Exploring the influence of functional architecture on cortical thickness networks in early psychosis - A longitudinal study.

Autor: Holton KM; Computational Neural Information Engineering Lab, University of Delaware, 139 The Green, Newark, DE 19716, USA. Electronic address: kmholton@udel.edu., Chan SY; Psychosis Neurobiology Laboratory, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA., Brockmeier AJ; Computational Neural Information Engineering Lab, University of Delaware, 139 The Green, Newark, DE 19716, USA., Öngür D; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA., Hall MH; Psychosis Neurobiology Laboratory, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478, USA. Electronic address: mhall@mclean.harvard.edu.
Jazyk: angličtina
Zdroj: NeuroImage [Neuroimage] 2023 Jul 01; Vol. 274, pp. 120127. Date of Electronic Publication: 2023 Apr 21.
DOI: 10.1016/j.neuroimage.2023.120127
Abstrakt: Cortical thickness reductions differ between individuals with psychotic disorders and comparison subjects even in early stages of illness. Whether these reductions covary as expected by functional network membership or simply by spatial proximity has not been fully elucidated. Through orthonormal projective non-negative matrix factorization, cortical thickness measurements in functionally-annotated regions from MRI scans of early-stage psychosis and matched healthy controls were reduced in dimensionality into features capturing positive covariance. Rather than matching the functional networks, the covarying regions in each feature displayed a more localized spatial organization. With Bayesian belief networks, the covarying regions per feature were arranged into a network topology to visualize the dependency structure and identify key driving regions. The features demonstrated diagnosis-specific differences in cortical thickness distributions per feature, identifying reduction-vulnerable spatial regions. Differences in key cortical thickness features between psychosis and control groups were delineated, as well as those between affective and non-affective psychosis. Clustering of the participants, stratified by diagnosis and clinical variables, characterized the clinical traits that define the cortical thickness patterns. Longitudinal follow-up revealed that in select clusters with low baseline cortical thickness, clinical traits improved over time. Our study represents a novel effort to characterize brain structure in relation to functional networks in healthy and clinical populations and to map patterns of cortical thickness alterations among ESP patients onto clinical variables for a better understanding of brain pathophysiology.
Competing Interests: Declaration of Competing Interest D.O acknowledges receipt of an honorarium from Neumora Inc. for scientific presentation in 1/2022. The rest of the authors declare no competing interests.
(Copyright © 2023. Published by Elsevier Inc.)
Databáze: MEDLINE
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