MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps
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 volum...
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Elsevier
2024-12-01
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| Series: | NeuroImage: Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666956024000333 |
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| author | Kellen K. Petersen Bhargav T. Nallapu Richard B. Lipton Ellen Grober Ali Ezzati |
| author_facet | Kellen K. Petersen Bhargav T. Nallapu Richard B. Lipton Ellen Grober Ali Ezzati |
| author_sort | Kellen K. Petersen |
| collection | DOAJ |
| description | 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 < 0.001), Clusters 1 and 3 (difference, 1.93; 95% CI, 1.41 to 2.44; P < 0.001), but not between Clusters 2 and 3 (difference, 0.45; 95% CI, −0.11 to 1.02; P = 0.146) in ADAS-14. Conclusions: Volumetric MRI can be used to identify homogenous clusters of amyloid positive individuals with mild dementia. The groups identified differ in baseline and longitudinal characteristics. Cluster 1 shows little ADAS-14 change over the first 40 weeks of study on placebo treatment and may be unsuitable for identifying early benefits of treatment. |
| format | Article |
| id | doaj-art-6c544ce44de2425788dfecc50d6f8bc7 |
| institution | OA Journals |
| issn | 2666-9560 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Reports |
| spelling | doaj-art-6c544ce44de2425788dfecc50d6f8bc72025-08-20T01:57:11ZengElsevierNeuroImage: Reports2666-95602024-12-014410022710.1016/j.ynirp.2024.100227MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing mapsKellen K. Petersen0Bhargav T. Nallapu1Richard B. Lipton2Ellen Grober3Ali Ezzati4Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Corresponding author.The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USAThe Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USAThe Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USAThe Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA; Department of Neurology, University of California-Irvine, Irvine, CA, USAIntroduction: 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 < 0.001), Clusters 1 and 3 (difference, 1.93; 95% CI, 1.41 to 2.44; P < 0.001), but not between Clusters 2 and 3 (difference, 0.45; 95% CI, −0.11 to 1.02; P = 0.146) in ADAS-14. Conclusions: Volumetric MRI can be used to identify homogenous clusters of amyloid positive individuals with mild dementia. The groups identified differ in baseline and longitudinal characteristics. Cluster 1 shows little ADAS-14 change over the first 40 weeks of study on placebo treatment and may be unsuitable for identifying early benefits of treatment.http://www.sciencedirect.com/science/article/pii/S2666956024000333Alzheimer's diseaseStructural MRIUnsupervised learningMachine learningSubtypesMild dementia |
| spellingShingle | Kellen K. Petersen Bhargav T. Nallapu Richard B. Lipton Ellen Grober Ali Ezzati MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps NeuroImage: Reports Alzheimer's disease Structural MRI Unsupervised learning Machine learning Subtypes Mild dementia |
| title | MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps |
| title_full | MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps |
| title_fullStr | MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps |
| title_full_unstemmed | MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps |
| title_short | MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps |
| title_sort | mri guided clustering of patients with mild dementia due to alzheimer s disease using self organizing maps |
| topic | Alzheimer's disease Structural MRI Unsupervised learning Machine learning Subtypes Mild dementia |
| url | http://www.sciencedirect.com/science/article/pii/S2666956024000333 |
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