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O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS
BACKGROUND: Psychiatric symptoms in childhood and adolescence have been associated with both delayed and accelerated patterns of grey matter development. This suggests that deviation in brain structure from a normative range of variation for a given age might be important in the emergence of psychop...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233979/ http://dx.doi.org/10.1093/schbul/sbaa028.008 |
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author | Cropley, Vanessa Tian, Ye Fernando, Kavisha Mansour, Sina Pantelis, Christos Cocchi, Luca Zalesky, Andrew |
author_facet | Cropley, Vanessa Tian, Ye Fernando, Kavisha Mansour, Sina Pantelis, Christos Cocchi, Luca Zalesky, Andrew |
author_sort | Cropley, Vanessa |
collection | PubMed |
description | BACKGROUND: Psychiatric symptoms in childhood and adolescence have been associated with both delayed and accelerated patterns of grey matter development. This suggests that deviation in brain structure from a normative range of variation for a given age might be important in the emergence of psychopathology. Distinct from chronological age, brain age refers to the age of an individual that is inferred from a normative model of brain structure for individuals of the same age and sex. We predicted brain age from a common set of grey matter features and examined whether the difference between an individual’s chronological and brain age was associated with the severity of psychopathology in children and adolescents. METHODS: Participants included 1313 youths (49.8% male) aged 8–21 who underwent structural imaging as part of the Philadelphia Neurodevelopmental Cohort. Independent Component Analysis was used to obtain 7 psychopathology dimensions representing Conduct, Anxiety, Obsessive-Compulsive, Attention, Depression, Bipolar, and Psychosis symptoms and an overall measure of severity (General Psychopathology). Using 10-fold cross-validation, support vector machine regression was trained in 402 typically developing youth to predict individual age based on a feature space comprising 111 grey matter regions. This yielded a brain age prediction for each individual. Brain age gap was calculated for each individual by subtracting chronological age from predicted brain age. The general linear model was used to test for an association between brain age gap and each of the 8 dimensions of psychopathology in a test sample of 911 youth. The regional specificity and spatial pattern of brain age gap was also investigated. Error control across the 8 models was achieved with a false discovery rate of 5%. RESULTS: Brain age gap was significantly associated with dimensions characterizing obsessive-compulsive (t=2.5, p=0.01), psychosis (t=3.16, p=0.0016) and general psychopathology (t=4.08, p<0.0001). For all three dimensions, brain age gap was positively associated with symptom severity, indicating that individuals with a brain that was predicted to be ‘older’ than expectations set by youth of the same chronological age and sex tended to have higher symptom scores. Findings were confirmed with a categorical approach, whereby higher brain age gap was observed in youth with a lifetime endorsement of psychosis (t=2.35, p=0.02) and obsessive-compulsive (t=2.35, p=0.021) symptoms, in comparison to typically developing individuals. Supplementary analyses revealed that frontal grey matter was the most important feature mediating the association between brain age gap and psychosis symptoms, whereas subcortical volumes were most important for the association between brain age gap and obsessive-compulsive and general symptoms. DISCUSSION: We found that the brain was ‘older’ in youth experiencing higher subclinical symptoms of psychosis, obsession-compulsion, and general psychopathology, compared to normally developing youth of the same chronological age. Our results suggest that deviations in normative brain age patterns in youth may contribute to the manifestation of specific psychiatric symptoms of subclinical severity that cut across psychopathology dimensions. |
format | Online Article Text |
id | pubmed-7233979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-72339792020-05-23 O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS Cropley, Vanessa Tian, Ye Fernando, Kavisha Mansour, Sina Pantelis, Christos Cocchi, Luca Zalesky, Andrew Schizophr Bull Oral Session: Digital Health/Methods BACKGROUND: Psychiatric symptoms in childhood and adolescence have been associated with both delayed and accelerated patterns of grey matter development. This suggests that deviation in brain structure from a normative range of variation for a given age might be important in the emergence of psychopathology. Distinct from chronological age, brain age refers to the age of an individual that is inferred from a normative model of brain structure for individuals of the same age and sex. We predicted brain age from a common set of grey matter features and examined whether the difference between an individual’s chronological and brain age was associated with the severity of psychopathology in children and adolescents. METHODS: Participants included 1313 youths (49.8% male) aged 8–21 who underwent structural imaging as part of the Philadelphia Neurodevelopmental Cohort. Independent Component Analysis was used to obtain 7 psychopathology dimensions representing Conduct, Anxiety, Obsessive-Compulsive, Attention, Depression, Bipolar, and Psychosis symptoms and an overall measure of severity (General Psychopathology). Using 10-fold cross-validation, support vector machine regression was trained in 402 typically developing youth to predict individual age based on a feature space comprising 111 grey matter regions. This yielded a brain age prediction for each individual. Brain age gap was calculated for each individual by subtracting chronological age from predicted brain age. The general linear model was used to test for an association between brain age gap and each of the 8 dimensions of psychopathology in a test sample of 911 youth. The regional specificity and spatial pattern of brain age gap was also investigated. Error control across the 8 models was achieved with a false discovery rate of 5%. RESULTS: Brain age gap was significantly associated with dimensions characterizing obsessive-compulsive (t=2.5, p=0.01), psychosis (t=3.16, p=0.0016) and general psychopathology (t=4.08, p<0.0001). For all three dimensions, brain age gap was positively associated with symptom severity, indicating that individuals with a brain that was predicted to be ‘older’ than expectations set by youth of the same chronological age and sex tended to have higher symptom scores. Findings were confirmed with a categorical approach, whereby higher brain age gap was observed in youth with a lifetime endorsement of psychosis (t=2.35, p=0.02) and obsessive-compulsive (t=2.35, p=0.021) symptoms, in comparison to typically developing individuals. Supplementary analyses revealed that frontal grey matter was the most important feature mediating the association between brain age gap and psychosis symptoms, whereas subcortical volumes were most important for the association between brain age gap and obsessive-compulsive and general symptoms. DISCUSSION: We found that the brain was ‘older’ in youth experiencing higher subclinical symptoms of psychosis, obsession-compulsion, and general psychopathology, compared to normally developing youth of the same chronological age. Our results suggest that deviations in normative brain age patterns in youth may contribute to the manifestation of specific psychiatric symptoms of subclinical severity that cut across psychopathology dimensions. Oxford University Press 2020-05 2020-05-18 /pmc/articles/PMC7233979/ http://dx.doi.org/10.1093/schbul/sbaa028.008 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Oral Session: Digital Health/Methods Cropley, Vanessa Tian, Ye Fernando, Kavisha Mansour, Sina Pantelis, Christos Cocchi, Luca Zalesky, Andrew O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS |
title | O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS |
title_full | O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS |
title_fullStr | O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS |
title_full_unstemmed | O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS |
title_short | O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS |
title_sort | o2.3. abnormal brain aging in youth with subclinical psychosis and obsessive-compulsive symptoms |
topic | Oral Session: Digital Health/Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233979/ http://dx.doi.org/10.1093/schbul/sbaa028.008 |
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