Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI

Abstract

A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).

Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data

“…Therefore, future work exploring the performance of ComBat harmonization in other combinations of connectivity metrics (e.g. partial correlation) and atlases (anatomical atlas: Brodmann, 1909; Desikan et al, 2006; functional atlas: Yeo et al, 2011; Wig et al, 2014; Glasser et al, 2016; Schaefer et al, 2017) is warranted. Finally, in this project we focused on the ability of ComBat harmonization to preserve age-related associations with several network connectivity and efficiency measures.…”

Precision Functional Mapping of Individual Human Brains

“…A lower standard deviation indicates higher functional homogeneity within the network piece. As (in)homogeneity depends critically on the size of the region in which it is tested (Gordon et al, 2016), the standard deviations were averaged over all network pieces while accounting for the size of those pieces, following Schaefer et al (in press): $$\frac{{\sum }_{l=1}^{L}s{d}_{l}false|lfalse|}{{\sum }_{l=1}^{L}false|lfalse|}$$where sd l is the standard deviation of task activation z-values for network piece l and | l | is the number of vertices in network piece l . This inhomogeneity value was calculated separately for each task contrast and averaged across contrasts for each subject.…”

Delineating the Macroscale Areal Organization of the Macaque Cortex In Vivo

“…To further test this point, we compared the fit of activations obtained for each of the animals with their respective parcellations and those of the five atlases. The inhomogeneity was calculated to access the fitness by computing the weighted SD of z-values from the task activation for each parcel (Schaefer et al, 2017). A lower inhomogeneity of a parcellation indicates higher fitness of a given parcellation and activation.…”

“…The validity of each parcellation was evaluated by the homogeneity using the Kendall coefficient (Xu et al., 2016). Consistent with prior work in humans, we also measured the fitness of functionally derived parcellations using data obtained during a somatosensory task; this involves calculation of the weighted standard deviation of z -values from the task activation within each parcel (Schaefer et al, 2017). Detailed are provided in Supplement Experimental Procedures.…”

“…In particular, Cohen et al (2008) proposed a novel approach to perform areal parcellation in the human brain that measures gradients in intrinsic functional connectivity among neighboring vertices, which can, in turn, be used to identify transitions across distinct functional units. This approach has been extended to the full neocortex, with applications revealing functionally discrete areas at the group-average level that are homogeneous and reproducible across studies (Glasser et al, 2016; Gordon et al, 2016; Schaefer et al, 2017). Several of the borders have been highlighted for their correspondence to task activations and cytoarchitectonically defined areas (Buckner and Yeo, 2014; Gordon et al, 2017a; Wig et al, 2014), though such relations are yet to be fully validated at the individual level.…”