Impaired topology and connectivity of grey matter structural networks in major depressive disorder : evidence from a multi-site neuroimaging data-set
BACKGROUND: Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD.
AIMS: Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes.
METHOD: A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings.
RESULTS: Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms.
CONCLUSIONS: Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:224 |
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| Enthalten in: |
The British journal of psychiatry : the journal of mental science - 224(2024), 5 vom: 10. Apr., Seite 170-178 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Long, Jing-Yi [VerfasserIn] |
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| Links: |
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| Themen: |
Brain network |
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| Anmerkungen: |
Date Completed 23.04.2024 Date Revised 23.04.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1192/bjp.2024.41 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370914775 |
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| 245 | 1 | 0 | |a Impaired topology and connectivity of grey matter structural networks in major depressive disorder |b evidence from a multi-site neuroimaging data-set |
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| 500 | |a Date Revised 23.04.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD | ||
| 520 | |a AIMS: Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes | ||
| 520 | |a METHOD: A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings | ||
| 520 | |a RESULTS: Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms | ||
| 520 | |a CONCLUSIONS: Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Magnetic resonance imaging | |
| 650 | 4 | |a brain network | |
| 650 | 4 | |a depression | |
| 650 | 4 | |a graph theory | |
| 650 | 4 | |a machine learning | |
| 700 | 1 | |a Qin, Kun |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Nanfang |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Wen-Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yi |e verfasserin |4 aut | |
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