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FMRI Adaptation to moving stimuli Spatiotopic, Retinotopic Or object based?. Maryam Vaziri Pashkam. Spatiotopic representation. Images in the retina change by eye movement Our percept of the world remains static We have to have a spatiotopic representation. Psychopysical evidence.
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FMRI Adaptation to moving stimuliSpatiotopic, Retinotopic Or object based? Maryam Vaziri Pashkam
Spatiotopic representation • Images in the retina change by eye movement • Our percept of the world remains static • We have to have a spatiotopic representation
Psychopysical evidence • Afraz et al: Remote motion after effect is stronger in spatiotopic location
Psychophysical evidence • Afraz et al: Remote motion after effect is stronger in spatiotopic location • Melcher and Morrone: Motion Integration Across eye movement is bigger when two locations are spatial (and not retinal) coordinates of each other.
fMRI evidence • Morrone: Motion Integration in MT cortex across eye movement is bigger when two locations are spatial (and not retinal) coordinates of each other.
Questions: • Where in the visual system this change from spasiotopic representation to retinotopic representation happens? • Does MT cortex have access to the spatiotopic map? • Are the same neurons involved in the perception of motion across eye movements? • FMRI ADAPTATION is a way to look at these questions.
fMRI adaptation • Repeated presentation of a stimulus acuses reduction in the BOLD. • Allows dissociation of different functional propertied within vixel (Grill-Spector & Malach 2001) • It can also be observed in rapid event related design (Grill spector et al 2006) • fMRI adaptation to motion direction is observed in MT cortex.(Huettel 2004) • This is a good tool to look at spatiotopy in MT
Experiment One • Number f subjects: 10 observers with normal vision participate in the experiment and will be trained before the scan with the paradigm • 3-T scanner with built in surface coil • Functional scan: T2 weighted TR=2 sec TE= 30 ms resolution= 3*3*4 mm 28 axial slices covering whole brain • Anatomical Scans: T1 image TR= 2 sec TE= 3 ms resolution=1*1*1 mm • Visual displays will be projected on a screen via an apple ibook G4 using Matlab and psychtoolbox
Experiment One • Two 8 minutes anatomical scans in each subject • MT localizer in each subject:200sec. (10 s moving pattern 10 s stationary pattern,10 times each) • Retinotopic mapping: same as localizer except for the fact that for eccentricity mapping we will show annuluses of moving patterns and for the polar mapping we will show wedges of moving pattern (4 wedges+4 annuluses+2fixation*5 each*10sec= 2blocks of 250 sec)
Experiment One • 3*2 conditions*2 direction*2 hemispheres =16 + 2 fixation conditions = 18 ( random ) • Each trial 2 seconds * !0 --> total time = 6 min each block • Each subject completes 10 functional scans
Experiment One • Data Analysis: SPM • ROI analysis on MT • Random effect analysis on all subjects • Compute the time series with respect to baseline in all conditions • Compute the average percent signal change in each condition in all subjects.
One Step Forward! • How brain manages to differentiate between a motion in the image due to eye movement and a motion due to real motion? • One possibility: When eyes move the whole scene moves but there is no relative movement of the objects with respect to each other. This is not the case for real motion. • Is there any Object-Based Adaptation? (Afraz et al)
Experiment Two • Fast FMRI adaptation, event related design. • Baseline rectangles that move in the same way as in the actual experiment. No moving stimulus. • See if fMRI adaptation is more pronounced in object based condition than in non-object based condition.
Predicted Outcomes • First Experiment: More spatiotopic than non-spatiotopic adaptation • Second Experiment: More object based than non-object based adaptation.
Costs: • Approximately 1:30 hour for each subject---> 8500$ • Conference and publication cost: 3400$ • Total cost: 11900$
Time: • Approximately 1:30 hour for each subject---> 15 hour • Programming--->10 hours • Data collection--->20 hour • Data analysis--->200 hour • Total:245 hour