Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1783622098940329984 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7732457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT renhuixia applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease AT zhujin applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease AT suxiaolin applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease AT chensiyan applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease AT zengsilin applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease AT lanxiaoyong applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease AT zouliangyu applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease AT sughruemichaele applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease AT guoyi applicationofstructuralandfunctionalconnectomemismatchforclassificationandindividualizedtherapyinalzheimerdisease |