Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study.

Brain complexity Cortical topology Largest Lyapunov exponent Non-linear dynamic model Schizophrenia

Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
10 Oct 2024
Historique:
received: 04 06 2024
revised: 07 10 2024
accepted: 07 10 2024
medline: 16 10 2024
pubmed: 16 10 2024
entrez: 15 10 2024
Statut: aheadofprint

Résumé

Measures of cortical topology are believed to characterize large-scale cortical networks. Previous studies used region of interest (ROI)-based approaches with predefined templates that limit analyses to linear pair-wise interactions between regions. As cortical topology is inherently complex, a non-linear dynamic model that measures the brain complexity at the voxel level is suggested to characterize topological complexities of brain regions and cortical folding. T1-weighted brain images of 150 first-episode antipsychotic-naïve schizophrenia (FES) patients and 161 healthy comparison participants (HC) were examined. The Chaos analysis approach was applied to detect alterations in brain structural complexity using the largest Lyapunov exponent (Lambda) as the key measure. Then, the Lambda spatial series was mapped in the frequency domain using the correlation of the Morlet wavelet to reflect cortical folding complexity. A widespread voxel-wise decrease in Lambda values in space and frequency domains was observed in FES, especially in frontal, parietal, temporal, limbic, basal ganglia, thalamic, and cerebellar regions. The widespread decrease indicates a general loss of brain topological complexity and cortical folding. An additional pattern of increased Lambda values in certain regions highlights the redistribution of complexity measures in schizophrenia at an early stage with potential progression as the illness advances. Strong correlations were found between the duration of untreated psychosis and Lambda values related to the cerebellum, temporal, and occipital gyri. Our findings support the notion that defining brain complexity by non-linear dynamic analyses offers a novel approach for identifying structural brain alterations related to the early stages of schizophrenia.

Sections du résumé

BACKGROUND BACKGROUND
Measures of cortical topology are believed to characterize large-scale cortical networks. Previous studies used region of interest (ROI)-based approaches with predefined templates that limit analyses to linear pair-wise interactions between regions. As cortical topology is inherently complex, a non-linear dynamic model that measures the brain complexity at the voxel level is suggested to characterize topological complexities of brain regions and cortical folding.
METHODS METHODS
T1-weighted brain images of 150 first-episode antipsychotic-naïve schizophrenia (FES) patients and 161 healthy comparison participants (HC) were examined. The Chaos analysis approach was applied to detect alterations in brain structural complexity using the largest Lyapunov exponent (Lambda) as the key measure. Then, the Lambda spatial series was mapped in the frequency domain using the correlation of the Morlet wavelet to reflect cortical folding complexity.
RESULTS RESULTS
A widespread voxel-wise decrease in Lambda values in space and frequency domains was observed in FES, especially in frontal, parietal, temporal, limbic, basal ganglia, thalamic, and cerebellar regions. The widespread decrease indicates a general loss of brain topological complexity and cortical folding. An additional pattern of increased Lambda values in certain regions highlights the redistribution of complexity measures in schizophrenia at an early stage with potential progression as the illness advances. Strong correlations were found between the duration of untreated psychosis and Lambda values related to the cerebellum, temporal, and occipital gyri.
CONCLUSIONS CONCLUSIONS
Our findings support the notion that defining brain complexity by non-linear dynamic analyses offers a novel approach for identifying structural brain alterations related to the early stages of schizophrenia.

Identifiants

DOI: 10.1016/j.nicl.2024.103686 PMID: 39406039
pubmed: 39406039
pii: S2213-1582(24)00127-X
doi: 10.1016/j.nicl.2024.103686
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103686

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Naici Liu (N)

Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.

Rebekka Lencer (R)

Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany; Institute for Translational Psychiatry and Otto-Creutzfeldt Center for Behavioral and Cognitive Neuroscience, University of Münster, Münster, Germany.

Christina Andreou (C)

Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany.

Mihai Avram (M)

Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany.

Heinz Handels (H)

Institute of Medical Informatics, University of Lübeck, Lübeck, Germany; German Research Center for Artificial Intelligence, Lübeck, Germany.

Wenjing Zhang (W)

Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.

Sun Hui (S)

Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.

Chengmin Yang (C)

Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.

Stefan Borgwardt (S)

Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany.

John A Sweeney (JA)

Psychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, USA.

Su Lui (S)

Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China. Electronic address: lusuwcums@tom.com.

Alexandra I Korda (AI)

Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany. Electronic address: alexandra.korda@uni-luebeck.de.

Classifications MeSH