Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study

Background: Findings from neurodevelopmental studies indicate that adolescents with psychosis spectrum disorders have delayed neurocognitive performance relative to the maturational state of their healthy peers. Using machine learning, we generated a model of neurocognitive age in healthy adults and...

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Main Authors: Lana Kambeitz-Ilankovic, Shalaila S. Haas, Eva Meisenzahl, Dominic B. Dwyer, Johanna Weiske, Henning Peters, Hans-Jürgen Möller, Peter Falkai, Nikolaos Koutsouleris
Format: Article
Language:English
Published: Elsevier 2019-01-01
Series:NeuroImage: Clinical
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158218303723
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spelling doaj-d9f0a54f23304d13899fd77cfeec6a102020-11-25T01:06:49ZengElsevierNeuroImage: Clinical2213-15822019-01-0121Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition studyLana Kambeitz-Ilankovic0Shalaila S. Haas1Eva Meisenzahl2Dominic B. Dwyer3Johanna Weiske4Henning Peters5Hans-Jürgen Möller6Peter Falkai7Nikolaos Koutsouleris8Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; Corresponding author.Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, GermanyDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität Düsseldorf, GermanyDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, GermanyDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, GermanyDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, GermanyDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, GermanyDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, GermanyDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, GermanyBackground: Findings from neurodevelopmental studies indicate that adolescents with psychosis spectrum disorders have delayed neurocognitive performance relative to the maturational state of their healthy peers. Using machine learning, we generated a model of neurocognitive age in healthy adults and investigated whether individuals in clinical high risk (CHR) for psychosis showed systematic neurocognitive age deviations that were accompanied by specific structural brain alterations. Methods: First, a Support Vector Regression-based age prediction model was trained and cross-validated on the neurocognitive data of 36 healthy controls (HC). This produced Cognitive Age Gap Estimates (CogAGE) that measured each participant's deviation from the normal cognitive maturation as the difference between estimated neurocognitive and chronological age. Second, we employed voxel-based morphometry to explore the neuroanatomical gray and white matter correlates of CogAGE in HC, in CHR individuals with early (CHR-E) and late (CHR-L) high risk states. Results: The age prediction model estimated age in HC subjects with a mean absolute error of ±2.2 years (SD = 3.3; R2 = 0.33, P < .001). Mean (SD) CogAGE measured +4.3 (8.1) years in CHR individuals compared to HC (−0.1 (5.5) years, P = .006). CHR-L individuals differed significantly from HC subjects while this was not the case for the CHR-E group. CogAGE was associated with a distributed bilateral pattern of increased GM volume in the temporal and frontal areas and diffuse pattern of WM reductions. Conclusion: Although the generalizability of our findings might be limited due to the relatively small number of participants, CHR individuals exhibit a disturbed neurocognitive development as compared to healthy peers, which may be independent of conversion to psychosis and paralleled by an altered structural maturation process.http://www.sciencedirect.com/science/article/pii/S2213158218303723
collection DOAJ
language English
format Article
sources DOAJ
author Lana Kambeitz-Ilankovic
Shalaila S. Haas
Eva Meisenzahl
Dominic B. Dwyer
Johanna Weiske
Henning Peters
Hans-Jürgen Möller
Peter Falkai
Nikolaos Koutsouleris
spellingShingle Lana Kambeitz-Ilankovic
Shalaila S. Haas
Eva Meisenzahl
Dominic B. Dwyer
Johanna Weiske
Henning Peters
Hans-Jürgen Möller
Peter Falkai
Nikolaos Koutsouleris
Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study
NeuroImage: Clinical
author_facet Lana Kambeitz-Ilankovic
Shalaila S. Haas
Eva Meisenzahl
Dominic B. Dwyer
Johanna Weiske
Henning Peters
Hans-Jürgen Möller
Peter Falkai
Nikolaos Koutsouleris
author_sort Lana Kambeitz-Ilankovic
title Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study
title_short Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study
title_full Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study
title_fullStr Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study
title_full_unstemmed Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study
title_sort neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: a pattern recognition study
publisher Elsevier
series NeuroImage: Clinical
issn 2213-1582
publishDate 2019-01-01
description Background: Findings from neurodevelopmental studies indicate that adolescents with psychosis spectrum disorders have delayed neurocognitive performance relative to the maturational state of their healthy peers. Using machine learning, we generated a model of neurocognitive age in healthy adults and investigated whether individuals in clinical high risk (CHR) for psychosis showed systematic neurocognitive age deviations that were accompanied by specific structural brain alterations. Methods: First, a Support Vector Regression-based age prediction model was trained and cross-validated on the neurocognitive data of 36 healthy controls (HC). This produced Cognitive Age Gap Estimates (CogAGE) that measured each participant's deviation from the normal cognitive maturation as the difference between estimated neurocognitive and chronological age. Second, we employed voxel-based morphometry to explore the neuroanatomical gray and white matter correlates of CogAGE in HC, in CHR individuals with early (CHR-E) and late (CHR-L) high risk states. Results: The age prediction model estimated age in HC subjects with a mean absolute error of ±2.2 years (SD = 3.3; R2 = 0.33, P < .001). Mean (SD) CogAGE measured +4.3 (8.1) years in CHR individuals compared to HC (−0.1 (5.5) years, P = .006). CHR-L individuals differed significantly from HC subjects while this was not the case for the CHR-E group. CogAGE was associated with a distributed bilateral pattern of increased GM volume in the temporal and frontal areas and diffuse pattern of WM reductions. Conclusion: Although the generalizability of our findings might be limited due to the relatively small number of participants, CHR individuals exhibit a disturbed neurocognitive development as compared to healthy peers, which may be independent of conversion to psychosis and paralleled by an altered structural maturation process.
url http://www.sciencedirect.com/science/article/pii/S2213158218303723
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