Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex

Abstract

Regions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into \"DN\" and \"non-DN\" subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.\n\nHighlightsO_LIThe topography of Default Network cortical regions varies across individuals.\nC_LIO_LIA community detection algorithm, spectral partitioning, was applied to rs-fMRI data.\nC_LIO_LIThe algorithm identified individualized Default Network regions in mPFC and PCC.\nC_LIO_LIDefault Network topography varied across individuals in mPFC, moreso than in PCC.\nC_LIO_LIOverlap of task effects with DN regions should be assessed at the individual level.\nC_LI