How reproducible are data-driven subtypes of Alzheimer's disease atrophy?
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914306957770752 |
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| author | Prevot, Emma Shand, Cameron Oxtoby, Neil Initiative, for Alzheimer's Disease Neuroimaging |
| author_facet | Prevot, Emma Shand, Cameron Oxtoby, Neil Initiative, for Alzheimer's Disease Neuroimaging |
| contents | Alzheimer's disease (AD) exhibits substantial clinical and biological heterogeneity, complicating efforts in treatment and intervention development. While new computational methods offer insights into AD progression, the reproducibility of these subtypes across datasets remains understudied, particularly concerning the robustness of subtype definitions when validated on diverse databases. This study evaluates the consistency of AD progression subtypes identified by the Subtype and Stage Inference (SuStaIn) algorithm using T1-weighted MRI data across 5,444 subjects from ANMerge, OASIS, and ADNI datasets, forming four independent cohorts. Each cohort was analyzed under two conditions: one using the full cohort, including cognitively normal controls, and another excluding controls to test subtype robustness. Results confirm the three primary atrophy subtypes identified in earlier studies: Typical, Cortical, and Subcortical, as well as the emergence of rare and atypical AD variants such as posterior cortical atrophy (PCA). Notably, each subtype displayed varying robustness to the inclusion of controls, with certain subtypes, like Subcortical, more influenced by cohort composition. This investigation underscores SuStaIn's reliability for defining stable AD subtypes and suggests its utility in clinical stratification for trials and diagnosis. However, our findings also highlight the need for improved dataset diversity, particularly in terms of ethnic representation, to enhance generalizability and support broader clinical application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00160 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How reproducible are data-driven subtypes of Alzheimer's disease atrophy? Prevot, Emma Shand, Cameron Oxtoby, Neil Initiative, for Alzheimer's Disease Neuroimaging Quantitative Methods Applications Alzheimer's disease (AD) exhibits substantial clinical and biological heterogeneity, complicating efforts in treatment and intervention development. While new computational methods offer insights into AD progression, the reproducibility of these subtypes across datasets remains understudied, particularly concerning the robustness of subtype definitions when validated on diverse databases. This study evaluates the consistency of AD progression subtypes identified by the Subtype and Stage Inference (SuStaIn) algorithm using T1-weighted MRI data across 5,444 subjects from ANMerge, OASIS, and ADNI datasets, forming four independent cohorts. Each cohort was analyzed under two conditions: one using the full cohort, including cognitively normal controls, and another excluding controls to test subtype robustness. Results confirm the three primary atrophy subtypes identified in earlier studies: Typical, Cortical, and Subcortical, as well as the emergence of rare and atypical AD variants such as posterior cortical atrophy (PCA). Notably, each subtype displayed varying robustness to the inclusion of controls, with certain subtypes, like Subcortical, more influenced by cohort composition. This investigation underscores SuStaIn's reliability for defining stable AD subtypes and suggests its utility in clinical stratification for trials and diagnosis. However, our findings also highlight the need for improved dataset diversity, particularly in terms of ethnic representation, to enhance generalizability and support broader clinical application. |
| title | How reproducible are data-driven subtypes of Alzheimer's disease atrophy? |
| topic | Quantitative Methods Applications |
| url | https://arxiv.org/abs/2412.00160 |