Abstract
BackgroundPrevious studies using hierarchical clustering approach to analyze resting-state fMRI data were limited to a few slices or regions-of-interest (ROIs) after substantial data reduction.PurposeTo develop a framework that can perform voxel-wise hierarchical clustering of whole-brain resting-state fMRI data from a group of subjects.Materials and MethodsResting-state fMRI measurements were conducted for 86 adult subjects using a single-shot echo-planar imaging (EPI) technique. After pre-processing and co-registration to a standard template, pair-wise cross-correlation coefficients (CC) were calculated for all voxels inside the brain and translated into absolute Pearson's distances after imposing a threshold CC≥0.3. The group averages of the Pearson's distances were then used to perform hierarchical clustering with the developed framework, which entails gray matter masking and an iterative scheme to analyze the dendrogram.ResultsWith the hierarchical clustering framework, we identified most of the functional connectivity networks reported previously in the literature, such as the motor, sensory, visual, memory, and the default-mode functional networks (DMN). Furthermore, the DMN and visual system were split into their corresponding hierarchical sub-networks.ConclusionIt is feasible to use the proposed hierarchical clustering scheme for voxel-wise analysis of whole-brain resting-state fMRI data. The hierarchical clustering result not only confirmed generally the finding in functional connectivity networks identified previously using other data processing techniques, such as ICA, but also revealed directly the hierarchical structure within the functional connectivity networks.
Highlights
Taking advantage of the rapidly expanding computational power in the past decade, several studies showed the feasibility to analyze whole-brain resting-state fMRI data with clustering algorithms
With the hierarchical clustering framework, we identified most of the functional connectivity networks reported previously in the literature, such as the motor, sensory, visual, memory, and the default-mode functional networks (DMN)
The DMN and visual system were split into their corresponding hierarchical sub-networks
Summary
Taking advantage of the rapidly expanding computational power in the past decade, several studies showed the feasibility to analyze whole-brain resting-state fMRI data with clustering algorithms. Benjaminsson et al used a dimensional scaling and vector quantization clustering technique to analyze resting-state fMRI data [1]. Previous studies on hierarchical clustering of resting-state fMRI data have been limited to a few slices or region-of-interests (ROIs) after substantial data reduction. Cordes et al used a hierarchical clustering algorithm and analyzed 4 slices of resting-state fMRI data [7]. Previous studies using hierarchical clustering approach to analyze resting-state fMRI data were limited to a few slices or regions-of-interest (ROIs) after substantial data reduction. Purpose: To develop a framework that can perform voxel-wise hierarchical clustering of whole-brain resting-state fMRI data from a group of subjects
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