ANOCVA in R: A Software to Compare Clusters between Groups and Its Application to the Study of Autism Spectrum Disorder

Abstract

Understanding how brain activities cluster can help in the diagnosis of neuropsychological disorders. Thus, it is important to be able to identify alterations in the clustering structure of functional brain networks. Here, we provide an R implementation of Analysis of Cluster Variability (ANOCVA), which statistically tests (1) whether a set of brain regions of interest (ROI) are equally clustered between two or more populations and (2) whether the contribution of each ROI to the differences in clustering is significant. To illustrate the usefulness of our method and software, we apply the R package in a large functional magnetic resonance imaging (fMRI) dataset composed of 896 individuals (529 controls and 285 diagnosed with ASD—autism spectrum disorder) collected by the ABIDE (The Autism Brain Imaging Data Exchange) Consortium. Our analysis show that the clustering structure of controls and ASD subjects are different (p < 0.001) and that specific brain regions distributed in the frontotemporal, sensorimotor, visual, cerebellar, and brainstem systems significantly contributed (p < 0.05) to this differential clustering. These findings suggest an atypical organization of domain-specific function brain modules in ASD.

Evidence for Brainstem Contributions to Autism Spectrum Disorders

“…These findings suggest that the brainstem may be modulating both higher- and lower-order cognitive functions. Supporting these theoretical considerations are recent findings from the ABIDE (Autism Brain Imaging Data Exchange) dataset showing that the altered brainstem activity may be associated with the altered cortical connectivity in ASD ( Vidal et al, 2017 ). These functional connectivity results are in line with tractography data demonstrating atypical white matter connectivity between the brainstem and both cortical (left postcentral gyrus) and subcortical (left caudate) regions in ASD ( Zhang et al, 2017 ).…”

Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI

“…Prior to K-means clustering, the number of clusters k was chosen according to the Silhouette Criterion (Rousseeuw, 1987; Vidal et al, 2017) with the highest silhouette index value between −1 and 1. The given criterion was based on how close each voxel in one cluster are from voxels in other clusters.…”

Process-Specific Alliances (PSAs) in Cognitive Neuroscience

“…As with individual brain regions, it is possible to characterize the functions of PSAs at different levels of abstraction. For example, one can describe a PSA between PFC and visual cortex (VisCtx) as responsible for mediating top-down visual attention during one task (Figure 1C-left), but this PSA could be split into a dorsal PSA for spatial top-down attention during some tasks and a ventral PSA for object top-down attention during other tasks (Figure 1C-right) [8]. Both levels of description are valid, and the optimal level of description should depend on the particular question(s) being investigated by the researcher.…”

“…PSAs are identified using functional neuroimaging (process-related, success-related activity and fCon) and other methods (e.g., lesions, electrophysiology) combined with theories about regional functions (e.g., modeling) and task component processes (cognitive theory). For example, the PFCVisCtx PSA is based on evidence that task-related fCon between these regions is associated with successful top-down visual attention, on neuropsychological data that PFC damage impairs control processes, and on electrophysiology findings showing VisCtx neuronal activity is modulated by attention, all combined with cognitive attention models [8, 68, 69]. In contrast, a cTRN just is the set of functional connections between brain regions in the network during a cognitive task or condition (e.g., “episodic retrieval network”).…”