sMRI-based Brain Age Estimation in MCI using Persistent Homology
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918193189093376 |
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| author | Bhattacharya, Debanjali Sinha, Neelam |
| author_facet | Bhattacharya, Debanjali Sinha, Neelam |
| contents | In this study, we propose the use of persistent homology -- specifically Betti curves for brain age prediction and for distinguishing between healthy and pathological aging. The proposed framework is applied to 100 structural MRI scans from the publicly available ADNI dataset. Our results indicate that Betti curve features, particularly those from dimension-1 (connected components) and dimension-2 (1D holes), effectively capture structural brain alterations associated with aging. Furthermore, clinical features are grouped into three categories based on their correlation, or lack thereof, with (i) predicted brain age and (ii) chronological age. The findings demonstrate that this approach successfully differentiates normal from pathological aging and provides a novel framework for understanding how structural brain changes relate to cognitive impairment. The proposed method serves as a foundation for developing potential biomarkers for early detection and monitoring of cognitive decline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | sMRI-based Brain Age Estimation in MCI using Persistent Homology Bhattacharya, Debanjali Sinha, Neelam Neurons and Cognition Computer Vision and Pattern Recognition Image and Video Processing In this study, we propose the use of persistent homology -- specifically Betti curves for brain age prediction and for distinguishing between healthy and pathological aging. The proposed framework is applied to 100 structural MRI scans from the publicly available ADNI dataset. Our results indicate that Betti curve features, particularly those from dimension-1 (connected components) and dimension-2 (1D holes), effectively capture structural brain alterations associated with aging. Furthermore, clinical features are grouped into three categories based on their correlation, or lack thereof, with (i) predicted brain age and (ii) chronological age. The findings demonstrate that this approach successfully differentiates normal from pathological aging and provides a novel framework for understanding how structural brain changes relate to cognitive impairment. The proposed method serves as a foundation for developing potential biomarkers for early detection and monitoring of cognitive decline. |
| title | sMRI-based Brain Age Estimation in MCI using Persistent Homology |
| topic | Neurons and Cognition Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2511.05520 |