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Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features

Antidepressant treatment, as an important method in clinical practice, is not suitable for all major depressive disorder (MDD) patients. Although magnetic resonance imaging (MRI) studies have found thalamic abnormalities in MDD patients, it is not clear whether the features of the thalamus are suita...

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Autores principales: Li, Hanxiaoran, Song, Sutao, Wang, Donglin, Zhang, Danning, Tan, Zhonglin, Lian, Zhenzhen, Wang, Yan, Zhou, Xin, Pan, Chenyuan, Wu, Yue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199000/
https://www.ncbi.nlm.nih.gov/pubmed/35720774
http://dx.doi.org/10.3389/fncom.2022.837093
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author Li, Hanxiaoran
Song, Sutao
Wang, Donglin
Zhang, Danning
Tan, Zhonglin
Lian, Zhenzhen
Wang, Yan
Zhou, Xin
Pan, Chenyuan
Wu, Yue
author_facet Li, Hanxiaoran
Song, Sutao
Wang, Donglin
Zhang, Danning
Tan, Zhonglin
Lian, Zhenzhen
Wang, Yan
Zhou, Xin
Pan, Chenyuan
Wu, Yue
author_sort Li, Hanxiaoran
collection PubMed
description Antidepressant treatment, as an important method in clinical practice, is not suitable for all major depressive disorder (MDD) patients. Although magnetic resonance imaging (MRI) studies have found thalamic abnormalities in MDD patients, it is not clear whether the features of the thalamus are suitable to serve as predictive aids for treatment responses at the individual level. Here, we tested the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) of the thalamus using multivariate pattern analysis (MVPA). A total of 74 MDD patients and 44 healthy control (HC) subjects were recruited. Thirty-nine MDD patients and 35 HC subjects underwent scanning twice. Between the two scanning sessions, patients in the MDD group received selective serotonin reuptake inhibitor (SSRI) treatment for 3-month, and HC group did not receive any treatment. Gaussian process regression (GPR) was trained to predict the percentage decrease in the Hamilton Depression Scale (HAMD) score after treatment. The percentage decrease in HAMD score after SSRI treatment was predicted by building GPRs trained with baseline thalamic data. The results showed significant correlations between the true percentage of HAMD score decreases and predictions (p < 0.01, r(2) = 0.11) in GPRs trained with GMD. We did not find significant correlations between the true percentage of HAMD score decreases and predictions in GMV (p = 0.16, r(2) = 0.00), ALFF (p = 0.125, r(2) = 0.00), and fALFF (p = 0.485, r(2) = 0.10). Our results suggest that GMD of the thalamus has good potential as an aid in individualized treatment response predictions of MDD patients.
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spelling pubmed-91990002022-06-16 Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features Li, Hanxiaoran Song, Sutao Wang, Donglin Zhang, Danning Tan, Zhonglin Lian, Zhenzhen Wang, Yan Zhou, Xin Pan, Chenyuan Wu, Yue Front Comput Neurosci Neuroscience Antidepressant treatment, as an important method in clinical practice, is not suitable for all major depressive disorder (MDD) patients. Although magnetic resonance imaging (MRI) studies have found thalamic abnormalities in MDD patients, it is not clear whether the features of the thalamus are suitable to serve as predictive aids for treatment responses at the individual level. Here, we tested the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) of the thalamus using multivariate pattern analysis (MVPA). A total of 74 MDD patients and 44 healthy control (HC) subjects were recruited. Thirty-nine MDD patients and 35 HC subjects underwent scanning twice. Between the two scanning sessions, patients in the MDD group received selective serotonin reuptake inhibitor (SSRI) treatment for 3-month, and HC group did not receive any treatment. Gaussian process regression (GPR) was trained to predict the percentage decrease in the Hamilton Depression Scale (HAMD) score after treatment. The percentage decrease in HAMD score after SSRI treatment was predicted by building GPRs trained with baseline thalamic data. The results showed significant correlations between the true percentage of HAMD score decreases and predictions (p < 0.01, r(2) = 0.11) in GPRs trained with GMD. We did not find significant correlations between the true percentage of HAMD score decreases and predictions in GMV (p = 0.16, r(2) = 0.00), ALFF (p = 0.125, r(2) = 0.00), and fALFF (p = 0.485, r(2) = 0.10). Our results suggest that GMD of the thalamus has good potential as an aid in individualized treatment response predictions of MDD patients. Frontiers Media S.A. 2022-06-01 /pmc/articles/PMC9199000/ /pubmed/35720774 http://dx.doi.org/10.3389/fncom.2022.837093 Text en Copyright © 2022 Li, Song, Wang, Zhang, Tan, Lian, Wang, Zhou, Pan and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Hanxiaoran
Song, Sutao
Wang, Donglin
Zhang, Danning
Tan, Zhonglin
Lian, Zhenzhen
Wang, Yan
Zhou, Xin
Pan, Chenyuan
Wu, Yue
Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features
title Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features
title_full Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features
title_fullStr Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features
title_full_unstemmed Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features
title_short Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features
title_sort treatment response prediction for major depressive disorder patients via multivariate pattern analysis of thalamic features
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199000/
https://www.ncbi.nlm.nih.gov/pubmed/35720774
http://dx.doi.org/10.3389/fncom.2022.837093
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