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

Abstract

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.

Differential patterns of age‐related cortical and subcortical functional connectivity in 6‐to‐10 year old children: A connectome‐wide association study

“…Functionally defined ROIs can be obtained using fMRI. Existing methods define regions to be either nonoverlapping (Blumensath et al., 2013; Shen, Tokoglu, Papademetris, & Constable, 2013; Yeo et al., 2011) or overlapping (Beckmann, 2012; van den Heuvel & Hulshoff Pol, 2010; Smith et al., 2012, 2013). For example, (Yeo et al., 2011) used functional MRI to define a cortical segmentation that maximized functional specialization within regions across subjects.…”

Altered brain connectivity in sagittal craniosynostosis

“…1). 35 Inverse transforms were used to bring ROIs back to individual space. Correlations between ROIs were run and transformed into z-scores with the Fisher transform to generate correlation matrices.…”

Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex

“…Spectral clustering is an alternative method that has been used for brain parcellation [25][26][50]. This method is based on a similarity matrix, each element of which measures the similarity of a pair of vertices.…”

“…The first class includes hard parcellations, in which each vertex belongs to exactly one parcel. The methods for this class are mainly originated from boundary detection [19][20][21][22] and various clustering techniques, such as K-means [23][24], spectral clustering [25][26], hierarchical clustering [27] or model-based clustering [28][29][30][31]. The second class includes soft parcellations, in which a vertex can belong to more than one parcel.…”