Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders
BackgroundBrain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.ObjectiveThis study aimed to e...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.975299/full |
| _version_ | 1828377461500411904 |
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| author | Sung-Woo Kim Yeong-Hun Song Hee Jin Kim Hee Jin Kim Hee Jin Kim Young Noh Young Noh Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Duk L. Na Duk L. Na Joon-Kyung Seong Joon-Kyung Seong Joon-Kyung Seong |
| author_facet | Sung-Woo Kim Yeong-Hun Song Hee Jin Kim Hee Jin Kim Hee Jin Kim Young Noh Young Noh Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Duk L. Na Duk L. Na Joon-Kyung Seong Joon-Kyung Seong Joon-Kyung Seong |
| author_sort | Sung-Woo Kim |
| collection | DOAJ |
| description | BackgroundBrain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.ObjectiveThis study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes.MethodsThis study established a framework that unifies the processes of identifying a brain connectivity-based biomarker and mapping its abnormality level into a single numerical value, called a biomarker abnormality summarized from the identified connectivity (BASIC) score. A connectivity-based biomarker was extracted in the form of a connected component associated with disease progression. BASIC scores were constructed to maximize Kendall's rank correlation with the disease, considering the spatial autocorrelation between adjacent edges. Using functional connectivity networks, we validated the BASIC scores in various scenarios.ResultsOur proposed framework was successfully applied to construct connectivity-based biomarker scores associated with disease progression, characterized by two, three, and five stages of Alzheimer's disease, and reflected the continuity of brain alterations as the diseases advanced. The BASIC scores were not only sensitive to disease progression, but also specific to the trajectory of a particular disease. Moreover, this framework can be utilized when disease stages are measured on continuous scales, resulting in a notable prediction performance when applied to the prediction of the disease.ConclusionOur unified framework provides a method to identify brain connectivity-based biomarkers and continuity-reflecting BASIC scores that are sensitive and specific to disease progression. |
| first_indexed | 2024-04-14T08:12:56Z |
| format | Article |
| id | doaj.art-7b61aab46060412b85a6403ef0c9329f |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-14T08:12:56Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-7b61aab46060412b85a6403ef0c9329f2022-12-22T02:04:30ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-09-011610.3389/fnins.2022.975299975299Unified framework for brain connectivity-based biomarkers in neurodegenerative disordersSung-Woo Kim0Yeong-Hun Song1Hee Jin Kim2Hee Jin Kim3Hee Jin Kim4Young Noh5Young Noh6Sang Won Seo7Sang Won Seo8Sang Won Seo9Sang Won Seo10Duk L. Na11Duk L. Na12Joon-Kyung Seong13Joon-Kyung Seong14Joon-Kyung Seong15Department of Bio-Convergence Engineering, Korea University, Seoul, South KoreaDepartment of Artificial Intelligence, Korea University, Seoul, South KoreaDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South KoreaDepartment of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South KoreaDepartment of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South KoreaDepartment of Neurology, Gil Medical Center, Gachon University of College of Medicine, Incheon, South KoreaNeuroscience Research Institute, Gachon University, Incheon, South KoreaDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South KoreaDepartment of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South KoreaDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Seoul, South KoreaAlzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South KoreaDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea0Neuroscience Center, Samsung Medical Center, Seoul, South KoreaDepartment of Artificial Intelligence, Korea University, Seoul, South Korea1School of Biomedical Engineering, Korea University, Seoul, South Korea2Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South KoreaBackgroundBrain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.ObjectiveThis study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes.MethodsThis study established a framework that unifies the processes of identifying a brain connectivity-based biomarker and mapping its abnormality level into a single numerical value, called a biomarker abnormality summarized from the identified connectivity (BASIC) score. A connectivity-based biomarker was extracted in the form of a connected component associated with disease progression. BASIC scores were constructed to maximize Kendall's rank correlation with the disease, considering the spatial autocorrelation between adjacent edges. Using functional connectivity networks, we validated the BASIC scores in various scenarios.ResultsOur proposed framework was successfully applied to construct connectivity-based biomarker scores associated with disease progression, characterized by two, three, and five stages of Alzheimer's disease, and reflected the continuity of brain alterations as the diseases advanced. The BASIC scores were not only sensitive to disease progression, but also specific to the trajectory of a particular disease. Moreover, this framework can be utilized when disease stages are measured on continuous scales, resulting in a notable prediction performance when applied to the prediction of the disease.ConclusionOur unified framework provides a method to identify brain connectivity-based biomarkers and continuity-reflecting BASIC scores that are sensitive and specific to disease progression.https://www.frontiersin.org/articles/10.3389/fnins.2022.975299/fullbrain connectivityconnectivity-based biomarkerbiomarker scoresconnected componentLaplacian regularizationKendall's rank correlation |
| spellingShingle | Sung-Woo Kim Yeong-Hun Song Hee Jin Kim Hee Jin Kim Hee Jin Kim Young Noh Young Noh Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Duk L. Na Duk L. Na Joon-Kyung Seong Joon-Kyung Seong Joon-Kyung Seong Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders Frontiers in Neuroscience brain connectivity connectivity-based biomarker biomarker scores connected component Laplacian regularization Kendall's rank correlation |
| title | Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders |
| title_full | Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders |
| title_fullStr | Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders |
| title_full_unstemmed | Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders |
| title_short | Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders |
| title_sort | unified framework for brain connectivity based biomarkers in neurodegenerative disorders |
| topic | brain connectivity connectivity-based biomarker biomarker scores connected component Laplacian regularization Kendall's rank correlation |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.975299/full |
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