Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics
Abstract Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This study investigated dynamic features of re...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-02018-7 |
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| author | Thawirasm Jungrungrueang Sawrawit Chairat Kasidach Rasitanon Praopim Limsakul Krit Charupanit |
| author_facet | Thawirasm Jungrungrueang Sawrawit Chairat Kasidach Rasitanon Praopim Limsakul Krit Charupanit |
| author_sort | Thawirasm Jungrungrueang |
| collection | DOAJ |
| description | Abstract Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This study investigated dynamic features of resting-state electroencephalography (EEG) functional connectivity to identify characteristic patterns of dementia subtypes, such as Alzheimer’s disease (AD) and frontotemporal dementia (FD), and to evaluate their potential as biomarkers. We extracted distinctive statistical features, including mean, variance, skewness, and Shannon entropy, from brain connectivity measures, revealing common alterations in dementia, specifically a generalized disruption of Alpha-band connectivity. Distinctive characteristics were found, including generalized Delta-band hyperconnectivity with increased complexity in AD and disrupted phase-based connectivity in Theta, Beta, and Gamma bands for FD. We also employed a convolutional neural network model, enhanced with these dynamic features, to differentiate between dementia subtypes. Our classification models achieved a multiclass classification accuracy of 93.6% across AD, FD, and healthy control groups. Furthermore, the model demonstrated 97.8% and 96.7% accuracy in differentiating AD and FD from healthy controls, respectively, and 97.4% accuracy in classifying AD and FD in pairwise classification. These establish a high-performance deep learning framework utilizing dynamic EEG connectivity patterns as potential biomarkers, offering a promising approach for early screening and diagnosis of dementia spectrum disorders using EEG. Our findings suggest that analyzing brain connectivity dynamics as a network and during cognitive tasks could offer more valuable information for diagnosis, assessing disease severity, and potentially identifying personalized neurological deficits. |
| format | Article |
| id | doaj-art-2afa139a93de47dfbde589abaaa80879 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2afa139a93de47dfbde589abaaa808792025-08-20T03:48:15ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-02018-7Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamicsThawirasm Jungrungrueang0Sawrawit Chairat1Kasidach Rasitanon2Praopim Limsakul3Krit Charupanit4Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla UniversityDepartment of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla UniversityDivision of Physical Science, Faculty of Science, Prince of Songkla UniversityDivision of Physical Science, Faculty of Science, Prince of Songkla UniversityDepartment of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla UniversityAbstract Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This study investigated dynamic features of resting-state electroencephalography (EEG) functional connectivity to identify characteristic patterns of dementia subtypes, such as Alzheimer’s disease (AD) and frontotemporal dementia (FD), and to evaluate their potential as biomarkers. We extracted distinctive statistical features, including mean, variance, skewness, and Shannon entropy, from brain connectivity measures, revealing common alterations in dementia, specifically a generalized disruption of Alpha-band connectivity. Distinctive characteristics were found, including generalized Delta-band hyperconnectivity with increased complexity in AD and disrupted phase-based connectivity in Theta, Beta, and Gamma bands for FD. We also employed a convolutional neural network model, enhanced with these dynamic features, to differentiate between dementia subtypes. Our classification models achieved a multiclass classification accuracy of 93.6% across AD, FD, and healthy control groups. Furthermore, the model demonstrated 97.8% and 96.7% accuracy in differentiating AD and FD from healthy controls, respectively, and 97.4% accuracy in classifying AD and FD in pairwise classification. These establish a high-performance deep learning framework utilizing dynamic EEG connectivity patterns as potential biomarkers, offering a promising approach for early screening and diagnosis of dementia spectrum disorders using EEG. Our findings suggest that analyzing brain connectivity dynamics as a network and during cognitive tasks could offer more valuable information for diagnosis, assessing disease severity, and potentially identifying personalized neurological deficits.https://doi.org/10.1038/s41598-025-02018-7Alzheimer’s diseaseFrontotemporal dementiaElectroencephalogramConvolutional neural networkAging disordersBrain connectivity |
| spellingShingle | Thawirasm Jungrungrueang Sawrawit Chairat Kasidach Rasitanon Praopim Limsakul Krit Charupanit Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics Scientific Reports Alzheimer’s disease Frontotemporal dementia Electroencephalogram Convolutional neural network Aging disorders Brain connectivity |
| title | Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics |
| title_full | Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics |
| title_fullStr | Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics |
| title_full_unstemmed | Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics |
| title_short | Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics |
| title_sort | translational approach for dementia subtype classification using convolutional neural network based on eeg connectome dynamics |
| topic | Alzheimer’s disease Frontotemporal dementia Electroencephalogram Convolutional neural network Aging disorders Brain connectivity |
| url | https://doi.org/10.1038/s41598-025-02018-7 |
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