A multi‐country, multi‐cohort examination of cortical volume, thickness, and surface area in the motoric cognitive risk (MCR) syndrome
Bibliographic record
Abstract
Abstract Background The motoric cognitive risk (MCR) syndrome is characterized by slow gait and cognitive complaint, and increases the risk for both Alzheimer’s disease and vascular dementia. Our recently established MCR neuroimaging consortium aims to identify the brain substrates and pathologies in MCR – and consists of > 3,000 MRIs from 6 different older adult cohorts and 5 different countries. The current study examined cortical volume, thickness, and surface area in MCR using a subset of 200 older adults from 4 different cohorts/countries, as a function of image processing methods that involved manual intervention and no manual intervention. Method Fifty MRIs from each of the four cohorts were examined (N = 200). FreeSurfer Version 6.0 and general linear statistical models with 1,000 bootstrapped samples (n‐1, with resampling) were used to determine if cortical volume (mm3), thickness (mm) and surface area (mm2) overall and in 34 different cortical regions were associated with MCR – following no manual intervention and manual error correction in the cortical surface. All models were adjusted for age, sex, education, white matter hyperintensities, total intracranial volume, and cohort status. Result The mean age was 72.62 years and 33 % met criteria for MCR. Overall cortical thickness – but not cortical volume or surface area – was different in older adults with MCR relative to those without MCR, although a trend in the expected direction was observed in cortical volume (p <.051). Smaller cortical thickness in MCR was pervasive, and included prefrontal (caudal middle frontal, medial orbitofrontal, pars orbitalis, pars opercularis, pars triangularis), insular, cingulate (posterior, isthmus), parietal (inferior parietal, precuneus), temporal (superior temporal, supramarginal) and precentral regions. The relationship between cortical volume, cortical thickness, surface area and MCR did not change following manual interventions (all 95% CIs overlapped). Conclusion These results suggest that a) cortical thinning in MCR is pervasive and involve regions associated with a number of social, cognitive, affective and motor functions, b) cortical thickness is more sensitive than cortical volume and surface area to the cortical changes observed in MCR, and c) that manual intervention does not influence the relationship between cortical volume, thickness, area and MCR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".