Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder

Functional magnetic resonance imaging (fMRI) has become one of the most widely used noninvasive neuroimaging technique in research of cognitive neurosciences and of neural mechanisms of neuropsychiatric/neurological diseases. A primary goal of fMRI-based neuroimaging studies is to identify biomarker...

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Autores principales:	Tianye Zhai, Hong Gu, Yihong Yang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	prediction modeling fMRI treatment outcome Cox regression functional connectivity neuromodulation implications Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/eeeb633359644db2ba2cbbcc347121c6
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id	oai:doaj.org-article:eeeb633359644db2ba2cbbcc347121c6
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spelling	oai:doaj.org-article:eeeb633359644db2ba2cbbcc347121c62021-11-11T10:25:54ZCox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder1662-453X10.3389/fnins.2021.768602https://doaj.org/article/eeeb633359644db2ba2cbbcc347121c62021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.768602/fullhttps://doaj.org/toc/1662-453XFunctional magnetic resonance imaging (fMRI) has become one of the most widely used noninvasive neuroimaging technique in research of cognitive neurosciences and of neural mechanisms of neuropsychiatric/neurological diseases. A primary goal of fMRI-based neuroimaging studies is to identify biomarkers for brain-behavior relationship and ultimately perform individualized treatment outcome prognosis. However, the concern of inadequate validation and the nature of small sample sizes are associated with fMRI-based neuroimaging studies, both of which hinder the translation from scientific findings to clinical practice. Therefore, the current paper presents a modeling approach to predict time-dependent prognosis with fMRI-based brain metrics and follow-up data. This prediction modeling is a combination of seed-based functional connectivity and voxel-wise Cox regression analysis with built-in nested cross-validation, which has been demonstrated to be able to provide robust and unbiased model performance estimates. Demonstrated with a cohort of treatment-seeking cocaine users from psychosocial treatment programs with 6-month follow-up, our proposed modeling method is capable of identifying brain regions and related functional circuits that are predictive of certain follow-up behavior, which could provide mechanistic understanding of neuropsychiatric/neurological disease and clearly shows neuromodulation implications and can be used for individualized prognosis and treatment protocol design.Tianye ZhaiHong GuYihong YangFrontiers Media S.A.articleprediction modelingfMRItreatment outcomeCox regressionfunctional connectivityneuromodulation implicationsNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	prediction modeling fMRI treatment outcome Cox regression functional connectivity neuromodulation implications Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	prediction modeling fMRI treatment outcome Cox regression functional connectivity neuromodulation implications Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Tianye Zhai Hong Gu Yihong Yang Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder
description	Functional magnetic resonance imaging (fMRI) has become one of the most widely used noninvasive neuroimaging technique in research of cognitive neurosciences and of neural mechanisms of neuropsychiatric/neurological diseases. A primary goal of fMRI-based neuroimaging studies is to identify biomarkers for brain-behavior relationship and ultimately perform individualized treatment outcome prognosis. However, the concern of inadequate validation and the nature of small sample sizes are associated with fMRI-based neuroimaging studies, both of which hinder the translation from scientific findings to clinical practice. Therefore, the current paper presents a modeling approach to predict time-dependent prognosis with fMRI-based brain metrics and follow-up data. This prediction modeling is a combination of seed-based functional connectivity and voxel-wise Cox regression analysis with built-in nested cross-validation, which has been demonstrated to be able to provide robust and unbiased model performance estimates. Demonstrated with a cohort of treatment-seeking cocaine users from psychosocial treatment programs with 6-month follow-up, our proposed modeling method is capable of identifying brain regions and related functional circuits that are predictive of certain follow-up behavior, which could provide mechanistic understanding of neuropsychiatric/neurological disease and clearly shows neuromodulation implications and can be used for individualized prognosis and treatment protocol design.
format	article
author	Tianye Zhai Hong Gu Yihong Yang
author_facet	Tianye Zhai Hong Gu Yihong Yang
author_sort	Tianye Zhai
title	Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder
title_short	Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder
title_full	Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder
title_fullStr	Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder
title_full_unstemmed	Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder
title_sort	cox regression based modeling of functional connectivity and treatment outcome for relapse prediction and disease subtyping in substance use disorder
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/eeeb633359644db2ba2cbbcc347121c6
work_keys_str_mv	AT tianyezhai coxregressionbasedmodelingoffunctionalconnectivityandtreatmentoutcomeforrelapsepredictionanddiseasesubtypinginsubstanceusedisorder AT honggu coxregressionbasedmodelingoffunctionalconnectivityandtreatmentoutcomeforrelapsepredictionanddiseasesubtypinginsubstanceusedisorder AT yihongyang coxregressionbasedmodelingoffunctionalconnectivityandtreatmentoutcomeforrelapsepredictionanddiseasesubtypinginsubstanceusedisorder
_version_	1718439178645012480

Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder

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