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
AuthorsKottaram, Akhil, Johnston, Leigh A., Tian, Ye, Ganella, Eleni P., Laskaris, Liliana, Cocchi, Luca, McGorry, Patrick, Pantelis, Christos, Kotagiri, Ramamohanarao, Cropley, Vanessa and Zalesky, Andrew
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.

Keywordsdefault mode network; dynamic functional connectivity; outcome prediction; schizophrenia; symptoms
Year2020
JournalHuman Brain Mapping
Journal citation41 (12), pp. 3342-3357
PublisherJohn Wiley & Sons
ISSN1065-9471
Digital Object Identifier (DOI)https://doi.org/10.1002/hbm.25020
Scopus EID2-s2.0-85085601336
Open accessPublished as ‘gold’ (paid) open access
Research or scholarlyResearch
Page range3342-3357
FunderNational Health and Medical Research Council (NHMRC)
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online29 May 2020
Publication process dates
Accepted13 Apr 2020
Deposited12 Jul 2021
Grant IDNHMRC/1065742
NHMRC/601253
NHMRC/628880
NHMRC/628386
NHMRC/1105825
NHMRC/1136649
NHMRC/1099082
NHMRC/1138711
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