Multiscale Analysis of Alzheimer’s Disease Using Feature Fusion in Cognitive and Sensory Brain Regions
Introduction: This research is focused on early detection of Alzheimer’s disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images. Methods: Using...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Karger Publishers
2024-12-01
|
| Series: | Digital Biomarkers |
| Online Access: | https://karger.com/article/doi/10.1159/000543165 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850133584830005248 |
|---|---|
| author | Aya Hassouneh Alessander Danna-dos-Santos Bradley Bazuin Saad Shebrain Ikhlas Abdel-Qader |
| author_facet | Aya Hassouneh Alessander Danna-dos-Santos Bradley Bazuin Saad Shebrain Ikhlas Abdel-Qader |
| author_sort | Aya Hassouneh |
| collection | DOAJ |
| description |
Introduction: This research is focused on early detection of Alzheimer’s disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images. Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier. Results: The research highlights the critical role of brain texture features, particularly in memory regions, for AD detection. Significant sex-specific differences are observed, with males showing significance in texture features in memory regions, volume in vision regions, and SUVR in speech regions, while females exhibit significance in texture features in memory and speech regions, and SUVR in vision regions. Additionally, the study analyzes how obesity affects features used in AD prediction models, clarifying its effects on speech and vision regions, particularly brain volume. Conclusion: The findings contribute valuable insights into the effectiveness of feature fusion, sex-specific differences, and the impact of obesity on AD-related biomarkers, paving the way for future research in early AD detection strategies and cognitive impairment classification. |
| format | Article |
| id | doaj-art-7b74f338ab4a4f668f2f274dfe3dcdd5 |
| institution | OA Journals |
| issn | 2504-110X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Digital Biomarkers |
| spelling | doaj-art-7b74f338ab4a4f668f2f274dfe3dcdd52025-08-20T02:31:55ZengKarger PublishersDigital Biomarkers2504-110X2024-12-0191233910.1159/000543165Multiscale Analysis of Alzheimer’s Disease Using Feature Fusion in Cognitive and Sensory Brain RegionsAya HassounehAlessander Danna-dos-SantosBradley BazuinSaad ShebrainIkhlas Abdel-Qader Introduction: This research is focused on early detection of Alzheimer’s disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images. Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier. Results: The research highlights the critical role of brain texture features, particularly in memory regions, for AD detection. Significant sex-specific differences are observed, with males showing significance in texture features in memory regions, volume in vision regions, and SUVR in speech regions, while females exhibit significance in texture features in memory and speech regions, and SUVR in vision regions. Additionally, the study analyzes how obesity affects features used in AD prediction models, clarifying its effects on speech and vision regions, particularly brain volume. Conclusion: The findings contribute valuable insights into the effectiveness of feature fusion, sex-specific differences, and the impact of obesity on AD-related biomarkers, paving the way for future research in early AD detection strategies and cognitive impairment classification.https://karger.com/article/doi/10.1159/000543165 |
| spellingShingle | Aya Hassouneh Alessander Danna-dos-Santos Bradley Bazuin Saad Shebrain Ikhlas Abdel-Qader Multiscale Analysis of Alzheimer’s Disease Using Feature Fusion in Cognitive and Sensory Brain Regions Digital Biomarkers |
| title | Multiscale Analysis of Alzheimer’s Disease Using Feature Fusion in Cognitive and Sensory Brain Regions |
| title_full | Multiscale Analysis of Alzheimer’s Disease Using Feature Fusion in Cognitive and Sensory Brain Regions |
| title_fullStr | Multiscale Analysis of Alzheimer’s Disease Using Feature Fusion in Cognitive and Sensory Brain Regions |
| title_full_unstemmed | Multiscale Analysis of Alzheimer’s Disease Using Feature Fusion in Cognitive and Sensory Brain Regions |
| title_short | Multiscale Analysis of Alzheimer’s Disease Using Feature Fusion in Cognitive and Sensory Brain Regions |
| title_sort | multiscale analysis of alzheimer s disease using feature fusion in cognitive and sensory brain regions |
| url | https://karger.com/article/doi/10.1159/000543165 |
| work_keys_str_mv | AT ayahassouneh multiscaleanalysisofalzheimersdiseaseusingfeaturefusionincognitiveandsensorybrainregions AT alessanderdannadossantos multiscaleanalysisofalzheimersdiseaseusingfeaturefusionincognitiveandsensorybrainregions AT bradleybazuin multiscaleanalysisofalzheimersdiseaseusingfeaturefusionincognitiveandsensorybrainregions AT saadshebrain multiscaleanalysisofalzheimersdiseaseusingfeaturefusionincognitiveandsensorybrainregions AT ikhlasabdelqader multiscaleanalysisofalzheimersdiseaseusingfeaturefusionincognitiveandsensorybrainregions |