Mesoscale brain dynamics reorganizes and stabilizes during learning

Abstract

<jats:p>Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions. How cross-regional interactions reorganize during learning remains elusive. We applied multi-fiber photometry to chronically record simultaneous activity of 12-48 mouse brain regions while mice learned a tactile discrimination task. We found that with learning most regions shifted their peak activity from reward-related action to the reward-predicting stimulus. We corroborated this finding by functional connectivity estimation using transfer entropy, which revealed growth and stabilization of mesoscale networks encompassing basal ganglia, thalamus, cortex, and hippocampus, especially during stimulus presentation. The internal globus pallidus, ventromedial thalamus, and several regions in frontal cortex emerged as hub regions. Our results highlight the cooperative action of distributed brain regions to establish goal-oriented mesoscale network dynamics during learning.</jats:p>

Conservative Significance Testing of Tripartite Interactions in Multivariate Neural Data

“…Next, we discuss related research in Functional Connectivity (FC) [33] and Effective Connectivity (EC) [39], and highlight potential implications of our results on estimation of related metrics. Metrics of FC and EC aim to estimate a matrix of pairwise connections between variables (also known as functional connectome [29]), to test if individual connections are significant, to describe the connectivity matrix by means of integral metrics of network neuroscience [7], and to study changes in network connectivity associated for example with learning [6, 73, 74] or disease [14]. Redundancy is a well-known problem in this field as well.…”

“…As past and present of the target come from the same signal, the impurity fractions of both of these variables in real data are equal or almost equal, significantly reducing the possible magnitude of false positive unique information atoms. In another study [74], we validated the performance of transfer entropy in the presence of noise for simulated neuronal recordings. We found that the metric was able to correctly reject false positives within a range of low impurity fractions.…”

“…A Frequentist approach to address the problem is to introduce a strict additional criterion on the specificity of inferred connectivity (such as optimal information transfer [49]) and to iteratively prune connections according to such criterion. Comparison of pairwise and pruned connectivity matrices can be used to approximate the range of possible functional networks [74].…”

“…This effect is undesirable, as we ultimately want to know the importance and specificity of individual sources as predictors. Importantly, high redundancy is common in both single-neuron recordings [35] and in multi-regional population-average recordings [36,74], and thus needs to be accounted for.…”