Disentangling disease heterogeneity from neuroimaging data via adaptive distribution modeling–based collaborative clustering
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".