Parsing Brain Network Specialization: A Replication and Expansion of Wang et al. (2014)

One organizing principle of the human brain is hemispheric specialization, or the dominance of a specific function or cognitive process in one hemisphere or the other. Previously, Wang et al. (2014) identified networks putatively associated with language and attention as being specialized to the left and right hemispheres, respectively; and a dual-specialization of the executive control network. However, it remains unknown which networks are specialized when specialization is examined within individuals using a higher resolution parcellation, as well as which connections are contributing the most to a given networks specialization. In the present study, we estimated network specialization across three datasets using the autonomy index and a novel method of deconstructing network specialization. After examining the reliability of these methods as implemented on an individual level, we addressed two hypotheses. First, we hypothesized that the most specialized networks would include those associated with language, visuospatial attention, and executive control. Second, we hypothesized that within-network contributions to specialization would follow a within-between network gradient or a specialization gradient. We found that the majority of networks exhibited greater within-hemisphere connectivity than between-hemisphere connectivity. Among the most specialized networks were networks associated with language, attention, and executive control. Additionally, we found that the greatest network contributions were within-network, followed by those from specialized networks. Significance StatementHemispheric specialization is a characteristic of brain organization that describes when a function draws on one hemisphere of the brain more than the other. We sought to identify the most specialized brain networks within individuals, as well as which connections contribute the most to a given networks specialization. Among the most specialized networks were those associated with language, attention, and executive control. Unexpectedly, we also identified networks associated with emotion/memory and theory of mind as highly specialized. Additionally, we found support for guiding principles of brain organization generally, such that within-network connections contributed most to a given networks specialization followed by connections from other specialized networks. These results have implications for identifying potential variations of network contributions in individuals with neurodevelopmental conditions.