title: True associations between resting fMRI time series based on innovations.
abstract: We calculated voxel-by-voxel pairwise crosscorrelations between prewhitened resting-state BOLD fMRI time series recorded from 60 cortical areas (30 per hemisphere) in 18 human subjects (nine women and nine men). Altogether, more than a billion-and-a-quarter pairs of BOLD time series were analyzed. For each pair, a crosscorrelogram was computed by calculating 21 crosscorrelations, namely at zero lag ± 10 lags of 2 s duration each. For each crosscorrelogram, in turn, the crosscorrelation with the highest absolute value was found and its sign, value, and lag were retained for further analysis. In addition, the crosscorrelations at zero lag (irrespective of the location of the peak) were also analyzed as a special case. Based on known varying density of anatomical connectivity, we distinguished four general brain groups for which we derived summary statistics of crosscorrelations between voxels within an area (group I), between voxels of paired homotopic areas across the two hemispheres (group II), between voxels of an area and all other voxels in the same (ipsilateral) hemisphere (group III), and voxels of an area and all voxels in the opposite (contralateral) hemisphere (except those in the homotopic area) (group IV). We found the following. (a) Most of the crosscorrelogram peaks occurred at zero lag, followed by ± 1 lag; (b) over all groups, positive crosscorrelations were much more frequent than negative ones; (c) average crosscorrelation was highest for group I, and decreased progressively for groups II-IV; (d) the ratio of positive over negative crosscorrelations was highest for group I and progressively smaller for groups II-IV; (e) the highest proportion of positive crosscorrelations (with respect to all positive ones) was observed at zero lag; and (f) the highest proportion of negative crosscorrelations (with respect to all negative ones) was observed at lag = 2. These findings reveal a systematic pattern of crosscorrelations with respect to their sign, magnitude, lag and brain group, as defined above. Given that these groups were defined along a qualitative gradient of known overall anatomical connectivity, our results suggest that functional interactions between two voxels may simply reflect the density of such anatomical connectivity between the areas to which the voxels belong.
authors.affiliation: Brain Sciences Center, Veterans Affairs Health Care System 11B, Minneapolis, MN 55417, USA.
authors.forename: P
authors.initials: P
authors.lastname: Christova
authors.forename: S M
authors.initials: SM
authors.lastname: Lewis
authors.forename: T A
authors.initials: TA
authors.lastname: Jerde
authors.forename: J K
authors.initials: JK
authors.lastname: Lynch
authors.forename: A P
authors.initials: AP
authors.lastname: Georgopoulos
mesh_terms: D000328:Adult; D000465:Algorithms; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000502:physiology*; D003627:Data Interpretation, Statistical; D005260:Female; D005403:Fixation, Ocular; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D008959:Models, Neurological; D010100:Oxygen / Q000097:blood; D011597:Psychomotor Performance / Q000502:physiology*; D012044:Regression Analysis; D012146:Rest / Q000502:physiology*; D055815:Young Adult
chemical_list: D010100:Oxygen