and
1,100
boys
with
the
 Cognitive
Assessment
System
(CAS)
battery,
girls
were
found
to
be
better
than
boys
in
 the
attention
subtest
(Naglieri
and
Johannes,
2001).
The
CAS
is
a
battery
informed
by
 A.R.
Luria’s
Planning,
Attention‐Arousal,
Simultaneous,
and
Successive
theory
of
 intelligence
and
the
CAS
battery
has
been
found
to
successfully
detect
frontal
lobe
 deficits.
In
another
study,
400
Finnish
children
were
tested
on
a
broad
array
of
cognitive
 tasks,
including
tasks
of
attention.
Tasks
were
constructed
to
be
age
appropriate
to
the
 participant,
however,
all
attention
task
required
the
participant
to
find
targets
in
an
  
  55
  array
of
distracters.
In
this
study,
girls
of
all
age
groups
performed
better
than
boys
in
 both
speed
and
accuracy
(Klenberg
et
al.,
2001).
 Overall,
there
is
much
variation
in
the
reports
of
sex
differences
in
attention
and
 processing
speed.
Partly
in
response
to
this
variation,
studies
have
attempted
to
 understand
whether
attention
is
produced
via
the
same
neural
pathways
in
women
and
 men.
There
is
some
evidence
that
women
and
men
employ
different
cognitive
strategies
 in
response
to
a
task
require
attention.
In
an
event‐related
potential
(ERP)
study
 employing
the
Attentional
Network
Test,
a
test
of
selective
attention,
alerting
and
 orienting,
increased
prefrontal
activity
was
seen
in
women
but
not
in
men
event
though
 both
sexes
performed
equally
well
on
the
task
(Neuhaus
et
al.,
2009).




 3.2.2
Verbal
learning
and
memory
 The
female
advantage
in
verbal
abilities
was
well
documented
by
Maccoby
and
 Jacklin’s
(1974)
influential
review
of
sex
differences
in
cognition.
The
body
of
literature
 developed
since
that
time
has
robustly
corroborated
their
findings.
Though
this
 literature
has
investigated
a
variety
of
verbal
abilities,
only
a
subset
of
these
findings
 pertains
to
verbal
learning
and
memory.
The
cognitive
tests
of
verbal
learning
and
 memory
include
the:
Controlled
oral
Word
Association
Test
(COWAT),
Rey
Auditory
 Verbal
Learning
Test
(RAVLT),
and
California
Verbal
Learning
Test
(CVLT)
(Andreano
and
 Cahill,
2009).
The
COWAT
is
a
measure
of
verbal
fluency;
in
this
test
the
participant
is
 given
a
letter
or
a
semantic
category
and
is
asked
to
generate
as
many
words
as
possible
 with
that
letter/category.
Studies
of
verbal
learning
can
be
thought
to
be
measuring
 vocabulary
and
semantic
verbal
memory.
On
the
other
hand,
the
CVLT
and
the
RAVLT
  
  56
  are
thought
to
more
concretely
measure
episodic
recall.
In
these
tasks,
the
participant
is
 asked
to
recall
word
lists
before
and
after
a
delay
of
several
minutes
(Andreano
and
 Cahill,
2009).

 Tasks
of
verbal
memory
and
fluency
show
a
distinct
advantage
for
women.
 Females
have
performed
better
on
studies
of
phonological
and
semantic
fluency
(Thilers
 et
al.,
2007).
Superior
recall
of
word
lists
is
observed
in
women
in
several
more
studies
 (Kimura
and
Seal,
2003).
In
addition
to
standardized
measures
such
as
the
CVLT,
RAVLT,
 and
Weschler
Adult
Intelligence
test,
better
episodic
recall
in
females
is
also
seen
in
 studies
that
measured
paired‐associates
learning,
story
recall,
and
verbal
recognition
 (Andreano
and
Cahill,
2009).
Furthermore,
the
female
advantage
in
broad
verbal
 capabilities
including
verbal
memory,
are
seen
even
before
puberty
(Kramer
et
al,
1997).
 Therefore,
it
is
likely
that
organizational
rather
than
activational
effects
produce
this
 wide‐ranging
female
verbal
advantage.
In
one
study
that
matched
male
and
female
 groups
by
estradiol
level,
women
nonetheless
performed
better
on
a
verbal
recall
task.
 There
is
also
evidence
to
support
that
this
female
verbal
advantage
is
seen
across
the
 lifetime;
in
studies
controlling
for
differences
in
education
level,
women
were
found
to
 perform
better
than
men
in
all
age
groups.
Decline
of
verbal
abilities
is
also
thought
to
 occur
at
a
significantly
earlier
age
in
men
(Andreano
and
Cahill,
2009).

 Given
these
lifetime
differences
seen
in
verbal
abilities,
researchers
have
 proposed
that
there
is
a
fundamental
difference
in
the
way
that
men
and
women
 neurobioloigcally
process
languages.
It
has
been
suggested
that
women
process
 language
more
bilaterally
than
men
who
process
language
in
a
left‐lateralized
manner.
  
  57
  In
support
of
this
is
theory
is
a
study
finding
that
language
capability
is
spared
after
left
 temporal
lobectomy
in
women
but
not
in
men
(Trenery
et
al.,
1995).

Additionally,
in
 neuroimaging
studies
where
participants
brains
are
imaged
during
the
learning
for
 foreign
words,
left‐lateralized
fusiform
activity
is
seen
in
men
while
bi‐lateral
fusiform
 activity
is
seen
in
women
(Chen
et
al.,
2007).
These
neural
distinctions
between
men
 and
women
may
also
be
expressed
in
terms
of
differences
in
cognitive
strategy.
It
is
 thought
that
women
show
a
higher
degree
of
semantic
and
phonological
clustering
in
 verbal
recall
than
men
(Andreano
and
Cahill,
2009).



  
  3.2.3
Executive
function
 
  Like
attention,
executive
function
is
a
cognitive
domain
that
has
not
traditionally
  been
associated
with
sex
differences
(Maccoby
and
Jacklin,
1974).
Generally,
a
 performance‐based
difference
is
not
noticed
in
these
higher
cognitive
tasks
of
working
 memory,
planning,
mental
flexibility,
attentional
set‐shifting,
and
problem
solving.
 However,
the
lack
of
a
performance‐based
difference
does
not
necessitate
that
men
and
 women
perform
these
mental
functions
in
the
exact
same
manner
(Cahill,
2006).
For
 example,
there
are
several
studies
that
indicate
that
working
memory
is
processed
 differentially
in
men
and
women
(Speck
et
al.,
2000).
Working
memory
is
defined
as
the
 ability
to
temporarily
maintain
and
manipulate
information
in
short‐term
memory.
The
 brain
regions
that
are
implicated
in
working
memory
include
the
dorsolateral
prefrontal
 cortex
(DLPFC),
inferior
prefrontal
cortex,
areas
of
the
parietal
lobe,
and
the
anterior
 cingulate.
Sex
differences
in
terms
of
both
volume
and
neuronal
density
have
been
  
  58
  found
in
several
regions
thought
to
be
involved
in
working
memory
(Janowsky
et
al.,
 2000).
 
 
  These
neuroanatomical
differences
between
men
and
women
seem
to
influence
  how
working
memory
is
processed
in
the
brain.
Neuroimaging
studies
have
found
 differing
activational
patterns
between
men
and
women
when
participating
in
working
 memory
tasks.
In
two
studies
employing
positron
emission
tomography
(PET),
sex
 differences
in
signal
intensity
was
found
in
somatosensory
cortex
and
anterior
cingulate
 gyrus
(Esposito
et
al.,
1996;
Speck
et
al.,
2000).
Functional
MRI
studies
have
found
that,
 while
the
same
brain
regions
were
activated
in
response
to
a
working
memory
task
 (DLPFC,
parietal
cortex,
and
the
caudate),
men
had
bilateral
or
right‐lateralized
 activation
in
these
regions
while
women
showed
left‐lateralized
activity.
Another
fMRI
 study
found
that
women
had
more
signal
intensity
in
the
middle,
inferior,
and
orbital
 prefrontal
cortices
(Goldstein
et
al.,
2005).
A
review
of
fMRI
studies
investigating
 working
memory
found
that
studies
in
which
men
and
women
are
analyzed
separately
 differ
from
studies
employing
mixed‐sex
samples
in
terms
of
activational
patterns.
 Therefore,
it
was
concluded
that
combining
men
and
women
on
fMRI
studies
of
 cognition
may
obscure
or
bias
results
(Goldstein
et
al.,
2005).
Studies
finding
sex
 differences
in
activational
patterns
of
working
memory
processing
have
been
more
 consistent
than
those
that
report
performance‐based
differences
in
working
memory;
 some
studies
of
visuo‐spatial
working
memory
have
found
sex
differences
favoring
 males,
although
studies
finding
the
null
effect
are
equivocal
(Andreano
and
Cahill,
 2009).


  
  59
  
  It
is
thought
that
these
differences
in
activational
patterns
between
men
and
  women
on
working
memory
tasks
reflect
distinct
problem
solving
strategies,
 neurodevelopmental
differences,
or
a
combination
of
both.
Supporting
the
 neurodevelopmental
theory
is
evidence
that
suggests
that
working
memory
is
 modulated
by
hormones.
In
studies
of
postmenopausal
women,
those
who
received
the
 estrogen‐based
hormone
replacement
therapy
fared
better
on
a
task
of
working
 memory
than
those
who
did
not
receive
hormone
replacement
therapy;
in
a
with‐in
 subjects
test‐retest
design,
women
fared
better
on
a
working
memory
task
after
having
 received
hormone
replacement
therapy
(Janowsky
et
al.,
2000).
Preclincal
evidence
has
 also
shown
that
extradiol
affects
working
memory
in
rats.
Another
study
found
that
 increasing
the
testosterone/estrogen
ratio
by
way
of
testosterone
replacement,
 improves
working
memory
in
men.
Sex‐steroids
and
menstrual
cycle
phase
have
also
 shown
to
affect
patterns
of
activation
in
an
fMRI
study
in
which
participants
perform
a
 visuo‐spatial
working
memory
task
(Janowski
et
al.,
2000).


 
  In
addition
to
working
memory,
there
is
limited
evidence
to
report
that
there
  may
be
sex
differences
in
the
planning
component
of
executive
function.
The
Tower
of
 London
task
is
a
task
of
planning
that
requires
the
participant
to
rearrange
colored
 beads
on
a
series
of
three
pegs
so
that
they
match
an
arrangement
provided
by
the
 experimenter.
Difficulty
of
the
task
can
increase
by
increasing
the
number
of
beads
and
 increasing
the
number
of
pegs.
This
task
is
thought
to
assess
frontal
lobe
function.
 Studies
have
found
a
male
advantage
on
a
computerized
Tower
of
London
task
and
an
 analogous
Tower
of
Hanoi
task
(De
Luca
et
al.,
2003;
Bishop
et
al.,
2001).
One
fMRI
  
  60
  study
also
found
different
activation
pattern
between
men
and
women
while
they
were
 participating
in
a
Tower
of
London
task,
although
others
have
failed
to
replicate
the
 finding
(Boghi
et
al.,
2006).
There
is
some
indication
that
planning
is
sexually
 differentiated
during
childhood
and
preadolescence
as
well.
In
a
study
testing
1100
boys
 and
1100
girls
found
a
female
advantage
on
the
planning
component
of
the
CAS
 (described
above;
(Naglieri
and
Johannes,
2001).).
 


  Several
other
components
of
executive
function
do
not
show
appreciable
sex
  differences;
among
others,
these
include
set‐shifting,
mental
flexibility,
problem‐solving
 capacity.
Though
there
are
studies
that
have
found
sex‐differences
in
multi‐tasking
and
 risk‐assessment/inhibition,
these
constructs
have
been
studied
through
unstandardized
 or
observational
means
in
healthy
samples.
Overall,
men
and
women
perform
similarly
 on
tasks
of
executive
function.
However,
they
may
take
different
neural
strategies
to
 receive
the
same
behavioral
outcome
(Goldstein
et
al.,
2005).
 
 
 
 
  
  61
  4.
The
influence
of
sex
on
cognitive
functioning
in
first‐episode
bipolar
 disorder
I
patients
 
 As
outlined
in
Chapter
3,
decades
of
research
have
revealed
that
the
human
 brain
is
sexually
dimorphic
at
every
level
of
neural
organization
from
cytoarchitecture
to
 gross
morphology
(Cahill,
2006).
However,
the
manner
in
which
this
sexual
 differentiation
extends
to
the
behavioral
and
cognitive
levels
is
not
easy
to
predict.
 Previously,
the
widespread
misconception
was
held
that
if
no
sex
difference
exists
for
a
 particular
behavior,
the
neural
substrates
that
are
responsible
for
that
behavior
function
 identically
in
both
sexes
(De
Vries,
2004).
Yet,
sex
differences
in
the
human
brain
are
far
 more
ubiquitous
than
are
the
behavioral
differences
observed
between
men
and
 women.
The
advent
of
neuroimaging
studies
has
helped
to
resolve
this
apparent
 discrepancy.
From
these
studies,
it
was
found
that
men
and
women
often
employ
 different
neural
strategies
to
reach
the
same
behavioural
endpoint
(De
Vries,
2004).
 That
is,
performance‐based
indices
on
tests
of
cognition
are
not
sufficient
in
detecting
 sex
differences
in
neural
mechanisms.
For
example,
while
visuo‐spatial
and
verbal
 cognitive
tasks
often
show
sex
differences
in
performance,
men
and
women
perform
 equally
well
on
most
tasks
of
executive
function;
nevertheless,
it
has
been
found
that
 men
and
women
activate
distinctly
different
brain
areas
when
performing
these
tasks
 (Goldstein
et
al.,
2005).

 These
neurobiological
distinctions
between
men
and
women
have
consequences
in
 the
psychiatric
realm.
There
are
often
pronounced
sex
differences
in
the
prevalence
 rates
of
both
neurological
and
neurophyciatric
disorders.
Illnesses
that
are
over
75%
  
  62
  more
common
in
women
than
in
men
include:
Rett
syndrome,
lymphocytic
 hypophysitis,
anorexia
nervosa,
and
bulimia;
Illnessess
that
are
over
75%
more
common
 in
men
include:
Tourette’s
syndrome,
autism,
ADHD,
and
dyslexia
(Bao
and
Swaab,
 2010).
Moreover,
in
several
neuropsychiatric
disorders,
sex
differences
are
seen
in
the
 signs,
symptoms,
and
course
of
the
illness.
SZ
is
2.7
times
more
common
in
men
than
in
 women.
In
addition,
males
with
SZ
are
prone
to
a
more
severe
form
of
the
illness,
have
 poorer
pre‐morbid
functioning,
earlier
onset,
more
negative
symptoms
and
cognitive
 deficits,
and
exhibit
a
greater
number
of
structural
brain
abnormalities.
Studies
have
 also
shown
that
males
with
SZ
experience
more
severe
relapses
and
are
more
treatment
 resistant
to
neuroleptic
medication
(Abel
et
al,
2010).
Cognitive
deficits
also
feature
 largely
in
the
pathophysiology
of
SZ;
recent
studies
suggest
that
healthy
patterns
in
 cognitive
functioning
are
disrupted
in
SZ
patients
such
that
male
and
female
patients
 perform
more
similarly
than
to
healthy
men
and
women
(Mendrek,
2007;
Vaskinn
et
al.,
 2011).

 Similar
to
SZ,
sex
differences
have
been
observed
in
the
phenomenology
of
BD.
 These
differences
in
clinical
presentation
and
course
were
summarized
in
Chapter
3.
Far
 fewer
studies
have
investigated
whether
healthy
patterns
of
cognitive
functioning
are
 maintained
in
BD.
The
finding
that
sex
differences
in
cognitive
functioning
are
 attenuated
in
SZ
is
of
relevance
to
BD
research
as
SZ
and
BD
share
aspects
of
their
 psychopathology,
neurobiology,
and
treatment
efficacy.
Genetic
studies
have
also
 shown
that
BD
and
SZ
share
some
degree
of
genetic
susceptibility
(Hill
et
al.,
2008).
 Given
the
close
relationship
between
SZ
and
BD,
sex
differences
in
cognitive
functioning
  
  63
  in
BD
samples
warrants
some
investigation.
Assessing
sex
difference
in
cognitive
 functioning
can
have
both
etiological
and
therapeutic
value.
For
example,
the
finding
 that
sex
differences
in
cognitive
functioning
are
attenuated
in
SZ
led
researchers
to
the
 understanding
that
the
factors
that
produce
sexual
dimorphisms,
which
in
turn
are
the
 factor
that
result
in
sex
differences
in
cognitive
functioning,
may
be
associated
with
the
 insults
that
produce
SZ
(Bao
and
Swaab,
2010).
Similarly,
the
finding
of
altered
patterns
 of
sex
differences
in
BD
is
potentially
important
because
it
suggests
that
the
biological
 mechanisms
underlying
normal
sex
differences
may
also
be
implicated
in
the
etiology
of
 BD.
A
greater
understanding
of
the
neural
mechanisms
that
produce
this
illness
in
both
 men
and
women
may
lead
to
better
therapeutic
strategies
and
greater
functional
 recovery
for
both
sexes.

 4.1
Introduction
 
 To
date,
only
three
studies
have
directly
investigated
sex
differences
in
cognitive
 functioning
within
a
BD
sample.
The
first
study,
conducted
by
Barrett
et
al.
(2008),
 examined
26
patients
diagnosed
with
BD
and
matched
healthy
control
subjects
on
 measures
of
spatial
working
memory
(SWM),
planning
and
attentional
set‐shifting
using
 the
Cambridge
Automated
Neuropsychological
Testing
Battery
(CANTAB),
as
well
as
 verbal
fluency
using
the
Controlled
Oral
Word
Association
Task
(COWAT).
They
found
a
 significant
diagnosis
by
sex
interaction
in
their
SWM
strategy
scores
such
that
their
male
 patients
performed
worse
than
their
female
patients
while
their
male
controls
 outperformed
their
female
controls.
In
the
second
study
by
Carrus
et
al.
(2010),
86
BDI
 patients
and
matched
healthy
controls
were
tested
on
various
tasks
of
general
  
  64
  intellectual
ability
and
declarative
memory
using
the
Wechsler
Memory
Scale‐III,
 Wechsler
Adult
Intelligence
Test‐Revised,
Hayling
Sentence
Completion
Task,
and
the
 Wisconsin
Card
Sorting
Test.
They
found
a
significant
diagnosis
by
sex
interaction
on
 measures
of
immediate
memory,
where
male
patients
performed
worse
than
female
 patients
and
healthy
controls.
In
the
largest
and
most
recent
study
conducted
by
 Vaskinn
et
al.
(2011),
106
patients
with
BD‐I
and
matched
healthy
controls
were
tested
 with
various
subtests
of
the
Wechsler
Adult
Intelligence
Scale.
Results
from
this
study
 failed
to
reveal
a
diagnosis
by
sex
interaction
across
the
full
sample
of
patients
with
 bipolar
disorder.

However,
there
was
a
suggestion
that
males
with
BD‐I
and
a
history
of
 psychosis
showed
preferentially
diminished
delayed
verbal
recall.
In
sum,
compared
to
 healthy
individuals,
the
existing
literature
suggests
that
males
with
BD
may
show
a
 relative
disadvantage
in
spatial
working
memory
and
memory
functioning.

 However,
 a
 clear
 interpretation
 of
 these
 results
 is
 obscured
 by
 heterogeneity
 present
 in
 the
 patient
 cohort
 of
 existing
 studies.
 For
 example,
 male
 patients
 in
 the
 Barrett
 et
 al.
 (2008)
 were
 older,
 more
 symptomatic,
 and
 had
 experienced
 a
 greater
 number
 of
 mood
 episodes
 than
 their
 female
 patients.
 These
 factors
 when
 paired
 with
 the
small
sample
size
of
this
study
may
have
biased
the
results.
Similarly,
in
the
Carrus
 et
 al.
 study
 (2010)
 a
 significantly
 greater
 number
 of
 female
 patients
 had
 a
 history
 of
 psychosis
than
male
patients.
Due
to
this
heterogeneity,
potential
confounding
clinical
 variables,
and
lack
of
cognitive
test
overlap
between
studies,
the
literature
in
this
area
is
 unclear
with
regard
to
the
extent
that
cognitive
impairment
in
BD
is
influenced
by
sex.

  
  65
  The
present
study
tested
66
BDI
euthymic
patients
and
90
matched
healthy
 control
subjects
on
a
broad
battery
of
neurocognitive
tasks.
This
study
attempted
to
 address
some
of
the
methodological
issues
plaguing
previous
studies
by
recruiting
a
 more
homogeneous
group
of
bipolar
I
patients
and
testing
them
immediately
after
 remission
from
their
first
manic
or
mixed
episode.
In
utilizing
a
first‐episode
sample,
the
 influence
of
variables
such
as
chronicity
of
illness
and
cumulative
treatment
effects
on
 cognition
is
likely
minimized.
Additionally,
the
patient
sample
used
is
homogeneous
in
 terms
of
age
and
clinical
characteristics.
Sex
differences
were
assessed
in
healthy
 controls
and
BDI
patients
using
tasks
that
have
been
shown
to
be
sensitive
to
cognitive
 impairments
seen
in
BD
as
well
as
in
unipolar
depression,
and
schizophrenia
(De
Luca
et
 al.,
2003;
Yatham
et
al.,
2010).
Additionally,
the
cognitive
domains
assessed
by
this
 battery
–
verbal
memory,
verbal
fluency,
executive
function,
working
memory
–
are
 those
in
which
sex
differences
have
been
repeatedly
observed
in
healthy
populations
as
 has
been
summarized
in
previous
chapters.
As
such,
the
assessment
of
a
first‐episode
 patient
population
with
this
testing
battery
allows
for
a
methodologically
stringent
 evaluation
of
whether
sex
influences
cognitive
impairment
in
BDI
early
in
its
course.
 4.2
Materials
and
methods
 
  Participants.
The
60
patients
and
90
healthy
controls
enrolled
in
this
study
were
  participants
 of
 the
 Systematic
 Treatment
 Optimization
 for
 Early
 Mania
 (STOP‐EM)
 project.
 STOP‐EM
 is
 a
 comprehensive
 prospective
 study
 assessing
 patients
 who
 have
 recently
experienced
their
first
bipolar
manic
or
mixed
episode
according
to
DSM‐IV‐TR
 criteria.
 A
 complete
 description
 of
 the
 study
 protocol
 has
 been
 provided
 in
 previous
  
  66
  papers
(Yatham
et
al.,
2009).
Briefly,
patients
aged
between
18
and
35
years
who
had
 experienced
a
first
manic
or
mixed
episode
within
the
last
3
months
were
recruited
from
 the
University
of
British
Columbia
affiliated
Hospitals
and
Clinics
via
referrals
from
local
 physicians
and
psychiatrists.
Particpants
were
required
to
be
clinically
stable
during
the
 initial
 study
 assessment.
 All
 diagnostic
 assessments
 were
 conducted
 by
 fully
 qualified
 psychiatrists
utilizing
both
comprehensive
clinical
interviews
and
the
Mini
International
 Neuropsychiatric
Interview.


 
For
 comparison
 purposes,
 healthy
 control
 subjects
 free
 of
 personal
 or
 familial
 psychiatric
 illness
 in
 first
 degree
 relatives
 were
 recruited
 into
 the
 study
 from
 the
 community
 and
 matched
 on
 the
 basis
 of
 sex,
 age,
 premorbid
 IQ,
 and
 educational
 attainment.
Patients
and
controls
were
assessed
at
least
every
six‐months
or
as
clinically
 indicated.
Only
baseline
visit
data
are
used
in
the
present
study.

 
  Psychiatric
assessment.
Clinical
variables
were
collected
following
the
protocol
of
  the
 STOP‐EM
 program
 in
 which
 patients
 were
 provided
 with
 clinically
 indicated
 treatment
 based
 on
 evidence
 based
 treatment
 guidelines
 (Yatham
 et
 al,
 2009)
 and
 clinically
accepted
standards.
Psychiatric
status
at
baseline
and
at
each
6
monthly
visit
 was
assessed
using
several
clinical
rating
scales
including
the:
Young
Mania
Rating
Scale
 (YMRS),
 Montgomery‐Asberg
 Depression
 Rating
 Scale
 (MADRS),
 Hamilton
 Rating
 Scale
 for
Depression
(HAM‐D),
Clinical
Global
Impression
Scale
for
Bipolar
Disorder
(CGI‐BP),
 and
Global
Assessment
of
Functioning
Scale
(GAF).
Information
regarding
the
patient’s
 psychiatric
 history
 including
 number
 and
 type
 of
 prior
 mood
 episodes,
 history
 of
 psychotic
symptoms
during
a
mood
episode,
duration
of
illness,
age
of
onset
of
illness,
  
  67
  number
 of
 hospitalizations,
 and
 lifetime
 substance
 abuse
 or
 dependence
 was
 also
 recorded
as
was
information
regarding
the
dose
and
duration
of
their
pharmacological
 treatments.

 
  Neurocognitive
assessment.
A
2‐3
hour
neurocognitive
battery
was
administered
  to
 participants
 in
 a
 quiet
 environment
 following
 standardized
 testing
 procedures.
 Premorbid
IQ
was
assessed
using
the
North
American
Adult
Reading
Test
Full
Scale
IQ
 (NAART
 FSIQ)
 and
 the
 Kaufman
 Brief
 Intelligence
 Test
 (KBIT)
 IQ
 composite
 score
 was
 used
to
evaluate
current
intelligence.
The
touch
screen
CANTAB
V2
System
was
used
to
 administer
the
cognitive
tests
of
attention,
planning,
and
executive
function.
These
tests
 were
 Stockings
 of
 Cambridge,
 Spatial
 Working
 Memory,
 Paired
 Associates
 Learning,
 Intra/Extra
 Dimensional
 Set
 Shift,
 and
 Rapid
 Visual
 Processing.
 In
 addition
 to
 the
 CANTAB
 tests,
 the
 CVLT‐II
 and
 COWAT
 were
 administrated
 to
 participants
 using
 traditional
paper
and
pencil
methods.
The
details
of
these
tasks
have
been
described
in
 other
papers
(De
Luca
et
al.,
2003).
However,
brief
descriptions
will
be
provided.


 Stockings
 of
 Cambridge
 (SOC):
 In
 this
 computer‐based
 task,
 the
 subject
 is
 presented
with
two
arrangements
of
coloured
balls
hanging
from
stockings
attached
to
 a
 beam.
 The
 participant
 is
 instructed
 to
 move
 one
 ball
 at
 a
 time
 to
 make
 the
 arrangements
 identical
 within
 a
 recommended
 number
 of
 moves.
 SOC
 is
 a
 computerized
 Tower
 of
 London
 Task
 and
 is
 a
 test
 of
 spatial
 planning
 ability
 and
 is
 a
 measure
 of
 frontal
 lobe
 function.
 The
 inhibitory
 control
 component
 required
 in
 such
 tasks
 of
 strategy
 and
 planning
 has
 shown
 moderate
 effects
 in
 bipolar
 disorder
 versus
 controls
 (Yatham,
 2010).
 In
 healthy
 samples,
 several
 studies
 across
 multiple
 testing
  
  68
  paradigms
 have
 found
 that
 males
 outperform
 females
 in
 tasks
 requiring
 planning
 (De
 Luca
 et
 al.,
 2003;
 Bishop
 et
 al.,
 2001).
 Raw
 scores
 for
 the
 SOC
 variable
 ‘number
 of
 problems
solved
in
the
minimum
number
of
moves’
was
used
in
analysis.
 Spatial
Working
Memory
(SWM):
In
this
task,
the
participant
is
presented
with
a
 number
of
boxes
on
a
computer
screen.
A
blue
token
is
hidden
beneath
one
box.
When
 found,
the
token
is
hidden
in
another
box
where
a
token
has
not
been
hidden
before.
 The
participant
is
required
to
find
as
many
blue
tokens
as
there
are
boxes,
eliminating
 the
number
of
boxes
that
need
to
be
searched
by
process
of
elimination.
SWM
is
a
task
 that
tests
the
participant’s
ability
to
manipulate
spatial
information
in
working
memory.
 This
 test
 has
 been
 shown
 to
 detect
 frontal
 lobe
 and
 executive
 dysfunction.
 Working
 memory,
both
spatial
and
verbal,
has
been
heavily
implicated
as
being
impaired
in
BD
 (Yatham
 et
 al.,
 2010).
 In
 healthy
 populations,
 the
 spatial
 component
 of
 this
 task
 likely
 confers
 a
 performance
 advantage
 to
 men
 (Andreano
 and
 Cahill,
 2009).
 Additionally,
 CANTAB
 normative
 data
 found
 a
 significant
 effect
 of
 sex
 in
 the
 SWM
 task
 across
 the
 lifespan
 (De
 Luca
 et
 al.,
 2003).
 There
 are
 some
 data
 to
 indicate
 that
 healthy
 sex
 differences
 SWM
 task
 performance
 seems
 to
 be
 altered
 in
 BD
 and
 in
 schizophrenia
 (Kurtz
 and
 Garrety,
 2009).
 Raw
 scores
 for
 SWM
 variables
 ‘between
 search
 errors’
 and
 ‘strategy’
was
used
in
analysis.
 Intra/Extra
Dimensional
Set
Shift
(IED).
IED
is
a
modified
computerized
version
of
 the
 Wisconsin
 Card
 Sorting
 task.
 The
 stimuli
 presented
 to
 the
 participants
 are
 solid
 color‐filled
novel
shapes,
white‐line
drawings,
or
the
combination
of
the
two.
The
rules
 are
based
on
the
color‐filled
shapes
or
the
white‐line
drawings.
Once
the
participant
has
  
  69
  acquired
 the
 rule
 and
 is
 consistently
 choosing
 the
 right
 pattern,
 the
 rule
 is
 then
 changed.
 IED
 is
 a
 test
 of
 rule
 acquisition
 and
 rule
 reversal.
 It
 is
 a
 complex
 task
 that
 assesses
 the
 participant’s
 visual
 discrimination
 skills
 as
 well
 as
 their
 attentional
 maintenance,
 flexibility,
 and
 set‐shifting
 ability.
 Performance
 on
 this
 type
 of
 task
 has
 been
 shown
 to
 be
 impaired
 in
 BD
 (Yatham
 et
 al.,
 2010).
 While
 sex
 differences
 in
 performance
 is
 not
 always
 found
 in
 this
 domain,
 there
 are
 numerous
 sexual
 dimorphisms
 found
 in
 the
 frontal
 lobe
 in
 healthy
 populations
 and
 activation
 in
 these
 regions
 in
 response
 to
 various
 cognitive
 tasks
 associated
 with
 executive
 functioning
 seems
to
vary
between
sexes
 (Cahill,
2006).
There
is
also
limited
evidence
to
show
that
 cognitive
tasks
involving
the
prefrontal
cortex
display
sex
differences
in
performance
in
 people
with
schizophrenia.
IED
‘EDS
errors’
and
‘total
errors’
raw
scores
were
used
for
 analysis.


 Rapid
Visual
Processing
(RVP):
In
this
task,
the
numbers
2‐9
flash
one
at
time
in
a
 box
in
the
center
of
the
computer
screen.
The
participants
are
asked
to
look
for
target
 sequences
 of
 three
 numbers
 (e.g.
 2‐4‐6)
 and
 are
 instructed
 to
 press
 a
 response
 pad
 when
the
third
digit
of
the
target
sequence
appears.
RVP
is
a
test
of
sustained
attention.
 It
 is
 thought
 to
 be
 sensitive
 to
 dysfunction
 in
 the
 frontal
 and
 parietal
 lobe
 areas.
 Sustained
attention
and
processing
speed
have
been
found
to
be
impaired
in
BD
(Torres
 and
Malhi,
2010).
Studies
assessing
attention
and
processing
speed
have
been
found
to
 show
distinctions
between
men
and
women
in
healthy
populations
using
both
ERP
and
 fMRI
paradigms
(Raja
and
Yang,
2012).
Studies
in
healthy
populations
have
shown
that
 females
 outperform
 males
 on
 tests
 of
 selective
 attention;
 however,
 males
 may
 show
  
  70
  greater
 visuo‐spatial
 processing
 speed
 than
 females
 (Andreano
 and
 Cahill,
 2009).
 Both
 processes
are
utilized
during
this
task.
RVP
raw
scores
for
the
variable
‘discriminability’
 and
‘mean
latency’
were
used
in
analysis.
 CVLT‐II:
 This
 task
 is
 thought
 to
 test
 verbal
 memory
 and
 ability
 to
 use
 semantic
 strategies
to
aid
in
verbal
memory.
In
this
task,
a
subject
is
read
16
words
aloud
over
18‐ 20
 seconds.
 The
 words
 can
 be
 grouped
 into
 four
 semantic
 categories
 (e.g.
 ways
 of
 traveling,
furniture,
animals,
etc.)
although
this
is
initially
unapparent
to
the
participant
 as
the
words
in
each
semantic
category
are
distributed
randomly.
The
participant
is
then
 required
 to
 repeat
 as
 many
 of
 the
 words
 that
 they
 can
 remember
 in
 any
 order.
 This
 sequence
is
repeated
until
the
participant
has
heard
the
list
read
aloud
5
times.
Verbal
 learning
and
memory
is
robustly
impaired
in
bipolar
disorder
(Torres
and
Malhi,
2010).
 Additionally,
females
outperform
males
on
verbal
memory
tasks
in
healthy
populations
 (Andreano
and
Cahill,
2009).
Raw
scores
for
the
CVLT‐II
variables
‘Trial
1’,
‘Trial
1‐5’,
and
 ‘long
delay
free
recall’
were
used
in
analysis.

 COWAT:
 This
 is
 a
 test
 of
 phonemic
 verbal
 fluency.
 The
 participant
 is
 given
 one
 minute
to
 say
 as
many
 words
that
 begin
with
 a
specific
letter
 as
fast
 as
 they
 can.
 The
 participant
is
told
to
avoid
proper
nouns
and
words
that
are
the
same
with
a
different
 ending
 such
 as
 ‘eat’
 and
 ‘eating’.
 Two
 sets
 of
 three
 letters,
 FAS
 and
 CFL,
 are
 counterbalanced
across
testing
sessions.
Verbal
fluency
is
impaired
in
BD
samples
(Kurtz
 and
 Gerraty,
 2009).
 In
 healthy
 samples,
 females
 have
 been
 frequently
 found
 to
 outperform
 males
 (Andreano
 and
 Cahill,
 2009).
 The
 COWAT
 raw
 ‘total
 score’
 variable
 was
used
in
analysis.

  
  71
  
  Statistical
 analysis.
 Statistical
 analysis
 was
 conducted
 to
 examine
 whether
 the
  pattern
 of
 sex
 differences
 in
 cognitive
 functioning
 observed
 in
 healthy
 controls
 is
 maintained
within
BD
patients.
All
statistical
analysis
was
conducted
using
PASW
version
 18
 for
 Windows.
 Clinical,
 demographic,
 and
 neurocognitive
 data
 were
 assessed
 for
 normality
 using
 histograms
 and
 the
 Shapiro‐Wilk
 test.
 For
 those
 variables
 that
 did
 not
 meet
 the
 criteria
 for
 normality,
 several
 transformations
 (including
 logarithmic,
 natural
 log
(ln),
square
root,
and
inverse)
were
attempted
in
order
to
adjust
the
data
so
that
the
 skew
and
kurtosis
lay
between
1
and
‐1.
Where
transformations
could
not
normalize
the
 data,
Mann‐Whitney
U‐tests
were
performed
to
assess
whether
mean
rank
differences
 lay
 across
 group
 and
 sex.
 Sex
 differences
 in
 clinical
 and
 demographic
 variables
 were
 assessed
 through
 univariate
 analysis
 of
 variance
 (ANOVA),
 using
 group
 and
 sex
 as
 between‐subject
 factors.
 Frequency
 data
 regarding
 patient
 medication
 use,
 patient
 history
of
psychosis,
and
hospitalization
was
assessed
using
chi‐squared
tests.
In
order
 to
 determine
 whether
 there
 were
 significant
 sex
 differences
 on
 any
 of
 the
 neurocognitive
 variables
 across
 diagnostic
 groups,
 normally
 distributed
 data
 and
 transformed
data
were
analyzed
using
ANOVA.

 4.3
Results
 
 
 Group
and
sex
differences
in
demographic
variables.
Table
1
presents
the
means
 and
 standard
 deviations
 (SD)
 for
 the
 demographic
 variables:
 age,
 years
 of
 education,
 NAART
and
KBIT
IQ.
The
p‐value
presented
in
the
last
column
of
Table
one
represents
 the
 p‐value
 for
 the
 interaction.
 As
 indicated
 by
 the
 ‘—‘,
 no
 interaction
 was
 tested
 for
 handedness
measures
as
these
data
did
not
display
normality.

  
  72
  Table
1.

Demographic
characteristics
of
study
participants
 Patients
 Males
 Females
 (N
=
29)
 (N
=
31)
 Mean
 SD
 Mean
 SD
 22.41

 3.93
 24.42

 4.43
 13.28
 1.92
 14.06

 2.31
  Measure
 Age
 Years
of
 education
 NAART
 107.45

 6.13
 106.74

 8.33
 KBIT
IQ
 104.83

 10.12
 103.87

 10.09
 Handedness
 12.62
 5.47
 14.77

 7.54
  Controls
 Males
 Females

 (N
=
42)
 (N
=
63)
 Mean
 SD
 Mean

 SD
 20.89

 2.83
 23.62

 4.81
 13.83

 4.81
 14.44

 2.18
  p‐value*
 0.65
 0.91
  107.74

 6.65
 109.83

 10.53
 12.20

 8.38
  0.45
 0.48
 

‐‐
  108.82

 6.79
 106.51

 10.11
 13.00

 6.89
  
 
  *these
figures
represent
the
p‐value
for
the
interaction
 Interactions
and
main
effects
for
each
variable
are
presented
below:
 Age:
Male
and
female
age
data
for
both
patients
and
controls
failed
the
Shapiro‐ Wilk
test
of
normality
(p
<
0.05).
However,
female
patient
and
female
control
data
had
a
 kurtosis
and
skew
that
lay
between
1
and
‐1.
Male
data
from
both
controls
and
patients
 had
a
skew
and
kurtosis
that
lay
between
2
and
‐2.
Transformations
failed
to
increase
 the
normality
of
this
sample.
The
data
was
judged
to
be
sufficiently
normal
to
allow
for
 ANOVA
analysis.
Histograms
of
age
data
are
provided
in
Appendix
A.
There
was
no
 significant
interaction
between
patients
and
controls
in
age
[F(1,150)
=
0.20,
p
=
0.65];
 however,
there
was
a
main
effect
of
sex
with
women
being
older
[F(1,150)
=
11.84,
p
=
 0.001]
and
a
trend
level
effect
of
group
with
patients
being
older
[F(1,150)
=
2.98,
p
=
 0.09].
Although
there
was
a
significant
main
effect
of
sex
and
group
for
age,
this
was
 likely
an
artifact
of
the
small
standard
deviation
values
due
to
the
homogeneity
of
age
in
 study
participants.
Females
were
on
average
only
three
years
older
than
males,
and
 patients
were
on
average
only
1
year
older
than
controls.
These
are
differences
that
are
 not
likely
to
be
clinically
meaningful
with
regard
to
their
influence
on
cognitive
testing.
  
  73
  Total
Years
of
Education:
Male
patient
data
and
male
and
female
control
data
for
 years
of
educations
met
the
Shapiro‐Wilk
test
of
normality
(p
>
0.05).

Male
patient
data
 were
both
skewed
and
kurtotic.
However,
transformations
only
decreased
the
overall
 normality
of
this
sample.
As
most
of
the
data
was
normally
distributed
and
as
non‐ parametric
tests
involve
a
substantial
drop
in
power,
years
of
education
data
was
 analyzed
using
ANOVA.
Histograms
for
these
data
are
provided
in
Appendix
A.
The
sex
x
 group
interaction
[F(1,150)
=
0.01,
p
=
0.91]
and
group
main
effect
[F(1,150)
=
1.49,
p
=
 0.23]
were
both
nonsignificant.
There
was
a
trend
level
effect
of
sex
favoring
more
years
 of
education
for
females
[F(1,150)
=
3.61,
p
=
0.06].
However,
females
only
had
one
 more
year
of
education
than
males.

 NAART
and
KBIT:
male
and
female
data
from
patients
and
controls
were
normally
 distributed
with
skew
and
kurtosis
lying
between
1
and
‐1.
In
NAART
data,
group
 [F(1,150)
=
1.02,
p
=
0.32],
sex
[F(1,150)
=
0.03,
p
=
0.88],
and
interaction
[F(1,150)
=
 0.57,
p
=
0.45]
effects
were
all
nonsignificant.
Additionally,
in
KBIT
data,
sex
[F(1,150)
=
 1.49,
p
=
0.23],
and
interaction
[F(1,150)
=
0.52,
p
=
0.48]
effects
were
nonsignificant.
 Controls
did
score
on
average
3
points
higher
than
patients
and
this
effect
was
 significant
[F(1,150)
=
4.82,
p
=
0.03].

 Handedness:
These
data
were
significantly
skewed
and
were
not
improved
by
 transformations;
they
were
analyzed
through
nonparametric
measures.
Handedness
 scores
were
matched
across
patients
and
controls
[U
=
2,216.50,
p
=
0.06]
and
males
 and
females
[U
=
3,049.00,
p
=
0.26].
 
  
  74
  
  Group
and
sex
differences
in
clinical
variables.
Means
and
standard
deviations
for
  clinical
variables
are
given
in
Table
2.

 Table
2.
Clinical
characteristics
of
study
patients
 Males
 N
=
29
 Mean
 SD
 
 
 5.90

 6.67
 0.83
 1.54
 
5.38

 6.30
 2.13

 1.30
 68.86

 15.08
 19.85

 4.80
 1.07

 1.41
  
  Females
 N
=
31
 Mean
 SD
 
 
 7.81


 8.60
 2.13


 4.01

 6.71


 8.36

 2.19


 1.36

 66.32

 11.41

 20.35

 5.68
 1.22

 1.80
  
  Measure
 
 p‐value*
 Scale:
 
 
 



HAM‐D
 
 0.34
 



YMRS
 
 0.18
 



MDRS
 
 0.49
 



CGI
 
 0.81
 



GAF
 
 0.46
 Age
of
Onset
 
 0.72
 Previous
Episodes
 
 0.88
 (Depression)
 Previous
Episodes
 0.14

 0.49
 
 0.74

 2.11
 0.14
 (Hypomania)

 
 Males
 
 Females
 
 
 Raw
 %
 
 Raw
 %
 
 st Hospitalized
for
1 
 
 
 
 
 
 0.83
 Mania:
 



Yes
 25
 86.21
 
 27
 87.10
 
 



No
 4
 13.79
 
 4
 12.90
 
 Past
History
of
 
 
 
 
 
 0.76
 Psychosis:
 



Yes
 21
 72.41
 
 24
 77.42
 
 



No
 8
 27.58
 
 7
 22.58
 
 Mood
Stabilizer:
 
 
 
 
 
 0.10
 



No
Medications
 7
 24.14
 
 1
 3.23
 
 



Divalproex
 12
 41.38
 
 16
 51.61
 
 



Lithium
 10
 34.48
 
 14
 45.16
 
 
 
 
 
 
 
 
 
 
 0.11
 Antipsychotics:
 
 



No
Medications
 10
 34.48
 
 6
 19.35
 
 



Risperidone
 7
 24.14
 
 13
 41.94
 
 



Olanzapine
 8
 27.59
 
 3
 9.68
 
 



Seroquel
 3
 10.34
 
 9
 29.03
 
 



Loxapine
 1
 3.44
 
 0
 0.00
 
 *p‐value
result
from
either
an
Independent
Samples
t‐test,
Mann‐Whitney
U‐test,
or
chi‐squared
test.

  Clinical
Scales:
GAF
data
were
normally
distributed
as
assessed
by
the
non‐ significant
Shapiro‐Wilk
test
statistic
(p
>
0.05).
The
CGI
data
failed
the
Shapiro‐Wilk
test
 of
normality
but
was
judged
to
be
sufficiently
normal
to
conduct
parametric
analysis,
  
  75
  the
skew
and
kurtosis
for
male
patients
were
within
1
and
‐1
as
was
the
skew
for
female
 patients,
while
the
kurtosis
for
female
patients
was
1.26.
Histograms
for
CGI
data
will
be
 provided
in
Appendix
A.
The
MADRS
and
HAM‐D
data
were
transformed
using
a
square‐ root
function.
The
GAF,
CGI,
MADRS,
and
HAM‐D
data
were
analyzed
using
the
 independent‐samples
t‐test.
As
the
normality
of
YMRS
data
were
not
improved
with
 transformations,
and
as
the
skew
and
kurtosis
for
the
male
and
female
data
were
well
 out
of
bounds,
the
nonparametric
Mann‐Whitney
U
test
was
used
to
analyze
this
data.
 From
these
analyses,
male
and
female
patients
were
found
to
be
well‐matched
on
all
of
 the
psychiatric
scales
used
in
assessment:
HAM‐D
[t(52)
=
‐1.16,
p
=
0.25],
YMRS
[t(52)
=
 ‐1.71,
p
=
0.09],
MADRS
[t(52)
=
‐0.58,
p
=
0.56],
CGI
[t(55)
=
‐0.04,
p
=
0.97],
and
GAF
 [t(55)
=
‐0.24,
p
=
0.81].
 Additional
Clinical
Variables:
Age
of
onset
data
for
male
and
female
patients
failed
 the
Shapiro‐Wilk
test
for
normality
(p
<
0.05),
however,
the
data
was
judged
to
be
 sufficiently
normal
as
their
skew
and
kurtosis
were
all
within
1.5.
Histograms
for
this
 data
are
provided
in
Appendix
A.
Data
for
number
of
past
episodes
(both
depression
 and
hypomania)
were
not
normally
distributed.
These
data
were
not
improved
by
 transformations;
as
such,
nonparametric
measures
were
used
to
analyze
these
 variables.
Male
and
female
patients
were
well
matched
for
age
of
onset
of
illness
[t(56)
 =
‐0.36,
p
=
0.72],
,
and
number
of
previous
mood
episodes
(depressive,
[U
=
424.25,
p
=
 0.88]
and
hypomanic
[U
=
499.50,
p
=
0.14]).There
were
no
significant
sex
differences
in
 the
number
of
patients
having
been
hospitalized
for
their
first
mania
[χ2
=
0.04,
p
=
  
  76
  0.83],
having
a
past
history
of
psychosis
[χ2
=
0.09,
p
=
0.77],
taking
any
particular
mood
 stabilizer
[χ2
=
4.67,
p
=
0.10],
or
any
particular
antipsychotic
[χ2
=
9.06,
p
=
0.11].

 
  Group
and
sex
differences
in
cognitive
function.
Mean
scores
for
the
cognitive
  measures
of
patients
and
controls
are
presented
in
Table
3.

 Table
3:
Descriptive
statistics
for
neurocognitive
variables
 
 
  Patients
 Males
 Females
 n
=
29
 n
=
31
 Mean

 SD
 Mean

 SD
 
 
 
 
 39.69
 8.56
 35.48

 9.03
 
 
 
 
 6.38

 1.72
 6.97

 2.09
 49.93
 8.90
 53.45

 12.15
 10.69
 2.97
 11.33

 2.96
  
 COWAT
 



Total
Score
 CVLT:


 



Trial
1
 



Trial
1‐5
 



Free
Recall
 (long
delay)
 IED:
 
 
 
 


EDS
Errors*
 4.65
 5.62
 10.93

 


Total
Errors

 26.64

 41.37
 29.97

 SOC:
 
 
 
 



Number
of
 8.79

 2.22
 8.87

 problems
solved
 in
min
moves
 RVP:


 
 
 
 

Discriminability
 0.89

 0.06
 0.89

 

Mean
Latency
 437.39

 64.74
 480.94

 
 
 
 
 SWM:
 
 
 
 



Between
Errors
 19.17

 16.92
 21.42

 



Strategy
 31.39

 6.37
 32.10

 *p‐values
reported
are
for
the
interaction
effect
  
  
  Controls
 Males
 Females
 n
=
35
 n
=
55
 Mean

 SD
 Mean

 SD
 
 
 
 
 43.03

 8.11
 41.58

 12.08
 
 
 
 
 7.09

 2.17
 7.60

 2.38
 58.18

 9.04
 60.18

 8.12
 13.15

 2.41
 13.62

 2.36
  
  
 10.45
 25.52
 
 2.09
  
 10.20

 17.23

 
 9.90

  
 10.00
 15.93
 
 1.79
  
 12.76

 19.28

 
 9.23

  
 10.68
 16.73
 
 1.89
  
 0.27
 0.88
 
 0.15
  
 0.04
 79.19
 
 
 20.57
 6.08
  
 0.93
 416.14

 
 
 6.51

 27.37

  
 0.03
 52.25
 
 
 6.55
 5.30
  
 0.91

 489.04

 
 
 14.93

 29.54

  
 0.04
 95.49
 
 
 18.16
 6.07
  
 0.12
 0.27
 
 
 0.23
 0.47
  
 p‐value*
 
 0.41
 
 0.92
 0.64
 0.85
  All
cognitive
variables
were
normally
distributed
as
assessed
by
a
nonsignificant
p‐ value
on
the
Shapiro‐Wilk
test
and/or
skew
and
kurtosis
values
of
one
or
less.
 Histograms
for
patient
and
control
data
for
cognitive
variables
are
provided
in
Appendix
 B.
All
cognitive
variables
were
assessed
by
ANOVA.

 Significant
group
effects,
favoring
controls,
were
observed
for
COWAT
Total
Score
 [F(1,150)
=
7.84,
p
=
0.006],
CVLT
Trials
1‐5
[F(1,149)
=
22.05,
p
<
0.001],
CVLT
Long
 
  77
  Delay
Free
Recall
[F(1,148)
=
28.23,
p
<
0.001],
IED
EDS
Errors
[F(1,145)
=
4.81,
p
=
0.03],
 IED
Total
Errors
[F(1,148)
=
5.67,
p
=
0.019],
SOC
Number
of
Problems
Solved
in
 Minimum
moves
[F(1,148)
=
7.83,
p
=
0.006],
and
RVP
discriminability
[F(1,149)
=
20.27,
 p
<
0.001].
Trend
level
significance
for
the
group
effect
was
observed
for
CVLT
Trial
1
 [F(1,149)
=
3.39,
p
=
0.068].
The
group
effect
for
RVP
mean
latency
was
nonsignificant
 [F(1,149)
=
0.25,
p
=
0.62].
 A
significant
main
effect
of
sex
favoring
males
was
observed
for
EDS
Errors
 [F(1,145)
=
6.91,
p
=
0.01],
RVP
discriminability
[F(1,149)
=
4.10,
p
=
0.045],
and
RVP
 mean
latency
[F(1,149)
=
19.33,
p
<
0.001].
A
trend
level
of
significance
for
the
main
 effect
of
sex
was
observed
for
COWAT
Total
Score
[F(1,150)
=
2.81,
p
=
0.096;
favoring
 males
–
surprising
males
were
favored
–
double
check
data
for
this
],
CVLT
Trials
1‐5
 [F(1,149)
=
3.00,
p
=
0.085;
favoring
females],
and
SWM
Between
Errors
[F(1,148)
=
1.43,
 p
=
0.23;
favoring
males].
Nonsignificant
main
effects
of
sex
were
found
for
CVLT
Trial
1
 [F(1,149)
=
2.28,
p
=
0.13],
CVLT
Long
Delay
Free
Recall
[F(1,148)
=
1.56,
p
=
0.21],
IED
 Total
Errors
[F(1,148)
=
0.405,
p
=
0.53],
SOC
number
of
problems
solve
in
minimum
 moves
[F(1,148)
=
1.39,
p
=
0.24],
and
SWM
strategy
[F(1,148)
=
2.02,
p
=
0.16]
 There
were
no
significant
interaction
effects
observed
for
any
of
the
cognitive
 variables.

The
p‐values
of
the
interactions
are
provided
above
in
Table
3.

 4.4
Discussion
 
  The
main
findings
of
this
study
are:
1.
Bipolar
patients
as
a
group
showed
poorer
  cognitive
performance
than
age
and
sex
matched
healthy
controls,
2.
Sex
was
an
 important
determinant
of
neurocognitive
function
in
that
males
performed
better
than
  
  78
  females
on
measures
of
sustained
attention
and
set
shifting,
whereas
there
was
a
trend
 for
females
to
perform
better
than
males
in
verbal
learning.

Most
importantly,
 however,
there
were
no
group
x
sex
interactions
indicating
that
sex
had
the
same
 impact
on
neurocognitive
function
in
bipolar
I
patients
as
in
healthy
controls.
These
 results
are
in
line
with
those
found
by
Vaskinn
et
al.
(2011),
but
disagree
somewhat
with
 the
results
found
by
Barrett
et
al.
(2008)
and
Carrus
et
al.
(2010).
While
Barrett
et
al.
 (2009),
and
Carrus
et
al.
(2010)
found
preservation
of
healthy
sex
differences
for
a
 majority
of
the
tasks
included
in
their
cognitive
batteries,
in
contrast
with
the
present
 findings,
these
previous
papers
also
reported
that
sex
differences
in
cognitive
 performance
in
bipolar
patients
differed
from
healthy
controls
on
measures
of
SWM
 and
immediate
memory.
Several
underlying
factors,
including
the
use
of
varying
testing
 materials,
may
contribute
to
this
discrepancy.
 
 The
methodological
limitations
of
the
previous
studies
have
already
been
 mentioned
and
it
is
possible
that
their
findings
were
an
artifact
of
small
sample
size
and
 large
sample
heterogeneity.
The
patients
and
healthy
controls
enrolled
in
the
present
 study
were
statistically
comparable
in
terms
of
intellectual
capacity.
Additionally
male
 and
female
patients
in
this
study
were
statistically
homogenous
in
terms
of
psychiatric
 status.

Although
patients
were
significantly
older
than
controls,
the
clinical
relevance
of
 this
difference
is
minimal
as
the
mean
age
of
the
patient
group
was
only
two
years
 above
that
of
the
control
group.
The
control
of
these
relevant
demographic
clinical
and
 variables
may
account
for
the
lack
of
group
x
sex
interactions
found
on
any
cognitive
 variable
including
SWM
in
this
study.

  
  79
  
 Another
possible
interpretation
of
the
present
findings
in
the
context
of
earlier
 studies
might
be
that
sex‐differences
in
cognitive
functioning
remains
intact
early
in
the
 course
of
BDI
but
may
be
altered
later
in
the
illness.
That
is,
cognitive
impairment
may
 take
differential
trajectories
in
men
and
women
with
BDI
as
the
illness
progresses.
 Furthermore,
differential
trajectories
between
sexes
may
be
more
apparent
for
certain
 cognitive
domains
such
as
SWM
or
immediate
memory.
This
hypothesis
helps
explain
 why
the
results
of
this
study
align
with
those
found
by
Vaskinn
et
al.
(2011);
compared
 to
the
samples
utilized
in
both
the
Barrett
et
al.
(2008)
and
Carrus
et
al.
(2010)
studies,
 the
sample
studied
by
Vaskinn
et
al.
(2011)
was
younger
and
had
been
symptomatic
for
 fewer
years.
It
is
possible
that
a
significant
cognitive
insult
must
first
occur
before
sex
 differences
in
cognitive
functioning
veers
away
from
healthy
patterns
in
BD.
The
next
 wave
of
questions
that
arise
from
this
finding
will
necessarily
concern
the
mechanisms
 that
protect
women
with
BD
from
greater
cognitive
deterioration,
or
alternatively,
the
 mechanisms
that
exacerbate
cognitive
deterioration
in
men
with
BD.
These
mechanisms
 may
encompass
both
illness‐related
and
iatrogenic
processes
as
medications
may
have
 different
effects
in
men
and
women.
As
many
these
medications
act
on
the
sexually
 dimorphic
dopaminergic
and
glutamatergic
neurochemical
systems,
the
notion
that
 medication
will
have
different
effects
in
men
and
women
seems
probable.
 This
hypothesis
that
the
alteration
of
healthy
patterns
of
cognitive
sex
differences
 emerge
over
the
course
of
the
illness
also
aligns
with
recent
neuroanatomical
evidence
 that
suggests
that
sexual
dimorphisms
that
are
present
in
healthy
controls
are
altered
in
 BD
patient
samples
that
have
experienced
multiple
mood
episodes.
In
voxel‐based
MRI
  
  80
  studies
conducted
with
BD
patients
and
healthy
controls,
significant
diagnostic
group
x
 sex
interactions
have
been
found
in
the
left
frontal,
left
temporal,
right
parietal,
right
 occipital
lobe,
and
the
cerebellar
vermis
(Mackay
et
al.,
2010;
Womer
et
al.,
2009).
 Similar
findings
of
altered
sexual
dimorphisms
have
been
found
in
subregions
of
the
 prefrontal
cortex
in
both
adult
and
pediatric
bipolar
populations
(Dickstein
et
al.,
2005;
 Najt
et
al.
2007).
Sexual
dimorphisms
are
phenotypically
impactful
and
are
thought
to
 be
the
basis
of
sex‐differences
in
cognitive
functioning
in
healthy
populations
(Adreano
 and
Cahill,
2009).
If
these
sexual
dimorphisms
are
altered
in
BD,
it
stands
to
reason
that
 sex
differences
in
cognitive
functioning
may
be
altered
as
well.

 There
is
parallel
evidence
of
altered
sexual
dimorphisms
co‐occurring
with
altered
 sex
differences
in
cognitive
performance
in
samples
with
schizophrenia
in
tasks
 involving
the
prefrontal
cortex
(Roesch‐Ely
et
al.,
2009).
These
results
are
particularly
 telling
as,
in
terms
of
cognitive
impairment,
BD
and
SZ
are
considered
by
many
to
be
on
 the
same
spectrum,
with
SZ
representing
the
more
severe
condition
(Hill
et
al,
2008).
 However,
until
the
present
study,
there
had
yet
to
be
an
investigation
of
sexual
 dimorphisms
in
first‐episode
BD
patients.
The
results
from
this
study
would
suggest
that
 as
healthy
sex
differences
in
cognitive
performance
are
maintained
in
first‐episode
 patients,
healthy
sexual
dimorphisms
may
be
maintained
as
well.
Again,
however,
 similar
performance
on
cognitive
tasks
between
men
and
women
does
not
guarantee
 that
the
neural
substrate
functions
equally
between
sexes.
Another
consideration
must
 be
the
effect
of
compensation;
neural
processing
in
men
and
women
with
BD
may
be
 abnormal
even
early
in
the
course
of
the
illness.
However,
compensatory
mechanisms
  
  81
  may
prevent
this
abnormality
from
registering
on
a
behavioural
level.

For
example,
 studies
have
demonstrated
that
in
response
to
tasks
of
planning
and
spatial
memory,
 women
show
more
activation
in
the
prefrontal
lobe
especially
on
the
right
side
while
 men
show
more
bilateral
activation
in
the
parietal
lobe
despite
performing
equivocally
 (Andreano
and
Cahill,
2009).
Further
studies
testing
first‐episode
BD
samples
using
 cognitive
batteries
paired
with
various
imaging
techniques
are
needed
in
order
to
 ascertain
whether
these
healthy
sexually
dimorphic
patterns
of
brain
activation
are
 maintained
in
psychiatric
populations.
 The
results
presented
in
this
study
need
to
be
considered
within
a
framework
of
 several
limitations.
Although
the
cognitive
battery
employed
in
this
study
effectively
 detected
group
differences
in
cognitive
functioning,
it
may
not
have
been
optimally
 sensitive
in
detecting
sex
differences.

While
group
effects
were
found
in
nearly
all
 measures
with
healthy
controls
performing
significantly
better
than
the
patients
sample,
 sex
differences
were
only
found
in
a
subset
of
the
tasks
tested.
In
accordance
with
 previous
literature,
significant
sex
differences
were
found
on
measures
of
sustained
 attention
(RVP
discriminablity
and
mean
latency),
executive
function
(IED
EDS
errors),
 and
verbal
declarative
memory
(CVLT
Trial
1
there
was
no
sex
difference
for
CVLT
Trial
 1,
CVLT
Trial
1‐5‐
this
was
a
trend).
Measures
of
planning
(SOC),
spatial
working
memory
 (SWM),
and
verbal
fluency
(COWAT)
did
not
show
any
significant
sex
differences.


 However,
the
finding
of
comparable
performance
between
sexes
in
these
tasks
is
 not
without
precedent.
While
some
tests
of
planning
using
Tower
of
London
and
the
 Tower
of
Hanoi
designs
have
shown
that
males
outperform
females,
several
studies
  
  82
  have
failed
to
replicate
these
findings
(De
Luca
et
al.,
2003).
Similarly,
while
many
 studies
have
shown
that
verbal
fluency
is
increased
in
women,
several
studies
including
 COWAT
normative
data
have
found
equivocal
performance
between
sexes
(Ruff
et
al.,
 1996).
Literature
regarding
visuospatial
working
memory
is
also
not
without
its
 ambiguities.
Spatial
working
memory
seems
to
be
a
multidimensional
construct
that
 assesses
several
distinct
cognitive
abilities
(Andreano
and
Cahill,
2009).
Over
the
years,
 several
tasks
have
been
designed
that
assess
spatial
working
memory
including:
CANTAB
 SWM,
spatial
span
on
the
Corsi‐Block
Tapping
Task,
Delayed
Response
Task,
Mental
 Rotation,
and
the
N‐back
working
memory
task.
The
most
robust
sex
difference
in
 cognitive
performance
is
seen
in
Mental
Rotation,
with
men
outperforming
women.
 However,
lack
of
sex
differences
in
spatial
working
memory
have
been
found
using
 several
of
these
paradigms,
while
other
measures
have
found
that
women
outperform
 men
(Andreano
and
Cahill,
2009).
Overall,
it
seems
that
the
complexity
of
these
 cognitive
domains
produces
a
high
degree
of
sensitivity
to
operationalization,
with
 variable
results
being
received
from
different
tasks
and
test
environments.
Again,
it
 should
be
emphasized
that
equivocal
performance
on
cognitive
tasks
does
not
 necessarily
indicate
that
sex
differences
do
not
exist
in
terms
of
cognitive
processing.

 Future
studies
investigating
cognitive
sex
differences
in
psychiatric
populations
 would
also
benefit
from
controlling
for
menstrual
cycle
in
their
designs.
Studies
in
 healthy
populations
have
shown
that
performance
on
various
cognitive
tasks
as
well
as
 the
detection
of
sex
differences
varies
according
to
phase
of
menstrual
cycle
(Kimura,
 1996;
Postma
et
al.,
1999).
Failure
to
account
for
phase
of
menstrual
cycle
represents
a
  
  83
  major
limitation
of
this
study,
as
is
the
case
with
prior
studies
in
BD.
While
this
overall
 picture
remains
unclear,
there
have
been
some
strong
suggestions
made
that
left/mixed
 handedness
predicts
better
cognitive
flexibility
while
strong
right‐handedness
predicts
 better
time
estimation
skills
(Andreano
and
Cahill,
2009).
Handedness
has
not
been
 previously
associated
with
the
specific
tasks
used
in
this
study
and
has
not
been
 associated
with
sex.
Nevertheless
handedness
may
have
mediated
or
moderated
some
 of
these
results.



 Despite
its
limitations,
this
study
remains
the
first
in
addressing
the
issue
of
 cognitive
sex
differences
in
BD
early
in
its
course;
whether
these
sex
differences
remain
 healthy
throughout
the
course
of
the
illness
represents
a
future
line
of
inquiry
that
may
 yield
informative
results.
Additionally,
too
few
studies
have
addressed
whether
healthy
 sexual
dimorphisms
and
patterns
of
neural
activation
are
conserved
in
these
patient
 samples.
Longitudinal
studies
following
patients
from
their
first‐episode
assessing
 neurocognition,
brain
morphology,
brain
function
are
needed
to
better
understand
the
 basis
of
sex
differences
observed
in
the
clinical
phenomenology
of
BD.
Recognizing
 distinctions
between
the
pattern
of
impairment
seen
in
men
and
women
with
BD
may
 help
to
generate
more
targeted
therapeutic
strategies.
For
example,
in
regards
to
the
 novel
pharmacological
cognitive‐enhancement
agents
that
are
currently
in
development
 to
ease
cognitive
dysfunction
in
BD,
some
agents
may
be
more
indicated
for
one
sex
 than
the
other
dependent
on
the
agent’s
cognitive
profile.

 
 
  
  84
  
  Summary.
Sex
influences
cognitive
functioning
in
BD.
However,
early
in
the
  course
of
the
illness,
the
influence
of
sex
in
BD
samples
is
equivalent
to
that
observed
in
 healthy
samples.
That
is,
healthy
patterns
of
cognitive
functioning
as
measured
by
 performance‐based
cognitive
tests,
are
maintained
early
in
the
course
BD.
This
pattern
 may
not
persist
over
the
course
of
the
illness.
As
the
repeated
insult
of
mood
episodes
 further
impacts
cognition,
sex
differences
that
veer
away
from
healthy
patterns
may
be
 observed.
A
promising
avenue
of
future
research
involves
investigating
sex
differences
 in
cognitive
functioning
in
a
longitudinal
manner.
Recognizing
the
distinctions
between
 the
profile
of
cognitive
impairment
seen
in
men
and
women
with
BD
as
they
emerge
 over
the
course
of
their
illness
may
help
in
designing
treatment
strategies
that
will
more
 effectively
remedy
the
functional
burden
in
both
sexes.




 
 
 
  
  85
  References
 Abel
KM,
Drake
R,
Goldstein
JM.
Sex
differences
in
schizophrenia.
International
Review
 of
Psychiatry.
2010;22(5):417‐28.
 Alansari
BM.
Psychometric
properties
of
the
Arabic
version
of
the
Dental
Cognition
 Questionnaire.
Community
Dental
Health.
2006;23(2):83‐90.
 
 Allen
JS,
Damasio
H,
Grabowski
TJ,
Bruss
J,
Zhang
W.
Sexual
dimorphism
and
 asymmetries
in
the
gray‐white
composition
of
the
human
cerebrum.
Neuroimage.
 2003;18(4):880‐94.
 Amadoboccara
I,
Gougoulis
N,
Littre
MFP,
Galinowski
A,
Loo
H.
Effects
of
 antidepressants
on
cognitive
functions
–
a
review.
Neuroscience
and
 Biobehavioral
Reviews.
1995;19(3):479‐93.
 Andreano
JM,
Cahill
L.
Sex
influences
on
the
neurobiology
of
learning
and
memory.
 Learning
&
Memory.
2009;16(4):248‐66.
 Angst
J.
Course
of
affective
disorders
2
typology
of
bipolar
manic‐depressive
illness.
 Archiv
Fur
Psychiatrie
Und
Nervenkrankheiten.
1978;226(1):65‐73.
 Antila
M,
Tuulio‐Henriksson
A,
Kieseppa
T,
Eerola
M,
Partonen
T,
Lonnqvist
J.
Cognitive
 functioning
in
patients
with
familial
bipolar
I
disorder
and
their
unaffected
 relatives.
Psychological
Medicine.
2007;37(5):679‐87.
 Ardila
A.
On
the
evolutionary
origins
of
executive
functions.
Brain
and
Cognition.
 2008;68(1):92‐9.
 Arnold
LM,
McElroy
SL,
Keck
PE.
The
role
of
gender
in
mixed
mania.
Comprehensive
 Psychiatry.
2000;41(2):83‐7.
 Balanza‐Martinez
V,
Selva
G,
Martinez‐Aran
A,
Prickaerts
J,
Salazar
J,
Gonzalez‐Pinto
A,
 et
al.
Neurocognition
in
bipolar
disorders‐A
closer
look
at
comorbidities
and
 medications.
European
Journal
of
Pharmacology.
2010;626(1):87‐96.
 Baldassano
CF,
Marangell
LB,
Gyulai
L,
Ghaemi
SN,
Joffe
H,
Kim
DR,
et
al.
Gender
 differences
in
bipolar
disorder:
retrospective
data
from
the
first
500
STEP‐BD
 participants.
Bipolar
Disorders.
2005;7(5):465‐70.
 Bao
A‐M,
Swaab
DF.
Sex
Differences
in
the
Brain,
Behavior,
and
Neuropsychiatric
 Disorders.
Neuroscientist.
2010;16(5):550‐65.
 Barrett
SL,
Kelly
C,
Bell
R,
King
DJ.
Gender
influences
the
detection
of
spatial
working
 memory
deficits
in
bipolar
disorder.
Bipolar
Disorders.
2008;10(5):647‐54.
 Bearden
CE,
Thompson
PM,
Dalwani
M,
Hayashi
KM,
Lee
AD,
Nicoletti
M,
et
al.
Greater
 cortical
gray
matter
density
in
lithium‐treated
patients
with
bipolar
disorder.
 Biological
Psychiatry.
2007;62(1):7‐16.
  
  86
  Ben
Abla
T,
Ellouze
F,
Amri
H,
Krid
G,
Zouari
A,
M'Rad
MF.
Unipolar
versus
bipolar
 depression
:
clues
toward
predicting
bipolarity
disorder.
Encephale‐Revue
De
 Psychiatrie
Clinique
Biologique
Et
Therapeutique.
2006;32(6):962‐5.
 Ben
Abla
T,
Ellouze
F,
Amri
H,
Krid
G,
Zouari
A,
M'Rad
MF.
Unipolar
versus
bipolar
 depression
:
clues
toward
predicting
bipolarity
disorder.
Encephale‐Revue
De
 Psychiatrie
Clinique
Biologique
Et
Therapeutique.
2006;32(6):962‐5.
 Benazzi
F.
A
comparison
of
the
age
of
onset
of
bipolar
I
and
bipolar
II
outpatients.
 Journal
of
Affective
Disorders.
1999;54(3):249‐53.
 Benazzi
F.
Prevalence
and
clinical
correlates
of
residual
depressive
symptoms
in
bipolar
 II
disorder.
Psychotherapy
and
Psychosomatics.
2001;70(5):232‐8.
 Benazzi
F.
Clinical
differences
between
bipolar
II
depression
and
unipolar
major
 depressive
disorder:
lack
of
an
effect
of
age.
Journal
of
Affective
Disorders.
 2003;75(2):191‐5.
 Benazzi
F.
Bipolar
II
disorder
family
history
using
the
family
history
screen:
Findings
 and
clinical
implications.
Comprehensive
Psychiatry.
2004;45(2):77‐82.
 Benedetti
A,
Fagiolini
A,
Casamassima
F,
Mian
MS,
Adamovit
A,
Musetti
L,
et
al.
Gender
 differences
in
bipolar
disorder
type
1
‐
A
48‐week
prospective
follow‐up
of
72
 patients
treated
in
an
Italian
tertiary
care
center.
Journal
of
Nervous
and
Mental
 Disease.
2007;195(1):93‐6.
 Bishop
DVM,
Aamodt‐Leeper
G,
Creswell
C,
McGurk
R,
Skuse
DH.
Individual
differences
 in
cognitive
planning
on
the
Tower
of
Hanoi
task:
Neuropsychological
maturity
or
 measurement
error?
Journal
of
Child
Psychology
and
Psychiatry
and
Allied
 Disciplines.
2001;42(4):551‐6.
 Boghi
A,
Rasetti
R,
Avidano
F,
Manzone
C,
Orsi
L,
D'Agata
F,
et
al.
The
effect
of
gender
 on
planning:
An
fMRI
study
using
the
Tower
of
London
task.
Neuroimage.
 2006;33(3):999‐1010.
 Bonnin
CM,
Martinez‐Aran
A,
Torrent
C,
Pacchiarotti
I,
Rosa
AR,
Franco
C,
et
al.
Clinical
 and
neurocognitive
predictors
of
functional
outcome
in
bipolar
euthymic
 patients:
A
long‐term,
follow‐up
study.
Journal
of
Affective
Disorders.
2010;121(1‐ 2):156‐60.
 Bora
E,
Vahip
S,
Akdeniz
F,
Gonul
AS,
Eryavuz
A,
Ogut
M,
et
al.
The
effect
of
previous
 psychotic
mood
episodes
on
cognitive
impairment
in
euthymic
bipolar
patients.
 Bipolar
Disorders.
2007;9(5):468‐77.
 Bryant‐Comstock
L,
Stender
M,
Devercelli
G.
Health
care
utilization
and
costs
among
 privately
insured
patients
with
bipolar
I
disorder.
Bipolar
Disorders.
 2002;4(6):398‐405.
 Burdick
KE,
Braga
RJ,
Goldberg
JF,
Malhotra
AK.
Cognitive
dysfunction
in
bipolar
 disorder
‐
Future
place
of
pharmacotherapy.
Cns
Drugs.
2007;21(12):971‐81.
  
  87
  Cahill
L.
Why
sex
matters
for
neuroscience.
Nature
Reviews
Neuroscience.
 2006;7(6):477‐84.
 Cassidy
F,
McEvoy
JP,
Yang
YK,
Wilson
WH.
Smoking
and
psychosis
in
patients
with
 bipolar
I
disorder.
Comprehensive
Psychiatry.
2002;43(1):63‐4.
 Chen
BG,
Zhou
HX,
Dunlap
S,
Perfetti
CA.
Age
of
acquisition
effects
in
reading
Chinese:
 Evidence
in
favour
of
the
arbitrary
mapping
hypothesis.
British
Journal
of
 Psychology.
2007;98:499‐516.
 Chen
WJ,
Hsiao
CK,
Hsiao
LL,
Hwu
HG.
Performance
of
the
continuous
performance
 test
among
community
samples.
Schizophrenia
Bulletin.
1998;24(1):163‐74.
 Clark
L,
Iversen
SD,
Goodwin
GM.
Sustained
attention
deficit
in
bipolar
disorder.
British
 Journal
of
Psychiatry.
2002;180:313‐9.
 Conners
CK,
Epstein
JN,
Angold
A,
Klaric
J.
Continuous
performance
test
performance
 in
a
normative
epidemiological
sample.
Journal
of
Abnormal
Child
Psychology.
 2003;31(5):555‐62.
 Coryell
W,
Endicott
J,
Keller
M.
Rapidly
cycling
affective
disorder
–
demographics,
 diagnosis,
family
history,
and
course.
Archives
of
General
Psychiatry.
 1992;49(2):126‐31.
 Cosgrove
KP,
Mazure
CM,
Staley
JK.
Evolving
knowledge
of
sex
differences
in
brain
 structure,
function,
and
chemistry.
Biological
Psychiatry.
2007;62(8):847‐55.
 Daban
C,
Martinez‐Aran
A,
Torrent
C,
Sanchez‐Moreno
J,
Goikolea
JM,
Benabarre
A,
et
 al.
Cognitive
functioning
in
bipolar
patients
receiving
lamotrigine
‐
Preliminary
 results.
Journal
of
Clinical
Psychopharmacology.
2006;26(2):178‐81.
 De
Luca
CR,
Wood
SJ,
Anderson
V,
Buchanan
JA,
Proffitt
TM,
Mahony
K,
et
al.
 Normative
data
from
the
Cantab.
I:
Development
of
executive
function
over
the
 lifespan.
Journal
of
Clinical
and
Experimental
Neuropsychology.
2003;25(2):242‐ 54.
 De
Vries
GJ.
Minireview:
Sex
differences
in
adult
and
developing
brains:
Compensation,
 compensation,
compensation.
Endocrinology.
2004;145(3):1063‐8.
 Deckersbach
T,
Savage
CR,
Reilly‐Harrington
N,
Clark
L,
Sachs
G,
Rauch
SL.
Episodic
 memory
impairment
in
bipolar
disorder
and
obsessive‐compulsive
disorder:
the
 role
of
memory
strategies.
Bipolar
Disorders.
2004;6(3):233‐44.
 Devous
MD,
Stokely
EM,
Chehabi
HH,
Bonte
FJ.
Normal‐distribution
of
regional
 cerebral
blood‐flow
measured
by
dynamic
single‐photon
emission
tomography.
 Journal
of
Cerebral
Blood
Flow
and
Metabolism.
1986;6(1):95‐104.
 Dickstein
DP,
Milham
MP,
Nugent
AC,
Drevets
WC,
Charney
DS,
Pine
DS,
Leibenluft
E:
 Frontotemporal
alterations
in
pediatric
bipolar
disorder
results
of
a
voxel‐based
 morphology
study.
Arch
Gen
Psychiatry
2005;
62:
734‐741.

  
  88
  Diehr
MC,
Heaton
RK,
Miller
W,
Grant
I.
The
paced
auditory
serial
addition
task
 (PASAT):
Norms
for
age,
education,
and
ethnicity.
Assessment.
1998;5(4):375‐87.
 Diflorio
A,
Jones
I.
Is
sex
important?
Gender
differences
in
bipolar
disorder.
 International
Review
of
Psychiatry.
2010;22(5):437‐52.
 Dilsaver
SC,
Chen
YW,
Swann
AC,
Shoaib
AM,
Krajewski
KJ.
Suicidality
in
patients
with
 pure
and
depressive
mania.
American
Journal
of
Psychiatry.
1994;151(9):1312‐5.
 Dittmann
J,
Abel
S.
Verbal
Working
Memory
and
Verbal
Learning:
Word
and
 Pseudoword
Learning
in
a
Case
of
Working
Memory
Deficit.
Sprache‐Stimme‐ Gehor.
2010;34(2):E1‐E9.
 Elias
MF,
Robbins
MA,
Walter
LJ,
Schultz
NR.
The
influence
of
gender
and
age
on
 Halstead‐Reitan
neuropsychological
test
performance.
Journals
of
Gerontology.
 1993;48(6):P278‐P81.
 Era
P,
Sainio
P,
Koskinen
S,
Ohlgren
J,
Harkanen
T,
Aromaa
A.
Psychomotor
speed
in
a
 random
sample
of
7979
subjects
aged
30
years
and
over.
Aging
Clinical
and
 Experimental
Research.
2011;23(2):135‐44.
 Esposito
G,
VanHorn
JD,
Weinberger
DR,
Berman
KF.
Gender
differences
in
cerebral
 blood
flow
as
a
function
of
cognitive
state
with
PET.
Journal
of
Nuclear
Medicine.
 1996;37(4):559‐64.
 Filley
CM,
Cullum
CM.
Attention
and
vigilance
functions
in
normal
aging.
Applied
 neuropsychology.
1994;1(1‐2):29‐32.
 Friedman
NP,
Miyake
A,
Young
SE,
DeFries
JC,
Corley
RP,
Hewitt
JK.
Individual
 differences
in
executive
functions
are
almost
entirely
genetic
in
origin.
Journal
of
 Experimental
Psychology‐General.
2008;137(2):201‐25.
 Galea
LAM,
Kimura
D.
Sex‐differences
in
route‐learning.
Personality
and
Individual
 Differences.
1993;14(1):53‐65.
 Gitlin
MJ,
Swendsen
J,
Heller
TL,
Hammen
C.
Relapse
and
impairment
in
Bipolar
 Disorder.
American
Journal
of
Psychiatry.
1995;152(11):1635‐40.
 Goldberg
JF,
Chengappa
KNR.
Identifying
and
treating
cognitive
impairment
in
bipolar
 disorder.
Bipolar
Disorders.
2009;11:123‐37.
 Goldberg
JF,
Berk
M.
Neurocognition
in
Bipolar
Disorder.
In
Yatham
LN,
Maj
M
(Eds.),
 Bipolar
disorder
clinical
and
neurobiological
foundations.
West
Sussex,
UK.
Wiley‐ Blackwell.

 Goodberg
JF,
Micheal
B
(2010).
Rapid
Cycling
Bipolar
Disorder:
Phenomenology
and
 Treatment.
In
Yatham
LN,
Maj
M
(Eds.),
Bipolar
disorder
clinical
and
 neurobiological
foundations.
West
Sussex,
UK.
Wiley‐Blackwell.

 Goldstein
JM,
Jerram
M,
Poldrack
R,
Anagnoson
R,
Breiter
HC,
Makris
N,
et
al.
Sex
 differences
in
prefrontal
cortical
brain
activity
during
fMRI
of
auditory
verbal
 working
memory.
Neuropsychology.
2005;19(4):509‐19.
 
  89
  Good
CD,
Johnsrude
I,
Ashburner
J,
Henson
RNA,
Friston
KJ,
Frackowiak
RSJ.
Cerebral
 asymmetry
and
the
effects
of
sex
and
handedness
on
brain
structure:
A
voxel‐ based
morphometric
analysis
of
465
normal
adult
human
brains.
Neuroimage.
 2001;14(3):685‐700.
 Goodwin
GM,
Anderson
I,
Arango
C,
Bowden
CL,
Henry
C,
Mitchell
PB,
et
al.
ECNP
 consensus
meeting.
Bipolar
depression.
Nice,
March
2007.
European
 Neuropsychopharmacology.
2008;18(7):535‐49.
 Goodwin
FK,
Lieberman
DZ
(2010).
Clinical
features
and
subtypes
in
bipolar
disorder.
In
 Yatham
LN,
Maj
M
(Eds.),
Bipolar
disorder
clinical
and
neurobiological
 foundations.
West
Sussex,
UK.
Wiley‐Blackwell.

 Gualtieri
CT,
Johnson
LG.
Comparative
neurocognitive
effects
of
5
psychotropic
 anticonvulsants
and
lithium.
MedGenMed
:
Medscape
general
medicine.
 2006;8(3):46.
 Gualtieri
CT,
Morgan
DW.
The
frequency
of
cognitive
impairment
in
patients
with
 anxiety,
depression,
and
bipolar
disorder:
An
unaccounted
source
of
variance
in
 clinical
trials.
Journal
of
Clinical
Psychiatry.
2008;69(7):1122‐30.
 Gur
RC,
Turetsky
BI,
Matsui
M,
Yan
M,
Bilker
W,
Hughett
P,
et
al.
Sex
differences
in
 brain
gray
and
white
matter
in
healthy
young
adults:
Correlations
with
cognitive
 performance.
Journal
of
Neuroscience.
1999;19(10):4065‐72.
 Hasler
G,
Drevets
WC,
Gould
TD,
Gottesman
II,
Manji
HK.
Toward
constructing
an
 endophenotype
strategy
for
bipolar
disorders.
Biological
Psychiatry.
 2006;60(2):93‐105.
 Hatazawa
J,
Brooks
RA,
Dichiro
G,
Campbell
G.
Global
cerebral
glucose‐utilization
is
 independet
of
brain
size
–
a
PET
study.
Journal
of
Computer
Assisted
 Tomography.
1987;11(4):571‐6.
 Healy
AF,
McNamara
DS.
Verbal
learning
and
memory:
Does
the
modal
model
still
 work?
Annual
Review
of
Psychology.
1996;47:143‐72.
 Hendrick
V,
Altshuler
LL,
Gitlin
MJ,
Delrahim
S,
Hammen
C.
Gender
and
bipolar
illness.
 Journal
of
Clinical
Psychiatry.
2000;61(5):393‐6.
 Hill
SK,
Harris
MSH,
Herbener
ES,
Pavuluri
M,
Sweeney
JA.
Neurocognitive
allied
 phenotypes
for
schizophrenia
and
bipolar
disorder.
Schizophrenia
Bulletin.
 2008;34(4):743‐59.
 Holmes
MK,
Erickson
K,
Luckenbaugh
DA,
Drevets
WC,
Bain
EE,
Cannon
DM,
et
al.
A
 comparison
of
cognitive
functioning
in
medicated
and
unmedicated
subjects
with
 bipolar
depression.
Bipolar
Disorders.
2008;10(7):806‐15.
 Honig
A,
Arts
BMG,
Ponds
R,
Riedel
WJ.
Lithium
induced
cognitive
side‐effects
in
 bipolar
disorder:
a
qualitative
analysis
and
implications
for
daily
practice.
 International
Clinical
Psychopharmacology.
1999;14(3):167‐71.
  
  90
  Kramer
JH,
Delis
DC,
Kaplan
E,
Odonnell
L,
Prifitera
A.
Developmental
sex
differences
in
 verbal
learning.
Neuropsychology.
1997;11(4):577‐84.
 Jamrozinski
K.
Do
euthymic
bipolar
patients
have
normal
cognitive
functioning?
 Current
Opinion
in
Psychiatry.
2010;23(3):255‐60.
 Janowsky
JS,
Chavez
B,
Orwoll
E.
Sex
steroids
modify
working
memory.
Journal
of
 Cognitive
Neuroscience.
2000;12(3):407‐14.
 Jones
K,
Johnson
KA,
Becker
JA,
Spiers
PA,
Albert
MS,
Holman
BL.
Use
of
singular
value
 decomposition
to
characterize
age
and
gender
differences
in
SPECT
cerebral
 perfusion.
Journal
of
Nuclear
Medicine.
1998;39(6):965‐73.
 Kawa
I,
Carter
JD,
Joyce
PR,
Doughty
CJ,
Frampton
CM,
Wells
JE,
et
al.
Gender
 differences
in
bipolar
disorder:
age
of
onset,
course,
comorbidity,
and
symptom
 presentation.
Bipolar
Disorders.
2005;7(2):119‐25.
 Kaye
NS,
Graham
J,
Roberts
J,
Thompson
T,
Nanry
K.
Effect
of
open‐label
lamotrigine
as
 monotherapy
and
adjunctive
therapy
on
the
self‐assessed
cognitive
function
 scores
of
patients
with
bipolar
I
disorder.
Journal
of
Clinical
Psychopharmacology.
 2007;27(4):387‐91.
 Kennedy
N,
Boydell
J,
Kalidindi
S,
Fearon
P,
Jones
PB,
van
Os
J,
et
al.
Gender
 differences
in
incidence
and
age
at
onset
of
mania
and
bipolar
disorder
over
a
35‐ year
period
in
Camberwell,
England.
American
Journal
of
Psychiatry.
 2005;162(2):257‐62.
 Kessing
LV.
Gender
differences
in
the
phenomenology
of
bipolar
disorder.
Bipolar
 Disorders.
2004;6(5):421‐5.
 Khan
A,
Ginsberg
LD,
Asnis
GM,
Goodwin
FK,
Davis
KH,
Krishnan
AA,
et
al.
Effect
of
 lamotrigine
on
cognitive
complaints
in
patients
with
bipolar
I
disorder.
Journal
of
 Clinical
Psychiatry.
2004;65(11):1483‐90.
 Kimura
D,
Saucier
DM,
Matuk
R.
Women
name
both
colours
and
forms
faster
than
 men.
Society
for
Neuroscience
Abstracts.
1996;22(1‐3):1860.
 Kimura
D,
Seal
BN.
Sex
differences
in
recall
of
real
or
nonsense
words.
Psychological
 Reports.
2003;93(1):263‐4.
 Klenberg
L,
Korkman
M,
Lahti‐Nuuttila
P.
Differential
development
of
attention
and
 executive
functions
in
3‐to
12‐year‐old
Finnish
children.
Developmental
 Neuropsychology.
2001;20(1):407‐28.
 Klimes‐Dougan
B,
Ronsaville
D,
Wiggs
EA,
Martinez
PE.
Neuropsychological
functioning
 in
adolescent
children
of
mothers
with
a
history
of
bipolar
or
major
depressive
 disorders.
Biological
Psychiatry.
2006;60(9):957‐65.
 Kocsis
JH,
Shaw
ED,
Stokes
PE,
Wilner
P,
Elliot
AS,
Sikes
C,
et
al.
Neuropsychologic
 effects
of
lithium
discontinuation.
Journal
of
Clinical
Psychopharmacology.
 1993;13(4):268‐76.
  
  91
  Krishnan
KRR.
Psychiatric
and
medical
comorbidities
of
bipolar
disorder.
 Psychosomatic
Medicine.
2005;67(1):1‐8.
 Kruger
S,
Young
LT,
Braunig
P.
Pharmacotherapy
of
bipolar
mixed
states.
Bipolar
 Disorders.
2005;7(3):205‐15.
 Kruger
S.
Bipolar
disorder,
pregnancy
and
the
postpartum‐‐risks
and
possibilities
of
 pharmacotherapy.
Therapeutische
Umschau
Revue
therapeutique.
 2009;66(6):475‐84.
 Kupka
RW,
Luckenbaugh
DA,
Post
RM,
Leverich
GS,
Nolen
WA.
Rapid
and
non‐rapid
 cycling
bipolar
disorder:
A
meta‐analysis
of
clinical
studies.
Journal
of
Clinical
 Psychiatry.
2003;64(12):1483‐94.
 Kurtz
MM,
Gerraty
RT:
A
meta‐analytic
investigation
of
neurocognitive
deficits
in
 bipolar
illness:
profile
and
effects
of
clinical
state.
Neuropsychology
2009;
23:
 551‐562
 Lahera
G,
Montes
JM,
Benito
A,
Valdivia
M,
Medina
E,
Mirapeix
I,
et
al.
Theory
of
mind
 deficit
in
bipolar
disorder:
Is
it
related
to
a
previous
history
of
psychotic
 symptoms?
Psychiatry
Research.
2008;161(3):309‐17.
 Lenroot
RK,
Giedd
JN.
Sex
differences
in
the
adolescent
brain.
Brain
and
Cognition.
 2010;72(1):46‐55.
 Levy
LJ,
Astur
RS,
Frick
KA.
Men
and
women
differ
in
object
memory
but
not
 performance
of
a
virtual
radial
maze.
Behavioral
Neuroscience.
2005;119(4):853‐ 62.
 Lieberman
DZ,
Kolodner
G,
Massey
SH,
Williams
KP.
Antidepressant‐Induced
Mania
 with
Concomitant
Mood
Stabilizer
in
Patients
with
Comorbid
Substance
Abuse
 and
Bipolar
Disorder.
Journal
of
Addictive
Diseases.
2009;28(4):348‐55.
 Luders
E,
Gaser
C,
Jancke
L,
Schlaug
G.
A
voxel‐based
approach
to
gray
matter
 asymmetries.
Neuroimage.
2004;22(2):656‐64.
 Maccoby
EE,
Jacklin
CN.
Myth,
reality
and
shades
of
gray
–
what
we
know
and
don’t
 know
about
sex
differences.
Psychology
Today.
1974;8(7):109‐12.
 Mackay
CE,
Roddick
E,
Barrick
TR,
Lloyd
AJ,
Roberts
N,
Crow
TJ,
Young
AH,
Ferrier
IN:
Sex
 dependence
of
brain
size
and
shape
in
bipolar
disorder:
an
exploratory
study.
 Bipolar
Disord
2010;
12:
306‐31.
 MacQueen
GM,
Young
LT,
Robb
JC,
Cooke
RG,
Joffe
RT.
Levels
of
functioning
and
well‐ being
in
recovered
psychotic
versus
nonpsychotic
mania.
Journal
of
Affective
 Disorders.
1997;46(1):69‐72.
 Marangell
LB,
Dennehy
EB,
Wisniewski
SR,
Bauer
MS,
Miyahara
S,
Allen
MH,
et
al.
 Case‐control
analyses
of
the
impact
of
pharmacotherapy
on
prospectively
 observed
suicide
attempts
and
completed
suicides
in
bipolar
disorder:
Findings
 from
STEP‐BD.
Journal
of
Clinical
Psychiatry.
2008;69(6):916‐22.
  
  92
  Martinez‐Aran
A,
Vieta
E,
Colom
F,
Torrent
C,
Reinares
M,
Goikolea
JM,
et
al.
Do
 cognitive
complaints
in
euthymic
bipolar
patients
reflect
objective
cognitive
 impairment?
Psychotherapy
and
Psychosomatics.
2005;74(5):295‐302.
 Martino
DJ,
Strejilevich
SA,
Scapola
M,
Igoa
A,
Marengo
E,
Ais
ED,
et
al.
Heterogeneity
 in
cognitive
functioning
among
patients
with
bipolar
disorder.
Journal
of
Affective
 Disorders.
2008;109(1‐2):149‐56.
 Mazaux
JM,
Dartigues
JF,
Letenneur
L,
Darriet
D,
Wiart
L,
Gagnon
M,
et
al.
Visuospatial
 attention
and
psychomotor
performance
in
elderly
community
residents
–
effects
 of
age,
gender,
and
education.
Journal
of
Clinical
and
Experimental
 Neuropsychology.
1995;17(1):71‐81.
 McClure
EB,
Treland
JE,
Snow
J,
Schmajuk
M,
Dickstein
DP,
Towbin
KE,
et
al.
Deficits
in
 social
cognition
and
response
flexibility
in
pediatric
bipolar
disorder.
American
 Journal
of
Psychiatry.
2005;162(9):1644‐51.
 McDonough‐Ryan
P,
DelBello
M,
Shear
PK,
Ris
MD,
Soutullo
C,
Strakowski
SM.
 Academic
and
cognitive
abilities
in
children
of
parents
with
bipolar
disorder:
A
 test
of
the
nonverbal
learning
disability
model.
Journal
of
Clinical
and
 Experimental
Neuropsychology.
2002;24(3):280‐5.
 McElroy
SL,
Strakowski
SM,
Keck
PE,
Tugrul
KL,
West
SA,
Lonczak
HS.
Differences
and
 similarities
in
mixed
and
pure
mania.
Comprehensive
Psychiatry.
1995;36(3):187‐ 94.
 Mendrek
A.
Reversal
of
normal
cerebral
sexual
dimorphism
in
schizophrenia:
Evidence
 and
speculations.
Medical
Hypotheses.
2007;69(4):896‐902.
 Merikangas
KR,
Akiskal
HS,
Angst
J,
Greenberg
PE,
Hirschfeld
RMA,
Petukhova
M,
et
al.
 Lifetime
and
12‐month
prevalence
of
bipolar
spectrum
disorder
in
the
national
 comorbidity
survey
replication.
Archives
of
General
Psychiatry.
2007;64(5):543‐ 52.
 Morgan
PT,
Desai
RA,
Potenza
MN.
Gender‐Related
Influences
of
Parental
Alcoholism
 on
the
Prevalence
of
Psychiatric
Illnesses:
Analysis
of
the
National
Epidemiologic
 Survey
on
Alcohol
and
Related
Conditions.
Alcoholism‐Clinical
and
Experimental
 Research.
2010;34(10):1759‐67.
 Morgan
VA,
Mitchell
PB,
Jablensky
AV.
The
epidemiology
of
bipolar
disorder:
 sociodemographic,
disability
and
service
utilization
data
from
the
Australian
 National
Study
of
Low
Prevalence
(Psychotic)
Disorders.
Bipolar
Disorders.
 2005;7(4):326‐37.
 Mur
M,
Portella
MJ,
Martinez‐Aran
A,
Pifarre
J,
Vieta
E.
Long‐term
stability
of
cognitive
 impairment
in
bipolar
disorder:
A
2‐year
follow‐up
study
of
lithium‐treated
 euthymic
bipolar
patients.
Journal
of
Clinical
Psychiatry.
2008;69(5):712‐9.
  
  93
  Naglieri
JA,
Rojahn
J.
Gender
differences
in
Planning,
Attention,
Simultaneous,
and
 Successive
(PASS)
cognitive
processes
and
achievement.
Journal
of
Educational
 Psychology.
2001;93(2):430‐7.
 Najt
P,
Glahn
D,
Bearden
CE,
Hatch
JP,
Monkul
ES,
Kaur
S,
et
al.
Attention
deficits
in
 bipolar
disorder:
a
comparison
based
on
the
Continuous
Performance
Test.
 Neuroscience
Letters.
2005;379(2):122‐6.
 Najt
P,
Nicoletti
M,
Chen
HH,
Hatch
JP,
Caetano
SC,
Sassi
RB,
Axelson
D,
Brambilla
P,
 Keshavan
MS,
Ryan
ND,
Birmaher
B,
Soares
JC:
Anatomical
measurments
of
the
 orbitofrontal
cortex
in
child
and
adolescents
patients
with
bipolar
disorders.
 Neursci
Lett
2007;
413:
183‐186.

 Negash
A,
Alem
A,
Kebede
D,
Deyessa
N,
Shibre
T,
Kullgren
G.
Prevalence
and
clinical
 characteristics
of
bipolar
I
disorder
in
Butajira,
Ethiopia:
A
community‐based
 study.
Journal
of
Affective
Disorders.
2005;87(2‐3):193‐201.
 Nehra
R,
Chakrabarti
S,
Pradhan
BK,
Khehra
N.
Comparison
of
cognitive
functions
 between
first‐
and
multi‐episode
bipolar
affective
disorders.
Journal
of
Affective
 Disorders.
2006;93(1‐3):185‐92.
 Neuhaus
AH,
Opgen‐Rhein
C,
Urbanek
C,
Gross
M,
Hahn
E,
Ta
TMT,
et
al.
 Spatiotemporal
Mapping
of
Sex
Differences
During
Attentional
Processing.
 Human
Brain
Mapping.
2009;30(9):2997‐3008.
 Nierenberg
AA,
Akiskal
HS,
Angst
J,
Hirschfeld
RM,
Merikangas
KR,
Petukhova
M,
et
al.
 Bipolar
disorder
with
frequent
mood
episodes
in
the
national
comorbidity
survey
 replication
(NCS‐R).
Molecular
Psychiatry.
2010;15(11):1075‐87.
 Nopoulos
P,
Flaum
M,
O'Leary
D,
Andreasen
NC.
Sexual
dimorphism
in
the
human
 brain:
evaluation
of
tissue
volume,
tissue
composition
and
surface
anatomy
using
 magnetic
resonance
imaging.
Psychiatry
Research‐Neuroimaging.
2000;98(1):1‐ 13.
 Ojemann
GA.
The
neurobiology
of
language
and
verbal
memory:
observations
from
 awake
neurosurgery.
International
Journal
of
Psychophysiology.
2003;48(2):141‐ 6.
 Pachet
AK,
Wisniewski
AM.
The
effects
of
lithium
on
cognition:
an
updated
review.
 Psychopharmacology.
2003;170(3):225‐34.
 Parasuraman
R,
Jiang
Y.
Individual
differences
in
cognition,
affect,
and
performance:
 Behavioral,
neuroimaging,
and
molecular
genetic
approaches.
Neuroimage.
 2012;59(1):70‐82.
 Pavuluri
MN,
Schenkel
LS,
Aryal
S,
Harral
EM,
Hill
SK,
Herbener
ES,
et
al.
 Neurocognitive
function
in
unmedicated
manic
and
medicated
euthymic
pediatric
 bipolar
patients.
American
Journal
of
Psychiatry.
2006;163(2):286‐93.
  
  94
  Penades
R,
Catalan
R,
Salamero
M,
Boget
T,
Puig
O,
Guarch
J,
et
al.
Cognitive
 Remediation
Therapy
for
outpatients
with
chronic
schizophrenia:
A
controlled
 and
randomized
study.
Schizophrenia
Research.
2006;87(1‐3):323‐31.
 Perala
J,
Saarni
SI,
Ostamo
A,
Pirkola
S,
Haukka
J,
Harkanen
T,
et
al.
Geographic
 variation
and
sociodemographic
characteristics
of
psychotic
disorders
in
Finland.
 Schizophrenia
Research.
2008;106(2‐3):337‐47.
 Perrin
JS,
Herve
P‐Y,
Leonard
G,
Perron
M,
Pike
GB,
Pitiot
A,
et
al.
Growth
of
white
 matter
in
the
adolescent
brain:
Role
of
testosterone
and
androgen
receptor.
 Journal
of
Neuroscience.
2008;28(38):9519‐24.
 Peters
M,
Jancke
L,
Staiger
JF,
Schlaug
G,
Huang
Y,
Steinmetz
H.
Unsolved
problems
in
 comparing
brain
sizes
in
Homo
sapiens.
Brain
and
Cognition.
1998;37(2):254‐85.
 Postma
A,
Winkel
J,
Tuiten
A,
Honk
VJ:
Sex
differences
and
menstrual
cycle
effects
in
 human
spatial
memory.
Psychoneuroendocrinology
1999;
24:
175‐192
 Post
RM,
Kaur‐Sant’Anna
M
(2010).
An
introduction
to
the
neurobiology
of
bipolar
 illness
onset,
recurrence,
and
progression.
In
Yatham
LN,
Maj
M
(Eds.),
Bipolar
 disorder
clinical
and
neurobiological
foundations.
West
Sussex,
UK.
Wiley‐ Blackwell.

 Reiman
EM,
Armstrong
SM,
Matt
KS,
Mattox
JH.
The
application
of
positron
emission
 tomography
to
the
study
of
the
normal
menstrual
cycle.
Human
Reproduction.
 1996;11(12):2799‐805.
 Rihmer
Z,
Fawcett
J
(2010).
Suicide
and
bipolar
disorder.
In
Yatham
LN,
Maj
M
(Eds.),
 Bipolar
disorder
clinical
and
neurobiological
foundations.
West
Sussex,
UK.
Wiley‐ Blackwell.

 Robinson
LJ,
Ferrier
IN.
Evolution
of
cognitive
impairment
in
bipolar
disorder:
a
 systematic
review
of
cross‐sectional
evidence.
Bipolar
Disorders.
2006;8(2):103‐ 16.
 Robinson
LJ,
Thompson
JM,
Gallagher
P,
Goswami
U,
Young
AH,
Ferrier
IN,
et
al.
A
 meta‐analysis
of
cognitive
deficits
in
euthymic
patients
with
bipolar
disorder.
 Journal
of
Affective
Disorders.
2006;93(1‐3):105‐15.
 Roesch‐Ely
D,
Hornberger
E,
Weiland
S:
Do
sex
differences
affect
prefrontal
cortex
 associated
cognition
in
schizophrenia?
Schiz
Res
2009;
107:
255‐261.
 Roy‐Byrne
P,
Post
RM,
Uhde
TW,
Porcu
T,
Davis
D.
The
longitudinal
course
of
recurrent
 affective
illness:
life
chart
data
from
research
patients
at
the
NIMH.
Acta
 psychiatrica
Scandinavica
Supplementum.
1985;317:1‐34.
 Ruff
RM,
Light
RH,
Parker
SB:
Benton
Controlled
Oral
Word
Association
Test:
Reliability
 and
updated
norms.
Arch
Neuropsychology
1996;
11:
329‐338.
  
  95
  Rybakowski
JK,
Wykretowicz
A,
Heymann‐Szlachcinska
A,
Wysocki
H.
Impairment
of
 endothelial
function
in
unipolar
and
bipolar
depression.
Biological
Psychiatry.
 2006;60(8):889‐91.
 Savitz
JB,
van
der
Merwe
L,
Stein
DJ,
Solms
M,
Ramesar
RS.
Neuropsychological
task
 performance
in
bipolar
spectrum
illness:
genetics,
alcohol
abuse,
medication
and
 childhood
trauma.
Bipolar
Disorders.
2008;10(4):479‐94.
 Sbrana
A,
Bizzarri
JV,
Rucci
P,
Gonnelli
C,
Doria
MR,
Spagnolli
S,
et
al.
The
spectrum
of
 substance
use
in
mood
and
anxiety
disorders.
Comprehensive
Psychiatry.
 2005;46(1):6‐13.
 Schaffer
A,
Cairney
J,
Cheung
A,
Veldhuizen
S,
Levitt
A.
Community
survey
of
bipolar
 disorder
in
Canada:
Lifetime
prevalence
and
illness
characteristics.
Canadian
 Journal
of
Psychiatry‐Revue
Canadienne
De
Psychiatrie.
2006;51(1):9‐16.
 Schneck
CD,
Miklowitz
DJ,
Allen
MH.
Mixed
depression
and
rapid
cycling
‐
Reply.
 American
Journal
of
Psychiatry.
2008;165(8):1049‐.
 Senturk
V,
Goker
C,
Bilgic
A,
Olmez
S,
Tugcu
H,
Oncu
B,
et
al.
Impaired
verbal
memory
 and
otherwise
spared
cognition
in
remitted
bipolar
patients
on
monotherapy
with
 lithium
or
valproate.
Bipolar
Disorders.
2007;9:136‐44.
 Silverman
DHS,
Hussain
SA,
Ercoli
LM,
Huang
SC,
Czernin
J,
Phelps
ME,
et
al.
Detection
 of
differences
in
regional
cerebral
metabolism
associated
with
genotypic
and
 educational
risk
factors
for
dementia.
Metmbs'00:
Proceedings
of
the
 International
Conference
on
Mathematics
and
Engineering
Techniques
in
 Medicine
and
Biological
Sciences,
Vols
I
and
Ii.
2000:422‐7.
 Silverman
I,
Choi
J,
Peters
M.
The
Hunter‐Gatherer
theory
of
sex
differences
in
spatial
 abilities:
Data
from
40
countries.
Archives
of
Sexual
Behavior.
2007;36(2):261‐8.
 Sobin
C,
Sackeim
HA.
Psychomotor
symptoms
of
depression.
American
Journal
of
 Psychiatry.
1997;154(1):4‐17.
 Soukup
VM,
Ingram
F,
Grady
JJ,
Schiess
MC.
Trail
Making
Test:
issues
in
normative
data
 selection.
Applied
neuropsychology.
1998;5(2):65‐73.
 Sowell
ER,
Thompson
PM,
Toga
AW.
Mapping
changes
in
the
human
cortex
throughout
 the
span
of
life.
Neuroscientist.
2004;10(4):372‐92.
 Speck
O,
Ernst
T,
Braun
J,
Koch
C,
Miller
E,
Chang
L.
Gender
differences
in
the
 functional
organization
of
the
brain
for
working
memory.
Neuroreport.
 2000;11(11):2581‐5.
 Strejilevich
S,
Martino
D.
Influence
of
obstetrical
complications
in
cognitive
functioning
 on
bipolar
disorder.
International
Journal
of
Neuropsychopharmacology.
 2008;11:182‐.
  
  96
  Suominen
K,
Mantere
O,
Valtonen
H,
Arvilommi
P,
Leppamaki
S,
Isometsa
E.
Gender
 differences
in
bipolar
disorder
type
I
and
II.
Acta
Psychiatrica
Scandinavica.
 2009;120(6):464‐73.
 Suppes
T,
Dennehy
EB.
Evidence‐based
long‐term
treatment
of
bipolar
II
disorder.
 Journal
of
Clinical
Psychiatry.
2002;63:29‐33.
 Tabares‐Seisdedos
R,
Balanza‐Martinez
V,
Sanchez‐Moreno
J,
Martinez‐Aran
A,
Salazar‐ Fraile
J,
Selva‐Vera
G,
et
al.
Neurocognitive
and
clinical
predictors
of
functional
 outcome
in
patients
with
schizophrenia
and
bipolar
I
disorder
at
one‐year
follow‐ up.
Journal
of
Affective
Disorders.
2008;109(3):286‐99.
 Taylor
Tavares
JV,
Clark
L,
Cannon
DM,
Erickson
K,
Drevets
WC,
Sahakian
BJ.
Distinct
 profiles
of
neurocognitive
function
in
unmedicated
unipolar
depression
and
 bipolar
II
depression.
Biological
psychiatry.
2007;62(8):917‐24.
 Taylor
MA,
Abrams
R.
Gender
differences
in
bipolar
affective‐disorder.
Journal
of
 Affective
Disorders.
1981;3(3):261‐71.
 Thilers
PP,
MacDonald
SWS,
Herlitz
A.
Sex
differences
in
cognition:
The
role
of
 handedness.
Physiology
&
Behavior.
2007;92(1‐2):105‐9.
 Tohen
M,
Hennen
J,
Zarate
CM,
Baldessarini
RJ,
Strakowski
SM,
Stoll
AL,
et
al.
Two‐year
 syndromal
and
functional
recovery
in
219
cases
of
first‐episode
major
affective
 disorder
with
psychotic
features.
American
Journal
of
Psychiatry.
 2000;157(2):220‐8.
 Tohen
M,
Waternaux
CM,
Tsuang
MT.
Outcome
in
mania
–
A
4‐year
prospective
 follow‐up
of
patients
utilizing
survival
analysis.
Archives
of
General
Psychiatry.
 1990;47(12):1106‐11.
 Tohen
M,
Zarate
CA,
Hennen
J,
Khalsa
HMK,
Strakowski
SM,
Gebre‐Medhin
P,
et
al.
The
 McLean‐Harvard
first‐episode
mania
study:
Prection
of
recovery
and
first
 recurrence.
American
Journal
of
Psychiatry.
2003;160(12):2099‐107.
 Tondo
L,
Baldessarini
RJ.
Rapid
cycling
in
women
and
men
with
bipolar
manic‐ depressive
disorders.
American
Journal
of
Psychiatry.
1998;155(10):1434‐6.
 Torres
IJ,
Boudreau
VG,
Yatham
LN.
Neuropsychological
functioning
in
euthymic
 bipolar
disorder:
a
meta‐analysis.
Acta
Psychiatrica
Scandinavica.
2007;116:17‐26.
 Torres
IJ,
Malhi
GS
(2010).
Neurocognition
in
Bipolar
Disorder.
In
Yatham
LN,
Maj
M
 (Eds.),
Bipolar
disorder
clinical
and
neurobiological
foundations.
West
Sussex,
UK.
 Wiley‐Blackwell.

 Trenerry
MR,
Jack
CR,
Cascino
GD,
Sharbrough
FW,
Ivnik
RJ.
Gender
differences
in
 post‐temporal
lobectomy
verbal
memory
and
relationships
between
MRI
 hippocampal
volumes
and
preoperative
verbal
memory.
Epilepsy
Research.
 1995;20(1):69‐76.
  
  97
  Tsaltas
E,
Kontis
D,
Boulougouris
V,
Papadimitriou
GN.
Lithium
and
cognitive
 enhancement:
leave
it
or
take
it?
Psychopharmacology.
2009;202(1‐3):457‐76.
 van
der
Schot
AC,
Vonk
R,
Brans
RGH,
van
Haren
NEM,
Koolschijn
PCMP,
Nuboer
V,
et
al.
 Influence
of
Genes
and
Environment
on
Brain
Volumes
in
Twin
Pairs
Concordant
 and
Discordant
for
Bipolar
Disorder.
Archives
of
General
Psychiatry.
 2009;66(2):142‐51.
 van
Gorp
WG,
Altshuler
L,
Theberge
DC,
Wilkins
J,
Dixon
W.
Cognitive
impairment
in
 euthymic
bipolar
patients
with
and
without
prior
alcohol
dependence
‐
A
 preliminary
study.
Archives
of
General
Psychiatry.
1998;55(1):41‐6.
 Vaskinn
A,
Sundet
K,
Simonsen
C,
Hellvin
T,
Melle
I,
Andreassen
OA.
Sex
Differences
in
 Neuropsychological
Performance
and
Social
Functioning
in
Schizophrenia
and
 Bipolar
Disorder.
Neuropsychology.
2011;25(4):499‐510.
 Vieta
E,
Suppes
T.
Bipolar
II
disorder:
arguments
for
and
against
a
distinct
diagnostic
 entity.
Bipolar
Disorders.
2008;10(1):163‐78.
 Wiederholt
WC,
Cahn
D,
Butters
NM,
Salmon
DP,
Kritzsilverstein
D,
Barrettconnor
E.
 Effects
of
age,
gender
and
education
on
selected
neuropsychological
tests
in
an
 elderly
community
cohort.
Journal
of
the
American
Geriatrics
Society.
 1993;41(6):639‐47.
 Wingo
AP,
Harvey
PD,
Baldessarini
RJ.
Neurocognitive
impairment
in
bipolar
disorder
 patients:
functional
implications.
Bipolar
Disorders.
2009;11(2):113‐25.
 Witelson
SF,
Beresh
H,
Kigar
DL.
Intelligence
and
brain
size
in
100
postmortem
brains:
 sex,
lateralization
and
age
factors.
Brain.
2006;129:386‐98.

 Womer
FY,
Wang
F,
Chepenik
LG,
Kalmar
JH,
Spencer
L,
Edmiston
E,
Pittman
BP,
 Constable
RT,
Papademetris
X,
Blumberg
HP:
Sexually
dimorphic
features
of
 vermis
morphology
in
bipolar.
Bipolar
Disord
2009;
753‐758
 Yatham
LN,
Kauer‐Sant'Anna
M,
Bond
DJ,
Lam
RW,
Torres
I.
Course
and
Outcome
After
 the
First
Manic
Episode
in
Patients
With
Bipolar
Disorder:
Prospective
12‐Month
 Data
From
the
Systematic
Treatment
Optimization
Program
for
Early
Mania
 Project.
Canadian
Journal
of
Psychiatry‐Revue
Canadienne
De
Psychiatrie.
 2009;54(2):105‐12.
 Young
RC,
Kiosses
D,
Heo
M,
Schulberg
HC,
Murphy
C,
Klimstra
S,
et
al.
Age
and
ratings
 of
manic
psychopathology.
Bipolar
Disorders.
2007;9(3):301‐4.
 Yucel
K,
McKinnon
MC,
Taylor
VH,
Macdonald
K,
Alda
M,
Young
LT,
et
al.
Bilateral
 hippocampal
volume
increases
after
long‐term
lithium
treatment
in
patients
with
 bipolar
disorder:
a
longitudinal
MRI
study.
Psychopharmacology.
 2007;195(3):357‐67.
 
 
  
  98
  Zobel
AW,
Schulze‐Rauschenbach
S,
von
Widdern
OC,
Metten
M,
Freymann
N,
 Grasmader
K,
et
al.
Improvement
of
working
but
not
declarative
memory
is
 correlated
with
HPA
normalization
during
antidepressant
treatment.
Journal
of
 Psychiatric
Research.
2004;38(4):377‐83.
 Zubieta
JK,
Taylor
SF,
Huguelet
P,
Koeppe
RA,
Kilbourn
MR,
Frey
KA.
Vesicular
 monoamine
transporter
concentrations
in
bipolar
disorder
type
I,
schizophrenia,
 and
healthy
subjects.
Biological
Psychiatry.
2001;49(2):110‐6.
 
  
 
 
 
 
 
 
 
  
  99
  Appendices
  
 Appendix
A:
Demographic
and
clinical
variable
histograms
 
 Age:
Male
Patient
Data
 
 
 Age:
Female
Patient
Data
  
 Age:
Male
Control
Data
  
 
  
  Years
of
Education:
Male
Patients

 
  
 
 
  
  Age:
Female
Control
Data
  
  
 Years
of
Education:
Female
Patients
  
  
  100
  Years
of
Education:
Male
Controls

 
  
 CGI:
Male
Patients
  
 
  
  
 Age
of
Onset:
Male
Patients
 
  
  
  Years
of
Education:
Female
Controls
  
  
  CGI:
Female
Patients
  
 
  Age
of
Onset:
Female
Patients
  
  
  101
  Appendix
B:
Cognitive
variable
histograms
 
 COWAT
Total
Score:
Male
Patients
 
 COWAT
Total
Score:
Female
Patients
  
 COWAT
Total
Score:
Male
Controls
 
  
 CVLT
Trial
1:
Male
Patients
 
  
 
 
  
  
  
 COWAT
Total
Score:
Female
Controls
  
 
  
 CVLT
Trial
1:
Female
Patients
  
  102
  CVLT
Trial
1:
Male
Controls
 
  
 CVLT
Trial
1­5:
Male
Patients
  
 CVLT
Trial
1­5:
Male
Controls
  
 
 
 
 
  
  
  CVLT
Trial
1:
Female
Controls
  
 
  
 CVLT
Trial
1­5:
Female
Patients
  
 
  
  CVLT
Trial
1­5:
Female
Controls
  
  
  103
  CVLT
Long
Delay
Free
Recall:


 Male
Patients
 
 
 
  
 CVLT
Long
Delay
Free
Recall:


 Male
Controls

 
 
  
 IED
EDS
Errors:
Male
Patients
  
 
 
 
 
 
  


 
  CVLT
Long
Delay
Free
Recall:

 Female
Patients
  
  
  


 
  CVLT
Long
Delay
Free
Recall:

 Female
Controls
  
 
  IED
EDS
Errors:
Female
Patients
  
  
  104
  IED
EDS
Errors:
Male
Controls
  
 IED
Total
Errors:
Male
Patients
  
 IED
Total
Errors:
Male
Controls
  
 
 
 
 
 
  
  
  IED
EDS
Errors:
Female
Controls
  
  
  
  IED
Total
Errors:
Female
Patients
  
  
  
  IED
Total
Errors:
Female
Controls
  
  
  105
  SOC
#
of
Problems
Solved
in
Min.
Moves:
 Male
Patients
 
 
 
 
  
 SOC
#
of
Problems
Solved
in
Min.
Moves:
 Male
Controls

 
 
 
 
  
 RVP
Discriminability:
Male
Patients


  
 
 
 
 
  SOC
#
of
Problems
Solved
in
Min.
Moves:
 Female
Patients
  
  
 SOC
#
of
Problems
Solved
in
Min.
Moves:
 Female
Controls
  
  
 RVP
Discriminability:
Female
Patients
  
  
  106
  RVP
Discriminability:
Male
Controls

  RVP
Discriminability:
Female
Controls
  
  
 RVP
Mean
Latency:
Male
Patients
 
  
 RVP
Mean
Latency:
Male
Controls
 
  
 
 
 
 
 
 
 
  
 RVP
Mean
Latency:
Female
Patients
  
  
 RVP
Mean
Latency:
Female
Controls
  
  
  107
  SWM
Between
Errors:
Male
Patients
  
 SWM
Between
Errors:
Male
Controls

 
  
 SWM
Strategy:
Male
Patients
  
 
 
 
 
 
 
  SWM
Between
Errors:
Female
Patients
  
  
 SWM
Between
Errors:
Female
Controls
  
 
  
 SWM
Strategy:
Female
Patients
  
  
  108
  SWM
Strategy:
Male
Controls
  
  SWM
Strategy:
Female
Controls
  
  
  
  
 
 
  
  
  
  
  
  

  
 
 
 
 
 
 
 
 
 
  
  109
  

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.24.1-0072759/manifest

Comment

Related Items