| Autor: |
Kellen K. Petersen, Bhargav T. Nallapu, Richard B. Lipton, Ellen Grober, Ali Ezzati |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Neuroimage: Reports, Vol 4, Iss 4, Pp 100227- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-9560 |
| DOI: |
10.1016/j.ynirp.2024.100227 |
| Popis: |
Introduction: Alzheimer's disease (AD) is a phenotypically and pathologically heterogenous neurodegenerative disorder. This heterogeneity can be studied and disentangled using data-driven clustering techniques. Methods: We implemented a self-organizing map clustering algorithm on baseline volumetric MRI measures from nine brain regions of interest (ROIs) to cluster 1041 individuals enrolled in the placebo arm of the EXPEDITION3 trial. Volumetric MRI differences were compared among clusters. Demographics as well as baseline and longitudinal cognitive performance metrics were used to evaluate cluster characteristics. Results: Three distinct clusters, with an overall silhouette coefficient of 0.491, were identified based on MRI volumetrics. Cluster 1 (N = 400) had the largest baseline volumetric measures across all ROIs and the best cognitive performance at baseline. Cluster 2 (N = 269) had larger hippocampal and medial temporal lobe volumes, but smaller parietal lobe volumes in comparison with the third cluster (N = 372). Significant between-group mean differences were observed between Clusters 1 and 2 (difference, 2.38; 95% CI, 1.85 to 2.91; P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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