Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study
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 com...
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Elsevier
2024-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221315822400127X |
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| author | Naici Liu Rebekka Lencer Christina Andreou Mihai Avram Heinz Handels Wenjing Zhang Sun Hui Chengmin Yang Stefan Borgwardt John A. Sweeney Su Lui Alexandra I. Korda |
| author_facet | Naici Liu Rebekka Lencer Christina Andreou Mihai Avram Heinz Handels Wenjing Zhang Sun Hui Chengmin Yang Stefan Borgwardt John A. Sweeney Su Lui Alexandra I. Korda |
| author_sort | Naici Liu |
| collection | DOAJ |
| description | 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: 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: 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: 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. |
| format | Article |
| id | doaj-art-e9bad84753974c8ea6488b428fb2c88c |
| institution | DOAJ |
| issn | 2213-1582 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj-art-e9bad84753974c8ea6488b428fb2c88c2025-08-20T02:49:00ZengElsevierNeuroImage: Clinical2213-15822024-01-014410368610.1016/j.nicl.2024.103686Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise studyNaici Liu0Rebekka Lencer1Christina Andreou2Mihai Avram3Heinz Handels4Wenjing Zhang5Sun Hui6Chengmin Yang7Stefan Borgwardt8John A. Sweeney9Su Lui10Alexandra I. Korda11Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, ChinaDepartment 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, GermanyDepartment of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, GermanyDepartment of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, GermanyInstitute of Medical Informatics, University of Lübeck, Lübeck, Germany; German Research Center for Artificial Intelligence, Lübeck, GermanyDepartment of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, ChinaDepartment of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, ChinaDepartment of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, ChinaDepartment of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, GermanyPsychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, USADepartment of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Corresponding authors at: Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany (Alexandra I. Korda). Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China (Su Lui).Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany; Corresponding authors at: Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany (Alexandra I. Korda). Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China (Su Lui).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: 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: 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: 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.http://www.sciencedirect.com/science/article/pii/S221315822400127XSchizophreniaNon-linear dynamic modelCortical topologyLargest Lyapunov exponentBrain complexity |
| spellingShingle | Naici Liu Rebekka Lencer Christina Andreou Mihai Avram Heinz Handels Wenjing Zhang Sun Hui Chengmin Yang Stefan Borgwardt John A. Sweeney Su Lui Alexandra I. Korda Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study NeuroImage: Clinical Schizophrenia Non-linear dynamic model Cortical topology Largest Lyapunov exponent Brain complexity |
| title | Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study |
| title_full | Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study |
| title_fullStr | Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study |
| title_full_unstemmed | Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study |
| title_short | Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study |
| title_sort | altered brain complexity in first episode antipsychotic naive patients with schizophrenia a whole brain voxel wise study |
| topic | Schizophrenia Non-linear dynamic model Cortical topology Largest Lyapunov exponent Brain complexity |
| url | http://www.sciencedirect.com/science/article/pii/S221315822400127X |
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