Disentangling disease heterogeneity from neuroimaging data via adaptive distribution modeling–based collaborative clustering. (December 2021)

• Record Type: Journal Article
• Authors: Liu, Hangfan; Grothe, Michel J.; Rashid, Tanweer; Labrador‐Espinosa, Miguel; Toledo, Jon B.; Habes, Mohamad
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.053118
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Neurodegenerative diseases, including Parkinson's disease (PD), are clinically heterogeneous, with distinct subtypes encompassed within the same clinical diagnosis. Heterogeneity in brain diseases makes it challenging to recruit ideal patients into trials for suitable treatment or develop markers that capture the disease's signals. Data‐driven clustering coupled with high dimensional neuroimaging data could further our understanding of heterogeneous disease biology and assigning specific patients groups to individualized treatments. Method: We propose an Adaptive Distribution based COllaborative Clustering (ADCoC) method, which simultaneously clusters subjects and features via nonnegative matrix tri‐factorization. For denoising, we further introduce adaptive regularization based on coefficient distribution modeling. Unlike related sparsity techniques [1], we form the distributions using the data of interest to better fit the coefficients. We studied structural MRI data from 170 PD patients and 77 healthy controls enrolled in the Parkinson Progression Markers Initiative (PPMI). ADCoC partitioned the patients into 2 clusters. We compared the distribution of ICV normalized grey matter volumes in different regions between the two patient clusters and the healthy control group by student's t‐test. Then we analyzed neuropsychological test data to compare the cognitive performance of the two patient clusters and the control group. Result: Test statistic values of regions with FDR corrected p‐ values smaller than 0.05 are plotted in Figure 1. Comparisons with the healthy control group show that PD cluster PD‐Post (54.7% of the PD samples) is characterized by a posterior cortical‐medial temporal atrophy pattern with worse cognition compared to PD cluster PD‐Front which had atrophy circumscribed to the frontal lobe areas. Analysis of neuropsychological test data further demonstrates that these MRI‐defined patient subtypes also show differences in clinical presentation, where PD‐Post show statistically significant lower cognitive performance compared to PD‐Front (Montreal Cognitive Assessment [MoCA] scores: 27.1 ± 2.4 [PD‐Post] vs 27.9 ± 1.7 [PD‐Front], p = 0.014). Conclusion: When applied to a clinical dataset of MRI data from PD patients, ADCoC identified two stable and highly reproducible patient clusters characterized by frontal and posterior cortical‐medial temporal atrophy patterns. References: [1] H. Liu et al ., "Adaptive sparsity regularization based collaborative clustering for cancer prognosis, " MICCAI' 19.