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

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.

The modular and integrative functional architecture of the human brain

“…Thus, we built five different networks-four with our data using four different atlases to define the nodes in the network [which we refer to as the Shen (55) (16,56). In each network, two nodes are connected by a weighted edge, with the weight being the Fisher-transformed Pearson correlation value (z) between the time series of activity in the two nodes, if z survives cost thresholding, where a cost of 0.15 retains the strongest 15% of possible edges and their edge weights (i.e., z values) in the network.…”

Imaging human brain networks to improve the clinical efficacy of non-invasive brain stimulation

“…Activity in the seed region is highly correlated with activity in the green region, whereas there is little correlated activity between the seed region and the region in red. C) Whole brain analysis of the resting-state BOLD signal reveals cortical areas with highly correlated patterns of activity (functional networks) (Panel C from Gordon et al, 2014; with permission). Each circle within a network represents a node, and the lines between them are the edges.…”

Striatal subdivisions that coherently interact with multiple cerebrocortical networks

“…The cerebrocortical parcels were calculated in a similar manner, as described previously (Cohen et al, 2008; Gordon et al, 2016; Laumann et al, 2015; Supporting Information Figure S1). Each vertex in the fiducial surface in the cerebral cortex of each participant was used as a seed to calculate its correlations with all of the vertices.…”

“…The similarity of the spatial patterns of the correlation maps was then evaluated using correlation coefficients, and similarity maps were generated. After spatial smoothing (FWHM = 6.0 mm) (Gordon et al, 2016; Laumann et al, 2015), spatial gradients of the similarity maps were computed for each seed vertex. A two‐dimensional watershed algorithm was applied to the gradient maps, and the binary watershed maps were averaged across the seed vertices after spatial smoothing (FWHM = 6.0 mm) to generate a boundary probability map.…”