Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations

TLDR

The human cortical surface contains many areas that have not been fully mapped, but abrupt transitions in resting‑state functional connectivity can noninvasively identify putative area borders. The authors propose a technique that uses RSFC‑boundary maps to delineate parcels representing putative cortical areas. The method generates parcels by applying RSFC‑boundary maps to locate abrupt connectivity transitions and defining contiguous regions. The resulting parcels exhibit highly homogeneous RSFC patterns, outperform null models and alternative parcellations, overlap known architectonic areas, mirror established brain network structures, and show reliable connectivity across subjects, indicating that RSFC‑boundary map‑derived parcels reveal cortical area location and extent. A parcellation produced by this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.

Abstract

The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.