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Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease
Moyamoya disease is a rare cerebrovascular disorder associated with cognitive dysfunction. It is usually treated by surgical revascularization, but research on the neurocognitive outcomes of revascularization surgery is controversial. Given that neurocognitive impairment could affect the daily activ...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9438963/ https://www.ncbi.nlm.nih.gov/pubmed/36072648 http://dx.doi.org/10.1093/braincomms/fcac213 |
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author | Gao, Mengxia Lam, Charlene L M Lui, Wai M Lau, Kui Kai Lee, Tatia M C |
author_facet | Gao, Mengxia Lam, Charlene L M Lui, Wai M Lau, Kui Kai Lee, Tatia M C |
author_sort | Gao, Mengxia |
collection | PubMed |
description | Moyamoya disease is a rare cerebrovascular disorder associated with cognitive dysfunction. It is usually treated by surgical revascularization, but research on the neurocognitive outcomes of revascularization surgery is controversial. Given that neurocognitive impairment could affect the daily activities of patients with moyamoya disease, early detection of postoperative neurocognitive outcomes has the potential to improve patient management. In this study, we applied a well-established connectome-based predictive modelling approach to develop machine learning models that used preoperative resting-state functional connectivity to predict postoperative changes in processing speed in patients with moyamoya disease. Twelve adult patients with moyamoya disease (age range: 23–49 years; female/male: 9/3) were recruited prior to surgery and underwent follow-up at 1 and 6 months after surgery. Twenty healthy controls (age range: 24–54 years; female/male: 14/6) were recruited and completed the behavioural test at baseline, 1-month follow-up and 6-month follow-up. Behavioural results indicated that the behavioural changes in processing speed at 1 and 6 months after surgery compared with baseline were not significant. Importantly, we showed that preoperative resting-state functional connectivity significantly predicted postoperative changes in processing speed at 1 month after surgery (negative network: ρ = 0.63, P(corr) = 0.017) and 6 months after surgery (positive network: ρ = 0.62, P(corr) = 0.010; negative network: ρ = 0.55, P(corr) = 0.010). We also identified cerebro-cerebellar and cortico-subcortical connectivities that were consistently associated with processing speed. The brain regions identified from our predictive models are not only consistent with previous studies but also extend previous findings by revealing their potential roles in postoperative neurocognitive functions in patients with moyamoya disease. Taken together, our findings provide preliminary evidence that preoperative resting-state functional connectivity might predict the post-surgical longitudinal neurocognitive changes in patients with moyamoya disease. Given that processing speed is a crucial cognitive ability supporting higher neurocognitive functions, this study’s findings offer important insight into the clinical management of patients with moyamoya disease. |
format | Online Article Text |
id | pubmed-9438963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-94389632022-09-06 Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease Gao, Mengxia Lam, Charlene L M Lui, Wai M Lau, Kui Kai Lee, Tatia M C Brain Commun Original Article Moyamoya disease is a rare cerebrovascular disorder associated with cognitive dysfunction. It is usually treated by surgical revascularization, but research on the neurocognitive outcomes of revascularization surgery is controversial. Given that neurocognitive impairment could affect the daily activities of patients with moyamoya disease, early detection of postoperative neurocognitive outcomes has the potential to improve patient management. In this study, we applied a well-established connectome-based predictive modelling approach to develop machine learning models that used preoperative resting-state functional connectivity to predict postoperative changes in processing speed in patients with moyamoya disease. Twelve adult patients with moyamoya disease (age range: 23–49 years; female/male: 9/3) were recruited prior to surgery and underwent follow-up at 1 and 6 months after surgery. Twenty healthy controls (age range: 24–54 years; female/male: 14/6) were recruited and completed the behavioural test at baseline, 1-month follow-up and 6-month follow-up. Behavioural results indicated that the behavioural changes in processing speed at 1 and 6 months after surgery compared with baseline were not significant. Importantly, we showed that preoperative resting-state functional connectivity significantly predicted postoperative changes in processing speed at 1 month after surgery (negative network: ρ = 0.63, P(corr) = 0.017) and 6 months after surgery (positive network: ρ = 0.62, P(corr) = 0.010; negative network: ρ = 0.55, P(corr) = 0.010). We also identified cerebro-cerebellar and cortico-subcortical connectivities that were consistently associated with processing speed. The brain regions identified from our predictive models are not only consistent with previous studies but also extend previous findings by revealing their potential roles in postoperative neurocognitive functions in patients with moyamoya disease. Taken together, our findings provide preliminary evidence that preoperative resting-state functional connectivity might predict the post-surgical longitudinal neurocognitive changes in patients with moyamoya disease. Given that processing speed is a crucial cognitive ability supporting higher neurocognitive functions, this study’s findings offer important insight into the clinical management of patients with moyamoya disease. Oxford University Press 2022-08-20 /pmc/articles/PMC9438963/ /pubmed/36072648 http://dx.doi.org/10.1093/braincomms/fcac213 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Gao, Mengxia Lam, Charlene L M Lui, Wai M Lau, Kui Kai Lee, Tatia M C Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease |
title | Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease |
title_full | Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease |
title_fullStr | Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease |
title_full_unstemmed | Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease |
title_short | Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease |
title_sort | preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9438963/ https://www.ncbi.nlm.nih.gov/pubmed/36072648 http://dx.doi.org/10.1093/braincomms/fcac213 |
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