% bigmat = big structure holding all the main fmri data % BETAS and T-VALUES are coded as different raws, one per each vertex, of % each hemisphere, of each subject, in each of the four testing conditions % and each of our 5 spatial frequency stimuli. % all other variables are coded in the same format. for example, the % estimated pRF eccentricity is a single value per voxel (of each hemisphere and % subject) but it is repeated for each condition and the 5 stimuli % % struct with fields: % % betas: [9213060x1 double] -> GLM betas expressed in units of signal change % tvals: [9213060x1 double] -> GLM t-values % ind2vals: [9213060x1 double] -> which of the 5 SF stimuli the beta/t-vals refer to % ispost: [9213060x1 logical] -> whether the data refers to the acquisitions after MD (with stimuli in either eye) % isdepeye: [9213060x1 logical] -> whether the data refers to the acquisitions with stimuli in the deprived eye (before or after MD) % islefteye: [9213060x1 logical] -> redundant to the above (some subjects were left-patched) % indsubj: [9213060x1 double] -> which subject the data comes from (index into the subjects cell) % isrighthemi: [9213060x1 logical] -> which hemisphere the data comes from % SFfit_bw: [9213060x1 double] -> population SF tuning (high sf cut-off of the tuning curve, computed from bandwith through a linear transformation) % SFfit_gof: [9213060x1 double] -> goodness of fit of the SF tuning % eccfit_peak: [9213060x1 double] -> pRF eccentricity % eccfit_gof: [9213060x1 double] -> pRF goodness of fit % rois: [9213060x1 double] -> rois (index into the roinames cell, e.g. 1 -> V1) % voxind: [9213060x1 double] -> vertex identifier (unique for each hemisphere and subject) % bm2pred = same as bigmat but holding data for the spit-half reliability % test: ind2vals goes from 1 to 10, 1:5 is the first block of the five SF % stimuli; 6:10 is the second block of the same stimuli % roinames = labels (read into this using bigmat.rois which varies in [1:6] range) % % 6x1 cell array % % 'V1' % 'V2' % 'V3' % 'V4' % 'MT' % 'V3a' % subjects = % % 20x1 cell array % % 'S1' % 'S2' % 'S3' % 'S4' % 'S5' % 'S6' % 'S7' % 'S8' % 'S9' % 'S10' % 'S11' %% Excluded % 'S12' % 'S13' % 'S14' % 'S15' % 'S16' % 'S17' % 'S18' % 'S19' % 'S20' % avghdr = average hdr in the V1 roi % % struct with fields: % % me: [8x1 double] -> mean across our 19 participants % se: [8x1 double] -> s.e. of the mean % binriv = psychophyisical performance in the binocular rivalry task % (pre & post deprivation) % % struct with fields: % % ismale: [1x1 struct] % mpd: [1x1 struct] -> mean phase durations ** % prop: [1x1 struct] -> proportion dominance ** % ** (each eye, pre + post + deprivation index, computed as stated in the paper) % bw2cutoff = linear transformation between bandwidth and cut-off % % 1.2594 -0.0451 % stim = average Fourier spectra of the 5 SF stimuli % % struct with fields: % % freq: [189×2 double] % take mean across the second dimension to plot % pow: [189×5 double] % each column is one of the 5 SF stimuli, from low to high