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

By Alexander Schaefer, Ru Kong, Evan M. Gordon, Timothy O. Laumann, Xi-Nian Zuo, Avram J. Holmes, Simon B. Eickhoff, B. T. T. Yeo, B. T. T. Yeo. bioRxiv, 2017

3,664 citations recorded by the source corpus.

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 one of two 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 four previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured sub-areal (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. Multi-resolution parcellations generated from 1489 participants are available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal)

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c3f634afff84fc0dc1b82d049eabf4d7c236d67e

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Citations in the corpus

Comparing spatial null models for brain maps (2021, neutral): “…referred to throughout the text as the “Schaefer atlas, ” has ten resolutions ranging from 100 to 1000 parcels in steps of 100 parcels (i.e., 100, 200, 300, and so on), and was generated via a gradient-weighted Markov Random Field model from resting state fMRI data ( Schaefer et al., 2018 ).”
CAT: a computational anatomy toolbox for the analysis of structural MRI data (2024, positive): “Ther efor e, CAT is ideall y suited to process not only small datasets (as demonstrated in the example application) but also big datasets, such as samples of the UK Biobank [ 61 ] or ENIGMA [ 62 ].”
BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets (2019, methodology): “Speci ﬁ cally, we sub-divided the conte69 surface into 400, 300, 200, and 100 parcels based on both a clustering of a well-established anatomical atlas 41 , as well as a recently published local-global functional clustering 42 and built FC gradients from these representations (Fig.”
Precision functional mapping of individual human brains (2017, positive): “…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. (2017): PL l =1sdljl jPL l = 1jl j where sdl is the standard deviation of task activation z-values for network piece l and jl j is…”
Mapping neurotransmitter systems to the structural and functional organization of the human neocortex (2021, methodology): “Due to the low spatial resolution of PET tracer binding, we opted to present our main results using a coarse resolution of 100 cortical regions 12 .”
Estimating trial-wise modulation of functional connectivity using event-related fMRI (2026, unknown): “These studies have provided insights into the organization of large-scale functional brain networks (Ladwig et al., 2026; Schaefer et al., 2018), and its alterations across neurological and psychiatric disorders (Greicius et al., 2004; Yan et al., 2019).”
Frontoparietal Hubs Leverage Probabilistic Representations and Integrated Uncertainty to Guide Cognitive Flexibility (2025, unknown): “2A) and probabilistic color belief (C i ) are integrated into a 2x2 joint distribution, which was used to infer the probabilistic task belief (T i ).”
Strengthening of alpha synchronization is a neural correlate of cognitive transfer (2024, methodology): “The reconstructions were parcellated into the 200-parcel Schaefer atlas with seven networks (Schaefer et al. 2018).”