Paakki, J-J, Rahko, JS, Kotila, A, et al. Co-activation pattern alterations in autism spectrum disorder–A volume-wise hierarchical clustering fMRI study. Brain Behav. 2021; 11:e02174. https://doi.org/10.1002/brb3.2174
Co-activation pattern alterations in autism spectrum disorder : a volume-wise hierarchical clustering fMRI study
|Author:||Paakki, Jyri-Johan1,2,3; Rahko, Jukka S.1,4,5; Kotila, Aija6;|
1Faculty of Medicine, Health and Biosciences Doctoral Programme, University of Oulu Graduate School, University of Oulu, Oulu, Finland
2The Faculty of Medicine, Research Unit of Medical Imaging, Physics and Technology, Oulu Functional NeuroImaging Group, University of Oulu, Oulu, Finland
3Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
4PEDEGO Research Unit, Faculty of Medicine, Child Psychiatry, University of Oulu, Oulu, Finland
5Institute of Clinical Medicine, Clinic of Child Psychiatry, University and University Hospital of Oulu, Oulu, Finland
6Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
7Research Unit of Clinical Neuroscience, Psychiatry, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021071241346
John Wiley & Sons,
|Publish Date:|| 2021-07-12
Introduction: There 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.
Methods: Inspired 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.
Results: We 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.
Conclusion: Nonsequential 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.
Brain and behavior
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3124 Neurology and psychiatry
This study received financial support from the Alma and K.A. Snellman Foundation (Oulu, Finland), Finnish Medical Foundation, Juhani Aho Foundation for Medical Research (Espoo, Finland), University of Oulu, and Radiological Society of Finland.
© 2021 The Authors. Brain and Behavior published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.