Comprehensively stratifying MCIs into distinct risk subtypes based on brain imaging genetics fusion learning
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Shang, Muheng Zhang, Jin Han, Junwei Du, Lei |
| author_facet | Shang, Muheng Zhang, Jin Han, Junwei Du, Lei |
| contents | Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD) and thus enrolling MCI subjects to undergo clinical trials is worthwhile. However, MCI groups usually show significant diversity and heterogeneity in the pathology and symptom, which pose great challenge to accurately select appropriate subjects. This study aimed to stratify MCI subjects into distinct subgroups with substantial difference in the risk of transitioning to AD by fusing multimodal brain imaging genetic data. The integrated imaging genetics method comprised three modules, i.e., the whole-genome-oriented risk genetic information extraction module (RGE), the genetic-to-brain mapping module (RG2PG), and the genetic-guided pseudo-brain fusion module (CMPF). We used data from AD Neuroimaging Initiative (ADNI) and identified two MCI subtypes, called low-risk MCI (lsMCI) and high-risk MCI (hsMCI). We also validated that the two subgroups showed distinct patterns of in terms of multiple biomarkers including genetics, demographics, fluid biomarkers, brain imaging features, clinical symptoms and cognitive functioning at baseline, as well as their longitudinal developmental trajectories. Furthermore, we also identified potential biomarkers that may implicate the risk of MCIs, providing critical insights for patient stratification at early stage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18058 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comprehensively stratifying MCIs into distinct risk subtypes based on brain imaging genetics fusion learning Shang, Muheng Zhang, Jin Han, Junwei Du, Lei Quantitative Methods Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD) and thus enrolling MCI subjects to undergo clinical trials is worthwhile. However, MCI groups usually show significant diversity and heterogeneity in the pathology and symptom, which pose great challenge to accurately select appropriate subjects. This study aimed to stratify MCI subjects into distinct subgroups with substantial difference in the risk of transitioning to AD by fusing multimodal brain imaging genetic data. The integrated imaging genetics method comprised three modules, i.e., the whole-genome-oriented risk genetic information extraction module (RGE), the genetic-to-brain mapping module (RG2PG), and the genetic-guided pseudo-brain fusion module (CMPF). We used data from AD Neuroimaging Initiative (ADNI) and identified two MCI subtypes, called low-risk MCI (lsMCI) and high-risk MCI (hsMCI). We also validated that the two subgroups showed distinct patterns of in terms of multiple biomarkers including genetics, demographics, fluid biomarkers, brain imaging features, clinical symptoms and cognitive functioning at baseline, as well as their longitudinal developmental trajectories. Furthermore, we also identified potential biomarkers that may implicate the risk of MCIs, providing critical insights for patient stratification at early stage. |
| title | Comprehensively stratifying MCIs into distinct risk subtypes based on brain imaging genetics fusion learning |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2508.18058 |