Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study.
Bipolar
Brain age
DTI
Machine learning
Psychosis
Schizophrenia
Journal
Biological psychiatry. Cognitive neuroscience and neuroimaging
ISSN: 2451-9030
Titre abrégé: Biol Psychiatry Cogn Neurosci Neuroimaging
Pays: United States
ID NLM: 101671285
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
06
01
2020
revised:
15
06
2020
accepted:
26
06
2020
pubmed:
30
8
2020
medline:
28
4
2021
entrez:
30
8
2020
Statut:
ppublish
Résumé
Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics. Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
Sections du résumé
BACKGROUND
Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts.
METHODS
We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.
RESULTS
Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics.
CONCLUSIONS
Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
Identifiants
pubmed: 32859549
pii: S2451-9022(20)30168-3
doi: 10.1016/j.bpsc.2020.06.014
pii:
doi:
Types de publication
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1095-1103Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/H008217/1
Pays : United Kingdom
Organisme : Medical Research Council
Pays : United Kingdom
Organisme : NIMH NIH HHS
ID : P50 MH071616
Pays : United States
Organisme : NIMH NIH HHS
ID : RL1 MH083268
Pays : United States
Organisme : NIMH NIH HHS
ID : RL1 MH083269
Pays : United States
Organisme : NIDA NIH HHS
ID : RL1 DA024853
Pays : United States
Organisme : NIMH NIH HHS
ID : RL1 MH083270
Pays : United States
Organisme : NLM NIH HHS
ID : RL1 LM009833
Pays : United States
Organisme : NIMH NIH HHS
ID : PL1 MH083271
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH056584
Pays : United States
Investigateurs
L Farde
(L)
L Flyckt
(L)
G Engberg
(G)
S Erhardt
(S)
H Fatouros-Bergman
(H)
S Cervenka
(S)
L Schwieler
(L)
F Piehl
(F)
I Agartz
(I)
K Collste
(K)
P Victorsson
(P)
A Malmqvist
(A)
M Hedberg
(M)
F Orhan
(F)
C Sellgren
(C)
Informations de copyright
Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.