Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up : Comparison of connectomic, structural, and clinical predictors
Journal article
Kottaram, Akhil, Johnston, Leigh A., Tian, Ye, Ganella, Eleni P., Laskaris, Liliana, Cocchi, Luca, McGorry, Patrick, Pantelis, Christos, Kotagiri, Ramamohanarao, Cropley, Vanessa and Zalesky, Andrew. (2020). Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up : Comparison of connectomic, structural, and clinical predictors. Human Brain Mapping. 41(12), pp. 3342-3357. https://doi.org/10.1002/hbm.25020
Authors | Kottaram, Akhil, Johnston, Leigh A., Tian, Ye, Ganella, Eleni P., Laskaris, Liliana, Cocchi, Luca, McGorry, Patrick, Pantelis, Christos, Kotagiri, Ramamohanarao, Cropley, Vanessa and Zalesky, Andrew |
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Abstract | In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates. |
Keywords | default mode network; dynamic functional connectivity; outcome prediction; schizophrenia; symptoms |
Year | 2020 |
Journal | Human Brain Mapping |
Journal citation | 41 (12), pp. 3342-3357 |
Publisher | John Wiley & Sons |
ISSN | 1065-9471 |
Digital Object Identifier (DOI) | https://doi.org/10.1002/hbm.25020 |
Scopus EID | 2-s2.0-85085601336 |
Open access | Published as ‘gold’ (paid) open access |
Research or scholarly | Research |
Page range | 3342-3357 |
Funder | National Health and Medical Research Council (NHMRC) |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 29 May 2020 |
Publication process dates | |
Accepted | 13 Apr 2020 |
Deposited | 12 Jul 2021 |
Grant ID | NHMRC/1065742 |
NHMRC/601253 | |
NHMRC/628880 | |
NHMRC/628386 | |
NHMRC/1105825 | |
NHMRC/1136649 | |
NHMRC/1099082 | |
NHMRC/1138711 |
https://acuresearchbank.acu.edu.au/item/8w5v3/predicting-individual-improvement-in-schizophrenia-symptom-severity-at-1-year-follow-up-comparison-of-connectomic-structural-and-clinical-predictors
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Publisher's version
OA_Kottaram_2020_Predicting_individual_improvement_in_schizophrenia_symptom.pdf | |
License: CC BY-NC-ND 4.0 | |
File access level: Open |
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