Large-Scale Brain Network Dynamics Provide a Measure of Psychosis and Anxiety in 22q11.2 Deletion Syndrome.


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:
10 2019
Historique:
received: 11 03 2019
accepted: 06 04 2019
pubmed: 7 6 2019
medline: 12 6 2020
entrez: 8 6 2019
Statut: ppublish

Résumé

Prodromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers. We retrieved and examined dynamic properties of large-scale functional brain networks using innovation-driven coactivation patterns. The study included resting-state functional magnetic resonance scans from 78 patients with 22q11DS and 85 healthy control subjects. After group comparison of temporal brain network activation properties, functional signatures of prodromal psychotic symptoms and anxiety were extracted using multivariate partial least squares correlation. Patients with 22q11DS had shorter activation in cognitive brain networks, longer activation in emotion processing networks, and generally increased segregation between brain networks. The functional signature of prodromal psychotic symptoms confirmed an implication of cingulo-prefrontal salience network activation duration and coupling. Further, the functional signature of anxiety uncovered an implication of amygdala activation and coupling, indicating differential roles of dorsal and ventral subdivisions of the anterior cingulate and medial prefrontal cortices. Coupling of amygdala with the dorsal anterior cingulate and medial prefrontal cortices was promoting anxiety, whereas coupling with the ventral anterior cingulate and medial prefrontal cortices had a protective function. Using innovation-driven coactivation patterns for dynamic large-scale brain network analysis, we uncovered patterns of brain network activation duration and coupling that are relevant in clinical risk factors for psychosis in 22q11DS. Our results confirm that the dynamic nature of brain network activation contains essential function to develop clinically relevant imaging markers of psychosis vulnerability.

Sections du résumé

BACKGROUND
Prodromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers.
METHODS
We retrieved and examined dynamic properties of large-scale functional brain networks using innovation-driven coactivation patterns. The study included resting-state functional magnetic resonance scans from 78 patients with 22q11DS and 85 healthy control subjects. After group comparison of temporal brain network activation properties, functional signatures of prodromal psychotic symptoms and anxiety were extracted using multivariate partial least squares correlation.
RESULTS
Patients with 22q11DS had shorter activation in cognitive brain networks, longer activation in emotion processing networks, and generally increased segregation between brain networks. The functional signature of prodromal psychotic symptoms confirmed an implication of cingulo-prefrontal salience network activation duration and coupling. Further, the functional signature of anxiety uncovered an implication of amygdala activation and coupling, indicating differential roles of dorsal and ventral subdivisions of the anterior cingulate and medial prefrontal cortices. Coupling of amygdala with the dorsal anterior cingulate and medial prefrontal cortices was promoting anxiety, whereas coupling with the ventral anterior cingulate and medial prefrontal cortices had a protective function.
CONCLUSIONS
Using innovation-driven coactivation patterns for dynamic large-scale brain network analysis, we uncovered patterns of brain network activation duration and coupling that are relevant in clinical risk factors for psychosis in 22q11DS. Our results confirm that the dynamic nature of brain network activation contains essential function to develop clinically relevant imaging markers of psychosis vulnerability.

Identifiants

pubmed: 31171499
pii: S2451-9022(19)30100-4
doi: 10.1016/j.bpsc.2019.04.004
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

881-892

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Auteurs

Daniela Zöller (D)

Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland. Electronic address: daniela.zoller@epfl.ch.

Corrado Sandini (C)

Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland.

Fikret Işik Karahanoğlu (FI)

Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts.

Maria Carmela Padula (MC)

Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland; Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.

Marie Schaer (M)

Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland.

Stephan Eliez (S)

Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland.

Dimitri Van De Ville (D)

Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.

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