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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Frontiers Media S.A.
2020-11-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2020.584430/full |
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| author | Huixia Ren Huixia Ren Jin Zhu Xiaolin Su Siyan Chen Silin Zeng Xiaoyong Lan Liang-Yu Zou Michael E. Sughrue Yi Guo |
| author_facet | Huixia Ren Huixia Ren Jin Zhu Xiaolin Su Siyan Chen Silin Zeng Xiaoyong Lan Liang-Yu Zou Michael E. Sughrue Yi Guo |
| author_sort | Huixia Ren |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T06:57:58Z |
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| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-11T06:57:58Z |
| publishDate | 2020-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-45c6e6b1b6984711b4c63a37bf17b8eb2022-12-22T01:16:41ZengFrontiers Media S.A.Frontiers in Public Health2296-25652020-11-01810.3389/fpubh.2020.584430584430Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer DiseaseHuixia Ren0Huixia Ren1Jin Zhu2Xiaolin Su3Siyan Chen4Silin Zeng5Xiaoyong Lan6Liang-Yu Zou7Michael E. Sughrue8Yi Guo9Department of Neurology, The Second Clinical Medical College, Shenzhen People's Hospital, Jinan University, Shenzhen, ChinaThe First Affiliated Hospital, Jinan University, Guangzhou, ChinaDepartment of Medical Imaging, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, ChinaCentre for Minimally Invasive Neurosurgery, Prince of Wales Hospital, Sydney, NSW, AustraliaDepartment of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, ChinaWhile 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.https://www.frontiersin.org/articles/10.3389/fpubh.2020.584430/fullbrain connectivitydiffusion tractography imagingAlzheimer's diseasebrain parcellationfunctional MRImachine learning |
| spellingShingle | Huixia Ren Huixia Ren Jin Zhu Xiaolin Su Siyan Chen Silin Zeng Xiaoyong Lan Liang-Yu Zou Michael E. Sughrue Yi Guo Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease Frontiers in Public Health brain connectivity diffusion tractography imaging Alzheimer's disease brain parcellation functional MRI machine learning |
| 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 | brain connectivity diffusion tractography imaging Alzheimer's disease brain parcellation functional MRI machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2020.584430/full |
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