Groupwise whole-brain parcellation from resting-state fMRI data for network node identification

By X. Shen, Fuyuze Tokoglu, X. Papademetris, R. Todd Constable. NeuroImage, 2013

1,018 citations recorded by the source corpus.

In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImageSuite and other analysis packages.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/e1ec5e1d2999b7375038fbcafcae3cd045e68407

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Citations in the corpus

Using connectome-based predictive modeling to predict individual behavior from brain connectivity (2017, methodology): “Alternatively, networks can be estimated directly from the connectivity matrices using methods described in Finn et al. 4 and Shen et al. 11 .”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, methodology): “Regions were delineated according to a 268-node whole-brain grey matter atlas (Shen et al. 2013).”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, methodology): “As illustrated in Figure 5, parcellations generated by the gwMRF model have comparable visuotopic alignment with parcellations from fully automatic approaches (Craddock et al. 2012; Shen et al. 2013; Gordon et al. 2016), but worse alignment than the semi-automatic multimodal approach (Glasser et al.”
Robust prediction of individual creative ability from brain functional connectivity (2018, methodology): “Whole-brain functional networks were constructed for each participant by extracting and correlating the task-related blood oxygen level-dependent (BOLD) signal from 268 brain regions (21).”
Graph Neural Networks in Network Neuroscience (2021, methodology): “matrices were generated using CONN toolbox or groupwise whole-brain parcellation approaches [21], [22].”
The modular and integrative functional architecture of the human brain (2015, methodology): “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), Power (16), Gordon (56), and Craddock (57) networks], and one with the publicly available data and a fifth brain atlas, which we refer to as ”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “It might be particularly important for subsequent network analysis where network nodes are typically represented by the average signal (e.g. BOLD timeseries) within each parcel (Shen et al., 2013; Gordon et al., 2016b).”