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Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease

While machine learning approaches to analyzing Alzheimer disease connectome neuroimaging data have been studied, many have limited ability to provide insight in individual patterns of disease and lack the ability to provide actionable information about where in the brain a specific patient's di...

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Autores principales: Ren, Huixia, Zhu, Jin, Su, Xiaolin, Chen, Siyan, Zeng, Silin, Lan, Xiaoyong, Zou, Liang-Yu, Sughrue, Michael E., Guo, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732457/
https://www.ncbi.nlm.nih.gov/pubmed/33330326
http://dx.doi.org/10.3389/fpubh.2020.584430
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author Ren, Huixia
Zhu, Jin
Su, Xiaolin
Chen, Siyan
Zeng, Silin
Lan, Xiaoyong
Zou, Liang-Yu
Sughrue, Michael E.
Guo, Yi
author_facet Ren, Huixia
Zhu, Jin
Su, Xiaolin
Chen, Siyan
Zeng, Silin
Lan, Xiaoyong
Zou, Liang-Yu
Sughrue, Michael E.
Guo, Yi
author_sort Ren, Huixia
collection PubMed
description While machine learning approaches to analyzing Alzheimer disease connectome neuroimaging data have been studied, many have limited ability to provide insight in individual patterns of disease and lack the ability to provide actionable information about where in the brain a specific patient's disease is located. We studied a cohort of patients with Alzheimer disease who underwent resting state functional magnetic resonance imaging and diffusion tractography imaging. These images were processed, and a structural and functional connectivity matrix was generated using the HCP cortical and subcortical atlas. By generating a machine learning model, individual-level structural and functional anomalies detection and characterization were explored in this study. Our study found that structural disease burden in Alzheimer's patients is mainly focused in the subcortical structures and the Default mode network (DMN). Interestingly, functional anomalies were less consistent between individuals and less common in general in these patients. More intriguing was that some structural anomalies were noted in all patients in the study, namely a reduction in fibers involving parcellations in the right anterior cingulate. Alternately, the functional consequences of connectivity loss were cortical and variable. Integrated structural/functional connectomics might provide a useful tool for assessing AD progression, while few concerns have been made for analyzing the mismatch between these two. We performed a preliminary exploration into a set of Alzheimer disease data, intending to improve a personalized approach to understanding individual connectomes in an actionable manner. Specifically, we found that there were consistent patterns of white matter fiber loss, mainly focused around the DMN and deep subcortical structures, which were present in nearly all patients with clinical AD. Functional magnetic resonance imaging shows abnormal functional connectivity different within the patients, which may be used as the individual target for further therapeutic strategies making, like non-invasive stimulation technology.
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spelling pubmed-77324572020-12-15 Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease Ren, Huixia Zhu, Jin Su, Xiaolin Chen, Siyan Zeng, Silin Lan, Xiaoyong Zou, Liang-Yu Sughrue, Michael E. Guo, Yi Front Public Health Public Health While machine learning approaches to analyzing Alzheimer disease connectome neuroimaging data have been studied, many have limited ability to provide insight in individual patterns of disease and lack the ability to provide actionable information about where in the brain a specific patient's disease is located. We studied a cohort of patients with Alzheimer disease who underwent resting state functional magnetic resonance imaging and diffusion tractography imaging. These images were processed, and a structural and functional connectivity matrix was generated using the HCP cortical and subcortical atlas. By generating a machine learning model, individual-level structural and functional anomalies detection and characterization were explored in this study. Our study found that structural disease burden in Alzheimer's patients is mainly focused in the subcortical structures and the Default mode network (DMN). Interestingly, functional anomalies were less consistent between individuals and less common in general in these patients. More intriguing was that some structural anomalies were noted in all patients in the study, namely a reduction in fibers involving parcellations in the right anterior cingulate. Alternately, the functional consequences of connectivity loss were cortical and variable. Integrated structural/functional connectomics might provide a useful tool for assessing AD progression, while few concerns have been made for analyzing the mismatch between these two. We performed a preliminary exploration into a set of Alzheimer disease data, intending to improve a personalized approach to understanding individual connectomes in an actionable manner. Specifically, we found that there were consistent patterns of white matter fiber loss, mainly focused around the DMN and deep subcortical structures, which were present in nearly all patients with clinical AD. Functional magnetic resonance imaging shows abnormal functional connectivity different within the patients, which may be used as the individual target for further therapeutic strategies making, like non-invasive stimulation technology. Frontiers Media S.A. 2020-11-23 /pmc/articles/PMC7732457/ /pubmed/33330326 http://dx.doi.org/10.3389/fpubh.2020.584430 Text en Copyright © 2020 Ren, Zhu, Su, Chen, Zeng, Lan, Zou, Sughrue and Guo. http://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 Public Health
Ren, Huixia
Zhu, Jin
Su, Xiaolin
Chen, Siyan
Zeng, Silin
Lan, Xiaoyong
Zou, Liang-Yu
Sughrue, Michael E.
Guo, Yi
Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease
title Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease
title_full Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease
title_fullStr Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease
title_full_unstemmed Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease
title_short Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease
title_sort application of structural and functional connectome mismatch for classification and individualized therapy in alzheimer disease
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732457/
https://www.ncbi.nlm.nih.gov/pubmed/33330326
http://dx.doi.org/10.3389/fpubh.2020.584430
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