Spontaneously emerging patterns in human visual cortex and their functional connectivity are linked to the patterns evoked by visual stimuli

Abstract

The function of spontaneous brain activity is an important issue in neuroscience. Here we test the hypothesis that patterns of spontaneous activity code representational patterns evoked by stimuli. We compared in human visual cortex multi-vertex patterns of spontaneous activity to patterns evoked by ecological visual stimuli (faces, bodies, scenes) and low-level visual features (e.g. phase-scrambled faces). Specifically, we identified regions that preferred particular stimulus categories during localizer scans (e.g. extra-striate body area for bodies), measured multi-vertex patterns for each category during event-related task scans, and then correlated over vertices these stimulus-evoked patterns to the pattern measured on each frame of resting-state scans. The mean correlation coefficient was essentially zero for all regions/stimulus categories, indicating that resting multi-vertex patterns were not biased toward particular stimulus-evoked patterns. However, the spread of correlation coefficients between stimulus-evoked and resting patterns, positive and negative, was significantly greater for the preferred stimulus category of an ROI. The relationship between spontaneous and stimulus-evoked multi-vertex patterns also governed the temporal correlation or functional connectivity of patterns of spontaneous activity between individual regions (pattern-based functional connectivity). Resting multi-vertex patterns related to an object category fluctuated preferentially between ROIs preferring the same category, and fluctuations of the pattern for a category (e.g. body) within its preferred ROIs were largely uncorrelated with fluctuations of the pattern for a disparate category (e.g. scene) within its preferred ROIs. These results support the proposal that spontaneous multi-vertex activity patterns are linked to stimulus-evoked patterns, consistent with a representational function for spontaneous activity.

A visual representation of the hand in the resting somatomotor regions of the human brain

“…This is interesting in light of the idea that spontaneous activity maintains statistical regularities (priors) to anticipate and even predict environmental demands 2 . This hypothesis has been tested using natural visual stimuli and common cognitive tasks 5,[19][20][21][22] . More specifically, during offline periods, the brain forms generic priors or low-dimensional representations, as categories or synergies, rather than individual instances or movements, that summarize the relative abundance of visual stimuli, objects, or motor patterns in the natural environment.…”

“…This procedure ultimately generated a distribution of correlation coefficients for each stimulus category, ROI, and subject. We used the approach of 5 who took the upper 90% (U90) of the distribution of correlation values to measure task-rest multi-voxel pattern similarity (Figure 2).…”

“…As expected, there were no differences in correlation in the non-hand-preferred early visual areas (Figure 4B) that extract low-to mid-level visual features controlled for in our stimuli 29,30 . For example, similarly, Kim and coworkers 5 show that the visual cortex codes intrinsic representations that are specific for the stimuli being coded in the area (for example, FFA for face stimuli). From birth, humans learn to use their hands in a more refined and precise fashion to interact with external objects.…”

“…Spontaneous activity has been hypothesized to reflect recapitulation of previous experiences or expectations of highly probable sensory events. More precisely, the ongoing activity could be related to the statistics of habitual cortical activations during real life, both in humans 5,21,31 and animals 32,33 . For example, 22 a higher similarity between motor and spontaneous patterns have been shown for natural hand sequences than for novel sequences.…”

“…This procedure ultimately generated a distribution of correlation coefficients for each stimulus category, ROI, and subject (Figure 2). As in 5 , we then took the upper 90% (U90) of each distribution to measure task/rest congruency. In the main analysis, we performed for each ROI an ANOVA, modeling REST and TASK (4 levels, according to the four stimulus categories).…”