ArticleBrain and behavior2021
Co-activation pattern alterations in autism spectrum disorder-A volume-wise hierarchical clustering fMRI study.
Article in Brain and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Age-related differences in axon pruning and myelination may alter neural signaling in autism spectrum disorder.Molecular autism · 2025Article
- Energy landscape analysis based on the Ising model: Tutorial review.PLOS complex systems · 2025Article
- Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder.European child & adolescent psychiatry · 2024Article
- Differential connectivity of the posterior piriform cortex in Parkinson's disease and postviral olfactory dysfunction: an fMRI study.Scientific reports · 2024Article
- State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis.BMC neuroscience · 2024Article
- Striatum-related spontaneous coactivation patterns predict treatment response on positive symptoms of drug-naive first-episode schizophrenia with risperidone monotherapy.Frontiers in psychiatry · 2023Article
- Co-activation pattern alterations in autism spectrum disorder-A volume-wise hierarchical clustering fMRI study.Brain and behavior · 2021Article
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11 authors.
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Abstract
introductionThere has been a growing effort to characterize the time-varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel-wise connectivity studies have examined different sliding window lengths, nonsequential volume-wise approaches have been less common.
methodsInspired by earlier co-activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS-fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups' volumes in CAPs as well as their voxel-wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z-scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group-specific patterns.
resultsWe found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel-wise means differed significantly within a matrix of 14 RSNs. The rest-activated default mode-positive and default mode-negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode-negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation.
conclusionNonsequential volume gathering into CAPs and the comparison of voxel-wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis.
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