Linked dimensions of psychopathology and connectivity in functional brain networks

Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical co...

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Published inNature communications Vol. 9; no. 1; pp. 3003 - 14
Main Authors Xia, Cedric Huchuan, Ma, Zongming, Ciric, Rastko, Gu, Shi, Betzel, Richard F., Kaczkurkin, Antonia N., Calkins, Monica E., Cook, Philip A., García de la Garza, Angel, Vandekar, Simon N., Cui, Zaixu, Moore, Tyler M., Roalf, David R., Ruparel, Kosha, Wolf, Daniel H., Davatzikos, Christos, Gur, Ruben C., Gur, Raquel E., Shinohara, Russell T., Bassett, Danielle S., Satterthwaite, Theodore D.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 01.08.2018
Nature Publishing Group
Nature Portfolio
Subjects
59/36
59/57
631/114/116
631/114/116/1925
692/699/476
Abnormalities
Adolescent
Adult
Biomarkers
Brain
Brain - physiology
Child
Cohort Studies
Correlation analysis
Diagnostic systems
Fear
Female
Gender aspects
Humanities and Social Sciences
Humans
Male
Mental disorders
Mood
Morbidity
multidisciplinary
Multivariate Analysis
Nerve Net - physiology
Neural networks
Psychiatry
Psychopathology
Psychosis
Reproducibility of Results
Science
Science (multidisciplinary)
Sex Characteristics
Sex differences
Young Adult
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Abstract Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated ( r  = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset ( n  = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry. Co-morbidity and symptom overlap make it difficult to associate psychiatric disorders with unique neural signatures. Here, the authors use a data-driven approach to show that the symptom dimensions of mood, psychosis, fear and externalizing behavior exhibit unique patterns of functional dysconnectivity.
AbstractList Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated ( r  = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset ( n  = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry. Co-morbidity and symptom overlap make it difficult to associate psychiatric disorders with unique neural signatures. Here, the authors use a data-driven approach to show that the symptom dimensions of mood, psychosis, fear and externalizing behavior exhibit unique patterns of functional dysconnectivity.
Abstract Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated ( r  = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset ( n  = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.
Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated (r = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset (n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.
Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology - mood, psychosis, fear, and externalizing behavior - are associated (r = 0.68-0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset (n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.
Co-morbidity and symptom overlap make it difficult to associate psychiatric disorders with unique neural signatures. Here, the authors use a data-driven approach to show that the symptom dimensions of mood, psychosis, fear and externalizing behavior exhibit unique patterns of functional dysconnectivity.
ArticleNumber 3003
Author Wolf, Daniel H.
Moore, Tyler M.
Bassett, Danielle S.
Calkins, Monica E.
Ciric, Rastko
García de la Garza, Angel
Betzel, Richard F.
Kaczkurkin, Antonia N.
Ruparel, Kosha
Roalf, David R.
Gu, Shi
Vandekar, Simon N.
Satterthwaite, Theodore D.
Davatzikos, Christos
Shinohara, Russell T.
Gur, Raquel E.
Gur, Ruben C.
Cook, Philip A.
Xia, Cedric Huchuan
Cui, Zaixu
Ma, Zongming
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  organization: Department of Statistics, The Wharton School, University of Pennsylvania
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  organization: Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
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  surname: Gu
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  organization: Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Department of Computer Science and Engineering, University of Electronic Science and Technology
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  givenname: Danielle S.
  orcidid: 0000-0002-6183-4493
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/30068943$$D View this record in MEDLINE/PubMed
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SSID ssj0000391844
Score 2.6927097
Snippet Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional...
Abstract Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests...
Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional...
Co-morbidity and symptom overlap make it difficult to associate psychiatric disorders with unique neural signatures. Here, the authors use a data-driven...
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Publisher
StartPage 3003
SubjectTerms 59/36
59/57
631/114/116
631/114/116/1925
692/699/476
Abnormalities
Adolescent
Adult
Biomarkers
Brain
Brain - physiology
Child
Cohort Studies
Correlation analysis
Diagnostic systems
Fear
Female
Gender aspects
Humanities and Social Sciences
Humans
Male
Mental disorders
Mood
Morbidity
multidisciplinary
Multivariate Analysis
Nerve Net - physiology
Neural networks
Psychiatry
Psychopathology
Psychosis
Reproducibility of Results
Science
Science (multidisciplinary)
Sex Characteristics
Sex differences
Young Adult
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Title Linked dimensions of psychopathology and connectivity in functional brain networks
URI https://link.springer.com/article/10.1038/s41467-018-05317-y
https://www.ncbi.nlm.nih.gov/pubmed/30068943
https://www.proquest.com/docview/2081521126
https://search.proquest.com/docview/2082089641
https://pubmed.ncbi.nlm.nih.gov/PMC6070480
https://doaj.org/article/3f26b7217d3b4ddb99ccbd289a6fae95
Volume 9
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