Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

Abstract Cognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional respon...

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Autores principales:	Lana Kambeitz-Ilankovic, Sophia Vinogradov, Julian Wenzel, Melissa Fisher, Shalaila S. Haas, Linda Betz, Nora Penzel, Srikantan Nagarajan, Nikolaos Koutsouleris, Karuna Subramaniam
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Psychiatry RC435-571
Acceso en línea:	https://doaj.org/article/8f308fa8606143dba33239c10eda993e
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spelling	oai:doaj.org-article:8f308fa8606143dba33239c10eda993e2021-12-02T16:45:40ZMultivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions10.1038/s41537-021-00165-02334-265Xhttps://doaj.org/article/8f308fa8606143dba33239c10eda993e2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41537-021-00165-0https://doaj.org/toc/2334-265XAbstract Cognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.Lana Kambeitz-IlankovicSophia VinogradovJulian WenzelMelissa FisherShalaila S. HaasLinda BetzNora PenzelSrikantan NagarajanNikolaos KoutsoulerisKaruna SubramaniamNature PortfolioarticlePsychiatryRC435-571ENnpj Schizophrenia, Vol 7, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Psychiatry RC435-571
spellingShingle	Psychiatry RC435-571 Lana Kambeitz-Ilankovic Sophia Vinogradov Julian Wenzel Melissa Fisher Shalaila S. Haas Linda Betz Nora Penzel Srikantan Nagarajan Nikolaos Koutsouleris Karuna Subramaniam Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions
description	Abstract Cognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.
format	article
author	Lana Kambeitz-Ilankovic Sophia Vinogradov Julian Wenzel Melissa Fisher Shalaila S. Haas Linda Betz Nora Penzel Srikantan Nagarajan Nikolaos Koutsouleris Karuna Subramaniam
author_facet	Lana Kambeitz-Ilankovic Sophia Vinogradov Julian Wenzel Melissa Fisher Shalaila S. Haas Linda Betz Nora Penzel Srikantan Nagarajan Nikolaos Koutsouleris Karuna Subramaniam
author_sort	Lana Kambeitz-Ilankovic
title	Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions
title_short	Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions
title_full	Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions
title_fullStr	Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions
title_full_unstemmed	Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions
title_sort	multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8f308fa8606143dba33239c10eda993e
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Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

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