Exploring heterogeneity in Motoric Cognitive Risk Syndrome using Volumetric MRI‐guided Clustering
Bibliographic record
Abstract
Abstract Background The Motoric Cognitive Risk Syndrome (MCR) is a predementia stage characterized by slow gait speed and subjective cognitive complaints. Defining the heterogeneity of brain volumetrics in individuals with MCR will improve current dementia risk assessments. Method We used data from 6 cohorts from the MCR consortium (N=2,007). We used K‐means clustering algorithm guided by volumetric MRI to identify distinct subgroups of participants. We compared the differences in cortical and subcortical volumes, comorbidities, and gait speeds across the identified subgroups using one‐way ANOVA and post‐hoc pairwise group comparisons. Result The sample had a mean age of 71.89 (±7.05) years, 48.7% were women, 32.9% were White and 63.2% were Asian (see Table 1). Four subgroups (A to D) were identified through MRI‐based clustering with significant differences in brain region volumes (Figure 1A), gait speeds (Figure 1B) and proportion of individuals with MCR (Figure 2). Subgroups A and C had the least amount of atrophy in all brain regions and had the least proportion of MCR. Subgroup D had the highest proportion of MCR subjects and highest atrophy specifically in hippocampus and cortical regions. The average gait speed of Subgroup D was lower than other subgroups. Subgroup D also had the highest rate of hypertension and diabetes among the subgroups (Figure 2). Conclusion Our results validate the previous findings linking MCR syndrome to MRI evidence of neurodegeneration. Heterogeneity in cortical and subcortical signatures are present in older adults and provides insights into brain substrates of MCR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".