Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis.

IQ diffusion MRI individual-level neuroimaging score psychotic disorders structural MRI symptoms

Journal

Schizophrenia bulletin
ISSN: 1745-1701
Titre abrégé: Schizophr Bull
Pays: United States
ID NLM: 0236760

Informations de publication

Date de publication:
16 Oct 2023
Historique:
medline: 16 10 2023
pubmed: 16 10 2023
entrez: 16 10 2023
Statut: aheadofprint

Résumé

Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease. We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes. All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05). Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.

Sections du résumé

BACKGROUND AND HYPOTHESIS OBJECTIVE
Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease.
STUDY DESIGN METHODS
We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes.
STUDY RESULTS RESULTS
All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05).
CONCLUSIONS CONCLUSIONS
Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.

Identifiants

pubmed: 37844289
pii: 7319340
doi: 10.1093/schbul/sbad149
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIMH NIH HHS
ID : MH077945
Pays : United States

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Auteurs

Amanda L Rodrigue (AL)

Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA.
Department of Psychiatry, Harvard Medical School, Boston, MA, USA.

Rebecca A Hayes (RA)

Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA.

Emma Waite (E)

Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA.

Mary Corcoran (M)

Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA.

David C Glahn (DC)

Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA.
Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA.

Maria Jalbrzikowski (M)

Department of Psychiatry, Boston Children's Hospital, Boston, MA, USA.
Department of Psychiatry, Harvard Medical School, Boston, MA, USA.

Classifications MeSH