Complementing several decades of research linking activation profiles to adaptive behavior, recent work demonstrates that topological features of functional brain networks can also predict task performance \cite{desposito2018} and the capacity for skill learning \cite{bassett2015learning,mattar2017network,schoenbaum2000changes}. These studies motivate the development of computational tools to directly compare constrained versus variable features of brain network structure and dynamics in a statistically principled fashion. One particularly promising candidate is intersubject correlation (ISC), which can be used to quantify the extent to which neuronal processes are shared or constrained across individuals \cite{hasson2004intersubject}. ISC estimates the similarity of brain activity across subjects by using the temporal signature of neuronal activity in a particular brain region as a predictor of the neuronal activity in the corresponding brain region of another individual. Thus, ISC is a model-free approach constructed without any a priori knowledge of the temporal composition of a task-evoked response \cite{hasson2004intersubject}, and it is thought to reflect the degree of inter-subject synchrony in cognitive states \cite{nummenmaa2012emotions,simony2016dynamic}. One can also calculate intersubject functional connectivity (ISFC), which measures the response of functional connectivity shared or constrained across subjects. The ISFC is estimated by calculating the Pearson correlation coefficient between a region's whole-brain functional connectivity in a single subject and the average whole-brain functional connectivity of that region across all remaining subjects. Generally, intersubject analyses such as ISC and ISFC quantify the extent to which a given functional measurement (brain activity or connectivity) in one subject statistically differs from the expected distribution of the measurement in other subjects and likely reflects constrained versus distinct neuronal processes.
Techniques to assess intersubject correspondence in brain activity and connectivity offer a powerful means to discriminate between brain regions that adhere to generalized constraints of functional architecture, and brain regions that break such constraints in order to contribute to subject-specific behaviors and their adaptation during learning. Here, we use the ISC and ISFC to assess the degree to wIhich each region's activity and connectivity are constrained across subjects during the course of learning, and we operationalize our study by testing three specific hypotheses. First, we hypothesized that subject-general constraints on activity and connectivity would be predominantly located in three general areas: (i) motor cortex, consistent with the shared demands of finger movements necessary to press buttons on the response box \cite{bassett2011dynamic,mattar2017network}, (ii) visual cortex, consistent with the shared demands of cognitive processing necessary to parse the visual stimuli of the novel objects, and (iii) other areas previously associated with the learning of value, including lateral occipital cortex \cite{persichetti2015value}. Second, we hypothesized that the coherence of stimulus-induced activity would place constraints on brain activity and connectivity during early learning, but that subject-specific activity and connectivity patterns would dominate later learning when greater neural real estate was available for contemporaneous, non-task-related processing as well as processing reflecting subject-specific learning strategies. Third and finally, we hypothesized that the extent to which a subject's brain activity or connectivity obeys subject-general constraints would be related to learning performance. Efforts to address these hypotheses could serve to more fundamentally elucidate dynamic constraints on task-dependent activity and functional connectivity during learning.