Brain connectomes in youth at risk for serious mental illness: a longitudinal perspective.

Autor: Shakeel MK; Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada. mohammed.kalathil@ucalgary.ca.; Department of Psychology, St.Mary's University, Calgary, AB, Canada. mohammed.kalathil@ucalgary.ca.; Mathison Centre, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada. mohammed.kalathil@ucalgary.ca., Metzak PD; Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada., Lasby M; Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada., Long X; Department of Radiology, University of Calgary, Calgary, AB, Canada.; Department of Radiology, Alberta Children's Hospital Research Institute, Calgary, AB, Canada.; Department of Radiology, Child and Adolescent Imaging Research Program, Calgary, AB, Canada., Souza R; Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada., Bray S; Department of Radiology, University of Calgary, Calgary, AB, Canada.; Department of Radiology, Alberta Children's Hospital Research Institute, Calgary, AB, Canada.; Department of Radiology, Child and Adolescent Imaging Research Program, Calgary, AB, Canada., Goldstein BI; Centre for Youth Bipolar Disorder, Center for Addiction and Mental Health, Toronto, ON, Canada.; Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.; Department of Pharmacology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada., MacQueen G; Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada., Wang J; Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Nova Scotia, Canada., Kennedy SH; Department of Psychiatry, University Health Network, Toronto, ON, Canada.; Department of Psychiatry, St. Michael's Hospital, Toronto, ON, Canada.; Arthur Sommer Rotenberg Chair in Suicide and Depression Studies, St. Michael's Hospital, Toronto, ON, Canada.; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.; Krembil Research Institute, University Health Network, Toronto, ON, Canada., Addington J; Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada., Lebel C; Department of Radiology, University of Calgary, Calgary, AB, Canada.; Department of Radiology, Alberta Children's Hospital Research Institute, Calgary, AB, Canada.; Department of Radiology, Child and Adolescent Imaging Research Program, Calgary, AB, Canada.
Jazyk: angličtina
Zdroj: Brain imaging and behavior [Brain Imaging Behav] 2024 Nov 08. Date of Electronic Publication: 2024 Nov 08.
DOI: 10.1007/s11682-024-00953-z
Abstrakt: Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one of 5 groups: healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9). Constrained spherical deconvolution was used to generate whole brain tractography maps, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional magnetic resonance imaging (fMRI) signal between pairs of brain regions. Linear mixed models revealed structural and functional abnormalities in global metrics of small world lambda, and resting state networks involving the fronto-parietal, default mode, and deep grey matter networks, along with the visual and dorsal attention networks. Machine learning analysis additionally identified changes in nodal metrics of betweenness centrality in the angular gyrus and bilateral temporal gyri as potential features which can discriminate between the groups. Our findings further support the view that abnormalities in large scale networks (particularly those involving fronto-parietal, temporal, default mode, and deep grey matter networks) may underlie transdiagnostic risk for SMIs.
(© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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