Spatially constrained hierarchical parcellation of the brain with resting-state fMRI

Abstract

We propose a novel computational strategy to partition the cerebral cortex into disjoint, spatially contiguous and functionally homogeneous parcels. The approach exploits spatial dependency in the fluctuations observed with functional Magnetic Resonance Imaging (fMRI) during rest. Single subject parcellations are derived in a two stage procedure in which a set of (~1000 to 5000) stable seeds is grown into an initial detailed parcellation. This parcellation is then further clustered using a hierarchical approach that enforces spatial contiguity of the parcels. A major challenge is the objective evaluation and comparison of different parcellation strategies; here, we use a range of different measures. Our single subject approach allows a subject-specific parcellation of the cortex, which shows high scan-to-scan reproducibility and whose borders delineate clear changes in functional connectivity. Another important measure, on which our approach performs well, is the overlap of parcels with task fMRI derived clusters. Connectivity-derived parcellation borders are less well matched to borders derived from cortical myelination and from cytoarchitectonic atlases, but this may reflect inherent differences in the data.

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.…”

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

“…Most of the recent open‐source fMRI datasets such as Human Connectome Project, ADHD‐200 or ABIDE, already support the functional parcellation of the data (Craddock, James, Holtzheimer, Hu, & Mayberg, 2012; Glasser et al., 2016; Rosenberg et al., 2016). Multiple functional parcellations are available in the field of fMRI (Bellec et al., 2006; Bellec, Rosa‐Neto, Lyttelton, Benali, & Evans, 2010; Blumensath et al., 2013; Chen et al., 2013; Craddock et al., 2012; Eickhoff et al., 2011; Flandin et al., 2002; Glasser et al., 2016; Golland, Golland, & Malach, 2007; Janssen, Jylänki, Kessels, & van Gerven, 2015; Janssen, Jylänki, & van Gerven, 2016; Kahnt, Chang, Park, Heinzle, & Haynes, 2012; Lashkari et al., 2012; Lashkari, Vul, Kanwisher, & Golland, 2010; Michel et al., 2012; Orban et al., 2014; Thirion et al., 2006; Tucholka et al., 2008; van den Heuvel, Mandl, & Pol, 2008; Yeo et al., 2011), and the issue of optimal functional parcellation is broadly discussed in the field (Stanley et al., 2013). In particular, in cognitive paradigms, the ROIs can be built in a data‐driven way and on the basis of the patterns of activation only (task localizers, Fedorenko, Hsieh, Nieto‐Castañón, Whitfield‐Gabrieli, & Kanwisher, 2010; Heinzle, Wenzel, & Haynes, 2012).…”

Genetics of the Connectome and the ENIGMA Project

“…Clearly, the brain can be divided into regions in many different ways, such as spectral clustering (Craddock et al 2012), hierarchical clustering (Blumensath et al 2013), or even genetic clustering (Chen et al 2012). Each one leads to a different definition of brain connectivity between the resulting regions.…”