Regional variability of functional constraints during learning
Next, we asked: if the dynamics of whole-brain inter-subject correlations map unto different phases of value learning, which brain systems might be most complicit in these phases? To investigate this question, we first partitioned brain regions into objectively defined functional modules using the GenLouvain community detection algorithm \cite{de2011generalized} (see Materials and Methods and SI). Briefly, community detection is applied to the functional network constructed from data of each task session and parses brain regions into functional modules such that brain regions within the same module exhibit strong functional connections and brain regions between different modules have weak functional connections. In order to obtain a single representative partitioning of brain regions across subjects and scans, we computed the module allegiance matrix \cite{bassett2011dynamic} – capturing the probability that two regions belong to the same functional module (Fig. 3A). By applying a final round of community detection to the module allegiance matrix, we identified seven modules that were associated with different putative brain systems, including fronto-temporal (FT) which covered most of the limbic lobe, sensorimotor network (SM), auditory network (AUD) including hippocampus and amygdala, the common default mode network (DMN), Language (LAN) network, Visual (VIS) network and three subcortical regions - putamen, caudate and thalamus (PCT) (Fig. 3B; see Table 1 for list of regions in each community).