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A functional MRI investigation into the neural correlates of the multiple-object tracking deficit in… Secen, John 2010

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A FUNCTIONAL MRI INVESTIGATION INTO THE NEURAL CORRELATES OF THE MULTIPLE-OBJECT TRACKING DEFICIT IN AMBLYOPIA  by  JOHN SECEN B.Sc. (Honours), Brock University, 2006  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)  August 2010  © John Secen, 2010         Abstract Amblyopia is a visual developmental disorder defined by reduced visual acuity in one (amblyopic) eye, while the other (fellow) eye has normal visual acuity and is otherwise healthy. Deficits in motion perception – such as multiple object tracking - affect both the amblyopic eye and the fellow eye. This thesis examined the neural correlates of the multiple-object tracking deficit to further understand the cortical deficit in amblyopia. Functional data were collected as participants with and without a history of amblyopia performed the multiple-object tracking task monocularly inside a 3T MRI scanner. Participants were asked to use their attention to track 0, 1, 2 or 4 of 9 moving balls (6 deg/s) for 12 seconds. MR signal change relative to fixation, as a function of target numerosity (track 0, track 1, track 2, track 4), group (control, amblyopia), and eye were examined in six regions of interest: putative V1, MT, superior parietal lobule, frontal eye fields, anterior intraparietal sulcus, and posterior intraparietal sulcus. For all four tracking conditions, area MT was found to be less active in participants with amblyopia with both fellow and amblyopic eye viewing. When tracking 4 balls, the anterior intraparietal sulcus was found to be less active in participants with amblyopia, only with amblyopic eye viewing. This finding suggests the functional differences in this region may be subtle. Future investigations targeting the network involved in sustained attention can determine the extent to which posterior parietal function may be impaired in amblyopia. Overall, this thesis provided neuroimaging evidence that the MT region is affected in human amblyopia, and that both eyes are affected by underlying cortical changes in dorsal extra-striate areas.  ii     Table of Contents    Abstract ........................................................................................................................... ii Table of Contents ............................................................................................................ iii  List of Tables ................................................................................................................... v  List of Figures ..................................................................................................................vi  Acknowledgements ....................................................................................................... viii  1. Introduction ................................................................................................................. 1  1.1. The Pathways of Vision......................................................................................... 1  1.2. Functions of Visual Cortex Regions ...................................................................... 6  1.3. Human Homologues of Motion Sensitive Areas .................................................... 7  1.4. Amblyopia ........................................................................................................... 10  1.4.1. Definition and Classification .......................................................................... 10  1.4.2. Neurophysiological Evidence in Macaques .................................................. 11  1.4.3. Human Amblyopia ........................................................................................ 12  1.4.3.1. Deficits in Form Perception ........................................................................ 12  1.4.3.2. Deficits in Motion Perception ..................................................................... 13  1.5. Neuroimaging of Human Amblyopia ................................................................... 15  1.5.1. Functional Differences in LGN ...................................................................... 15  1.5.2. Functional Differences in V1/V2.................................................................... 16  1.5.3. Functional differences beyond V2................................................................. 17  1.5.4. Structural Abnormalities ................................................................................ 19  1.6. Motion, Motion Systems and Visual Attention ..................................................... 19  1.6.1. Visual Attention ............................................................................................. 24  1.7. Multiple-Object Tracking ..................................................................................... 25  1.7.1. Performance Across Age during Multiple-Object Tracking ........................... 27  1.8. Functional Neuroimaging Evidence During Multiple-Object Tracking ................. 27  1.9. Multiple-Object Tracking in Amblyopia ................................................................ 33  1.10. Visual Attention Studies in Amblyopia ............................................................... 35  1.11. Rationale and Hypotheses ................................................................................ 36  2. Experiment 1 ............................................................................................................. 38  2.1. Background and Hypothesis ............................................................................... 38  2.2. Methods .............................................................................................................. 38  2.2.1. Recruitment .................................................................................................. 38  2.2.2. Participants ................................................................................................... 39  2.2.3. Apparatus ..................................................................................................... 43  2.2.4. Stimulus ........................................................................................................ 43  2.2.5. Procedure ..................................................................................................... 43  2.2.6. Data Analysis ................................................................................................ 48  2.3. Results ................................................................................................................ 49  2.4. Discussion........................................................................................................... 54  3. Experiment 2 ............................................................................................................. 55  3.1. Background and Hypothesis ............................................................................... 55  3.2. Methods .............................................................................................................. 56  3.2.1. Participants ................................................................................................... 56  3.2.2. Apparatus ..................................................................................................... 60  iii     3.2.3. Procedure ..................................................................................................... 61  3.2.3.1. Multiple-object tracking ........................................................................... 61  3.2.3.2. MT localizer ............................................................................................ 62  3.2.4. Psychophysics Data Analysis ....................................................................... 66  3.2.5. Functional MRI Data Analysis ....................................................................... 66  3.2.5.1. Preprocessing ........................................................................................ 66  3.2.5.2. Whole-brain voxelwise analysis .............................................................. 68  3.2.5.3. Region of interest (ROI) Analyses .......................................................... 69  3.2.5.4. Event-Related Analysis .......................................................................... 75  3.2.5.5. Statistical Analysis .................................................................................. 76  3.3. Results ................................................................................................................ 76  3.3.1. Psychophysical Data .................................................................................... 76  3.3.2. Functional MRI Data ..................................................................................... 81  3.3.2.1. MR Signal Change in MT ....................................................................... 88  3.3.2.2. MR Signal Change in Superior Parietal Lobule ...................................... 95  3.3.2.3. MR Signal Change in Posterior IPS........................................................ 97  3.3.2.4. MR Signal Change in Anterior IPS ......................................................... 98  3.3.2.5. MR Signal Change in Frontal Eye Fields .............................................. 101  3.3.2.6. MR Signal Change in Putative V1 ........................................................ 102  3.3.2.7. Correlations Between the Behavioural and Functional MRI Data ......... 102  3.4. Discussion......................................................................................................... 106  3.4.1. Psychophysics Data ................................................................................... 106  3.4.2. Functional MRI data .................................................................................... 108  3.4.2.1. Task-dependent Versus Load-dependent Agreement with Other Studies .................................................................................................... 109  3.4.2.2. Differences in Activation between the Control and Amblyopic Groups . 111  3.4.2.3. Correlations between the Behavioural and Functional MRI Data ......... 113  3.4.3. Limitations................................................................................................... 114  3.4.3.1. Eye Movements .................................................................................... 114  3.4.3.2. Functional MRI ..................................................................................... 115  3.4.4. Future Directions ........................................................................................ 117  4. Conclusion .............................................................................................................. 119  References .................................................................................................................. 120  APPENDIX A ............................................................................................................... 134  APPENDIX B ............................................................................................................... 137               iv     List of Tables Table 1- Details on ten control participants that completed Experiment 1. .................... 41  Table 2 - Details on nine participants with amblyopia that completed Experiment 1. .... 42  Table 3- Correlation matrix of psychophysical performance, visual acuity and stereopsis in participants with amblyopia. ..................................................................... 51 Table 4- Details on the seven control participants who completed Experiment 2 .......... 58  Table 5- Details on the seven participants with amblyopia for Experiment 2................. 59 Table 6- Active voxels in MT during MT localizer .......................................................... 82  Table 7- Active voxels in bilateral posterior parietal cortex............................................ 83  Table 8- Talairach co-ordinates of the anterior IPS region of interest ........................... 84  Table 9- Talairach co-ordinates of the posterior IPS region of interest ......................... 85  Table 10- Talairach co-ordinates of the superior parietal lobule region of interest ........ 86  Table 11- Talairach coordinates of the frontal eye fields region of interest ................... 87 Table 12- Correlations between the behavioural deficit at 4 balls and activity in MT and anterior IPS .................................................................................................... 105   v     List of Figures Figure 1 – Schematic of retinal inputs into six major layers of monkey LGN ................... 3  Figure 2 – Schematic of the pathways from LGN to extra-striate regions and beyond in monkey. .......................................................................................................... 5  Figure 3 – Schematic of the anatomic variability of human intraparietal sulcus presented from the surface. ............................................................................................ 8  Figure 4 – Schematic diagram of IPS regions that respond to motion stimuli.................. 9  Figure 5 - Schematic model of how a motion detector would operate ........................... 22  Figure 6- Classification of the two motion systems used for this thesis. ........................ 23 Figure 7 - The four stages of multiple object tracking .................................................... 26  Figure 8 - MR signal change in various motion sensitive regions. ................................. 29  Figure 9 – Task versus load dependent activation during multiple-object tracking ........ 30  Figure 10 – Functional connectivity model of brain regions engaged during multiple-object tracking.................................................................................................. 32  Figure 11 – Schematic diagram of the multiple-object tracking performance in participants with and without amblyopia. ......................... Error! Bookmark not defined.  Figure 12 - Cartoon images used to identify targets and items ..................................... 46  Figure 13– The four stages of the multiple-object tracking in Experiment 1 .................. 47  Figure 14– Psychophysical performance in Experiment 1............................................. 52  Figure 15 – Psychophysical accuracy across group, eye, and target numerosity ......... 53  Figure 16 - The stages of multiple-object tracking for Experiment 2 ............................. 63  Figure 17– Schematic of one run of the functional MRI multiple-object tracking paradigm ....................................................................................................................... 64  Figure 18- Schematic of MT localizer task. ................................................................... 65  Figure 19- Sample slices of activation during multiple-object tracking. ......................... 72  Figure 20- Sample slices of activation from MT localizer. ............................................. 73  Figure 21 – A screen shot taken in BrainVoyager to show the relative locations of the regions of interest ................................................................................................ 74  Figure 22- Accuracy scores across target numerosity for the 10 participants. .............. 78  Figure 23- Average group accuracy scores collapsed across target numerosity and eye in Experiment 2................................................................................................ 79  Figure 24- Accuracy scores as a function of group, eye, and target numerosity in Experiment 2 ................................................................................................................. 80  Figure 25 - MR signal change means for each of the 12 s of tracking in MT................. 90  Figure 26– MR signal change as a function of time, averaged across the four conditions. .............................................................................................................. 91  Figure 27 – Average (last 6 s of tracking) MR signal change in MT as a function of target numerosity, group and eye. ............................................................................. 92  Figure 28- Mean MR signal change in MT across the twelve time points of tracking for each of the four tracking conditions............................................................. 94  Figure 29 – MR signal in superior parietal lobule. ......................................................... 96  Figure 30 – MR signal change in posterior IPS. ............................................................ 99  Figure 31 – MR signal change in anterior IPS. ............................................................ 100  Figure 32 – MR signal change in frontal eye fields. ..................................................... 103  Figure 33 – MR signal change in putative V1 .............................................................. 104  vi     Acknowledgements First and foremost to my supervisor Dr. Debbie Giaschi, for giving me an opportunity and taking me on as a graduate student in her lab. Debbie, I could not have done it without your time investment, patience, and challenge to be more in-depth and thorough in my writing and thinking. Thank you. Second, I would like to thank the Ophthalmology staff at BC Children’s Hospital. To Dr. Chris Lyons and Dr. Jane Gardiner, Andrea Quan and Christy Gilligson, and the many medical and orthoptist students who helped recruit participants from the clinic. Also to the staff at the UBC 3T MRI center, including Trudy Harris, Paul Hamill and Linda James for their technical assistance during MRI scans. Also to Jody Culham for all her neuroimaging help. I would like to thank the funding agencies that helped support this project: The Michael Smith Foundation for Health Research, the Canadian Optometric Education Trust Fund and the Human Early Learning Partnership. Thank you for your support. To my thesis committee, Dr. Jason Barton, Dr. Todd Handy and Dr. Robert Douglas, and also the external examiner Dr. Nick Swindale, thank you for your valuable insight.   Special mention to four people in particular: Emily Rae Harrison, Kevin Fitzpatrick, Dr. Sathyasri Narasimhan, and Marita Partanen. Emily, from assisting with the scans to carrying out the most trivial task, I will always be grateful for your help. Kevin, without your expertise, I would probably still be sacrificing evenings and weekends trying to sort issues with Brain Voyager. Sathya, in such a short time, our discussions on multipleobject tracking enhanced my knowledge and forced me to think deeper on some issues that I would not have otherwise considered. Marita, in addition to the help with statistics it was an honour to share the graduate experience with you. To all the people in the lab over the years: Dr. Cindy Ho, Dr. Catherine Boden, Alan Yau, Andrew Lam, Tom Tang, Grace Truong, Rawn Stokoe and Jake Hayward, in one way or another thank you for your assistance. To the participants, parents and families, there would be no thesis without your commitment and patience. I hope that these findings will contribute to the understanding of amblyopia in the future. To my friends, who helped balance the academic lifestyle with enjoying everything the beautiful city of Vancouver has to offer. Finally, to mom and dad, for all your unconditional support and words of encouragement from 4000 km away.  vii     1. Introduction Amblyopia, a developmental visual disorder affecting 3% of the population (von Noorden, 1990) is characterized by reduced visual acuity in one (amblyopic) eye, while the other (fellow) eye has normal visual acuity and is otherwise healthy. It is evident in the literature that individuals with amblyopia have deficits in form perception, as shown by their performance on tests of visual acuity and contrast sensitivity. What has not been as extensively studied is that individuals with amblyopia also display deficits in motion processing. Whereas the deficit in form perception exists primarily in the amblyopic eye, deficits in motion perception exist in both fellow and amblyopic eyes. Although, deficits in motion perception implicate impairments along the M/dorsal visual pathway, and deficits in form perception implicate impairments along the P/ventral pathway, the full extent of the neural changes in amblyopia remains unknown. The purpose of this thesis was to determine by using functional MRI, the neural correlates behind one aspect of motion processing – multiple-object tracking, in order to further enhance the understanding of the neural basis behind amblyopia.  1.1. The Pathways of Vision In monkey, three parallel pathways run from the retina to the primary visual cortex: the magnocellular (M) pathway, the parvocellular (P) pathway, and the koniocellular (K) pathway (Mather, 2006). The M pathway contains parasol ganglion cells, which are characterized by having center-surround receptive fields, and are not sensitive to color. The P pathway contains midget ganglion cells which are characterized by having centersurround receptive fields, and are sensitive to red-green light. The koniocellular pathway 1     contains bistratified ganglion cells which do not have center-surround receptive fields, but are sensitive to blue-yellow light. The axons of these ganglion cells project to the lateral geniculate nucleus (LGN). The axons from parasol retinal ganglion cells project to the bottom two magnocellular layers of the LGN, while the axons from midget retinal ganglion cells project to the top four parvocellular layers of the LGN. Meanwhile, the axons from the bistratified ganglion cells project to layers 3 and 4 of the LGN (Hendry & Reid, 2000; Szmajda, Grunert & Martin, 2008). The LGN has six major layers (Figure 1). The top four layers are parvocellular (P) layers, while the bottom two layers are magnocellular (M) layers. Each of the major six layers can be further divided into two sub-layers. The first sub-layer is either the primary parvocellular (layers 3-6) or magnocellular (layers 1 and 2), while the second sub-layer is a koniocellular sublayer. Layers 1, 4 and 6 of each LGN receive input from the contralateral eye, while layers 2, 3 and 5 receive input from the ipsilateral eye.  2     Figure 1 – Schematic of retinal inputs into six major layers of monkey LGN. Midget ganglion inputs (red) project to the top 4 parvocellular layers (P3-P6) of the LGN. Bistratified ganglion inputs (yellow) project to the koniocellular layers (K3-K4) of the LGN. Parasol ganglion inputs (gray) project to the two magnocellular layers (M1-M2) of the LGN. Layers 1, 4 and 6 receive inputs from the contralateral eye (pink), while layers 2, 3, and 5 (white) receive ipsilateral inputs. Source: Mather (2006)  The M, P, and K layers of the LGN project to primary visual cortex (V1). Inputs from the M layer terminate in layer 4Cα, while inputs from the P layers terminate in layer 4Cβ. The inputs from koniocellular layers terminate in layers 2, 3 and 4A of V1. V1 cortex is characterized by blobs and interblobs, which can be visualized with the use of cytochrome oxidase. Blobs, which have high levels of cytochrome oxidase, appear as cylinders spanning all layers of V1 (Sincich & Horton, 2002), and are centered on ocular dominance columns which are arranged in parallel rows approximately 0.5 mm apart. Interblobs represent the areas of cortex in between blobs. The blobs and interblobs are 3     the two sources of output from V1 into V2 (Sincich & Horton, 2002), and the segregation of the M, P and K pathways are kept up to this level. Blobs, which mostly receive input from K layers, primarily project to thin stripes of cytochrome oxidase in V2. Interblobs which mostly receive input from M and P layers, project to thick and pale stripes of V2. Beyond V2, the thick stripes project into regions affiliated with the dorsal stream primarily responsible for motion processing, while the pale and thick stripes project into regions affiliated with the ventral stream, primarily responsible for form and color processing. Figure 2 provides a simplified schematic of the projections involved for each stream, considering about 300 projections have been calculated to exist between the different areas of visual cortex (Hilgetag, O’Neill, & Young, 1996). The dorsal stream involves projections through medial temporal area (MT), medial superior temporal area (MST) and into posterior parietal cortex, while the ventral stream involves projections through V4 and into inferior temporal cortex (ITC). For this thesis, I will refer to these two streams as the M/dorsal and P/ventral pathways, as information from the M pathway at the subcortical level feeds mainly into the cortical dorsal pathway1, while information from the P and K pathways feeds mainly into the ventral pathway.                                                               1  For example, physiological evidence has shown blocking parvocellular LGN inputs leads to slightly reduced responses in MT (Maunsell, Nealey & DePriest, 1990), a region associated with the dorsal stream. This thesis does not take into consideration the crosstalk between the two streams. 4     Figure 2 – Schematic of the pathways from LGN to extra-striate regions and beyond in monkey. Inputs from K layers project to layers 2/3 in V1, while M and P layers project to layer 4. V1 blobs, which receive inputs primarily from K layers, project to V2 thin stripes and contribute to ventral stream processing. Interblob regions which project to V2 pale stripes also contribute to ventral stream processing, while interblob projections to V2 thick stripes contribute to dorsal stream processing. Source: Sincich and Horton (2002)  5     1.2. Functions of Visual Cortex Regions The dorsal visual pathway is responsible for processing of motion and depth, while the ventral stream is responsible for processing form and color. Ungerleider & Mishkin (1982) emphasized the ventral stream is responsible for what the objects in the visual field are, while the dorsal visual pathway emphasized where the locations are. As multiple object tracking is a motion perception task, I will only discuss further in detail processing along the M/dorsal stream. V1 is the site where many spatiotemporal filters process visual input for higher visual areas. 25-35% of cells in V1 are directionally selective (Schiller et al., 1976). The number of direction selective cells increases moving higher up along the M/dorsal stream from V1 to V3. By area MT, almost all cells are direction selective (Lagae, Maes, Raiguel, Xiao & Orban, 1994). MT is the major motion processing region of the brain, as it integrates all local motion signals from direction selective neurons into a global motion percept. In macaques, lesions to MT impair motion processing (Newsome & Pare, 1988). MT projects to MST, a region which also contains a high proportion of directionally selective cells (Lagae et al., 1994) and responds to rotating or expanding/contracting stimuli (Saito et al., 1986). Neurophysiological studies in macaque posterior parietal cortex have identified several areas within the intraparietal sulcus (IPS) that are involved in visuo-motor processing. These regions include the lateral intraparietal area (LIP), a region specialized for saccades (Colby, Duhamel & Goldberg, 1996); the anterior intraparietal area (AIP), a region involved in grasping movements (Taira, Mine, Georgopoulous, Murata & Sakata, 1990); the ventral intraparietal area (VIP), a region involved in perceiving movements  6     towards the head (Muri, Iba-Zizen, Derosier, Cabanis & Pierrot-Deseillgny, 1996) and caudal IPS, responsible for processing of shape and orientation (Sakata, Taira, Kusunoki, Murata & Tanaka, 1997).  1.3. Human Homologues of Motion Sensitive Areas The use of neuroimaging shed great insight into motion processing in humans. Area V5 was the first motion region to be identified (Zeki, Watson, Lueck, Friston, Kennard & Frackowiak, 1991). This region was found to respond to a wide variety of motion stimuli, such as flickering checkerboards, moving gratings, or moving dot patterns. This region is the homolog of macaque motion area MT and MST, and has been referred to as the human MT complex, hMT+ or MT in the literature (Dumoulin et al., 2000; Watson et al., 1993) For this thesis, I will refer to this region as MT. Tootell, Tsao & Vanduffel (2003) found a second motion sensitive region in V3A, located below the transverse occipital sulcus (TOS) in the occipital cortex. This region is the homologue to V3, a motion sensitive region in macaque monkey. Recently, human MST, located anterior to MT, was found to be sensitive to optic flow stimuli (Smith, Wall, Williams & Singh, 2006). Human IPS separates the superior parietal lobule from the inferior parietal lobule (see Figure 3) and extends into the occipital lobe. Four motion sensitive regions within human IPS (Figure 4) were identified in response to moving versus stationary random texture patterns (Sunaert, Van Hecke, Marchal & Orban, 1999; Orban et al., 2003). The dorsal medial (DIPSM) and dorsal anterior (DIPSA) were two regions found within the parietal portion of human intraparietal sulcus. Specifically, the dorsal anterior region was  7     located near the junction with the post-central sulcus, while the dorsal medial region was located more medial to this location. A third region, the parieto-occipital intra-parietal sulcus (POIPS) was located at the lateral end of the parieto-occipital junction. The last region was the posterior IPS (or ventral IPS as described in Sunaert et al., 1999), located at the junction of IPS with the transverse occipital sulcus.  Figure 3 – Schematic of the anatomic variability of human intraparietal sulcus presented from the surface. The IPS spans parietal (grey) and occipital (peach) regions. At the surface, there is a high degree of variability of the sulcus as one (left brain, left hemisphere), two (left brain, right hemisphere) or three (right brain, left hemisphere) segments may be visible. The anterior end of the IPS connects with the post-central sulcus, either at the surface (left brain), or deeper within the cortex (right brain). At the posterior end, IPS extends into occipital cortex. The posterior end of the IPS can intersect with the transverse occipital sulcus (right brain, right hemisphere), although in some cases it does not (right brain, left hemisphere). Posterior IPS can also connect to the parieto-occipital fissure at the medial end, or connect with superior temporal sulcus at the lateral end (Ono et al., 1990). Furthermore, the IPS separates the superior parietal lobule from inferior parietal lobule, and a side branch of the IPS separates the angular from supramarginal gyrus within the inferior parietal lobule (not shown). Source: Ono, Kubik & Abernathey (1990).  8     Figure 4 – Schematic diagram of IPS regions that respond to motion stimuli. DIPSA= dorsal anterior IPS; DIPSM = dorsal medial IPS; POIPS = posterior-occipital IPS; VIPS = ventral IPS; TOS = transverse occipital sulcus; IPL = inferior parietal lobule; SPL = superior parietal lobule. Source: Sunaert et al., (1999).  9     1.4. Amblyopia 1.4.1. Definition and Classification Unilateral amblyopia is defined as reduced visual acuity in one eye, which cannot be optically corrected, but the eye is otherwise healthy. The other, fellow eye has normal visual acuity and is clinically considered to be unaffected. Any condition that deprives an eye of normal vision for a prolonged period of time during development can result in amblyopia. These conditions include cataracts, a droopy eyelid (ptosis), corneal lesions, strabismus or anisometropia. As amblyopia is most commonly caused by strabismus or anisometropia, these are the only causes I will consider for this thesis. Strabismus is a misalignment of the visual axes. The deviating eye can either turn inwards (esotropia), outwards (exotropia), upwards (hypertropia) or downwards (hypotropia) from the center of gaze. Strabismus poses two problems for the visual system: double vision, the perception of two images of a single object, and confusion, the stimulation by two different objects at the same retinal point. To prevent both problems in children, the brain suppresses the image from the misaligned eye, and this leads to amblyopia (reviewed in Barrett, Bradley & McGraw, 2004). Anisometropia is a difference in refractive error between the two eyes. This produces a difference in image quality such that the image in the weaker eye is blurry. Amblyopia can also occur in the presence of both strabismus and anisometropia. Either the strabismus arises first and induces anisometropia, or the anisometropia arises first and induces strabismus. Strabismus can be treated by surgery on the extra-ocular muscles to correct the misalignment of the eyes, while glasses are prescribed for anisometropia. If detected and treated early,  10     amblyopia can be prevented by patching of the fellow eye following removal of the amblyogenic factor(s). Next to the etiological differences between strabismic and anisometropic amblyopia noted above, many investigations have looked at whether individuals with strabismic or anisometropic amblyopia display different patterns of loss in form perception (e.g. Hess & Holliday, 1992; Levi & Klein, 1982, 1986). A recent study by McKee, Levi & Movshon (2003) showed that the presence or absence of binocular vision is more predictive of the types of visual loss, and not strabismic or anisometropic etiology. The individuals with amblyopia who had residual binocularity had better contrast sensitivity thresholds, but worse optotype and Vernier acuity than the individuals with amblyopia who had no residual binocularity.  1.4.2. Neurophysiological Evidence in Macaques Many studies show that the retina (e.g. Hendrickson, Movshon, Eggers, Gizzi, Boothe & Kiorpes, 1987) and LGN (Movshon, Eggers, Gizzi, Hendrickson, Kiorpes & Boothe, 1987; Sasaki, Cheng & Smith, 1998) functions normally in amblyopia2, but V1 does not. The primary deficit in V1 is a reduced response by neurons driven by the amblyopic eye in response to viewing mid to high spatial frequencies (reviewed in Kiorpes , 2006; Kiorpes & Movshon, 1998), with the deficit amplified at subsequent levels of processing beyond V1. Ocular dominance columns in V1, which equally represent inputs from either eye in normal vision, shrink for the deprived eye, and expand for the                                                               2   LGN function has been shown to be normal even when shrinkage occurs in the cell bodies of  the LGN (see Levitt, Schumer, Sherman, Spear & Movshon, 2001).  11     non-deprived eye in amblyopia (Wiesel & Hubel, 1977). This finding has also been confirmed via cytochrome oxidase staining (reviewed in Horton, 2006).  1.4.3. Human Amblyopia Visual deficits in amblyopia involve more than the deficit in visual acuity that is tested clinically. Laboratory evidence has revealed other deficits in form and motion perception; some of these deficits affect not only the amblyopic eye, but also the fellow eye.  1.4.3.1. Deficits in Form Perception Early investigations revealed amblyopic eye deficits in contrast sensitivity (Gstalder & Green, 1971; Levi & Harwerth, 1977; Hess & Howell, 1977), positional acuity (Bedell & Flom, 1981), Vernier acuity (Levi & Klein, 1982), and crowding (Hess & Jacobs, 1979; Stuart & Burian, 1962; Giaschi, Regan, Kraft & Kothe, 1993). While the deficit in form perception exists in the amblyopic eye, some studies have noted deficits in the fellow eye with regards to visual acuity (Kandel, Grattan & Bedell, 1980) and texturedefined form (Wang, Ho & Giaschi, 2007). Furthermore, visual evoked potential amplitudes in both fellow and amblyopic eyes of amblyopic adults diagnosed after 18 months of age were found to be reduced compared to control participants 30 minutes after exposure to checkerboard stimuli of different contrasts (Davis, Sloper, Neveu, Hogg, Morgan & Holder, 2003). This provided electrophysiological evidence that the fellow eye may be affected with regards to deficits in form perception.  12     The under-sampling (Levi & Klein, 1986) and neural disarray theories (Hess et al., 1999) attempt to explain the deficit in form perception for the amblyopic eye. In the under-sampling hypothesis, a reduction of neurons driven by the amblyopic eye leads to the deficit in spatial vision. In the neural disarray hypothesis, the deficit in form perception arises from the disorganization between the neurons, and not the number of neurons stimulated by the amblyopic eye.  1.4.3.2. Deficits in Motion Perception Some studies have found deficits in motion perception only in the amblyopic eye, while others have found the deficit exists in both fellow and amblyopic eyes. Deficits pertaining to the amblyopic eye only involved tasks such as oscillatory movement detection (Kelly et al., 1998; Buckingham et al, 1991), and motion after effect (Hess, Demanins & Bex, 1997). Tasks showing deficits in both eyes include global motion (Simmers, Ledgeway, Hess & McGraw, 2003, Simmers, Ledgeway, Mansouri, Hutchinson & Hess,2006), motion-defined form (Giaschi, Regan, Kraft & Hong, 1992; Ho, Giaschi, Boden, Dougherty, Cline & Lyons, 2005; Wang et al, 2007), maximum motion displacement (Dmax) (Ho et al., 2005; Ho & Giaschi, 2006; Ho & Giaschi, 2007) and attentive motion tracking3 (Ho, Paul, Asirvatham, Cavanagh, Cline & Giaschi, 2006). In the global motion task, participants viewed a random-dot kinematogram (RDK) display. At 100% coherence, all of the dots in the random-dot kinematogram moved in the same direction. As coherence decreased, it became more difficult to report the direction of the dots as fewer dots moved in the same direction. Simmers et al. (2003)                                                              3   Attentive tracking deficits will be discussed in the Multiple‐Object Tracking section of this  literature review  13     reported a deficit in both eyes as participants with amblyopia had higher thresholds than participants without amblyopia. In contrast, Ho et al. (2005, 2006) reported no global motion deficit in their amblyopic group when the speed of the dots (1.26 deg/s) was slower than that in the Simmers et al. study (6 deg/s). In the motion-defined form task (Giaschi, Regan, Kraft & Hong, 1992) subjects viewed a random dot kinematogram, where shapes were only visible when the dots outside of the shape moved in a direction opposite to dots inside the shape. Participants were asked to identify the shape while speed thresholds were collected to determine the minimum speed at which letters could be recognized. There were elevated thresholds in both fellow and amblyopic eyes for motion-defined letters. Follow-up studies (Giaschi, Hayward, Truong & Partanen, 2010) showed that the deficit was at slow speeds (0.1 degrees/second), but not at medium (0.9 degrees/second) or fast speeds (5.1 degrees/second). At slow speeds, the task may be biased towards the P/ventral pathway, which is known to process slow motion. At fast speeds, the task may be biased towards the M/dorsal pathway, which is known to process fast motion. This suggests that the deficit on this task may be due to altered processing within the P/ventral stream rather than the M/dorsal stream. In the maximum motion displacement (Dmax) task, participants view a random dot display in which all of the dots either move up or down along the vertical axis, or move left or right along the horizontal axis. As the displacement of the dots between the frames of the motion movie increases, the perception of smooth motion deteriorates. Thus, Dmax is the largest displacement between the dots that produces the perception of smooth apparent motion in a single direction. Abnormal Dmax values have been found in  14     both fellow and amblyopic eyes (Ho et al., 2006). A follow-up study (Ho & Giaschi, 2007) revealed no significant differences in thresholds between individuals with strabismic and anisometropic amblyopia, but thresholds were elevated for children with poor stereoacuity regardless of etiology.  1.5. Neuroimaging of Human Amblyopia 1.5.1. Functional Differences in LGN Miki , Liu, Goldsmith, Liu & Haselgrove (2003), reported a case study of a 13-year old child with anisometropic amblyopia who had bilateral activation of the LGN when viewing a checkerboard through the fellow eye, but this activation was absent when viewing the same stimuli with the amblyopic eye. Experimental evidence later emerged that the LGN was structurally (Barnes, Li, Thompson, Singh, Dumoulin & Hess, 2009) and functionally (Hess, Thompson, Gole & Mullen, 2009) abnormal. The Hess et al. study found that activation of the LGN of adult participants with amblyopia was reduced with amblyopic eye (as opposed to fellow eye) viewing of a chromatic/achromatic highcontrast square wave checkerboard. As 6% of LGN cells are concerned with feed forward transmission from the retina (Sherman & Guillery, 1998), with the rest of the cells involved in modifying the feed forward response via feedback projections from V1 (Van Horn & Sherman, 2004), Hess et al. concluded the reduced LGN activation may reflect changes of the modulatory cells within LGN, and not the cells responsible for relaying input from the retina. Thus, the functional abnormality in the LGN could reflect retrograde changes from cortical impairment.  15     1.5.2. Functional Differences in V1/V2 Barnes, Hess, Dumoulin, Achtman & Pike (2001) presented retinotopic stimuli monocularly to subjects with strabismic or aniso-strabismic amblyopia. They found that compared to activation from the fellow eye, activation of visual cortex areas was first reduced in V1, then subsequently in V2. In contrast, Sireteanu et al. (1998) found that V1 was reliably activated when strabismic or anisometropic adults viewed a sinusoidal grating stimulus through their amblyopic eye. Sireteanu et al. proposed that the source of the cortical deficit is V2, and is enhanced at higher cortical levels. Some experimental evidence supported Sireteanu’s findings. Muckli, Kiess, Tonhausen, Singer, Goebel & Sireteanu (2006) examined functional differences in retinotopically mapped cortical areas (V1, V2, V3, V4, and V8) in response to a grating stimulus. Their results indicated V1 and V2 were reliably activated in both eyes during this task, but as one moved higher up the visual stream, activity was reduced with amblyopic eye viewing. In contrast, Li, Dumoulin, Mansouri & Hess (2007) measured grating and acuity thresholds in adults with and without amblyopia and correlated them to functional MRI activity when viewing retinotopic stimuli. They found no difference in activation between amblyopic and fellow eyes, but reported reduced V1 to V4 activity with fellow eye or amblyopic eye viewing relative to control eyes. Additionally, Li et al (2007) found a high correlation between the reduced activation in V2 and the loss in V1, concluding that the loss first appears in V1, with additional extra-striate loss occurring after V1 loss. Other investigations, have found functional differences in V1 or V2 with amblyopic eye viewing, but not with fellow eye viewing. Goodyear, Nicolle, Humphrey & Menon (2000) measured contrast sensitivity thresholds in four adult participants with amblyopia  16     and six controls. They found elevated thresholds only in the amblyopic eye. The authors then found that viewing of a sinusoidal grating at 22% contrast resulted in no differences in MR signal magnitude, but the number of voxels was significantly less with amblyopic eye viewing. Similar findings were reported by Connor, Odom, Schwartz & Mendola (2007), who found activity in V1 and V2 was reduced with amblyopic eye viewing of standard retinotopic mapping stimuli, but activity with fellow eye viewing was similar to that of controls. Several studies have noted differences in functional activation between the amblyopia subtypes. In a study by Choi et al. (2001) participants with a history of strabismic amblyopia or anisometropic amblyopia viewed checkerboard patterns from 0.5 to 2 cycles/degree of visual angle. In all participants, there was reduced activation in the amblyopic eye, compared to the fellow eye. At lower spatial frequencies, there was a greater difference for participants with strabismic amblyopia, but at higher spatial frequencies, there was a greater difference for participants with anisometropic amblyopia.  1.5.3. Functional differences beyond V2 Bonhomme et al. (2006) investigated functional differences in area MT between the two eyes in participants with and without amblyopia during a motion paradigm. The motion paradigm consisted of passively viewing an expanding and contracting ring. All four participants without amblyopia had normal vision. Of these four participants without amblyopia, two participants viewed the task normally, one participant viewed the task with blurred vision out of one eye, and one participant viewed the task with a filter over  17     one eye. The findings revealed no differences in activation between the two eyes in the two participants who viewed the task normally, and in the two participants who viewed the task with reduced vision. The two participants with anisometropic amblyopia displayed reduced activation in MT with amblyopic eye, as opposed to fellow eye viewing.  Bonhomme et al. concluded that the deficits in motion perception extend  beyond that of deficits in blur and visual acuity, as the two participants with anisometropic amblyopia performed worse than controls who viewed the task with reduced vision out of one eye. A recent functional MRI study (Ho & Giaschi, 2009) investigated differences in participants with strabismic amblyopia, participants with anisometropic amblyopia and control participants during the viewing of low-level versus high-level RDKs. Low-level RDKs contained small dot sizes at a high dot density, while high-level RDKs either contained larger dot sizes, or decreased dot density. Sato (1998) proposed that as the dot size increases, or dot density decreases, there is a switch from reliance of low-level motion detectors, to high-level feature-matching motion mechanisms4. Ho et al. (2009) found that this switch occurred in both participants with and without amblyopia, but there was significantly reduced activation in both strabismic and anisometropic groups in both eyes. In participants without amblyopia, low-level RDKs elicited stronger responses in posterior occipital areas, while high-level RDKs elicited stronger responses in occipitalparietal regions. Participants with anisometropic amblyopia displayed a similar pattern, but activation was significantly less than that in the control group. Meanwhile, participants with strabismic amblyopia had no difference in cortical activity between the two eyes.                                                              4   This will be discussed further in the Motion, Motion Systems, and Visual Attention section of  this literature review  18     1.5.4. Structural Abnormalities Mendola et al. (2005) used voxel-based morphometry, a technique which quantified gray matter in the brain, to investigate structural differences in children and adults with and without amblyopia. The amblyopic group had reduced gray matter volume along the calcarine sulcus, parieto-occipital cortex, and in ventral temporal cortex. In another voxel based morphometry study, Xiao et al. (2007) replicated the findings by Mendola et al. (2005), but found additional frontal and temporal regions with reduced gray matter. One study by Lv et al. (2008) correlated the reduction in cortical thickness to the reduction in functional activation in adult participants with amblyopia as they viewed retinotopic stimuli. They found that the correlations were the highest in occipital and calcarine areas, suggesting a relationship between functional differences and abnormal changes in structure. Using voxel-based morphometry, Barnes et al. (2009) found reduced quantities of gray matter in the LGN of adult participants with amblyopia. They then had the same participants view retinotopic stimuli and found that reduced LGN gray matter correlated with decreased V1 activity. Yet, as the authors point out, it remained unclear if the LGN deficit is primary or secondary to the cortical deficit, considering the majority of LGN processing deals with feedback projections.  1.6. Motion, Motion Systems and Visual Attention A crude definition of a motion system is a computation carried out in the brain, which takes inputs that are correlated in space and time and outputs velocity and direction of the motion (Lu & Sperling, 2001). Several multisystem theories have been  19     proposed in order to account for all human motion perception. Early theories of the motion systems such as the short-range and long-range distinction (Braddick 1974, 1980; Anstis 1980) attributed the short-range process to motion sensors that extract motion due to changes in luminance and the long-range process to extraction of spatial features matched over time by a higher-level system. Cavanagh and Mather (1989) critiqued this classification pointing out that stimuli used to test both short-range and long-range process were luminance-defined, thus both processes could be explained by one set of motion detectors that detect motion at different spatial resolutions. Cavanagh et al. (1989) concluded that different mechanisms should not exist for luminance-based stimuli, but there should be a distinction in the motion systems that process first-order, or luminance-defined motion, versus motion that is second-order, or texture- or contrastdefined motion. Chubb & Sperling (1988) proposed that the processing of first-order motion occurred with a Fourier motion system, where luminance cues were extracted by a simple Reichardt-type motion detector. The processing of second-order motion occurred with a non-Fourier motion system that first computes the amount of texture in the stimulus by a “texture-grabbing” mechanism, and then submits the outputs of the texture grabbers to motion detectors5. As Lu and Sperling (2001) point out, there are similarities between the different multi-system theories. For example, both short-range processes and Fourier motion detectors are likely luminance-defined Reichardt-type motion detectors, but differ in their                                                              5   Lu and Sperling (1995) proposed a third motion system, the third‐order system, that defines  motion based on figure‐ground saliency, and would not be detected by first‐order or second‐ order motion.   20     computational models of motion perception. As Sato (1998) noted, common algorithms used to model motion perception likely involve a combination of spatiotemporal frequency filters for first-order motion and a second approach based on feature matching. An example of how a simple Reichardt-type motion detector would operate is demonstrated in Figure 5. For this thesis, I will apply the passive versus active motion system classification described in Cavanagh (1991) to the multiple-object tracking task. Cavanagh (1991, 1992) proposed the passive motion system as an automatic process that contains dense arrays of localized motion detectors that monitor all areas of the retina; the active motion system involves tracking individual targets with attention as they move about the visual field an attentive process that involves feature matching. Cavanagh suggested that the passive system can detect motion in the absence of attention, while the active system derives motion from signals that move the attention window. In this regard, Cavanagh attributed “low-level” motion processing to that of the passive motion system, and “highlevel” motion processing to processing which involves attention. Figure 6 explains the interplay between the two systems. Both motion systems can process motion that is firstorder or luminance defined, and second-order or texture-defined.     21     Figure 5 - Schematic model of how a motion detector would operate. Reichardt (1986) developed this model based on insect vision. A centipede moving at fast speeds from left to right would first pass through the receptive field of neuron A, then pass through the receptive field of neuron B a short time later. In order to correlate the activity of neuron A and neuron B as motion, neuron A projects to neuron C, which delays transmission of neuron A’s activity. Neuron C, along with neuron B, are connected to a fourth cell, neuron D. Neuron D is the motion detector, and will only fire when both neuron B and neuron C are active at the same time, reflecting the temporal sequence of an object in motion. In the event that neuron C and neuron B are not active at the same time, due to motion in the wrong direction, or at a velocity such that activity from neuron C occurs sooner or later than activity from neuron B, the motion of the centipede will not be detected. Source: Mather (2006).  22     Figure 6- Classification of the two motion systems used for this thesis. There are two motion systems: a passive, low-level motion system that detects motion from Reichardt motion detectors, and an active, high-level motion system that detects motion from internal signals that move the focus of attention. Both motion systems can process first-order motion (defined by luminance, and or color), and second-order motion (defined by contrast and/or texture). Based on this classification system, multiple-object tracking is considered to access both passive and active systems. The passive system is engaged when attention is not required to track any of the items, while the active system is engaged when attention is required to track a subset of the items. Source: Cavanagh (1991).  23     1.6.1. Visual Attention The visual system cannot process everything that falls onto the retina. Thus, the visual system relies on the process of attention to select specific details for further processing, while other details in the visual world are ignored. Attention can be allocated by making eye movements to a location or by attending to an area in the periphery without directing one’s gaze towards it. Focusing on an object by directly gazing on it is called overt attention, while viewing that same object in the periphery is referred to as covert attention. There are two types of covert attention systems that help select information from the world for further processing: an endogenous system and an exogenous system. The endogenous system is a voluntary system where attention can be directed at will and for extended periods of time, while the exogenous system is an involuntary system that orients to a sudden event that occurs within the visual world. As a result, endogenous attention is referred to as sustained attention, while exogenous attention is referred to as transient attention (reviewed in Carrasco and Yeshurun, 2009). These terms refer to temporal properties of each type of attention: where transient attention has short durations of engagement and is stimulus driven, sustained attention can be internally engaged as long as one is able. Some studies have suggested it takes 100 ms for attention to be engaged in a transient manner (Carrasco et al., 2009), while it takes about 300 ms for the endogenous sustained attention to be engaged.  24     1.7. Multiple-Object Tracking The multiple-object tracking paradigm involves a number of visually identifiable items (usually 8-10) on a screen. The goal of the multiple-object tracking paradigm is to track the locations of several moving items at once, such that their final positions can be identified when motion of the items ceases (Pylyshyn & Storm, 1988). The paradigm can be broken down into four steps (see Figure 7): •  Stage 1- Initialization – The appearance of several moving items on a screen  •  Stage 2 - Target Selection– Some brief event (e.g. a flash, a colour change) will identify which of the items are targets. In some trials, only one item may be a target, while for other trials, several items will be targets. Except for this brief event in which targets are selected, targets and distractors are visually identical throughout the entire trial.  •  Stage 3 - Tracking – The participant is asked to track items indicated as targets in stage 2, for a duration of time.  •  Stage 4- Report – After tracking, motion of all items ceases. The participant then reports which of the items were targets. The report can either be a full report, or a partial report, depending on the experimental design. In a full report, the participant selects which of the items they thought were the targets they had been tracking. In a partial report, one of the items is selected at random, and the participant has to indicate if that was a target item, or if it was not a target item  25     Initialization  Tracking  Target Acquisition  Report  Figure 7 - The four stages of multiple object tracking. In the Initialization phase, participants fixate their attention on the center white dot (top left) while nine moving balls (not shown) appear on the screen. In the Target Acquisition stage (top right), some of the nine moving balls are selected as target items to be tracked after the target cues (white rings) disappear. In the Tracking phase (bottom left), the participant uses their attention to track the moving targets, while maintaining fixation. In the report stage (bottom right), the participant either reports all tracked balls (full report) or if a specific ball was a target item (partial report). Partial report is shown in the bottom right figure.  26     1.7.1. Performance Across Age during Multiple-Object Tracking Trick, Jaspers-Feyer & Sethi (2005) investigated the performance of five age groups (6, 8, 10, 12 & 19 year olds) during multiple object tracking. When it came to tracking one item, only the 6 year old group performed significantly worse than the other four groups. For tracking four items, age-related differences in performance were more evident. The 6, 8, and 10 year old groups performed significantly worse than the 12 and 19 year old groups. Furthermore, the 8 year old group performed significantly worse than the 10 year old group. In a subsequent study, Trick, Perl & Sethi (2005) found that there were no differences in tracking one target between young (mean age 19 years) and elderly (mean age 72 years) participants, but there were significant differences in tracking four items. Sekuler, McLaughlin & Yotsumoto (2008) ruled out that differences in memory function between the two groups was the cause of the age-related decline in performance, as they found that elderly adults performed just as well when the disks were stationary and the task was to report the location of up to 5 items held in memory.  1.8. Functional Neuroimaging Evidence During Multiple-Object Tracking To date, four studies (Culham, Brandt, Cavanagh, Kanwisher, Dale & Tootell, 1998; Culham, Cavanagh & Kanwisher, 2001; Howe, Horowitz, Morocz, Wolfe & Livingstone, 2001; Jovicich, Peters, Koch, Braun, Chang & Ernst, 2001) have examined the neural correlates behind multiple-object tracking using functional MRI. In the passive viewing condition, the items move around the screen and the participant is not required to use their attention to track any of the moving items. Participants are only asked to maintain fixation throughout the trial and observe the  27     items. In this regard, the passive, automatic motion system as described by Cavanagh (1991) could capture this type of motion. Comparing the activation from passive viewing to that of fixation, several brain regions are engaged including V1 (Culham et al., 1998; Jovicich et al., 2001), MT (Culham et al., 1998; Jovicich et al., 2001), intraparietal sulcus (Culham et al, 1998), and superior parietal lobule (Culham et al., 2001; Jovicich et al., 2001). Activation has also been noted in the frontal eye fields (FEF), located at the junction of the pre-central and superior frontal sulci (Culham et al., 1998; Jovicich et al., 2001). When participants are asked to use their attention to track some of the objects, the active motion system is likely to be involved. In the multiple-object tracking neuroimaging evidence, some brain regions engaged during passive viewing increase in brain activity when attention is engaged, while others do not (see Figure 8). Activity in brain areas including MT, intraparietal sulcus, superior parietal lobule, and frontal eye fields are further enhanced by attention. V1 is not further enhanced by attention. These results suggest that each brain region may be involved in different components of the task. Culham et al. (1998) and Jovicich et al. (2001) further examined this possibility by parametrically manipulating the attentional demand of the task. They examined the level of brain activity as the attentional demand (the number of balls tracked) increased. This parametric analysis determined which of the brain regions engaged during attentive tracking were task-dependent, or load-dependent. The profile of task-dependent activation would show MR signal significantly increasing in the shift from passive viewing to attentive tracking (e.g. from track 0 to track 1 items), then MR signal remaining constant as attentional load (increasing target numerosity) increased (Figure 9, left  28     graph). Load-dependent activation would also show MR signal significantly increasing in the shift from passive viewing to attentive tracking, but in addition, a linear increase in MR signal with increasing target numerosity (Figure 9, right graph) would be found. Culham et al. (2001) found that regions such as the superior parietal lobule and frontal eye fields were more responsible for task-related components such as monitoring of eye movements, while anterior and posterior regions of the IPS, were responsible for the attentive tracking components of the task.  Figure 8 - MR signal change in various motion sensitive regions. Passive viewing of the task engages several areas within the occipital, parietal and frontal cortex (blue bars). When attention is engaged (red bars) some brain areas do not increase in MR signal (V1-V3), while others (MT, IPS, SPL, PostCS, Precun, FEF) do. Brain regions which increase in MR signal during attentive viewing are modulated by attention. Adapted from Culham et al (1998). Precun - Precuneus; SMA/SEF = supplementary motor area/supplementary eye fields; FEF – Frontal eye fields; PostCs – post-central sulcus. Source: Culham et al., (1998).  29     Figure 9 – Task versus load dependent activation during multiple-object tracking. Some areas (left graph) increased in brain activity upon the onset of attentional demand, and then levelled off in brain activity. Other areas (right graph) initially increased in brain activity upon the onset of attentional demand, but continued to increase in MR signal as attentional demand increased. Source: Culham et al., (2001).  A recent examination by Howe et al. (2009) has shed additional insight into the neural network underlying multiple-object tracking. In their study, eight green balls appeared on the screen in which four were stationary, and four moved at 6 degrees/s. At the beginning of the trial, two of the eight disks were red indicating they were target items, and could either be a moving ball or a stationary ball. This paradigm led to three separate manipulations of attention: the “attend moving” condition, where the red target balls were moving; the “attend stationary” condition, where the red target balls were stationary; and passive viewing where participants were instructed to not attend to any of the balls. The authors found that in the “attend moving” versus “passive viewing” contrast, five brain regions were engaged: anterior IPS, posterior IPS, MT, superior parietal lobule, and frontal eye fields. This suggested that all regions were engaged when 30     the targets moved. In the “attend stationary” versus “passive viewing” contrast, the posterior IPS was the only active brain region. This suggested that the posterior IPS was the sole brain region responsible for attending to an object, and the other four brain regions were active only when the targets moved. Using a cyclic causal discovery algorithm (see Richardson, 1996), Howe et al. (2009) determined how the five areas of the multiple-object tracking network functionally connected with each other (Figure 10). At the center of the network is the anterior IPS. This region is only active when targets are moving, and not when they are stationary. Thus, the anterior IPS has been identified as the brain region responsible for actively tracking the objects. As this region has been proposed in the visual short term memory literature (see Xu and Chun, 2006) to be involved with remembering features of an object, Howe et al. concluded that the anterior IPS tracks moving objects, and also provides additional information about the features of the target. In their model, Howe et al. found three connections to anterior IPS: the frontal eye fields, the superior parietal lobule, and MT. The superior parietal lobule has been proposed to be involved in the planning or generation of saccadic eye movements (Burman and Bruce, 1997), while the frontal eye fields have been proposed to be involved in the suppression of eye movements (Paus et al., 1996). The anterior IPS communicates with these regions to suppress eye movements under fixation, or to coordinate eye movements in real world examples of multiple-object tracking (Culham et al., 2001).  Meanwhile, Howe et al.  proposed that MT was responsible for representing the locations of moving targets. According to their model, MT interacts with the posterior IPS, the brain region solely engaged during the attending of stationary targets. In the short term memory literature,  31     posterior IPS has been proposed to function as a spatial index (Xu et al., 2006). Howe et al. proposed that this same region would thus be involved in indexing target balls. If so, posterior IPS would be responsible for indexing which of the items in MT are targets during the task.  Figure 10 – Functional connectivity model of brain regions engaged during multiple-object tracking. The core of the multiple-object tracking network is the anterior IPS, which actively tracks moving targets. This region connects with superior parietal lobule and frontal eye fields, responsible for the generation and suppression of saccades, respectively, and also MT, which updates the locations of moving targets. MT connects with the posterior IPS, a region responsible for engaging attention, and may index the locations of the targets MT is updating. Source: Howe et al., (2009)  32     1.9. Multiple-Object Tracking in Amblyopia Children with amblyopia displayed a deficit when asked to track 1-4 of 8 moving discs on a screen moving at 6 deg/s (Ho et al., 2006). For children with and without amblyopia, psychophysical performance declined as the number of tracked balls increased. Yet, regardless of the number of balls being tracked, and which eye they used, children with amblyopia performed worse than children without amblyopia at all four tracking conditions. Furthermore, when examining the rate of change of performance across increasing attentional load (number of balls tracked), the slope of the decline in accuracy was steeper in children with amblyopia than in children without amblyopia (see Figure 11). Levi & Tripathy (2006) investigated performance of adults with amblyopia in a multiple trajectory tracking task. This task is similar to multiple-object tracking, but trials are much shorter in duration (850 ms). Furthermore, whereas in multiple-object tracking, the prior position of an item is not visible, the positions or history of the locations of the dots are used to determine the direction in which the dots are deviating across trajectories. In this task, 4 dots moved (4 deg/s) in a straight line from left to right for 450 ms. After 450 ms, one of the dots deviated from its trajectory either in a clockwise or counter-clockwise fashion. Participants then had to indicate which direction the dot deviated. Levi et al. originally found that neither fellow nor the amblyopic eye displayed a deficit. In a follow-up study, Tripathy & Levi (2008) found that at small angles of deviation (19 o), small differences were found in the amblyopic eye, but not in the fellow eye relative to controls.  33     Figure 11 – Schematic diagram of the multiple-object tracking performance in participants with and without amblyopia. As the attentional load (number of balls tracked) increases, performance declines for both groups. The rate of decline is steeper in the amblyopic group, for both fellow and amblyopic eyes. Source: Ho et al., (2006).  34     1.10. Visual Attention Studies in Amblyopia Evidence has shown that participants with amblyopia display deficits in various other attentional tasks. Sharma, Levi & Klein (2000) found that participants with strabismic amblyopia undercounted the number of features missing from a uniform grid briefly presented to their amblyopic eye.  The authors were able to rule out that  performance was due to low-level factors such as blur, crowding, or topographical jitter, but rather reflected a limitation to which attention could individuate features. Whereas this study dealt with static stimuli, others have examined high-level attention processes during dynamic or moving stimuli. Ho et al. (2006) found that children with amblyopia did not display a deficit in apparent motion perception. As Battelli et al. (2001, 2003) suggested apparent motion is a transient attention task; there may not be a problem with transient attention in amblyopia. Participants were asked to track a single moving object for 1500 ms. Three arrays of four discs were alternated in time and space to create the perception of four white discs rotating around a central target. One of the discs was selected at random, and completed 12 steps in one revolution at eight different rotation speeds ranging from slow (0.114 revolutions/s) to fast (0.50 revolutions/s). Participants were instructed to track the target. After 1500 ms, one of the white discs turned red again and the participant had to indicate whether the disc that turned red was the same disc that they were tracking. Both the fellow eye and amblyopic eye displayed a deficit on the task, suggesting that children with amblyopia displayed attentive tracking problems on tasks involving the spatial selection of targets and rejection of distractors.  35     1.11. Rationale and Hypotheses In amblyopia, psychophysical deficits in multiple-object tracking exist for both fellow and amblyopic eyes. The purpose of this thesis was to use functional MRI to determine the neural basis behind the psychophysical deficits. Examining the neural correlates behind the multiple-object tracking deficit can provide further information into the extent of abnormal M/dorsal processing in amblyopia. The specific goals of this thesis were to:  1) Examine functional differences within cortical regions engaged during multipleobject tracking in participants with and without amblyopia  Hypothesis: Participants with amblyopia will display functional differences during the task. Reasons: Prior neuroimaging evidence revealed that MT was less active during the passive viewing of motion stimuli in participants with amblyopia (Bonhomme et al., 2006). I predict that this MT region may also be activated differently during the multiple-object tracking task. I further predict that functional differences will be found in posterior parietal cortex.  The neuroimaging evidence revealed posterior parietal regions such as the  anterior IPS were involved in actively tracking objects (Howe et al., 2009). This region also increased in activity as target numerosity increased (Culham et al., 1998; Jovicich et al., 2001). Considering the known deficit in amblyopia as target numerosity increases (Ho et al., 2006), I predict that functional differences will also be found in anterior IPS.  36     2) Determine the extent to which functional differences correlate with the behavioural deficit  Hypothesis: There will be a correlation between the behavioural deficit and the reduced activational differences. Reasons: Jovicich et al. (2001) found no correlation between behavioural performance and MR signal in 4 participants with normal vision. These participants were able to track 5 balls with over 90% accuracy, but Jovicich et al. did not indicate the extent to which performance could or could not have dropped as a function of increasing the number of targets. The behavioural evidence discussed earlier showed a significant drop off in performance as the number of targets increased.  Indirect  evidence in right posterior parietal cortex (Battelli et al., 2001, 2003) showed that impairments in right posterior parietal cortex led to deficits on the task. As the anterior IPS was found to be load-dependent (Culham et al., 2001), MR signal in this region should correlate with the known behavioural deficit. This thesis is divided into two experiments. As the functional MRI study required older participants than that studied in Ho et al. (2006), the first experiment was conducted to confirm that the psychophysical deficit in multiple-object tracking existed in our sample of children and adults. The second experiment examined differences in cortical activation by multiple-object tracking between control participants with and without amblyopia using functional MRI.  37     2. Experiment 1 2.1. Background and Hypothesis Ho et al. (2006) found that children with amblyopia displayed deficits in multipleobject tracking in both fellow and amblyopic eyes. Recent evidence (Tripathy et al., 2008) has also shown elevated thresholds for multiple trajectory tracking, but only for the amblyopic eye. For this thesis, a sample of children and adults was recruited. Before investigating the neural correlates of the multiple-object tracking experiment, the purpose of Experiment 1 was performed to determine if the psychophysical deficit existed in this sample of participants with amblyopia. I hypothesize that the results obtained will be similar to that of Ho et al. (2006), where significant differences in performance a) between groups and b) within target numerosity were found.  2.2. Methods 2.2.1. Recruitment Control subjects were recruited throughout the city of Vancouver communities, including the University of British Columbia. Participants with a history of amblyopia were recruited through BC Children’s Hospital, the University of British Columbia, and several optometrists in the Vancouver area. All participants were screened with the UBC MRI screening form (see Appendix A). This form was administered to participants via email or phone interview. Potential participants who reported past experience of claustrophobia, or non-removable metal inside or outside of the body were not recruited for the study.  38     Participants without a history of amblyopia were eligible if they had no history of neurological or other chronic health conditions. Meanwhile, participants with a history of amblyopia were included if a) they also had a history of strabismus, anisometropia or both b) they had a visual acuity difference of one line or greater c) they had no reported nystagmus or other visual disorders that would affect the ability to fixate during the tasks. Handedness for each participant was not recorded. Eligible participants were asked to participate in two 90 minute sessions for this study. The first session took place in our lab at BC Children’s Hospital. Here, each participant’s vision was briefly assessed, and psychophysical data were collected during the multiple-object tracking task as described below. In addition, participants were trained to lie still inside an MRI simulator, as they practiced the multiple-object tracking task (as described in Experiment 2) that they would perform for the functional MRI study one week later at the 3T UBC MRI center. For participants under 18, child assent and parental consent forms were administered and signed by the participant and parent of the participant, respectively. For participants over the age of 18, adult assent forms were signed by the participant. All forms (see Appendix) were approved by the University of British Columbia Research Ethics Board. The second session consisted of the functional MRI scan, which will be described further in Experiment 2.  2.2.2. Participants 10 control participants (mean age = 18.52 years, SD = 8.13 years) and 9 participants with amblyopia (mean age = 19.01 years, SD = 8.52 years) completed Experiment 1.  Table 1 provides the details for the 10 control participants without  39     amblyopia that participated in this study. Note that of the 10 controls, six were children or adolescents under the age of 18, while four were adults over the age of 18. The data from one of the 10 subjects, ACJ04 was excluded from analysis, as decimal visual acuity assessments indicated poor vision that was not corrected at the time of testing. Her vision was corrected before Experiment 2, and her data were included in that experiment. Table 2 provides clinical details on the 9 participants with amblyopia. Of these nine participants, five were adults, and four were children or adolescents.  Five  participants had amblyopia in their right eye (OD), while four participants had amblyopia in their left (OS) eye. Note that four participants had a history of strabismus, four participants had a history of anisometropia, and 1 participant had anisometropia and strabismus. Two additional adult participants with amblyopia were recruited to participate in Experiment 1, but they were excluded from further participation as they could not maintain fixation during the task. Their details are not included in Table 2. A preliminary one-way between subjects ANOVA revealed no difference in age between the two groups, or any violation of the heterogeneity of variance assumption. Decimal visual acuity (DVA), was measured with the Regan 96% contrast letter chart. This was used because it has a logarithmic progression of letter size, minimizing the crowding effect (Regan, 1988) which tends to be significantly greater in children with amblyopia (Giaschi, Regan, Kraft & Kothe, 1993). A DVA of 1.0 represents 20/20 visual acuity on the Snellen Chart. Stereoacuity was then assessed using the Randot Preschool Stereotest (Stereo Optical Co, Inc). A control participant’s data were included if corrected decimal visual acuity (DVA) was 1.0 or higher in each eye, and stereoacuity was 40 seconds of arc or lower.  40     Table 1- Details on ten control participants that completed Experiment 1. Code Age Sex Glasses a ACJ01 14.41 M N ACJ02a 14.60 M N a ACJ03 15.79 M N  OD DVA 1.13 1.48 1.40  OS DVA 1.50 1.48 1.40  Stereoacuity 40 40 40  OD prescription Plano Plano Plano  OS prescription Plano Plano Plano  Visual problems none none none below 20/20 vision at time ACJ04a 16.70 F N 0.24* 0.20* 40 Plano Plano of testing myopia with b ACJ05 19.57 M Y 1.20 1.20 40 -5.5 -4.00 -1 x 170 astigmatism myopia with b ACJ06 24.76 M Y 1.05 1.20 40 -6.50 -0.50 X 180 -7.75 -0.50 x 78 astigmatism b ACJ07 8.91 M N 1.30 1.43 40 Plano Plano none ACJ08b 10.49 M N 1.20 1.20 40 Plano Plano none b ACJ11 36.80 F N 1.20 1.20 40 Plano Plano none b ACJ12 23.14 F N 1.40 1.40 40 Plano Plano none a indicates eye movements were monitored manually; b indicates eye movements were monitored through a video camera * This participant’s data was excluded in Experiment 1 due to poor VA. She was properly refracted for Experiment 2.      41         Table 2 - Details on nine participants with amblyopia that completed Experiment 1.   Code  Age  Sex  Glasses  OD DVA (best corrected)  OS DVA (best corrected)  Stereo acuity  OD prescription  OS prescription  AEJ02a  12.16  M  Y  1.08  0.10  800  Plano  AEJ03a  9.15  M  Y  0.28  0.85  400  AEJ04a  15.78  F  Y  1.08  0.80  200  +3.75 -1.75 X 010 -2.75  3.75+0.75 X 70 +2.25 -1.75 X 162 1.25  AEJ05b  19.74  M  Y  1.00  1.20  60  -4.001.00x170  -5.000.75x170  AEJ06b  25.10  M  Y  0.93  0.68  800  -0.75 + 0.5 x 90  -3.75 +0.5 x90  AEJ09b  9.83  M  Y  1.28  0.88  60  Plano  4  AEJ10b  35.90  M  N  0.45  1.40  800  Plano  Plano  AEJ11b  20.26  F  Y  0.22  0.88  100  AEJ12b  23.16  F  Y  0.80  1.00  100  -2.00 -1.00 x 007 +4.00 -0.75 x 080  -0.5 -0.75 X 020 +4.00 -0.5 X 090  Diagnosis OS anisometropic amblyopia OD anisometropic amblyopia OS anisometropic amblyopia  OD strabismic amblyopia  OS anisostrabismic amblyopia OS anisometropic amblyopia OD strabismic amblyopia,  OD anisometropic amblyopia OD strabismic amblyopia  Clinical details and ocular deviation history of patching history of patching history of patching; unequal hypermetropia history of patching; intermittent right o 15 extropia history of patching; strabismus surgery no history of patching history of patching; alternating 10o esotropia history of patching intermittent o 30 right esotropia  a  indicates eye movements were monitored manually; b indicates eye movements were monitored through a video camera  42     2.2.3. Apparatus The psychophysical task was programmed in Matlab, and run on a Macintosh PowerBook G4 computer. The stimuli were displayed on a 17” Mac flat screen LCD monitor with a 33.6 cm X 25.35 cm viewing area, and a resolution of 1024 X 768 pixels. Participant responses were collected with a Logitech Gamepad.  2.2.4. Stimulus All stimuli were presented within a viewing field subtending 14 X 14 degrees of visual angle when viewed from a distance of 57 cm. A white circular fixation point of 0.5 degrees located at the center of the viewing field was maintained on the screen throughout the entire run. At no point did any target or distractor item interfere with the viewing of the fixation point. A cartoon tree 1 degree in diameter (see Figure 12), was used as a moving item for this task. There were 8 items. Trees moved around the screen independently of each other. To prevent overlap, a repulsion mechanism within the Matlab code redirected an item to move away from any other item or the frame if it was less than 2.8 pixels away. Trees travelled up to a constant speed of 6 deg/s in all directions.  2.2.5. Procedure For Experiment 1, a multiple-object tracking paradigm designed to be engaging to children was used, based on Trick et al. (2005). Our version was based on the television show “Dora the Explorer”, using the cartoon character “Swiper the Fox” (see Figure 12) to identify target items. Prior to the presentation of the stimuli, a Microsoft  43     PowerPoint presentation explained to the participants that Swiper the Fox likes to steal from Dora, and hide behind moving trees. The participant had to help identify the trees that Swiper the Fox was hiding behind so that he could not steal from Dora.  If  participants did not understand the task, it was repeated until they understood the task.  There were five stages to this paradigm (see Figure 13): •  Initialization - Participants pushed a button on the Logitech game pad, which caused the 8 moving trees to appear on a screen and move at a speed of 6 deg/s. Participants were instructed to maintain their fixation on the center dot throughout the whole trial.  •  Target Acquisition - 1000 ms after the 8 moving trees appeared on the screen, 1 to 4 Swiper the Fox images replaced the image of the tree for 1200 ms. This identified which of the moving trees were targets.  •  Tracking- Each Swiper the Fox was replaced by a tree, and participants tracked the target trees for 5 s as they maintained fixation on the center dot.  •  Report - All trees ceased motion and an orange box outlined one of the eight trees. The participant indicated with the game pad whether this tree was one of the target items they had been tracking (a tree that Swiper was hiding behind), or not a target item.  •  Feedback - After each response, the participant received feedback from the program about the accuracy of their response. The participant initiated the next trial by pressing a button on the game pad  44     Participants first completed a practice run of 8 trials binocularly to ensure they understood the task of tracking targets amongst the distractor items. Then, an eye patch was placed over one eye and four runs consisting of 20 trials each were completed, two for each eye. A total of 10 trials per eye were performed for each of the four tracking conditions (1, 2, 3 or 4 targets). The order of testing was counterbalanced. For the control group, half of the participants performed the first run with their right eye, while the other half of the children started with their left. For the amblyopic group, half of the participants started with their amblyopic eye, while the other half started with their fellow eye. Each eye was tested once before the second run of testing occurred for each eye. Eye movements were measured in two different ways. The eye movements of 12 of 19 participants were recorded with a Sony HandyCam video camera while they performed the task. This was done to ensure participants were fixating on the fixation point during tracking, and not making any saccadic or smooth pursuit eye movements during the task. One observer (JS) watched their movements through the camera display and gave feedback at the end of each run if they were not fixating at the center of the screen. For the other six participants, eye movements were watched by the same observer, but not recorded. Tables 1 and 2 indicate how eye movements were monitored in every participant. Limitations in the measurement of eye movements will be commented on in the Discussion section of Experiment 2  45                         Figure 12 - Cartoon images used to identify targets and items. An image of Swiper the Fox (top) was used to identify targets. An image of a tree (bottom) was used for the moving items in Experiment 1.  46                     Initialization                                 Target Acquistion                Tracking                        Response      Figure 13– The four stages of the multiple-object tracking in Experiment 1. In the first stage (Initalization), 8 trees appeared and moved randomly in all directions at 6 deg/s. In the second stage (Target acquisition), 1 to 4 of the trees was replaced by a Swiper the Fox image for 1.2 seconds, indicating these were the targets to follow. In the third stage (Tracking), Swiper the Fox images disappeared and the participant tracked the target items. For the last stage (Response), the trees stopped moving and one of the trees was randomly selected. The participant indicated if this was or was not one of the targets they had been tracking.  47     2.2.6. Data Analysis For every participant, the proportion of correct responses out of 10 was calculated for each of the four tracking conditions. In the likelihood that participants lost track of some of the items during tracking, and guessed when they made a response, accuracy was corrected for guessing according to the formula (Ho et al., 2006):  Accuracy = 100 * (p – n/t) / (1-n/t) Where, p = proportion of correct responses n = number of targets (1, 2, 3 or 4) t = total number of items (8)  Statistical analyses were performed using SPSS 15.0. A three-way, repeatedmeasures ANOVA with one between factor (group: control, amblyopia), and two within factors (target numerosity: one, two, three, four; eye: fellow, amblyopic) was conducted on the corrected-for-guessing accuracy scores. Amblyopic eyes were age-matched to five right eyes and four left eyes from the control group, because there were five right and four left amblyopic eyes. In turn, fellow eyes were age-matched to the remaining five left and four right eyes from the control group.  48     2.3. Results The ANOVA revealed significant main effects of target numerosity (F(1.96, 31.34= 5.33, p= .01, η2 = .25) and group (F(1,16)= 6.61, p = .02, η2 = .29), but no main effect of eye (F(1, 16) = .66, p >.05, η2 = .02). No significant two-way or three-way interactions were present. The effect sizes were large for target numerosity and group, and small for eye. Bonferroni post-hoc testing on the numerosity main effect revealed higher accuracy scores for tracking 1 ball versus tracking 4 (Xtrack1- Xtrack4 = 15.71, p = .05, 95% C.I.’s = .093-31.52), and tracking 1 ball versus tracking 3 (X  track1-X track3  =  12.83, p = <.01, 95% C.I.’s = 3.2-22.45). Control participants (Figure 14) scored higher than participants with amblyopia (Xcontrol –Xamblyopia = 12.51, p = .02, 95% C.I.’s = 2.19- 22.83). Bonferroni post-hoc testing revealed the differences between the groups were significant for tracking 3 balls (XcontrolXamblyopia= 19.56, p= .01, 95% C.I.’s = 5.27- 33.84) and for tracking 4 balls (XcontrolXamblyopia= 20.00, p = .04, 95% C.I.’s = 3.67-36.63). Figure 15 displays the means of both groups collapsed across target numerosity and eye. A between (group)-within (eye) ANOVA determined if the slope of decline in accuracy scores as a function of increasing target numerosity was significant. ANOVA testing revealed a significant group by eye interaction (F(1,16)= 5.06, p = .04, η2 = .24). Simple effects testing of group for each eye revealed the difference between groups was significant for the fellow eye (F(1,16)= 8.05, p = .01, η2 = .33), but not for the amblyopic eye (F(1,16) = .36, p>.05, η2 =.02). Both fellow (slope: -22.44) and amblyopic eyes (slope: -11.06) had a greater decline in slope than for control eye 1 (slope: -3.51) and control eye 2 (slope: -  49     1.26), but only the difference between fellow and matched control eye 1 was of significance. For participants with amblyopia, an additional analysis determined that psychophysical performance was not significantly correlated with visual acuity or stereo acuity (Table 3).  50     Table 3- Correlation matrix of psychophysical performance, visual acuity and stereopsis in participants with amblyopia. Variable 1 2 3 4 1. amblyopic eye average performance -.96* .03 -.24 2. fellow eye average performance -.19 -.07 3. amblyopic eye visual acuity -.36 4. fellow eye visual acuity -5. stereopsis * p < .05 significant  5 -.02 -.02 -.51 .08 --  51     Figure 14– Psychophysical performance in Experiment 1. Accuracy scores were corrected for guessing and collapsed across target numerosity and eye. Control participants performed better (M= 94.61, SE = ± 3.44) than participants with amblyopia (M=82.10, SE = ± 3.44). Error bars represent standard error.  52     Figure 15 – Psychophysical accuracy across group, eye, and target numerosity. Control eye 1 was matched to the fellow eye; control eye 2 was matched to the amblyopic eye. Slope of decline as a function of target numerosity was greater in both fellow and amblyopic eyes relative to control eyes. Error bars represent standard error.  53     2.4. Discussion The results confirmed the hypothesis that a sample of children and adult participants with amblyopia displayed a deficit in multiple-object tracking. Performance was significantly worse in participants with amblyopia than in control participants. These findings replicated the prior study by Ho et al (2006). It should be noted that while the sample in Ho et al (2006) consisted of children, the sample for this thesis consisted of children and adults. This suggests that the conflicting findings between our work and the Levi & Tripathy group (Levi & Tripathy, 2006; Tripathy & Levi, 2008) who studied adults with amblyopia were not due to a difference in the age of the participants. The differences are more likely task dependant. This will be discussed further in the Discussion for Experiment 2. Overall, the objective of Experiment 1 was met as a multiple-object tracking deficit was evident in the participants for Experiment 2. A greater discussion of the psychophysical findings will also occur in the Discussion in Experiment 2.  54     3. Experiment 2 3.1. Background and Hypothesis The participants with amblyopia in Experiment 1 displayed a deficit in multipleobject tracking. Patients with posterior parietal cortex lesions also showed multipleobject tracking deficits (Battelli et al., 2001, 2003). This suggests that the posterior parietal cortex is important for successful multiple-object tracking. The posterior parietal cortex is part of the M/dorsal stream. As previously discussed in the Introduction, cortical processing in amblyopia has been found to be abnormal in the brain regions of the M/dorsal stream before the posterior parietal cortex. Yet to date, no studies have directly investigated whether processing within the posterior parietal cortex is normal in amblyopia. Neuroimaging evidence with the multiple-object tracking paradigm has revealed that this task engages areas along the M/dorsal stream, beginning in early visual areas (V1), continuing along to MT and then into posterior parietal cortex (Culham et al., 1998; Culham et al., 2001; Jovicich et al., 2001). Passive viewing of moving balls without the engagement of attention leads to activation of these brain regions, but when attention is required to track a specific subset of moving balls, some brain regions further increase in brain activity (MT, posterior parietal cortex), while others (V1) do not. A task like multiple-object tracking is advantageous in targeting posterior parietal cortex function, as visual attention can be parametrically manipulated to examine increases in brain activity when attention is not required (tracking zero balls) to conditions were the attentional load is high  (tracking 4 balls). Based on this premise, the purpose of  Experiment 2 was to investigate the extent to which function in the posterior parietal  55     cortex, and other regions along the M/dorsal stream may be compromised in amblyopia. This is based on the functional MRI assumption that MRI signal correlates with neural processing during the task (Logothetis, 2002; 2003; Rees et al., 2000). I predict that the quantity of MRI signal will be abnormal in the posterior parietal cortex regions in amblyopia. I also predict that the quantity of MRI signal in area MT will be abnormal during the task, given prior neuroimaging evidence (Bonhomme et al., 2006) that this region is less active in amblyopia during the viewing of motion stimuli.  3.2. Methods 3.2.1. Participants The 19 participants who completed Experiment 1 were invited to participate in Experiment 2 approximately one week later at the 3T UBC MRI center. Of the 19 participants who completed Experiment 1, 14 participants completed Experiment 2. Four participants were lost owing to: failure to show up for the scanning session (two participants), failure to complete scanning due to claustrophobia (one participant), and falling asleep inside the scanner (one participant). One participant completed the experiment (ACJ01), but their psychophysical data were excluded due to issues with response recording at the time of scanning, and their functional MRI data did not meet the requirements for analysis6. One participant (ACJ04), whose psychophysical data were excluded in Experiment 1 due to poor visual acuity, had her vision corrected to normal for Experiment 2.                                                               6   This will be discussed in the Data Pre‐processing section of this experiment  56     Of the 14 participants from whom usable psychophysical data was obtained in the scanner, functional MRI data were analyzed in 10 of them. Data from four children all under the age of 11, were excluded due to excessive head movements made over multiple runs of the multiple-object tracking task (AEJ03, AEJ09), or their activation only consisted of small clusters of active voxels scattered throughout the whole brain which did not meet the established cluster threshold at 50 voxels (ACJ07, ACJ08). Participant ACJ08 experienced claustrophobia and was removed from the scanner, but was able to complete the experiment. Tables 4 and 5 lists the participants used from Experiment 1. Psychophysical data was analyzed in seven control participants without amblyopia (mean age = 20.06, SD = 9.48) and seven participants with amblyopia (mean age = 20.45, SD= 9.21). Note that in the control group, there were four adults and three children, while there were five adults over the age of 18, and two children in the amblyopic group. The amblyopic group comprised five participants with amblyopia in their right eye and two with amblyopia in their left eye. Functional MRI data was analyzed in 5 control participants and 5 participants with amblyopia. There were four adults and one child in the control group, meanwhile all 5 participants with amblyopia were adults over the age of 18. Of the five participants with amblyopia, four were right eye amblyopic. Also note that four of the participants with amblyopia also had a history of strabismus, while the other participant with amblyopia had a history of anisometropia.            57     Table 4- Details on the seven control participants who completed experiment 2 OD OS Stereo Code Age Sex Glasses DVA DVA acuity OD prescription OS prescription ACJ04* 16.70 F N 1.00* 1.00* 40 -2 -2 ACJ05* 19.57 M Y 1.20 1.20 40 -5.5 -4.00 -1 x 170 ACJ06* 24.76 M Y 1.05 1.20 40 -6.50 -0.50 X 180 -7.75 -0.50 x 78 ACJ07 8.91 M N 1.30 1.43 40 plano plano ACJ08 10.49 M N 1.20 1.20 40 plano plano ACJ11* 36.80 F N 1.20 1.20 40 plano plano ACJ12* 23.14 F N 1.40 1.40 40 plano plano Note: *are participants whose functional MRI data were analyzed; **indicates is corrected visual acuity at the refraction noted to the right     58          Table 5- Details on the seven participants with amblyopia for Experiment 2 OD OS Stereo OD prescription OS prescription Code Age Sex Glasses DVA DVA acuity (best corrected) (best corrected) Diagnosis +3.75 -1.75 X OD anisometropic AEJ03 9.15 M Y 0.28 0.85 400 010 +2.25 -1.75 X 162 amblyopia AEJ05* 19.74 M Y 1.00 1.20 60 -4.00-1.00x170 -5.00-0.75x170 OD strabismic amblyopia OS aniso-strabismic AEJ06* 25.10 M Y 0.93 0.68 800 -0.75 + 0.5 x 90 -3.75 +0.5 x90 amblyopia OS anisometropic AEJ09 9.83 M Y 1.28 0.88 60 Plano 4 amblyopia AEJ10* 35.90 M N 0.45 1.40 800 Plano Plano OD strabismic amblyopia -2.00 -1.00 x OD anisometropic AEJ11* 20.26 F Y 0.20 0.88 100 007 -0.5 -0.75 X 020 amblyopia +4.00 -0.75 x AEJ12* 23.16 F Y 0.80 1.00 100 080 +4.00 -0.5 X 090 OD strabismic amblyopia Note: * indicates participants whose functional MRI data were analyzed;      59          3.2.2. Apparatus Participants were comfortably placed inside the 3T MRI Phillips Gyroscan Intera scanner. Visual stimuli were back projected with an 800 X 600 resolution LCD projector (refresh rate: 60 Hz) onto a screen 53 cm behind the participant’s head. Participants were able to view the screen through an angled mirror placed 15 cm from their eyes. Participant responses were obtained using a fiber optic response system (Lumitouch, Inc). A phased array head coil was used for the collection of functional and anatomical images. An anatomic brain image was acquired with a T1-weighted scan. The field of view for anatomic image acquisition was 256 mm. A 256 X 256 matrix was used with a 1 mm slice thickness giving a voxel size of 1 mm X 1 mm X 1 mm. Echo-planar imaging (EPI) was used to collect functional data in four to six T2*-weighted scans (TR = 2000 ms, TE = 30 ms). The field of view for functional data acquisition was 240 mm. 3 mm isotropic voxels were acquired using an 80 X 80 mm matrix. Functional volumes were collected in 36 interleaved axial slices with slice thickness of 3 mm and an inter-slice gap of 1 mm. Images were then reconstructed with a 128 mm X 128 mm matrix resulting in an effective voxel size of 1.88 mm X 1.88 mm X 3 mm. All participants wore MR compatible eye glass frames throughout the scan. This served two purposes. The first was that participants requiring prescriptive lenses could view the task with corrected vision. The second purpose was to achieve monocular testing by placing a red filter over one eye and a green filter over the other eye. By placing a red or green filter over the projector lens, this allowed the manipulation of which eye viewed the task inside the scanner. Insertion of a secondary red filter over the projector lens, would allow the eye covered with the red filter to view the task, while  60          nothing could be seen out of the eye covered with the green filter. Consequently, only the eye covered with the green filter could see the task with the insertion of a green filter over the projector lens, while the eye covered with the red filter could not see anything. Differences in luminance between the red and green filter were compensated with a 0.3 neutral density filter over the red filter in the MR compatible frames and on the projector.  3.2.3. Procedure All participants in this study completed a scanning session that lasted approximately one hour. One scanning session comprised 4-6 runs of a multiple-object tracking paradigm and one run of a task designed to localize brain region MT.  3.2.3.1. Multiple-object tracking The multiple-object tracking paradigm used was adapted from Culham et al.’s (1998) “bouncing-ball” paradigm, a version of the standard multiple-object tracking task (see Figure 16). In this version, participants were instructed to fixate on a white dot (0.5 deg) throughout the trial. Nine balls (1 deg in size) appeared on the screen and moved at a speed of 6 deg/s. Then, a subset of the balls (0, 1, 2 or 4) was highlighted with a white ring for 2 s which indicated that they were target items. The highlights disappeared so that target items appeared the same as distractor balls. Participants then used their attention to track the target items for 12 s while also maintaining fixation. A repulsion mechanism within the Matlab code redirected an item to move away from any other item or the frame if it was less than 2.8 pixels away. At the end of the 12 s of tracking, the balls stopped moving, and one of the balls was highlighted. The selected  61          ball had a 50% chance of being a target ball cued earlier in the trial. The participant indicated with the response pad whether or not the highlighted item was a target item. All the balls then disappeared, and the trial ended. Figure 17 demonstrates one run of the multiple-object tracking paradigm. One run consisted of 3 cycles. Within each cycle, there were four trials, with each condition (track 0, track 1, track 2 and track 4) presented once. Between each trial, there was a 4-8 second fixation period designed to maximize the event-related response. Each trial lasted 16 s for 192 s of trial time, in addition to 104 s of fixation dispensed at the beginning, ending and in between trials. Thus, one run of the multiple-object tracking paradigm lasted 296 s.  3.2.3.2. MT localizer In addition to the multiple-object tracking task, participants also completed a run designed to localize area MT (Giaschi et al., 2007). Grey dots (0.2 deg) at a density of 0.9 dots/deg2 were presented on a black background.  There were two conditions:  moving dots and stationary dots (Figure 18). For the moving dots condition, four trials of radial expanding dots (2.5 deg/s) alternated with four trials of radial contracting dots, with each trial lasting 1.75 s, or 14 s total. This composed one block of moving dots. For the stationary dots condition, the dots were presented on the screen for the entire 14 s block. One cycle comprised one block of moving dots and one block of stationary dots. One cycle lasted 28 s, and the cycle was repeated 6 times for a total run time of 168 s. Participants did not make any responses during the run and were only instructed to maintain fixation at the fixation point located at the center of the screen.  62            Fixation             Target Acquisition – 2 s            Tracking – 12 s               Response – 2 s         Figure 16 - The stages of multiple-object tracking for Experiment 2. At time 0, participants were instructed to maintain fixation on a white dot at the center of the screen (top left). The trial began (top right) with the appearance of 9 moving blue balls on the screen, in which some of the balls were high-lighted ( 2 s) indicating they were the target balls to be tracked. The highlights then disappeared (bottom left) and participants used their attention to track the target balls for 12 s. After the balls stopped moving, one of the balls was selected at random (bottom right) with a 50% probability of being a target or distractor ball. The participant indicated if the selected ball was or was not a target.  63          Figure 17– Schematic of one run of the functional MRI multiple-object tracking paradigm. One run was broken down into 3 cycles of 4 trials each in a pseudorandom (not known to the participant) order. Each cycle contained the four tracking conditions presented once. After every trial, a 4-8 second fixation period occurred during which MR signal was acquired.  64          Figure 18- Schematic of MT localizer task. Participants were asked to maintain fixation and passively view 14 s of stationary dots alternating with 14s of radially moving dots. Dot density and size were smaller and denser in the actual task.  65          3.2.4. Psychophysics Data Analysis The proportion of correct responses out of 6 was recorded for each eye for the three tracking conditions in which a target was present (track 1, track 2 and track 4). Accuracy was corrected for guessing using the formula from Experiment 1 adjusted for the increase in the number of items: Accuracy = 100 * (p – n/t) / (1-n/t) Where, p = proportion of correct responses n = number of targets (1, 2, 3 or 4) t = total number of balls (9)  Statistical analyses were conducted in SPSS 15. A three-way repeatedmeasures ANOVA with one between factor (group: control, amblyopia), and two within factors (target numerosity: one, two, four; eye: fellow, amblyopic) examined group differences in corrected tracking accuracy. Amblyopic eyes were age-matched to five right eyes and four left eyes from the control group, because there were five right and two left amblyopic eyes. In turn, fellow eyes were age-matched to the remaining five left and two right eyes from the control group.  3.2.5. Functional MRI Data Analysis 3.2.5.1. Preprocessing Data analyses from volume preprocessing to computation of MR signal change values were conducted with BrainVoyager QX 1.10 (Brain Innovation, Maastricht,  66          Netherlands). Prior to analysis, inter-slice time differences were removed from the data with an algorithm involving linear interpolation over time. All volumes were then corrected for small translational and rotational head movements by aligning to the first volume of each run using a nine-parameter rigid-body intensity-based algorithm with trilinear interpolation across eight neighbouring voxels.  Temporal high-pass filtering  (three cycles in time course) and a linear trend removal algorithm were used to eliminate temporal drifts (physiological and scanner noise) from the data. Functional runs were excluded from the analysis if translational movements greater than 1 mm in the x (left to right), y (anterior to posterior) and z (superior to inferior) or rotational movements greater than 1 deg in the x, y and z directions were made during the 148 dynamics (296 s) of scanning. Each participant completed 4-6 runs, 2-3 runs per every eye. Their data were included in the analysis if the first two runs of each eye met the preprocessing criteria above. Participant data that did not meet this requirement (ACJ01, ACJ07, ACJ08, AEJ03 & AEJ09) were excluded from analysis. This was due to findings from preliminary analyses which revealed that averaging across at least two runs of the multiple-object tracking task was required to ensure reliable activation at the threshold used for the experiment. One run of the task alone was not sufficient. For every eye, each of the two pre processed volumes was co-registered with the anatomic image. Both anatomic and functional data were spatially normalized to stereotaxic space (Talairach& Tournoux, 1988). The two volumes for each eye were averaged together so that every participant had one functional set of data for each eye.  67          3.2.5.2. Whole-brain Voxelwise Analysis For every participant, a single-subjects fixed-effects general linear model (GLM) was used to determine whole-brain, voxel-wise activity. The four predictors for multipleobject tracking (track 0, track 1, track 2, track 4)7 were derived by convolution of a boxcar function with the BrainVoyager default hemodynamic response function (doublegamma function model; Friston et al., 1998), which corrected for the hemodynamic delay in MR signal. This convolution modeled the functional MRI data. Maps of the t statistic were created from a contrast which consisted of all four conditions (track 0, track 1, track 2, track 4) versus fixation, with a Bonferroni correction for multiple comparisons at an alpha value of p<.05. A cluster threshold of 50 voxels was employed so that any active clusters under this threshold were interpreted as noise. Appendix B1 displays the predictors and the predicted hemodynamic response for each event. Note that although one trial lasted 16 s (2 s cueing + 12 s tracking + 2 s response), the predictors applied to only the first 14 s, and the 2 s of response was not included. Results from the whole-brain, voxel-wise analysis revealed four main regions of activation that were present in all 10 selected participants (Figure 19): bilateral MT, bilateral frontal eye fields, bilateral posterior occipital cortex (putative V1), and bilateral posterior parietal cortex. MT was found to be a contiguous cluster of activated voxels at the junction between the inferior temporal sulcus and the ascending limb of the inferior temporal sulcus (Dumoulin et al., 2000). The frontal eye fields were defined as the cluster of contiguous activated voxels at the junction of the pre-central sulcus with the superior frontal sulcus (Culham et al., 1998; Paus, 1996). V1 was putatively defined as                                                              7   The predictor for MT localizer was moving dots versus stationary dots  68           the cluster of contiguous activated voxels near the calcarine sulcus. The posterior parietal cortex was defined as all active voxels within four anatomic sulci (Ono et al., 1990): the post-central sulcus at the anterior end; the transverse occipital sulcus at the posterior end; the intraparietal sulcus at the lateral end, and the inter-hemispheric fissure. For some participants, additional clusters of activation were found in various frontal and occipital regions, but as they were not present in all participants, they were not examined further (Howe et al., 2009). Based on the neuroimaging multiple-object tracking literature, the posterior parietal cortex activation was sub-divided into three areas (Culham et al, 1998; Howe et al, 2009; Jovicich et al, 2001): anterior IPS, located at the junction of IPS and post-central sulcus, posterior IPS, located at the junction of intraparietal sulcus with the transverse occipital sulcus, and the superior parietal lobule. For a visualization of the activation of this contrast, appendix B2-B4 shows clusters of activation at every 4th Talaraich co-ordinate along the axial orientation (superior to inferior) for the multiple-object tracking task from one control eye, one fellow eye and one amblyopic eye.  3.2.5.3. Region of interest (ROI) Analyses ROI locations of the six regions of interest for every participant were defined from the whole-brain, voxel-wise analysis.  As every participant completed an MT  localizer scan, activation from the MT localizer (Figure 20) was used to define area MT in each participant (discussed below). The other four regions of interest (anterior IPS, posterior IPS, superior parietal lobule, frontal eye fields) were defined according to methods used by Howe et al. (2009). For each participant, ROI locations were centered  69          according to the peak voxels of activation within the known anatomical landmarks of each region, during an attentive tracking versus passive viewing contrast in the other observers. In this regard, the regions of interest are defined functionally, but constrained by anatomy. This meant a participant’s own activation was not used to define their regions of interest, avoiding the problems of bias, non-independence and circularity (Vul & Kanwisher, in press; Kriegeskorte, Simmons, Bellgowan & Baker, 2009). Peak voxels in each of the six regions for every participant were established by raising the threshold until no activation was present. The threshold was then turned down one step revealing small clusters of activation in both hemispheres. The ROI was then placed in each brain such that it contained these significant voxels. 8 mm X 8 mm X 8 mm cubes were selected as the size of the region of interest based on a similar size to the 5 mm spheres (523 mm3) used in the Howe et al (2009) study8. For anterior IPS, posterior IPS, and frontal eye fields, ROI’s were placed around the peak voxels according to the sulcal landmarks documented earlier in the introduction. For the superior parietal lobule, the sulcal landmark of the superior parietal sulcus (Ono et al., 1990) was used. This sulcus is found within the superior parietal lobule, as it lies posterior to the superior post-central sulcus at the anterior end; and anterior to the parieto-occipital sulcus at the posterior end, running parallel with the intraparietal sulcus and the inter-hemispheric fissure line.  Observation during the  single-subject, whole-brain, voxel-wise analysis revealed that at stringent thresholds (p<.001, bonferroni-corrected), a cluster of activation encompassed both the superior                                                              8   Of the four multiple object tracking neuroimaging studies, this was the only study that  mentioned size of region of interest examined. Limitations of this method will be discussed in  the Discussion section.  70          parietal sulcus region, and anterior IPS region. When the thresholds were turned up higher, peak voxels identified from the whole-brain, voxelwise analysis fell within this sulcus9. As the superior parietal lobule contains all cortex dorsomedial to the intraparietal sulcus, the superior parietal sulcus was chosen as a landmark for this region. For the MT ROI, each participant’s activation from the MT localizer was used to define the MT region. Subsequent analysis revealed that this region overlapped with activation from the whole-brain, voxel-wise analysis in 19/20 hemispheres from the 10 participants whose data were analyzed. The only non-overlapping hemisphere (left hemisphere, AEJ05) was due to the failure of voxels being active in the known anatomic region. For this non-active hemisphere in the MT localizer task, a 512 mm cube was placed at the peak active voxel corresponding to the anatomic region of MT described above during the multiple-object tracking activation. For V1, the ROI was putatively defined by placing a 20 mm X 20 mm X 20 mm cube which encompassed the posterior calcarine sulcus in both hemispheres. Thus, V1 was defined according to the anatomical landmark of the calcarine sulcus. Figure 21 displays the locations of regions of interest along the 3 views in Brain Voyager: sagittal (left to right, pictured left), coronal (anterior to posterior; pictured right) and axial (superior to inferior, pictured bottom right) orientations.                                                               9   As Culham et al. (1998) noted, increasing the threshold often fails to separate IPS/SPL activation due to the close proximity of the two locations. For this thesis, the superior parietal sulcus was defined as a second sulcus which branches off the post-central sulcus, but at a more superior location than the intraparietal sulcus.  71                   Figure 19- Sample slices of activation during multiple-object tracking. Activation was obtained with a whole-brain, single subject GLM analysis, from an all conditions (0,1,2,4) versus fixation contrast, p<.05 bonferroni-corrected. Yellow-orange colors represent significant positive t-statistic values (stronger activation relative to fixation), while blue-green colors represent negative t-statistic values (weaker activation relative to fixation). Top row images: sagittal (left), coronal (middle) and axial (right) slices showing activation from one amblyopic eye. Middle row images: slices showing activation from one fellow eye of the same participant. Bottom row images: Slices showing activation from one control eye. In posterior parietal cortex, there were no significant differences between the two groups in the total number of active voxels.    72                Figure 20- Sample slices of activation from MT localizer. Yellow-orange colors represent significant positive t-statistic values (stronger activation of moving dots relative to stationary dots). Blue-green colors represent significant negative t-statistic values (weaker activation of moving dots relative to stationary dots). Top row images: sagittal (left), coronal (middle) and axial (right) slices showing area MT activation and additional activation in frontal, occipital, and parietal regions in one control (ACJ05) participant. Bottom row images: sagittal (left), coronal (middle) and axial (right) slices showing activation in MT in one participant (AEJ11) with amblyopia. There were no significant differences between the two groups in the total number of active voxels                73          Figure 21 – A screen shot taken in BrainVoyager to show the relative locations of the regions of interest. Frontal eye fields (yellow; top left); Superior parietal lobule (light blue; top right and bottom right); Anterior IPS (rose; top right); Posterior IPS (orange; top left); MT (purple; bottom right); V1 (green; bottom right). MT was independently defined from the MT localizer; the other regions of interest were anatomically defined but functionally guided according to Howe et al. (2009).  74          3.2.5.4. Event-Related Analysis For each condition, an epoch time course was extracted from the ROI to examine MR signal change10 at each of the 14 s within each condition. As the first three time points were attributed to the two second cueing phase, they were excluded from further analysis. As each of the 10 participants had six regions of interest, applied to two eyes, 120 total data sets of MR signal change were collected. Thus, 120 total datasets consisting of MR signal change values (relative to fixation) at each of the 12 s of tracking for each condition, for the four conditions, were analyzed. It should be noted that MR signal change is a more meaningful scale than the arbitrary raw MR signal that could vary from session to session. MR signal change was calculated according to the formula:  MR signal change = (signal magnitude – baseline)/baseline X 100%  The fixation baseline for one run was calculated by collecting MR signal values one second prior to the beginning of a trial, then averaging those values. One dataset comprised 15 MR signal change values reflecting the 2 s cueing (that was excluded) and 12 s of total tracking time for each condition.                                                               10  MR signal is more commonly known as the blood‐oxygen‐level dependence (BOLD) signal. I  will be using the MR signal terminology for this thesis.  75          3.2.5.5. Statistical Analysis A global average of MR signal change across the 12 time points for the four conditions (0, 1, 2, 4) was input into SPSS. A one-between (group: control, amblyopia), two-within (attentional load: 0, 1, 2, 4; eye: fellow, amblyopic) repeated-measures ANOVA was used to determine significant effects of average MR signal change. As there were four right and one left amblyopic eyes, eyes from the control group were matched to represent the equal representation of right and left, to the amblyopic and fellow eye groups respectively.    3.3. Results 3.3.1. Psychophysical Data A preliminary, one-way between (filter colour: red, green) - within (target numerosity: one, two, four) repeated measures ANOVA was conducted on the data for seven control participants to determine if performance differed as a function of filter colour. The one-way ANOVA revealed no significant differences between the two filter colours (p = .47), indicating that filter colour had no effect on accuracy. The three-way repeated-measures ANOVA revealed a significant main effect of target numerosity (F(2,24) = 15.44, p<.01, η2 =.56), and group (F(1,12)= 8.80, p = .01, η2 =.42), but not eye (F(1,12)= .40, p>.05, η2 =.02). The effect sizes were large for target numerosity and group, and small for eye. No other two-way or three-way interactions were significant.  76          Bonferroni post-hoc testing on the numerosity main effect revealed accuracy was higher for tracking 2 balls versus tracking 4 balls (Xtrack2- Xtrack4 = 26.47, p = .02, 95% C.I.’s = 3.41-49.13), and higher for tracking 1 ball versus tracking 4 balls (X track1-Xtrack4 = 31.63, p = .001, 95% C.I.’s =18.40-60.87). Figure 22 displays the accuracy means across target numerosity. Figure 23 displays the group means collapsed across numerosity and eye. Accuracy scores were higher for the control group (M= 86.61, SE = ± 4.65) than for the amblyopia group (M= 66.67, SE = ± 4.65). The differences between the groups were significant for tracking 2 balls (Xcontrol-Xamblyopia= 22.98, p = .05, 95% C.I.’s = .01- 45.97) and for four balls (Xcontrol – Xamblyopia = 31.56, p = .05, .47-62.64). Figure 24 plots the means as a function of numerosity and eye. A between (group)-within (eye) ANOVA determined if the slope of decline in accuracy scores as a function of increasing target numerosity was significant. A between (group)-within (eye) ANOVA determined if the slope of decline in accuracy scores as a function of increasing target numerosity was significant. The ANOVA revealed a main group effect (F(1,12) = 6.17, p =.03, η2 =.34). Post hoc testing revealed the slope averaged across both fellow and amblyopic eyes was greater than the slope averaged across both control eyes (Xamblyopia-Xcontrol = 18.03, p = .03, 95%C.I.’s = 2.22-33.02). For amblyopic participants, a correlation matrix was used to explore significant correlations between visual acuity, stereopsis, and performance inside the scanner for each eye. Similar to the findings in Experiment 1, no significant correlations were found.  77          Figure 22- Accuracy scores across target numerosity for the 10 participants. Accuracy was significantly lower in all conditions as target numerosity increased. Error bars represent standard error.  78          Figure 23- Average group accuracy scores collapsed across target numerosity and eye in Experiment 2. Error bars represent standard error.  79          Figure 24- Accuracy scores as a function of group, eye, and target numerosity in Experiment 2. Control eye 1 is matched with the fellow eye, control eye 2 is matched with the amblyopic eye. The decline in slope as a function of target numerosity was greater for fellow and amblyopic eyes than for both control eyes. Error bars represent standard error.  80          3.3.2. Functional MRI Data Table 6 lists for every participant, the number of active voxels for left and right MT during the MT localizer task. A between (group: control, amblyopia)-within (eye: fellow, amblyopic) ANOVA revealed no significant mean differences between groups, or eyes. Table 7 lists the number of active voxels for posterior parietal cortex during the single-subject, whole-brain, voxel-wise analysis, for the contrast of the four conditions versus fixation. ANOVA testing also revealed no significant differences (p>.05) between eyes. Tables 8-11 list the Talaraich co-ordinates of the anterior IPS (Table 8), posterior IPS (Table 9), superior parietal lobule (Table 10) and frontal eye fields (Table 11) regions of interest for every participant.  81          Table 6- Active voxels in MT during MT localizer Group Subject Left hemisphere ACJ04 1836 ACJ05 1207 Control ACJ06 2141 ACJ11 2666 ACJ12 1579  Amblyopia  Right hemisphere 5676 2303 1251 3137 1142  Average SD  1885.80 554.80  2701.8 1852.84  AEJ05 AEJ06 AEJ10 AEJ11 AEJ12  407 1234 1340 5507 1836  0 4361 550 3805 5676  2064.8 1991.54  2878.4 2479.41  average SD p <.001, uncorrected threshold  82          Table 7- Active voxels in bilateral posterior parietal cortex Participant Group code Number of active voxels Right eye ACJ04 26224 ACJ05 24333 Control ACJ06 43260 ACJ11 14562 ACJ12 35914  Amblyopia  Left eye 55225 25834 32918 29660 32573  Average SD  28858.60 11056.60  35242.00 11527.29  AEJ05 AEJ06 AEJ10 AEJ11 AEJ12  Fellow eye 30192 32914 23114 30885 42317  Amblyopic eye 23298 13314 14193 23809 38718  Average 31884.40 22666.40 SD 6904.39 10230.15 *Number of active voxels during whole-brain, voxel-wise analysis, all conditions (0, 1, 2, 4) versus fixation contrast, p<.05 bonferroni-corrected                                    83          Table 8- Talairach co-ordinates of the anterior IPS region of interest Participant Code Hemisphere Talairach co-ordinates X Y Z  ACJ04  R L  39 -38  -30 -39  44 -52  ACJ05  R L  35 -33  -38 -41  42 42  ACJ06  R L  36 -37  -40 -37  48 45  ACJ11  R L  42 -40  -35 -41  41 40  ACJ12  R L  34 -35  -37 -39  44 43  AEJ05  R L  34 -31  -44 -46  47 41  AEJ06  R L  33 -35  -42 -42  47 38  AEJ10  R L  43 -45  -43 -43  44 47  AEJ11  R L  34 -32  -33 -36  46 48  AEJ12  R L  40 -35  -43 -44  43 44  84          Table 9- Talairach co-ordinates of the posterior IPS region of interest Participant code Hemisphere Talairach Co-ordinates X Y  Z  ACJ04  R L  33 -23  -78 -78  15 14  ACJ05  R L  23 19  -73 -73  32 34  ACJ06  R L  23 -22  -71 -71  29 28  ACJ11  R L  24 -26  -73 -71  29 19  ACJ12  R L  31 -28  -74 -74  16 14  AEJ05  R L  27 -23  -78 -78  24 25  AEJ06  R L  26 -22  -79 -85  13 13  AEJ10  R L  27 -20  -76 -77  26 31  AEJ11  R L  -25 27  -74 -78  28 22  AEJ12  R L  27 -20  -70 -70  23 34  85          Table 10- Talairach co-ordinates of the superior parietal lobule region of interest Participant code Hemisphere Talairach co-ordinates X Y Z ACJ04  R L  14 -29  -59 -59  51 46  ACJ05  R L  15 -27  57 -57  53 49  ACJ06  R L  19 -26  -58 -58  50 50  ACJ11  R L  15 -28  -58 -58  48 47  ACJ12  R L  32 32  -46 -57  50 50  AEJ05  R L  21 -28  -58 -58  50 48  AEJ06  R L  15 -28  -57 -57  53 48  AEJ10  R L  13 -31  -60 -60  53 46  AEJ11  R L  21 -28  -58 58  49 48  AEJ12  R L  17 -29  -62 -52  55 45  86          Table 11- Talairach coordinates of the frontal eye fields region of interest Participant code Hemisphere Talairach Co-ordinates X Y Z ACJ04  R L  23 20  -4 -5  54 51  ACJ05  R L  22 -22  -5 -5  50 51  ACJ06  R L  24 -21  -6 -6  51 50  ACJ11  R L  22 21  -5 -5  53 54  ACJ12  R L  27 20  -9 -9  46 51  AEJ05  R L  24 -21  -5 -5  55 50  AEJ06  R L  26 -20  -7 -7  49 52  AEJ10  R L  20 -22  -6 -6  51 55  AEJ11  R L  22 -21  -5 -5  52 51  AEJ12  R L  23 -22  -6 -6  52 51  87          3.3.2.1. MR Signal Change in MT Across 12 s of tracking, there was a main effect of average MR signal for target numerosity (F(3,24) = 10.05, p<.001, ηp  2  =.56), but no significant effect of eye  (F(1,8)=.13, p>.05, ηp 2 = .02) or group (F(1,8)= 2.16, p>.05, ηp 2 = .21). No two-way or three-way interactions were significant. The effect sizes were large for both target numerosity and group, and small for eye. Post-hoc testing on the numerosity effect revealed significant differences in average MR signal between tracking 4 versus tracking 0 (X  track 4-X track 0  tracking 0 (X  track 2-  = .32, p = .03, 95% C.I.’s = .002 - .67) and tracking 2 versus  X track 0 = .32, p= .05, 95% C.I.’s = .03- .62). Figure 25 displays the  average MR signal change across target numerosity for each eye. A second series of post-hoc analyses collapsed average MR signal across the four conditions as a function of time. According to the convolution of the box-car function with the hemodynamic response function, MR signal for all four predictors (Appendix B1) was predicted to be the lowest at the beginning of the 12 s of tracking, and the highest at the end of twelve seconds of tracking. Depending on how well/poor this model fit the functional MRI data, non-significant differences in MR signal could have been masked by the efficiency of this convolution. Figure 26 illustrates the average MR signal as a function of time. As observed in this figure, a similar pattern existed in the eyes of all participants for the first 4 s of tracking. Starting at 5 s of tracking though, the rise in the slope of the MR signal for control eyes was higher compared to both fellow and amblyopic eyes. By the 7 s of tracking, significant differences in average MR signal change were revealed and remained significant until the end of the tracking period. Whereas MR signal linearly increased with time for  88          control eyes (slopes = .10, both eyes), MR signal for both eyes of the amblyopic group appeared to level off after 6 s of tracking (slopes = .01, both eyes). ANOVA testing on the last 6 s of tracking showed significant main effects of target numerosity (F(1.34, 10.71)= 15.36, p <.01, ηp 2 = .66), and group (F(1,8)= 5.78, p=.04, ηp 2 = .42). Both of these effect sizes were large. Figure 27 demonstrates the mean MR signal change at the four conditions for the last 6 s of tracking. These means were higher in the control eyes for all four conditions, with a significant difference at 0 balls (Xcontrol-Xamblyopia = .48, p = .05, 95% C.I.’s = .02-.97). As Figure 26 reflects the averaged MR signal change collapsed across the four tracking conditions, I then assessed MR signal change as a function of time at each of the four tracking conditions to see if a similar pattern existed (Figure 28). One can see from each of the four graphs that MR signal change means are relatively equal for the first 6 seconds of tracking. From six seconds to twelve seconds of tracking, MR signal change means are higher for both control eyes relative to the fellow and amblyopic eyes for all four tracking conditions.  89          Figure 25 - MR signal change means for each of the 12 s of tracking in MT. Note that after the sixth time point, MR signal change continues to increase with time in both control eyes, but levels off in both fellow and amblyopic eyes. After this time point, MR signal is significantly lower in the amblyopic group. Error bars represent standard error.  90                Figure 26– MR signal change as a function of time, averaged across the four conditions. Control eye 1 data was matched with fellow eye data, while control eye 2 was matched with amblyopic eye data. No significant differences between groups were identified. Error bars represent standard error.  91          Figure 27 – Average (last 6 s of tracking) MR signal change in MT as a function of target numerosity, group and eye. Significant differences existed between the two groups, where MR signal means are higher at all four tracking conditions in both control eyes. Error bars represent standard error.  92          0 balls  1 ball  93          2 balls  4 balls  Figure 28- Mean MR signal change in MT across the twelve time points of tracking for each of the four tracking conditions. Whereas no significant differences were found in the first 6 s of tracking, significant differences between the groups emerge after 6 seconds of tracking (0 balls), 7 seconds of tracking (2 balls), 8 seconds of tracking (4 balls) and 9 seconds of tracking (1 ball). MR signal means are higher in both control eyes relative to the fellow and amblyopic eyes. Error bars represent standard error. 94          3.3.2.2. MR Signal Change in Superior Parietal Lobule Across 12 s of tracking, there was a significant effect of target numerosity (F(2.69, 11.83)= 31.39, p<.001, ηp 2 =.57), with no effect of group (F(1,8)= .09, p>.05, ηp 2  = .12) or eye (F(1,8)=1.35, p>.05, ηp 2 =.08). The effect sizes were large, medium, and  small respectively. Figure 29 (top graph) reveals the mean MR signal change across the four tracking conditions. Post hoc testing on the target numerosity main effect revealed significant differences in MR signal in tracking 4 balls versus tracking 0 balls (Xtrack 4-Xtrack 0= .48, p= .01, 95% C.I.’s = .26-.70) and tracking 2 balls versus tracking 0 balls (X track 2- X track 0= .57, p<.01, 95% C.I.’s = .16 - .96). A secondary set of analyses as described for MT examined average MR signal change collapsed across the four conditions, as a function of time. Whereas examining MR signal change at each of the 12 time points of tracking revealed significant group differences in MT, Figure 29 (bottom graph) reveals that the slopes of MR signal change were nearly identical for all four eyes.  95          Figure 29 – MR signal in superior parietal lobule. Top graph: Average (12 s) MR signal change in superior parietal lobule across target numerosity, eye, and group. Only the main effect of target numerosity was significant, with no differences between group or eye. Bottom: MR signal change in superior parietal lobule across the 12 time points in tracking. Despite low MR signal means between 3 to 6 s for the fellow eye, no significant group differences were found. Error bars represent standard error. 96          3.3.2.3. MR Signal Change in Posterior IPS Across 12 s of tracking, there was a significant main effect of target numerosity (F(1.77, 14.14)= 8.11, p=.001, ηp 2 =.50) , with no effect of group (F(1,8)= .01, p>.05, ηp 2  = 0) or eye (F(1,8)= .651, p>.05, ηp 2 =.03). No significant interactions were found.  The effect sizes for target numerosity were large, and small for eye. There was no effect size for group. Figure 30 displays the average MR signal change across target numerosity for 12 s of tracking. Post-hoc testing of the main effect of target numerosity revealed significant differences in MR signal change in tracking 2 balls versus tracking 0 (Xtrack 2-Xtrack 0 = .27, p = .03, 95% C.I.’s = .05-.49) and tracking 4 balls versus tracking 0 (Xtrack 4-Xtrack0 = .31, p = .03, 95% C.I.’s = .06 - .56). Figure 30 (bottom graph) displays mean MR signal change for tracking 4 balls across the 12 s. No differences were reported. Follow-up investigations into average MR signal change at each of the 12 s of tracking also revealed no significant differences at any of the 12 points (Figure 30, bottom graph).  97          3.3.2.4. MR Signal Change in Anterior IPS There was a significant 3-way interaction (Figure 31, top graph) between target numerosity, eye, and group. (F(3,24)= 3.68, p = .03, ηp  2  = .31). Follow-up of the  significant 3-way interaction via simple effects testing of the group X eye interaction at each numerosity condition revealed no significant interactions at 0 balls (F(1,8)=2.02, p> .05, ηp 2 = .20), 1 ball (F(1,8)= 4.65, p >.05, ηp 2 = .2), or 2 balls (F(1,12)=.04, p>.05, ηp  2  = .004), but a significant interaction at 4 balls (F(1,8)=6.18, p = .04, ηp  2  = .44).  Follow-up testing of the significant interaction at 4 balls via simple, simple effects testing of eye for each group revealed no significant difference between control eyes, but a significant difference between fellow and amblyopic eyes (Xfellow eye- X amblyopic eye = .19, p<.001, = 95% C.I’s = .04-.29). MR signal change means for the fellow eye were not significantly different relative to controls. The slopes of MR signal change across target numerosity were slightly higher in both control eyes (0.18 and 0.14) than for fellow (0.11) and amblyopic eyes (0.10), but ANOVA testing revealed these differences were not significant. MR signal change as a function of time (Figure 31, bottom graph) revealed no significant differences at any of the 12 time points. Post hoc testing revealed MR signal change means for tracking 4 balls was significantly higher than tracking 2 balls (Xtrack 4 – X track 2 = .19, p = .04, 95% C.I.’s = .07-.31), tracking 1 ball (Xtrack 4 – X  track 1  = .29, p =  .01, 95% C.I.’s = .14 - .44) and passive viewing (X track 4 –X track 0 = .40, 95% C.I.’s = .18 .61), p = .02. MR signal change means for tracking 2 balls was significantly higher than passive viewing (X track 2 – X track 0 = .1, p= .05 , 95% C.I.’s = .1-.19).  98          Figure 30 – MR signal change in posterior IPS. Top graph: average (12 seconds) MR signal change in posterior IPS as a function of target numerosity, group and eye. ANOVA testing revealed significant effects of target numerosity, with no difference in group or eye. Bottom Graph: Mean MR signal change in posterior IPS for each of the 12 time points. No significant differences could be established as a function of time. Error bars represent standard error.  99          Figure 31 – MR signal change in anterior IPS. Top graph: average (12 seconds) MR signal change in posterior IPS as a function of target numerosity, group and eye. Follow up of the three-way interaction via simple, simple effects testing revealed significantly lower MR signal in the amblyopic eye, relative to the fellow eye for tracking 4 balls. Bottom graph: Mean MR signal change in anterior IPS for each of the 12 time points. No significant differences were found. Error bars represent standard error.  100          3.3.2.5. MR Signal Change in Frontal Eye Fields There was a significant 3-way interaction (Figure 32, top graph) between target numerosity, eye, and group (F(3,24)=3.89, p =.02, ηp  2  =.33).  Follow-up of the  significant 3-way interaction via simple effects testing of the group X eye interaction at each numerosity condition revealed no significant interactions at 0 balls (F(1,8)=1.05, p>.05, ηp 2 = .09), 1 ball (F(1,8)=4.03, p>.05, ηp 2 = .34), or 2 balls (F(1,8)= 1.34, p>.05, ηp 2 = .14), but a significant interaction at 4 balls (F(1,8)= 7.71, p=.02, ηp 2 =.49). Simple, simple effects testing of eye for each group revealed no significant differences for control eyes, but a significant difference for amblyopic eye relative to fellow eye (Xfellow eye -Xamblyopic eye  = .33, p=.01, 95% C.I.’s = .12-.53).  MR signal change as a function of time (Figure 32, bottom graph) revealed no significant differences at any of the 12 time points. Post hoc testing on the secondary main effect of target numerosity revealed MR signal change for passive viewing was significantly lower than tracking 4 (Xtrack4 – X track 0 = .37, p = .01, 95% C.I.’s = .04- .7), tracking 2 (X track 2 – X track 0 = .03, 95% C.I.’s = .09 - .74) and tracking 1 (X track1- X track 0 = .27, p = .05, 95% C.I.’s = .01- .53) balls.  101          3.3.2.6. MR Signal Change in Putative V1 No significant main effects or interactions were found. Figure 33 (top graph) displays MR signal change across the four tracking conditions. Although the means for both control eyes are higher than both amblyopic eyes, the differences are not of significance. Figure 33 also displays that the means across 12 s of tracking (bottom graph) were higher in controls, but differences were not of significance. Due to the lack of any significant effects, no further post-hoc testing was conducted.  3.3.2.7. Correlations Between the Behavioural and Functional MRI Data Another investigation examined if there were correlations between the behavioural deficit at four balls, MR signal change in MT at each of the four conditions in MT, and MR signal change in anterior IPS for tracking four balls in the five participants with amblyopia. There were no significant correlations between the behavioural deficit, MR signal change in MT or in anterior IPS. Table 12 lists the correlations.  102                      Figure 32 – MR signal change in frontal eye fields. Top graph: average (12 seconds) MR signal change in frontal eye fields as a function of target numerosity, group and eye. Follow up of the three-way interaction via simple, simple effects testing revealed significantly lower MR signal in the amblyopic eye, relative to the fellow eye for tracking 4 balls. Bottom graph: Mean MR signal change in fellow eye for each of the 12 time points. No significant differences were found. Error bars represent standard error.  103          Figure 33 – MR signal change in putative V1. Top graph: Average (12 s) MR signal change across target numerosity, eye, and group. No main effects were found. Bottom graph: MR signal change across the 12 time points in tracking. No significant difference between groups were found. Error bars represent standard error.  104          Table 12- Correlations between the behavioural deficit at 4 balls and activity in MT and anterior IPS MT Anterior IPS fellow eye amblyopic eye fellow eye amblyopic eye track 0 track 1 track 2 track 4 track 0 track 1 track 2 track 4 track 4 track 4 Fellow eye -0.54 -0.25 -0.40 -0.06 0.56 -0.44 -0.22 -0.62 -0.53 -0.40 Amblyopic eye -0.60 -0.43 -0.39 -0.45 0.18 -0.33 -0.47 -0.48 -0.15 -0.18  105          3.4. Discussion 3.4.1. Psychophysics Data The results from Experiment 2 indicated significant effects of target numerosity and group on accuracy scores when the targets were tracked for 12 s. The amblyopic group performed worse than the control group for all three target numerosity conditions, with the differences being significant at tracking 2 and 4 balls. These results were similar to the psychophysics data in Experiment 1, as both sets of data indicated main effects for target numerosity and group. The key differences between the two paradigms used in Experiment 1 and Experiment 2 were the duration of tracking and the number of balls.  The duration of tracking in Experiment 2 (12 s) was longer than tracking in  Experiment 1 (5 s). Also, there were 9 total balls in Experiment 2, but 8 total balls in Experiment 1. Both target numerosity and group effect sizes were larger in Experiment 2 than in Experiment 1. Furthermore, the drop off in accuracy as a function of increasing target numerosity was greater in Experiment 2 than for Experiment 1. These two findings indicated that increasing the duration of tracking, and increasing the number of targets was a more challenging task for all participants. Attentional resolution is limited by spatial (Shim, Alvarez, & Jiang, 2008) and temporal factors (Alvarez & Franconeri, 2007; Verstraten, Cavanagh, & Labianca, 2000). Some studies have shown spatial deficits primarily for the amblyopic eye in undercounting the number of features on a grid (Sharma et al., 2000) and in angle discrimination tasks (Levi et al., 2006). Furthermore, these deficits existed when lowlevel factors such as blur or jitter were taken into account (Sharma et al., 2000). Spatial  106          deficits were also found in multiple-trajectory tracking, as the amblyopic eye was slightly worse relative to controls when the deviations were at smaller angles (19o). It is thought in the multiple-object tracking literature that each target is tracked by one independent spotlight of attention (reviewed in Cavanagh & Alvarez, 2005). As the number of targets increase, so too do the number of attentional spotlights deployed. Each attentional spotlight deployed is thought to enhance processing of the target location, while suppressing surrounding regions around the target (Muller, Mollenhauer, Rosler & Kleinschmidt, 2005). The suppression of the surrounding region enhances the “contrast” between targets and distractors, and optimizes the spatial resolution of attention. Shim et al. (2008), proposed that when target-to-target distance decreases, such as for the case of increasing the number of targets, one target can fall inside the suppression zone of another target. In turn, this reduces the contrast between targets and distractors. Such a mechanism can explain why targets are often mixed up with distractors. As experimental evidence suggests spatial attention may be disrupted in amblyopia, perhaps a coarser spatial resolution could result in bigger suppression zones for each target. For small target-to-target distances such as at tracking 4 balls, this could explain why control participants could still individuate targets, but participants with amblyopia could not. Deficits were also found in the attentional blink paradigm (Popple et al., 2008), a task which implicates a disruption in temporal attention. Psychophysical evidence to date has shown no deficit in transient attention, but deficits when attention must be sustained. Ho et al. (2006) found that children with amblyopia performed similar to control children on apparent motion tasks which engaged transient attention (Battelli et  107          al., 2006), but deficits in both eyes occurred when attention was required to be engaged at longer durations for single-object and multiple-object tracking. The experimental evidence in this thesis shown that the decline in performance was greater in both groups when tracking time increased, although the confound of an extra item in Experiment 2 was not accounted for. In this regard, future investigations extensively manipulating the duration of tracking, and keeping the total number of items constant could provide direct evidence of an enhanced deficit with increasing tracking time. This would provide further evidence of a disruption of temporal attention in amblyopia. The assumption of the multiple-object tracking deficit in this study is that participants with amblyopia were successfully seeing the number of cued targets at the beginning of the trial. Yet, it is possible that some incorrect trials were due to problems with target acquisition, where participants miscounted the number of targets. Modifications of the multiple-object tracking paradigm, such as having participants report how many target items were cued immediately after the cues disappear, could provide insight into the proportion of incorrect responses due to acquisition versus tracking.  3.4.2. Functional MRI data The focus of this thesis was to determine functional differences within cortical regions engaged during multiple-object tracking in participants with and without amblyopia. The strongest evidence of a functional difference was found in MT, as for all four conditions, MR signal means were lower in fellow and amblyopic eyes relative to  108          controls. The anterior IPS was also implicated, as for tracking 4 balls, MR signal change means were lower for the amblyopic eye. This thesis adds to the neuroimaging findings that MT is functionally less active during motion processing in both eyes of participants with amblyopia. Bonhomme et al. (2006) found that in two adults with anisometropic amblyopia, there was significantly less activation with amblyopic eye viewing of expanding/contracting rings compared to their fellow eye. As their study only examined inter-eye differences, it was unknown if activity for the fellow eye was less active than activity for control eyes. Recently, Ho et al. (2009) found the MR signal was significantly reduced in a network involving MT during the viewing of low-level and high-level RDKs. Activation was reduced relative to controls in both amblyopic eye and fellow eyes.  3.4.2.1. Task-dependent Versus Load-dependent Agreement with Other Studies All regions of interest, except for V1, showed a main effect of target numerosity, which matched the profiles of activation with increasing target numerosity as described by Culham et al. (1998) and Jovicich et al. (2001). Post-hoc analyses on the main effect of target numerosity revealed whether each region of interest was task or load dependent. The criteria for a task-dependent region was that significant differences in MR signal were found for the three attentive tracking conditions relative to the passive viewing condition, but no significant differences found within the three attentive tracking conditions. The criteria for a load-dependent region was that significant differences in MR signal were found within the three attentive tracking conditions in addition to differences relative to passive viewing.  109          Both studies were in agreement that the anterior IPS was a region that was loaddependent because activation increased with the number of balls tracked. The frontal eye fields and superior parietal lobule were found to be task-dependent because activation increased in the shift from passive viewing to attentive tracking, and then remained constant as attentional load increased. A load-dependent change in MR signal was found in the anterior IPS (Figure 31), and a task-dependent change in the frontal eye fields (Figure 32) and superior parietal lobule (Figure 29). These two papers differed in the activation profile regarding posterior IPS. Whereas Culham et al. (1998) stated this region was load-dependent, Jovicich et al. (2001) stated this region was task-dependent. I found the posterior IPS to be taskdependent, in agreement with Jovicich et al. (2001). In the case of posterior IPS, our region of interest was restricted to the intersection of intraparietal sulcus and transverse occipital sulcus. In the neuroimaging literature, this region often encompassed V3a, which sits posterior and ventral to the transverse occipital sulcus (Tootell et al., 2003). The lower MR signal change values obtained for posterior IPS in this thesis may reflect the fact that this ROI likely did not capture V3a. Both Culham et al. (1998) and Jovicich et al. (2001) found that MT was weakly load dependent. Figure 27 indicates that MR signal in MT increases linearly from 0 to 2 targets, then levels off from 2 to 4 targets. This may agree with evidence that MT is not fully load-dependent, but there is modulation of activity by MT in this region. Only the Jovicich et al. (2001) study examined MR signal change as a function of target numerosity in V1. The data from putative V1 (Figure 31) in this thesis support the Jovicich et al. finding that V1 activation was not enhanced by increasing attention.  110          3.4.2.2. Differences in Activation between the Control and Amblyopic Groups The largest difference in cortical activation between the groups was in area MT. Smaller differences in cortical activation between the groups was found in anterior IPS. Howe et al (2009) proposed a functional connectivity model (Figure 10) with the anterior IPS at the core, and responsible for tracking targets when they moved. This region connected with the superior parietal lobule for the planning of saccades (Doricchi et al., 1987) and also the frontal eye fields for the suppression of eye movements (Burman and Bruce, 1997). Anterior IPS also connects with MT, which is hypothesized to be responsible for updating the locations of objects.  In turn, MT  connects with posterior IPS, which is proposed to be involved in indexing which objects were targets. My results suggest that MT is the core of some of the problems in amblyopia. The two main findings of the deficit in MT were: a) For all four tracking conditions, percent signal change in both control eyes continued to increase as tracking duration increased, but in fellow and amblyopic eyes, percent signal change levelled off after 6 s (Figure 28); b) differences in percent signal change were higher in all four tracking conditions and during passive viewing, especially during the last 6 s of tracking (Figure 27) The levelling off of percent signal change after 6 s in fellow and amblyopic eyes is intriguing considering the hemodynamic response function predicted a linear increase in MR signal from 0 to 12 s (Appendix B1). Whereas the control data appeared to fit this hemodynamic prediction, the amblyopic group clearly did not. Logothetis (2002, 2003; Logothetis & Wandell, 2004) found evidence that the amplitude of the MR signal reflects  111          the summation of excitatory and inhibitory post-synaptic potentials strength of input into a brain region. Roden et al (2004) stated that MR signal is not a measure of spiking potentials, but rather the synaptic activity between neurons. It has been suggested in animals, neural deficits in amblyopia reflect the decreased synchronization of neurons (Roelfsema, Konig, Engel, Sireteanu & Singer, 1994), or a decrease in the number of neurons firing (reviewed in Kiorpes, 2006; Kiorpes & Movshon, 1998). Meanwhile MRI studies in human amblyopia have shown reduced gray matter in occipital cortex (Mendola et al., 2005). Thus, the levelling off of MR signal after 6 seconds in both eyes could reflect a constraint in anatomy (reduced number of cells in this region) or function (reduced synchronization) in this region. Furthermore, if reduced input into MT was the cause of reduced MR signal in this region, there should be significant differences in regions that project to MT, such as V1. The control eyes did appear to have higher activation in putative V1 relative to amblyopic and fellow eyes (Figure 33), but the difference was not significant. According to Cavanagh’s model (1991, 1992), the passive motion system detects motion via Reichardt-like motion-detectors tuned to temporal changes in luminance at two different locations during the passive viewing conditions. During passive viewing, the passive motion system would be involved. Significantly lower MR signal change means for both fellow and amblyopic eyes relative to controls in MT indicate problems with the passive motion system in amblyopia. This is in agreement with Bonhomme et al. (2006), who found this region was less active during the passive viewing of moving stimuli in their study. When attention is engaged during tracking of 1, 2 or 4 targets, there is involvement of the active motion system. In the Howe et al. (2009) model, the  112          anterior IPS was responsible for actively tracking the objects. The anterior IPS data shows that for tracking 4 balls, MR signal change means were lower for the amblyopic eye. This indicates problems with the active motion system in amblyopia. One interpretation of the findings in anterior IPS is that functional differences in anterior IPS are small, or smaller than functional differences in MT in amblyopia. Considering the confirmation with the literature that anterior IPS was load-dependent (Culham et al., 2001; Jovicich et al., 2001), it is unlikely that the small group difference is due to a problem with ROI definition. In the Howe et al. model, posterior IPS was proposed to index the locations of the objects that MT was updating, while the superior parietal lobule was involved in planning eye movements. There was no evidence to suggest the posterior IPS or superior parietal regions were abnormal in these participants with amblyopia. Finally, MR signal for the frontal eye fields were found to be significantly lower at 4 balls for the amblyopic eye relative to the fellow eye. This likely reflects that participants with amblyopia had difficulty suppressing eye movements only at 4 balls.  3.4.2.3. Correlations between the Behavioural and Functional MRI Data The finding that behavioural deficits did not correlate with MR signal change in MT or anterior IPS (Table 12) contradicted the hypothesis that there would be a significant correlation. The only other study that compared behavioural performance to functional MRI activation in multiple-object tracking, also found no significant correlation (Jovicich et al., 2001). In other tasks, Li et al (2007) found a non significant correlation between deficits in acuity and contrast sensitivity with the deficit in MR signal activity in  113          V1 or V2 in their sample of participants with amblyopia. Also, Ho et al. (2009) failed to find a significant correlation between reduced binocularity and reduced functional MRI activation in their sample of participants with amblyopia. To date, no study has correlated the deficit in brain activity to the behavioural deficit. On the other hand, correlations have been found between functional and anatomical changes, as discussed in the Introduction (Lv et al., 2008), suggesting functional deficits do correlate with structural changes in the brain. In this thesis, the lack of correlation between the behavioural deficit and the functional MRI data could be due to low power based from the low number of recruited participants.  3.4.3. Limitations 3.4.3.1. Eye Movements One limitation of this experiment was the crude method by which eye movements were recorded. During psychophysical testing, eye movements were assessed with a video camera. All participants received a practice session with both eyes. If participants showed evidence of saccadic eye movements and/or smooth pursuit eye movements, they were reminded by the experimenter (JS) to fixate on the center of the screen. In control participants and some participants with amblyopia, there were no observable saccades or smooth pursuit movements, indicating that fixation was in fact being maintained. For other participants with amblyopia, saccades were made during target cueing at the beginning and end of the trial, but not during tracking. As long as participants did not make observable eye movements during the 5 s of tracking, they were assumed to maintain fixation, and continued on with the study. Two participants  114          that were recruited for Experiment 1 were excluded, as they were unable to fixate during the 5 s of attentive tracking, and could not do the task without making these eye movements. In Experiment 2, an attempt was made to monitor eye movements inside the MRI scanner. The combination of wearing lenses with filters (to test for monocular viewing) altered the angle at which the infrared laser hit the eye. This caused inaccurate measurements of any eye movements and the plan was discontinued. Before the scanning session took place, participants were given a practice session consisting of 4 trials, and the importance of fixating while completing the task was stressed. Considering that all participants demonstrated the ability to fixate in Experiment 1, and were reminded again in Experiment 2, I could only assume they were not making any eye movements during the task. The functional MRI data for the frontal eye fields may indirectly indicate that eye movements were not significantly different relative to controls.  3.4.3.2. Functional MRI The biggest limitation in the functional MRI analysis was determining both the size and precise location for the posterior parietal cortex and frontal eye field regions of interest for which there were no known localizers. At the time this project began, only three papers had investigated the neural correlates of multiple-object tracking. Two of these used a regression formula to identify brain regions which were task-activated as opposed to attentional load-activated (Culham et al., 1998; Jovicich et al., 2001). The third paper (Culham et al., 1998) listed  115          regions that were more active during attentive tracking than during passive viewing. Clusters of activation in these studies were defined relative to their anatomical location, but the size and individual variability of these regions was not described. Howe et al. (2009) localized ROI’s anatomically and functionally, and also at the level of single subjects. Due to the high-degree of inter-subject variability of IPS branching (Ono et al, 1990), I decided that analyzing data at the level of single subjects would be more precise than a group analysis, choosing to mimic the Howe et al (2009) methods to define ROI’s. Yet, problems arose as Howe et al. did not report Talaraich co-ordinates in their paper, and only identified regions of interest based on the previous three findings in multiple-object tracking. I decided on a single-subject general linear model analysis for the voxel-wise, whole-brain analysis, and a group-defined, independent analysis to pin the exact location of the ROI cubes within each of the regions for the three posterior parietal cortex ROI’s and the frontal eye field ROI. There are some limitations to the method I chose to employ. First, the size of the regions of interest could have been too small, especially for the superior parietal lobule. Second is the close proximity between superior parietal lobule, and the anterior IPS regions. By definition, the superior parietal lobule is all cortex above the intraparietal sulcus, and a 512 mm3 region may not have been representative of activity in this region. Yet at the same time, the agreement in these results with the task-dependent and load-dependent findings suggest that the methods used to capture the regions of interest were adequate. The third limitation is that V1 was localized using the calcarine sulcus as a landmark. A proper retinotopic mapping localizer would have helped establish if early visual areas were functionally different during this task.  116          An important point to discuss is that the average MR signal change values reported in this thesis are lower than those obtained in Jovicich et al. (2001). Several possible reasons exist as to why this may be the case. First, the number of runs differed across studies. The participants in the Jovicich et al (2001) study completed eight runs of multiple-object tracking; while my participants only completed two for each eye. Their increase in power due to reduced variability of the data likely reflected greater MR signal. Finally, in the case of posterior IPS, the region of interest was restricted to the intersection of intraparietal sulcus and transverse occipital sulcus. In the multipleobject tracking neuroimaging literature, this region is often said to encompass V3a (Culham et al., 1998), which sits posterior and ventral to the transverse occipital sulcus (Tootell et al., 2003). The lower MR signal change values obtained for posterior IPS in this thesis may reflect the fact that this ROI likely did not capture V3a.  3.4.4. Future Directions A recent report by Thakral & Slotnick (2009) used functional MRI to localize the brain regions active during sustained attention. Their experimental design consisted of a motion protocol alternating between periods of moving and stationary dots, and an additional flicker protocol, also alternating between periods of flickering or stationary checkerboards. The moving and flickering periods were sub-divided into attention and perception periods. For attending to motion, participants had to indicate when dots moving towards, and away from the center of fixation changed speeds, as opposed to the perception of motion where participants passively viewed the dots. For attending to  117          flicker, participants viewed a circular checkerboard and had to respond when a red square appeared within the checkerboard. In the flicker perception condition, participants passively viewed the flickering checkerboard. Via a conjunction analysis, a neural network containing the right IPS and right middle frontal gyrus, right insula, and right superior temporal gyrus was identified. Considering the psychophysical evidence in amblyopia suggesting that problems may exist with sustained visual attention, perhaps this type of experimental paradigm may be more effective in targeting regions responsible for sustained attention, and determining if they are impaired in amblyopia. From the anatomical perspective, the use of diffusion tensor imaging tractography, a method used to image the neural axons of white matter, could shed insight into the anatomical changes in human amblyopia. Prior anatomical studies have shown reduced grey matter in LGN ( Barnes et al., 2010), occipital cortex (Mendola et al., 2006, Xiao et al, 2007) and other regions in frontal cortex (Xiao et al, 2007). The use of diffusion tensor imaging can provide unique insight into the changes along the M/dorsal pathway that occur in human amblyopia.   118          4. Conclusion The main objective of this thesis was to determine the functional cortical differences underlying the multiple-object tracking deficit in amblyopia. It was hypothesized that functional differences would be found in regions of the anterior IPS, and in MT. Area MT was found to be less active in participants with amblyopia relative to controls across all attention conditions. In anterior IPS, MR signal was significantly lower in the amblyopic eye relative to the fellow eye when tracking 4 balls.  This  suggests a problem with both the passive and the active motion systems. One interpretation of these findings is that functional differences in anterior IPS are small, or smaller than functional differences in MT in amblyopia. The second main objective of this thesis was to determine if the behavioural deficit correlated with the neural deficit. Consistent with previous studies, no significant correlations were found. 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The Journal of Neuroscience, 11, 641-649.  133          APPENDIX A  134          135          136          APPENDIX B Appendix B1    Sample time course of MR signal across the four trackign conditions        Prediction of the hemodynamic response.                    137             Appendix B2  Sample activation in one participant without amblyopia along the dorsal‐ventral axis                       138           Appendix B2 (continued)  Sample activation in one participant without amblyopia along the dorsal‐ventral axis                           139         Appendix B2 (continued)  Sample activation in one participant without amblyopia along the dorsal‐ventral axis                       140         Appendix B2 (continued)  Sample activation in one participant without amblyopia along the dorsal‐ventral axis                     141         Appendix B3  Sample activation with amblyopic eye viewing  along the dorsal‐ventral axis             142         Appendix B3 (Continued)  Sample activation with amblyopic eye viewing  along the dorsal‐ventral axis                   143         Appendix B3 (Continued)  Sample activation with amblyopic eye viewing  along the dorsal‐ventral axis                 144         Appendix B3 (Continued)  Sample activation with amblyopic eye viewing  along the dorsal‐ventral axis             145         Appendix B4 (Continued)  Sample activation with fellow eye viewing  along the dorsal‐ventral axis                       146         Appendix B4 (Continued)  Sample activation with fellow eye viewing  along the dorsal‐ventral axis                     147         Appendix B4 (Continued)  Sample activation with fellow eye viewing  along the dorsal‐ventral axis                   148         Appendix B4 (Continued)  Sample activation with fellow eye viewing  along the dorsal‐ventral axis             149     

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