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Functional neural correlates of reduced physiological falls risk Nagamatsu, Lindsay S; Hsu, Liang C; Handy, Todd C; Liu-Ambrose, Teresa Aug 16, 2011

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RESEARCH Open AccessFunctional neural correlates of reducedphysiological falls riskLindsay S Nagamatsu1,2,3,4, Chun Liang Hsu2,4, Todd C Handy1 and Teresa Liu-Ambrose 2,3,4*AbstractBackground: It is currently unclear whether the function of brain regions associated with executive cognitiveprocessing are independently associated with reduced physiological falls risk. If these are related, it would suggestthat the development of interventions targeted at improving executive neurocognitive function would be aneffective new approach for reducing physiological falls risk in seniors.Methods: We performed a secondary analysis of 73 community-dwelling senior women aged 65 to 75 years oldwho participated in a 12-month randomized controlled trial of resistance training. Functional MRI data wereacquired while participants performed a modified Eriksen Flanker Task - a task of selective attention and conflictresolution. Brain volumes were obtained using MRI. Falls risk was assessed using the Physiological ProfileAssessment (PPA).Results: After accounting for baseline age, experimental group, baseline PPA score, and total baseline white matterbrain volume, baseline activation in the left frontal orbital cortex extending towards the insula was negativelyassociated with reduced physiological falls risk over the 12-month period. In contrast, baseline activation in theparacingulate gyrus extending towards the anterior cingulate gyrus was positively associated with reducedphysiological falls risk.Conclusions: Baseline activation levels of brain regions underlying response inhibition and selective attention wereindependently associated with reduced physiological falls risk. This suggests that falls prevention strategies may befacilitated by incorporating intervention components - such as aerobic exercise - that are specifically designed toinduce neurocognitive plasticity.Trial Registration: ClinicalTrials.gov Identifier: NCT00426881IntroductionFalls are a major health care problem for seniors andhealth care systems. They are the third leading cause ofchronic disability worldwide [1] and approximately 30%of community-dwellers over the age of 65 years experi-ence one or more falls every year [2]. Importantly, 5% offalls result in fracture, with one-third of those being hipfractures.Key risk factors for falls include reduced physiologicalfunction, such as impaired balance, [3,4] and cognitiveimpairment [2]. Recent evidence suggests that even mildreductions in cognitive abilities among otherwise healthycommunity-dwelling older adults increase physiologicalfalls risk [5-8]. Specifically, evidence suggests thatreduced executive functions – the ability to concentrate,to attend selectively, and to plan and strategize – areassociated with increased falls risk among seniors with-out cognitive impairment and dementia [5,6,9-11].Currently, the neural basis for the association betweenreduced executive functions and falls is unclear. Evi-dence from neuroimaging studies provides insight topossible underlying mechanisms. Specifically, cerebralwhite matter lesions (or leukoaraiosis) are associatedwith both reduced executive functions [12] and gait andbalance abnormalities [13-16]. Cerebral white matterlesions may interrupt frontal lobe circuits responsiblefor normal gait and balance or they may interfere withlong loop reflexes mediated by deep white matter sen-sory and motor tracts [15]. In addition, the* Correspondence: tlambrose@exchange.ubc.ca2Centre for Hip Health and Mobility, Vancouver Coastal Research Institute,University of British Columbia, 7/F 2635 Laurel Street, Vancouver BC, V6H2K2, CanadaFull list of author information is available at the end of the articleNagamatsu et al. Behavioral and Brain Functions 2011, 7:37http://www.behavioralandbrainfunctions.com/content/7/1/37© 2011 Nagamatsu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.periventricular and subcortical distribution of whitematter lesions could interrupt the descending motorfibers arising from medial cortical areas, which areimportant for lower extremity motor control [16]. How-ever, while the results of these neuroimaging studiescontribute to our appreciation of the importance ofbrain structure to physiological falls risk, they do notprovide specific guidance for refining or developing fallsprevention strategies because white matter lesions arenot currently modifiable once they present. Studies havealso demonstrated the contribution of brain volume tophysiological falls risk. Specifically, reduced grey mattervolume within sensorimotor and frontal parietal regionsof the brain is associated with both reduced gait speedand impaired balance [17,18].Of particular relevance to falls prevention, targetedexercise training is beneficial for both brain volume, asassessed by MRI, and brain function, as assessed byfMRI [19]. What has not been well examined to date isthe contribution of brain function to physiological fallsrisk. Using functional magnetic resonance imaging(fMRI), we previously demonstrated that reduced activ-ity in the posterior lobe of the right cerebellum duringan executive-challenging cognitive task may be anunderlying neural mechanism for increased falls risk[20].To our knowledge, it is currently unknown whetherthe function of brain regions responsible for executivefunctions are independently associated with reducedphysiological falls risk after accounting for relevant fac-tors such as baseline age, baseline physiological fallsrisk, and baseline brain volume. Yet, such knowledgewould facilitate the development and refinement of tar-geted interventions to reduce physiological falls risk inolder adults. Thus, we used fMRI to examine the func-tional neural correlates of executive functioning that areindependently associated with reduced physiological fallsrisk among community-dwelling senior women.MethodsParticipantsThe sample for this analysis consisted of a subset of 155women who consented and completed a 12-month ran-domized controlled trial of exercise (NCT00426881)that primarily aimed to examine the effect of once-weekly or twice-weekly resistance training comparedwith a twice-weekly balance and tone exercise interven-tion on cognitive performance of executive functions.The design and the primary results of the study havebeen reported elsewhere [21].We recruited and randomized 155 senior women who:1) were aged 65-75 years; 2) were living independentlyin their own home; 3) obtained a score ≥ 24 on theMini-Mental Status Examination (MMSE) [22]; and 4)had a visual acuity of at least 20/40, with or withoutcorrective lenses. We excluded those who: 1) had a diag-nosed neurodegenerative disease (e.g., AD) and/orstroke; 2) were taking psychotropic drugs; 3) did notspeak and understand English; 4) had moderate to sig-nificant impairment with ADLs as determined by inter-view; 5) were taking cholinesterase inhibitors within thelast 12 months; 6) were taking anti-depressants withinthe last six months; or 7) were on oestrogen replace-ment therapy within the last 12 months.Ethical approval was obtained from the VancouverCoastal Health Research Institute (V06-0326) and theUniversity of British Columbia’s Clinical Research EthicsBoard (H06-0326). All participants provided writteninformed consent.RandomizationThe randomization sequence was generated by http://www.randomization.com and was concealed until inter-ventions were assigned. This sequence was held inde-pendently and remotely by the Research Coordinator.Participants were enrolled and randomised by theResearch Coordinator to one of three groups: once-weekly resistance training (1x RT), twice-weekly resis-tance training (2x RT), or twice-weekly balance andtone (BAT).Exercise InterventionResistance TrainingAll classes were 60 minutes in duration. The protocolfor this program was progressive and high-intensity innature. Both a Keiser® Pressurized Air system and freeweights were used to provide the training stimulus.Other key strength exercises included mini-squats, mini-lunges, and lunge walks.Balance and ToneThis program consisted of stretching exercises, range ofmotion exercises, kegals, balance exercises, and relaxa-tion techniques. This group served to control for con-founding variables such as physical training received bytraveling to the training centres, social interaction, andlifestyle changes secondary to study participation.Descriptive VariablesGlobal cognition was assessed using the MMSE [22].We used the 15-item Geriatric Depression Scale (GDS)[23] to screen for depression. Functional ComorbidityIndex was calculated to estimate the degree of comor-bidity associated with physical functioning [24]. Thisscale’s score is the total number of comorbidities.Dependent Variable: Physiological Falls RiskPhysiological falls risk was assessed using the short formof the physiological profile assessment (PPA; Prince ofNagamatsu et al. Behavioral and Brain Functions 2011, 7:37http://www.behavioralandbrainfunctions.com/content/7/1/37Page 2 of 9Wales Medical Research Institute, AUS) to assess phy-siological falls risk. The PPA measures five domains ofphysiological functioning - dominant hand reactiontime, postural sway, contrast sensitivity, proprioception,and dominant quadriceps strength - and computes aglobal falls risk score that has 75% accuracy for predict-ing falls. Global PPA scores < 0 indicate low falls risk, 0to 1 indicate mild falls risk, 1 to 2 indicate moderatefalls risk, and scores > 2 indicate high falls risk. We cal-culated change in physiological falls risk as the differ-ence score between the baseline global PPA score andthe trial completion PPA score; higher PPA changescores indicate greater reductions in physiological fallsrisk.Independent Variables of InterestBrain Structure: Anatomical MRIBaseline brain volume was measured via high-resolution,T1-weighted structural MRI images obtained using aPhilips Achieva 3T scanner (TR = 8 ms, TE = 3.7 ms,bandwidth = 2.26 kHz, voxel size = 1 × 1 × 1 mm).Brain tissue volume, normalized for subject head size,was estimated with SIENAX [25], part of FSL (FMRIB’sSoftware Library, Version 4.1.4) [26]. SIENAX starts byextracting brain and skull images from the single whole-head T1 image [27]. The brain image was then affine-registered to Montreal Neurological Institute (MNI) 152space [28,29]. Next, tissue-type segmentation with par-tial volume estimation was carried out [30] in order tocalculate baseline total volume of brain tissue, totalwhite matter volume, and total grey matter volume.Brain Function: Functional MRITransverse echo-planar imaging (EPI) images in-planewith the AC-PC line were acquired using a gradient-echo pulse sequence and sequential slice acquisition (TR= 2000 ms, TE = 30 ms, flip angle = 90°, 36 contiguousslices at 3 mm skip 1 mm, in-plane resolution of 128 ×128 pixels reconstructed in a FOV of 240 mm). Eachfunctional run began with four TR’s during which nodata were acquired to allow for steady-state tissue mag-netization. A total of 148 EPI volumes were collected ineach functional run, and a total of 6 functional runswere collected for each participant.During scanning, participants performed a modifiedEriksen flanker task [31] – a task that engages theexecutive cognitive processes of selective attention andconflict resolution (Figure 1). Participants viewed dis-plays with an arrow at central fixation, flanked by a pairof arrows on either side. In half the trials, the flankingarrows pointed in the same direction as the centralarrow cue (e.g., < < < < <; congruent condition), and inthe other half, the flanking arrows pointed in the oppo-site direction (e.g., > > < > >; incongruent condition).There were four event types based on whether thecentral arrow was congruent versus incongruent withthe distracter arrows and whether it pointed to the leftor right. A central fixation cross was presented for 500milliseconds at the beginning of each trial. The targetstimulus (arrows) was then shown for 2000 millise-conds. An average of 13500 milliseconds of blank screenwas presented between each trial, jittered between11500 and 15000 milliseconds. Each participant under-went six successive five-minute blocks where they werepresented with 17 trials that were first-order counterba-lanced such that congruent and incongruent trials fol-lowed each other equally. The participants’ task on eachtrial was to signal the direction the central arrow pointsvia a simple key press. Reaction time was recorded inmilliseconds. At the end of the sessions, a high-resolu-tion scan allowed each participant’s anatomical andfunctional images to be co-registered during dataanalysis.Functional MRI data were processed and analyzedusing SPM2 (http://www.fil.ion.ucl.ac.uk/spm). For eachparticipant, the EPI images were corrected for motionusing the INRIalign toolbox for SPM2 (http://www-sop.inria.fr/epidaure/software/INRIAlign/). The resultingimages were spatially-normalized into MNI stereotaxiccoordinates using the EPI template provided with SPM2[32], and spatially smoothed using an isotropic 8 mmFigure 1 The Flanker Task. Participants were presented with a13.5-sec fixation cross, which was followed by a 500 millisecondspre-cue that informed participants that the critical stimulus willappear soon. Finally, an array of five arrows was on the screen.Participants responded to the orientation of the central arrow cueby pressing a button with their left hand if the central arrow cuepointed to the left and with their right hand if the central arrowcue pointed to the right. During one half of the trials, the flankingarrows faced in the same direction as the central arrow cue (i.e.,congruent trials), and during the other half, they pointed in theopposite direction as the central arrow cue (i.e., incongruent trials).These stimuli remained on the screen for 2,000 milliseconds. Eachparticipant underwent six successive five-minute blocks where theywere presented with 17 trials that are first-order counterbalancedsuch that consistent and inconsistent trials followed each otherequally [31]. This paradigm is sensitive to age-related decrements inattention control [48].Nagamatsu et al. Behavioral and Brain Functions 2011, 7:37http://www.behavioralandbrainfunctions.com/content/7/1/37Page 3 of 9Gaussian kernel. For each participant, the smoothed,normalized EPI data were analyzed via multiple regres-sion using a fixed-effects general linear model [33]. Inparticular, the event-related responses to the onsets ofthe stimuli was examined, with each participant’s modelincluding four event-related regressors: 1) one for eachcombination of target type (i.e., left or right); 2) and dis-tracter condition (i.e., congruent or incongruent).Regressors were based on the canonical event-relatedhemodynamic response function, temporal derivatives ofthe event-related responses were included as additionalregressors, and low-frequency scanner and/or physiolo-gical noise was modeled via linear, quadratic, and cubicregressors of non-interest. Group-level analyses werethen based on a random-effects model using one-samplet-tests, with a threshold of p < 0.05, corrected, and aminimum extent threshold of 10 contiguous voxels.Mean beta values reported for clusters identified in thegroup-level data were extracted from the SPM2 datafiles using custom scripts implemented in MATLAB(The MATHWORKS Inc., Natick, MA). The group-levelcluster means were calculated by first determining eachparticipant’s mean beta across all voxels in the givencluster. All reported voxel coordinates were convertedto Talairaich coordinates [34] using the mni2talMATLAB script (http://imaging.mrc-cbu.cam.ac.uk/ima-ging/MniTalairach). The mean beta values were thenimported to SPSS.Statistical AnalysesDescriptive data are reported for variables of interest.Data were analyzed using SPSS Windows Version 18.0(SPSS Inc., Chicago, IL). The associations between thevariables were determined using the Pearson productmoment coefficient of correlation.A multiple linear regression model was constructed todetermine the independent association of the neuralcorrelates of executive functioning, as assessed by fMRI,with change in physiological falls risk over the 12-month intervention study, as assessed by PPA. Baselineage, experimental group, and baseline physiological fallsrisk were statistically controlled by entering these threevariables into the regression model first. These indepen-dent variables were determined from the results of thePearson product moment coefficient of correlation ana-lyses (i.e., baseline PPA score) and based on biologicalrelevance, such as experimental group and age.Baseline total brain volume, total white mattervolume, and total grey matter volume were then enteredinto regression model and only those that significantlyimproved the model were included (i.e., stepwise).Finally, regions of the brain (i.e., clusters) showingincreases in the hemodynamic response on incongruentrelative to congruent trials of the flanker task were thenentered into the model using a stepwise approach.Alpha was set at p ≤ 0.05.ResultsParticipants and Variables of InterestOf the 155 participants who consented and were rando-mized at baseline, 135 completed the 12-month trial.Seventy-three of the 135 participants consented andcompleted baseline MRI and fMRI scanning.Table 1 reports the baseline descriptive statistics forthis cohort. The mean baseline PPA score was 0.10,indicating mild falls risk. At the end of the 12-monthtrial, the 73 women demonstrated a mean change of0.10 in the PPA score. A paired t-test indicated that thiswas not a statistically significant change (p = 0.06).Behavioural performance on the flanker task was cal-culated as percent increase in reaction time to incongru-ent stimuli, over and above the average reaction time tocongruent stimuli {[(incongruent reaction time - congru-ent reaction time)/congruent reaction time] × 100} [31].The percent increase measure is derived to reflect inter-ference unbiased by differences in base reaction time.Only correct responses were included in the analysis.Mean interference score for BAT, RT1, and RT2 were16.59 (SD = 13.07), 19.92 (SD = 2.52), and 27.98 (SD =13.77), respectively.Consistent with previous studies using the flankertask, regions showing increases in the hemodynamicresponse on incongruent relative to congruent trialsincluded bilateral inferior and middle frontal gyri, fron-tal orbital cortex, anterior cingulate cortex (ACC), bilat-eral precuneus, and the right cerebellum (Figure 2); 14clusters were identified (Table 2).Correlation CoefficientsTable 3 reports the bivariate correlation coefficients ofthose variables included in the final multiple linearregression model. Baseline physiological falls risk waspositively and significantly associated with change inphysiological falls risk (p < 0.001). Baseline total brainvolume, total white matter volume, and activation (i.e.,hemodynamic response) in the left frontal orbital cortexextending towards the insula (OFC/In) were negativelyand significantly associated with change in physiologicalfalls risk (p < 0.05). In our bivariate analysis, age, experi-mental group, and activation in the right paracingulategyrus extending towards the anterior cingulate cortex(PCG/ACC) were not associated with change in physio-logical falls risk (p > 0.26).Linear Regression ModelBaseline age, experimental group, and baseline physiologi-cal falls risk, accounted for 31.9% of the variance in changein physiological falls risk (Table 3). Adding baseline totalNagamatsu et al. Behavioral and Brain Functions 2011, 7:37http://www.behavioralandbrainfunctions.com/content/7/1/37Page 4 of 9white matter volume resulted in an R-square change of6.4% and significantly improved the regression model (FChange = 7.1, p = 0.01). Adding activation in the leftOFC/In to the model resulted in an R-square change of10.4% and significantly improved the model (F Change =13.6, p < 0.001). Finally, the inclusion of activation in theright PCG/ACC resulted in significant R-square change of4.4% (F Change = 6.6, p = 0.02). The total varianceaccounted by the final model was 53.1% (Table 3). Basedon the standardized betas, the left OFC/In was most asso-ciated with reduced physiological falls risk.DiscussionRecent evidence strongly suggests that changes in brainstructure with age contribute to problems with mobility[35-39]. However, less is known about the role of brainfunction [20]. To our knowledge, our study is the firstto demonstrate the independent contribution of brainfunction to reduced physiological falls risk among com-munity-dwelling seniors. Specifically, after accountingfor baseline age, experimental group, baseline physiolo-gical falls risk, and baseline total white matter volume,activation in the left OFC/In was negatively and inde-pendently associated with reduced physiological fallsrisk in community-dwelling senior women over a 12-month period. In contrast, activation in the PCG/ACCwas positively and independently associated withreduced physiological falls risk.The two regions included in our multiple linearregression model – the left OFC/In and the right PCG/ACC – are both part of the neural network associatedwith response inhibition and selective attention [40-43].Response inhibition - the ability to avoid unwanted,inappropriate responses - is associated with falls inseniors. For example, Anstey and colleagues [44]reported that senior fallers (both single and recurrent)performed significantly worse on a measure of responseinhibition compared to non-fallers. The authors sug-gested that reduced inhibition results from age-relateddeclines in functioning of the prefrontal cortex, whichcontributes to falls. Given that movement through theenvironment requires attending to relevant stimuli andinhibiting prepotent, yet potentially unsafe, responses, itis not surprising that brain regions associated withresponse inhibition and selective attention are related tofalls risk.Importantly, we found that activation in the left OFC/Inwas negatively associated with reduced physiological fallsTable 1 Descriptive statistics for variables of interest (N = 73)Variable 1 BAT(n = 22)Mean (SD)1x RT (n = 28)Mean (SD)2x RT (n = 23)Mean (SD)Total (N = 73)Mean (SD)Age (yr) 69.6 (3.1) 69.5 (2.7) 69.1 (3.1) 69.4 (2.9)Height (cm) 161.5 (6.2) 162.0 (7.5) 162.4 (6.9) 161.9 (6.9)Weight (kg) 67.1 (10.9) 67.9 (13.6) 68.6 (13.0) 67.9 (12.5)EducationLess than Grade 9 2 0.0 (0.0) 0.0 (0.0) 0.0 (0.0) 0.0 (0.0)Grade 9 to 12 without Certificate or Diploma 2 2.0 (9.1) 2.0 (7.1) 0.0 (0.0) 4.0 (5.5)High School Certificate or Diploma 2 5.0 (22.7) 3.0 (10.7) 5.0 (21.7) 13.0 (17.8)Trades or Professional Certificate or Diploma 2 3.0 (13.6) 6.0 (21.4) 2.0 (8.7) 11.0 (15.1)University Certificate or Diploma 2 4.0 (18.2) 5.0 (17.9) 4.0 (17.4) 13.0 (17.8)University Degree 2 8.0 (36.4) 12.0 (42.9) 12.0 (52.2) 32.0 (43.8)MMSE Score (max. 30 pts) 28.8 (1.3) 28.6 (1.3) 28.8 (1.0) 28.7 (1.2)Falls in the Last 12 Months (yes/no) 2 8 (36.4) 7 (25.0) 9 (39.1) 24 (32.9)Geriatric Depression Scale (/15 pts) 0.7 (2.2) 0.1 (0.8) 0.6 (1.6) 0.5 (1.6)Functional Comorbidity Index (/18 pts) 2.2 (1.3) 1.9 (1.7) 1.7 (1.5) 1.9 (1.5)Baseline Physiological Profile Assessment Score 0.10 (0.91) 0.06 (0.89) 0.16 (1.11) 0.10 (0.96)Total Brain Volume 3 1404767.07 (61101.38) 1392824.85 (74770.29) 1425571.35 (53607.47) 1406741.26 (65216.88)White Matter Volume 3 673259.09 (37763.87) 668611.61 (33667.89) 680775.90 (30457.92) 673844.81 (33920.23)Gray Matter Volume 3 731508.20 (30004.57) 731957.93 (35834.91) 746535.05 (35339.77) 736415.19 (34256.96)Change in Physiological Falls Risk 0.25 (0.97) 0.04 (0.88) 0.34 (0.82) 0.10 (0.96)1 BAT = Balance and Tone; 1x RT = once-weekly resistance training; 2x RT = twice-weekly resistance training; yr = year; kg = kilogram; MMSE = Mini-Mental StateExamination; sec = seconds.2 Count = number of “yes” cases within each group. % = percent of “yes” within each group.3 Brain volume = mm3.Nagamatsu et al. Behavioral and Brain Functions 2011, 7:37http://www.behavioralandbrainfunctions.com/content/7/1/37Page 5 of 9Right PCG/ACC Left OFC/In Figure 2 Brain Regions Demonstrating an Increased Hemodynamic Response on Incongruent Relative to Congruent Trials. Data aregroup-averaged across all 83 participants and shown on a rendered brain provided with SPM2. Data were thresholded at P < 0.05 (corrected)and a minimum cluster size of 10 contiguous voxels. The left OFC/In and right PCG/ACC both contributed significantly to our model predictingchange in physiological falls risk.Table 2 Voxel Cluster Statistics from fMRIHemisphere Structure BA 1 K 2 t 3 MNI TALX Y Z X Y ZRight Lateral occipital cortex 19 4247 9.51 28 -78 42 28 -74 42Right Frontal orbital cortex 47 597 8.18 36 24 -4 36 23 -5Right Posterior cerebellum 1131 7.95 8 -80 -34 8 -79 -25Left Lateral occipital cortex 37 626 7.67 -48 -70 -12 -48 -68 -7Right Paracingulate gyrus 32 1634 7.49 8 20 44 8 21 39Left Lateral occipital cortex 7 2915 7.43 -22 -72 32 -22 -68 33Left Middle frontal gyrus 6 1620 7.20 -26 0 50 -26 2 46Right Middle frontal gyrus 6 631 7.03 26 2 48 26 4 44Right Inferior frontal gyrus 9 699 6.97 54 14 28 53 15 25Left Frontal orbital cortex 47 198 6.51 -32 24 -6 -32 23 -6Left Frontal orbital cortex 47 122 6.19 -46 20 -10 -46 19 -9Right Supramarginal gyrus 40 82 6.04 28 -68 -28 28 -67 -20Right Posterior cerebellum 25 5.72 14 -76 -50 14 -76 -38Right Anterior cerebellum 21 5.69 16 -38 -34 16 -38 -27Reported coordinates and t values are for the cluster maxima.1 BA = Brodmann’s area. 2 K = # of voxels in the cluster. 3 All p values < 0.05.Nagamatsu et al. Behavioral and Brain Functions 2011, 7:37http://www.behavioralandbrainfunctions.com/content/7/1/37Page 6 of 9risk, whereas activation in the PCG/ACC was positivelyassociated with reduced physiological falls risk. Increasedactivation in the frontal cortex during an executive task,such as the flanker, is associated with better task perfor-mance [31]. In contrast, increased activation of the ante-rior cingulate cortex in older adults is associated withreduced task performance [31]. In particular, increasedanterior cingulate cortex activation is hypothesized to bean indicator of greater cognitive effort, such that the ante-rior cingulate cortex is less efficient at triggering the pre-frontral system to engage cognitive control [45].Our volumetric brain results also suggest that totalwhite matter volume, rather than total grey mattervolume, is associated with change in physiological fallsrisk. Previous research suggests that white matterdeclines at a faster rate than grey matter in otherwisehealthy older adults [46]. Our results extends this find-ing by suggesting the loss of total white matter volumemay be an early indicator of increased falls risk amongcommunity-dwelling older adults.Of particular clinical relevance, the results of ourstudy suggest that individuals at higher risk for futurefalls have greater potential for risk reduction than thoseat lower risk for falls. Specifically, our multiple regres-sion model showed that baseline physiological falls riskwas positively associated with change in physiologicalfalls risk. Hence, our current study results concurs andextends that of a previous meta-analysis that concludedexercise-based falls prevention strategies are most effec-tive among those at the greatest risk [47]. This suggeststhat one intervention strategy for falls prevention maybe to target those who are at greatest risk for falls.We note that of the independent variables included inour regression model, baseline activation of the leftOFC/In was most associated with reduced physiologicalfalls risk. Hence, while many falls interventions focus onbalance training, our study suggests that future falls pre-vention strategies should potentially incorporate inter-vention components that induce neurocognitiveplasticity (i.e., changes in brain function). Future work isneeded to establish whether such interventions wouldbe effective. Current evidence suggests that targetedaerobic exercise training has specific benefits on neuro-cognitive plasticity in brain regions that are responsiblefor selective attention and response inhibition [31].Therefore, promoting plasticity in brain regionsTable 3 Multiple linear regression model summary for improved physiological falls riskΔ PPA Score (Baseline Score - Trial Completion Score)Independent Variable r R2 R2 Change Unstandardized B(Standard Error)Standardized b p - valueModel 1 0.565 0.319 0.319Group 0.040 0.015 (0.112) 0.013 0.896Age -0.078 -0.064 (0.031) -0.211 0.043Baseline PPA Score 0.526** 0.529 (0.094) 0.575 <0.001Model 2 0.619 0.383 0.064Group 0.040 0.040 (0.107) 0.035 0.713Age -0.078 -0.068 (0.030) -0.224 0.026Baseline PPA Score 0.526** 0.521 (0.090) 0.566 <0.001White Matter Volume -0.263* -6.670E-6 (0.000) -0.255 0.010Model 3 0.698 0.487 0.104Group 0.040 0.034 (0.099) 0.030 0.733Age -0.078 -0.088 (0.028) -0.287 0.003Baseline PPA Score 0.526** 0.504 (0.083) 0.548 <0.001White Matter Volume -0.263* -8.800E-6 (0.000) -0.337 <0.001Cluster 3 1 -0.258 -0.654 (0.177) -0.339 0.014Model 4 0.729 0.531 0.044Group 0.040 0.023 (0.095) 0.021 0.809Age -0.078 -0.087 (0.027) -0.286 0.002Baseline PPA Score 0.526** 0.474 (0.081) 0.515 <0.001White Matter Volume -0.263* -1.000E-5 (0.000) -0.383 <0.001Cluster 3 1 -0.258* -1.159 (0.266) -0.601 <0.001Cluster 7 2 -0.055 0.637 (0.271) 0.329 0.016* P ≤ 0.05** P ≤ 0.0011 Cluster 3 is the region of left frontal orbital cortex extending towards the insula.2 Cluster 7 is the region of right paracingulate gyrus extending towards the anterior cingulate cortex.Nagamatsu et al. Behavioral and Brain Functions 2011, 7:37http://www.behavioralandbrainfunctions.com/content/7/1/37Page 7 of 9associated with these key executive functions may havea positive impact on falls prevention.We acknowledge that our finding that a negativeassociation between baseline total white matter volumeand change in physiological falls risk is significantlyassociated with reduced falls risk contrasts previouscross-sectional studies on gray matter volume, balance,and mobility. Specifically, Rosano and colleagues[17,18] found that reduced gait speed and impairedbalance - key risk factors for falls – were significantlycorrelated with reduced grey matter volume withinsensorimotor and frontal parietal regions in the brain.However, we highlight that our study examined theindependent contribution of baseline volumetric brainmeasures to change in falls risk (i.e., longitudinal studydesign versus cross-sectional design) and hence ourconclusion that those at the greater risk for future falls(i.e., smaller baseline total white volume) have greaterpotential for falls risk reduction (i.e., greater change inPPA scores).We recognize the limitations of our study. A key lim-itation is that we did not quantify white matter lesionswithin total white matter volume. We note that ourstudy sample consisted exclusively of independent com-munity-dwelling senior women who were without signif-icant physical and cognitive impairments and without asignificant history of falls. Thus, the results of our studymay not generalize beyond this population of seniorwomen and we may have underestimated the contribu-tion of brain function to change in physiological fallsrisk. Future prospective studies are needed to testwhether the present findings also apply to larger, moreheterogeneous populations.To conclude, the function of brain regions underlyingresponse inhibition and selective attention was indepen-dently associated with reduced physiological falls risk.Hence, future falls prevention strategies should poten-tially incorporate intervention components, such asaerobic exercise training, that induce neurocognitiveplasticity in the neural network that supports responseinhibition and selective attention.Acknowledgements and FundingThe authors would like to thank the Vancouver South Slope YMCAmanagement and members who enthusiastically supported the study byallowing access to participants for the training intervention. We thank theinstructors for their commitment to the participants’ health and safety. TLAis a Michael Smith Foundation for Health Research (MSFHR) Scholar. LSN is aMSFHR Senior Graduate trainee and a NSERC Doctoral trainee.This work was supported by the Vancouver Foundation (BCM06-0035), theMichael Smith Foundation for Health Research Establishment Grant (CI-SCH-063(05-1)CLIN), and the Canadian Institutes of Health Research (MOB-93373)to TLA.Author details1Department of Psychology, University of British Columbia, 2136 West Mall,Vancouver BC, V6T 1Z4, Canada. 2Centre for Hip Health and Mobility,Vancouver Coastal Research Institute, University of British Columbia, 7/F 2635Laurel Street, Vancouver BC, V6H 2K2, Canada. 3Brain Research Centre,University of British Columbia, 2211 Wesbrook Mall, Vancouver BC, V6T 2B5,Canada. 4Department of Physical Therapy, University of British Columbia,#212 2177 Wesbrook Mall, Vancouver BC, V6T 1Z3, Canada.Authors’ contributionsTLA conceived and designed the study, acquired data, and analyzed andinterpreted the data. LSN and CLH acquired data and participated in thestatistical analysis. 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ActaPsychol (Amst) 1999, 101:339-378.doi:10.1186/1744-9081-7-37Cite this article as: Nagamatsu et al.: Functional neural correlates ofreduced physiological falls risk. Behavioral and Brain Functions 2011 7:37.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitNagamatsu et al. Behavioral and Brain Functions 2011, 7:37http://www.behavioralandbrainfunctions.com/content/7/1/37Page 9 of 9


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