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Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms

OBJECTIVES: To investigate the relationship between diffusion tensor imaging (DTI) indicators and cerebral small vessel disease (CSVD) with depressive states, and to explore the underlying mechanisms of white matter damage in CSVD with depression. METHOD: A total of 115 elderly subjects were consecu...

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Autores principales: Gu, Yumeng, Zhao, Ping, Feng, Wenjun, Xia, Xiaoshuang, Tian, Xiaolin, Yan, Yu, Wang, Xiaowen, Gao, Decheng, Du, Yanfen, Li, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270825/
https://www.ncbi.nlm.nih.gov/pubmed/35810313
http://dx.doi.org/10.1186/s12877-022-03245-7
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author Gu, Yumeng
Zhao, Ping
Feng, Wenjun
Xia, Xiaoshuang
Tian, Xiaolin
Yan, Yu
Wang, Xiaowen
Gao, Decheng
Du, Yanfen
Li, Xin
author_facet Gu, Yumeng
Zhao, Ping
Feng, Wenjun
Xia, Xiaoshuang
Tian, Xiaolin
Yan, Yu
Wang, Xiaowen
Gao, Decheng
Du, Yanfen
Li, Xin
author_sort Gu, Yumeng
collection PubMed
description OBJECTIVES: To investigate the relationship between diffusion tensor imaging (DTI) indicators and cerebral small vessel disease (CSVD) with depressive states, and to explore the underlying mechanisms of white matter damage in CSVD with depression. METHOD: A total of 115 elderly subjects were consecutively recruited from the neurology clinic, including 36 CSVD patients with depressive state (CSVD+D), 34 CSVD patients without depressive state (CSVD-D), and 45 controls. A detailed neuropsychological assessment and multimodal magnetic resonance imaging (MRI) were performed. Based on tract-based spatial statistics (TBSS) analysis and structural network analysis, differences between groups were compared, including white matter fiber indicators (fractional anisotropy and mean diffusivity) and structural brain network indicators (global efficiency, local efficiency and network strength), in order to explore the differences and correlations of DTI parameters among the three groups. RESULTS: There were no significant differences in terms of CSVD burden scores and conventional imaging findings between the CSVD-D and CSVD+D groups. Group differences were found in DTI indicators (p <  0.05), after adjusting for age, gender, education level, and vascular risk factors (VRF), there were significant correlations between TBSS analysis indicators and depression, including: fractional anisotropy (FA) (r = − 0.291, p <  0.05), mean diffusivity (MD) (r = 0.297, p < 0.05), at the same time, between structural network indicators and depression also show significant correlations, including: local efficiency (E(Local)) (r = − 0.278, p < 0.01) and network strength (r = − 0.403, p < 0.001). CONCLUSIONS: Changes in FA, MD values and structural network indicators in DTI parameters can predict the depressive state of CSVD to a certain extent, providing a more direct structural basis for the hypothesis of abnormal neural circuits in the pathogenesis of vascular-related depression. In addition, abnormal white matter alterations in subcortical neural circuits probably affect the microstructural function of brain connections, which may be a mechanism for the concomitant depressive symptoms in CSVD patients.
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spelling pubmed-92708252022-07-10 Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms Gu, Yumeng Zhao, Ping Feng, Wenjun Xia, Xiaoshuang Tian, Xiaolin Yan, Yu Wang, Xiaowen Gao, Decheng Du, Yanfen Li, Xin BMC Geriatr Research OBJECTIVES: To investigate the relationship between diffusion tensor imaging (DTI) indicators and cerebral small vessel disease (CSVD) with depressive states, and to explore the underlying mechanisms of white matter damage in CSVD with depression. METHOD: A total of 115 elderly subjects were consecutively recruited from the neurology clinic, including 36 CSVD patients with depressive state (CSVD+D), 34 CSVD patients without depressive state (CSVD-D), and 45 controls. A detailed neuropsychological assessment and multimodal magnetic resonance imaging (MRI) were performed. Based on tract-based spatial statistics (TBSS) analysis and structural network analysis, differences between groups were compared, including white matter fiber indicators (fractional anisotropy and mean diffusivity) and structural brain network indicators (global efficiency, local efficiency and network strength), in order to explore the differences and correlations of DTI parameters among the three groups. RESULTS: There were no significant differences in terms of CSVD burden scores and conventional imaging findings between the CSVD-D and CSVD+D groups. Group differences were found in DTI indicators (p <  0.05), after adjusting for age, gender, education level, and vascular risk factors (VRF), there were significant correlations between TBSS analysis indicators and depression, including: fractional anisotropy (FA) (r = − 0.291, p <  0.05), mean diffusivity (MD) (r = 0.297, p < 0.05), at the same time, between structural network indicators and depression also show significant correlations, including: local efficiency (E(Local)) (r = − 0.278, p < 0.01) and network strength (r = − 0.403, p < 0.001). CONCLUSIONS: Changes in FA, MD values and structural network indicators in DTI parameters can predict the depressive state of CSVD to a certain extent, providing a more direct structural basis for the hypothesis of abnormal neural circuits in the pathogenesis of vascular-related depression. In addition, abnormal white matter alterations in subcortical neural circuits probably affect the microstructural function of brain connections, which may be a mechanism for the concomitant depressive symptoms in CSVD patients. BioMed Central 2022-07-09 /pmc/articles/PMC9270825/ /pubmed/35810313 http://dx.doi.org/10.1186/s12877-022-03245-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Gu, Yumeng
Zhao, Ping
Feng, Wenjun
Xia, Xiaoshuang
Tian, Xiaolin
Yan, Yu
Wang, Xiaowen
Gao, Decheng
Du, Yanfen
Li, Xin
Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms
title Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms
title_full Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms
title_fullStr Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms
title_full_unstemmed Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms
title_short Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms
title_sort structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270825/
https://www.ncbi.nlm.nih.gov/pubmed/35810313
http://dx.doi.org/10.1186/s12877-022-03245-7
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