Brain connectomes in youth at risk for serious mental illness : an exploratory analysis
© 2022. The Author(s)..
BACKGROUND: Identifying early biomarkers of serious mental illness (SMI)-such as changes in brain structure and function-can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI.
METHODS: Participants were classified as 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) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data.
RESULTS: Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections.
CONCLUSIONS: Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
BMC psychiatry - 22(2022), 1 vom: 15. Sept., Seite 611 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Metzak, Paul D [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 19.09.2022 Date Revised 01.10.2022 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12888-022-04118-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM346264731 |
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| 100 | 1 | |a Metzak, Paul D |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Brain connectomes in youth at risk for serious mental illness |b an exploratory analysis |
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| 500 | |a Date Revised 01.10.2022 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022. The Author(s). | ||
| 520 | |a BACKGROUND: Identifying early biomarkers of serious mental illness (SMI)-such as changes in brain structure and function-can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI | ||
| 520 | |a METHODS: Participants were classified as 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) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data | ||
| 520 | |a RESULTS: Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections | ||
| 520 | |a CONCLUSIONS: Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Connectome | |
| 650 | 4 | |a Constrained spherical deconvolution | |
| 650 | 4 | |a Functional connectivity | |
| 650 | 4 | |a Linear support vector machine | |
| 650 | 4 | |a Major depressive disorder | |
| 650 | 4 | |a PROCAN | |
| 650 | 4 | |a Resting-state fMRI | |
| 650 | 4 | |a Structural connectivity | |
| 650 | 4 | |a Transdiagnostic risk | |
| 700 | 1 | |a Shakeel, Mohammed K |e verfasserin |4 aut | |
| 700 | 1 | |a Long, Xiangyu |e verfasserin |4 aut | |
| 700 | 1 | |a Lasby, Mike |e verfasserin |4 aut | |
| 700 | 1 | |a Souza, Roberto |e verfasserin |4 aut | |
| 700 | 1 | |a Bray, Signe |e verfasserin |4 aut | |
| 700 | 1 | |a Goldstein, Benjamin I |e verfasserin |4 aut | |
| 700 | 1 | |a MacQueen, Glenda |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, JianLi |e verfasserin |4 aut | |
| 700 | 1 | |a Kennedy, Sidney H |e verfasserin |4 aut | |
| 700 | 1 | |a Addington, Jean |e verfasserin |4 aut | |
| 700 | 1 | |a Lebel, Catherine |e verfasserin |4 aut | |
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