Dynamic and Static Structure–Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease

ABSTRACT The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure–function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure–function intera...

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Vydáno v:Human brain mapping Ročník 46; číslo 5; s. e70202 - n/a
Hlavní autoři: Wu, Han, Lu, Yinping, Wang, Luyao, Wu, Jinglong, Liu, Ying, Zhang, Zhilin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.04.2025
Témata:
Aged
Aged, 80 and over
Alzheimer Disease - diagnosis
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - physiopathology
Alzheimer's disease
Biomarkers
Brain
Cognitive ability
Cognitive Dysfunction - diagnosis
Cognitive Dysfunction - diagnostic imaging
Cognitive Dysfunction - physiopathology
Coupling
Disease
Disease Progression
dynamic structure–function coupling
Early Diagnosis
Education
Feature selection
Female
Functional anatomy
Hospitals
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Learning algorithms
Machine Learning
Magnetic Resonance Imaging
Male
Medical imaging
Mental disorders
Middle Aged
Neurodegenerative diseases
Neuroimaging
Physiology
Scanners
static structure–function coupling
Structure-function relationships
Canada
Montreal Quebec Canada
static structure–function coupling
magnetic resonance imaging
dynamic structure–function coupling
machine learning
Alzheimer's disease
ISSN:1065-9471, 1097-0193, 1097-0193
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Abstract ABSTRACT The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure–function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure–function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection. This study investigates the integration of static and dynamic structure–function coupling (SFC) for early Alzheimer's disease (AD) classification. By combining machine learning with SFC metrics, it reveals how AD alters brain network stability, offering new insights for early AD detection and pathophysiological understanding.
AbstractList The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure–function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure–function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection.
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure-function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection.The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure-function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection.
ABSTRACT The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure–function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure–function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection.
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure–function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure–function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection. This study investigates the integration of static and dynamic structure–function coupling (SFC) for early Alzheimer's disease (AD) classification. By combining machine learning with SFC metrics, it reveals how AD alters brain network stability, offering new insights for early AD detection and pathophysiological understanding.
ABSTRACT The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure–function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure–function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection. This study investigates the integration of static and dynamic structure–function coupling (SFC) for early Alzheimer's disease (AD) classification. By combining machine learning with SFC metrics, it reveals how AD alters brain network stability, offering new insights for early AD detection and pathophysiological understanding.
Author Liu, Ying
Zhang, Zhilin
Wu, Han
Lu, Yinping
Wu, Jinglong
Wang, Luyao
AuthorAffiliation 2 Research Center for Medical Artificial Intelligence Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences Shenzhen Guangdong China
3 Institute of Biomedical Engineering, School of Life Sciences Shanghai University Shanghai China
1 School of Software Northeastern University Shenyang China
AuthorAffiliation_xml – name: 3 Institute of Biomedical Engineering, School of Life Sciences Shanghai University Shanghai China
– name: 1 School of Software Northeastern University Shenyang China
– name: 2 Research Center for Medical Artificial Intelligence Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences Shenzhen Guangdong China
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  fullname: Wu, Han
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  fullname: Lu, Yinping
  organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
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  givenname: Luyao
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  fullname: Wang, Luyao
  email: wangly1018@shu.edu.cn
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  surname: Zhang
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  email: zhangzhilin@siat.ac.cn
  organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
BackLink https://www.ncbi.nlm.nih.gov/pubmed/40193134$$D View this record in MEDLINE/PubMed
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Issue 5
Keywords static structure–function coupling
magnetic resonance imaging
dynamic structure–function coupling
machine learning
Alzheimer's disease
Language English
License Attribution-NonCommercial-NoDerivs
2025 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
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Notes Funding
This work was supported by GuangDong Basic and Applied Basic Research Foundation, 2023A1515012929, Science and Technology Planning Project of Guangdong Province, China, 2023A0505050162.
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Funding: This work was supported by GuangDong Basic and Applied Basic Research Foundation, 2023A1515012929, Science and Technology Planning Project of Guangdong Province, China, 2023A0505050162.
ORCID 0009-0008-6217-7916
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PublicationTitle Human brain mapping
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Snippet ABSTRACT The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making...
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making...
ABSTRACT The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making...
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StartPage e70202
SubjectTerms Aged
Aged, 80 and over
Alzheimer Disease - diagnosis
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - physiopathology
Alzheimer's disease
Biomarkers
Brain
Cognitive ability
Cognitive Dysfunction - diagnosis
Cognitive Dysfunction - diagnostic imaging
Cognitive Dysfunction - physiopathology
Coupling
Disease
Disease Progression
dynamic structure–function coupling
Early Diagnosis
Education
Feature selection
Female
Functional anatomy
Hospitals
Humans
Learning algorithms
Machine Learning
Magnetic Resonance Imaging
Male
Medical imaging
Mental disorders
Middle Aged
Neurodegenerative diseases
Neuroimaging
Physiology
Scanners
static structure–function coupling
Structure-function relationships
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Title Dynamic and Static Structure–Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease
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