Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture

Ji Chen; Veronika I Müller; Juergen Dukart; Felix Hoffstaedter; Justin T Baker; Avram J Holmes; Deniz Vatansever; Thomas Nickl-Jockschat; Xiaojin Liu; Birgit Derntl; Lydia Kogler; Renaud Jardri; Oliver Gruber; André Aleman; Iris E Sommer; Simon B Eickhoff; Kaustubh R Patil

doi:10.1016/j.biopsych.2020.09.024

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Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture

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Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture

Ji Chen, Veronika I Müller, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickl-Jockschat, Xiaojin Liu, Birgit Derntl, …

Biological psychiatry (1969), Vol.89(3), pp.308-319

02/01/2021

DOI: 10.1016/j.biopsych.2020.09.024

PMID: 33357631

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Abstract

Despite the marked interindividual variability in the clinical presentation of schizophrenia, the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients remains unclear. Here, we address this question using network-based predictive modeling of individual psychopathology along 4 data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. We investigated resting-state functional magnetic resonance imaging data from 147 patients with schizophrenia recruited at 7 sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using a relevance vector machine based on functional connectivity within 17 meta-analytic task networks following repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of 9 receptors/transporters from prior molecular imaging in healthy populations. Tenfold and leave-one-site-out analyses revealed 5 predictive network-symptom associations. Connectivity within theory of mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory of mind and socioaffective default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 receptor and serotonin reuptake transporter densities as well as dopamine synthesis capacity. We revealed a robust association between intrinsic functional connectivity within networks for socioaffective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

Machine Learning

Schizophrenia

Neurotransmitter receptor

Brain imaging

Meta-analytic network

Symptom dimension

Details

Title: Subtitle: Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture
Creators: Ji Chen - Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
Veronika I Müller - Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
Juergen Dukart - Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
Felix Hoffstaedter - Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
Justin T Baker - Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, Massachusetts
Avram J Holmes - Department of Psychology, Yale University, New Haven, Connecticut
Deniz Vatansever - Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
Thomas Nickl-Jockschat - Iowa Neuroscience Institute and Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa
Xiaojin Liu - Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
Birgit Derntl - Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Tübingen, Germany
Lydia Kogler - Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Tübingen, Germany
Renaud Jardri - Université de Lille, INSERM U1172, Lille Neuroscience & Cognition Centre, Plasticity & SubjectivitY team & CHU Lille, Fontan Hospital, CURE platform, Lille, France
Oliver Gruber - Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
André Aleman - Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
Iris E Sommer - Department of Biomedical Science of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
Simon B Eickhoff - Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
Kaustubh R Patil - Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
Resource Type: Journal article
Publication Details: Biological psychiatry (1969), Vol.89(3), pp.308-319
DOI: 10.1016/j.biopsych.2020.09.024
PMID: 33357631
NLM abbreviation: Biol Psychiatry
ISSN: 0006-3223
eISSN: 1873-2402
Publisher: Elsevier Inc
Grant note: DOI: 10.13039/501100001659, name: Deutsche Forschungsgemeinschaft, award: EI 816/4-1; DOI: 10.13039/100000025, name: National Institute of Mental Health, award: R01-MH074457
Language: English
Date published: 02/01/2021
Academic Unit: Psychiatry; Iowa Neuroscience Institute; Neuroscience and Pharmacology
Record Identifier: 9984070603102771

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