Dynamic thalamus parcellation from resting‐state fMRI data

Abstract

The thalamus is a relay center between various subcortical brain areas and the cerebral cortex with delineation of its constituent nuclei being of particular interest in many applications. While previous studies have demonstrated efficacy of connectivity-based thalamus segmentation, they used approaches that do not consider the dynamic nature of thalamo-cortical interactions. In this study, we explicitly exploited the dynamic variation of thalamo-cortical connections to identify different states of functional connectivity and performed state-specific thalamus parcellation. With normalized spectral clustering successively applied in temporal and spatial domains, nine thalamo-cortical connectivity states were identified and the dynamic thalamus parcellation revealed finer thalamic structures with improved atlas correspondence. The present results extend our understanding of thalamo-cortical connectivity and provide a more comprehensive view of the thalamo-cortical interaction.

Delineating functional segregations of the human middle temporal gyrus with resting‐state functional connectivity and coactivation patterns

“…Thus, we cannot exclude the potential effects of the different smoothing kernels on the results. Finally, our parcellation results were based on RSFC, a new parcellation technique is needed to verify our results, such as dynamic functional connectivity‐based on resting‐state fMRI (Ji et al, ), which might be helpful to further uncover the functional segregations of the MTG at a different level.…”

Fine-scale patterns driving dynamic functional connectivity provide meaningful brain parcellations

“…Avoiding the need of a-priori choosing regions of interest, these parcellations have the advantage of being whole brain, completely data-driven and taking into account the dynamic nature of FC. So far, only few previous reports consider dFC to determine a parcellation [26,27], but no previous work has performed the dFC analysis in the whole brain at the voxel level. It should be emphasized that the parcellation is obtained from temporal clustering of the dFC dominant patterns, and thus no spatial constraint (e.g., voxel neighborhood) was taken in account.…”

“…The obtained parcels do not always precisely resemble the known thalamic anatomy, suggesting that the dynamic behavior of the thalamus can lead to finer subdivisions or aggregate known anatomically distinct regions. Only one previous study attempted a parcellation of the thalamus based on dFC [27], by clustering (spatially and temporally) patterns of connectivity between thalamic voxels and five main cortical regions. In that case, however, the segmentation was dependent on the a-priori chosen regions, therefore a comparison is not straightforward.…”

Functional atlas of the awake rat brain: A neuroimaging study of rat brain specialization and integration

“…This connectivity-derived parcellation is based on the premise that each functionally specialized brain region is characterized by a distinct connectivity profile. For instance, using resting-state functional magnetic resonance imaging (rsfMRI) technology (Biswal et al, 1995; Biswal et al, 2010; Fox and Raichle, 2007), researchers have obtained fine-grained functional parcellations of various brain structures such as the thalamus (Fan et al, 2015; Ji et al, 2016), striatum (Choi et al, 2012; Jung et al, 2014), numerous cortical regions (Cauda et al, 2010; Goulas et al, 2012; Kahnt et al, 2012; Kim et al, 2010; Long et al, 2014; Nelson et al, 2010; Zhang and Li, 2012), and even parcellations of the whole brain (Blumensath et al, 2013; Craddock et al, 2012; Gordon et al, 2016; Shen et al, 2013; Wig et al, 2014; Yeo et al, 2011). Without the presence of overt tasks, rsfMRI measures resting-state functional connectivity (RSFC) between different brain regions based on temporal correlations of spontaneously fluctuating blood oxygenation level-dependent (BOLD) signals (Biswal et al, 2010; Fox and Raichle, 2007).…”