Verifying small vessel disease and mild cognitive impairment with a computational мagnetic resonance imaging analysis
Bibliographic record
Abstract
Aim. To illustrate capabilities of the computational brain мagnetic resonance imaging (MRI) analyses on a small vessel disease (SVD) sample. Materials and methods. Thirty-one patients underwent brain MRI in standard sequences. We used Lesion Segmentation Tool to assess white matter hyperintensities (WMH) volume and Computational Anatomy Toolbox to calculate cortical thickness. Both software plug-ins work within the Statistical Parametric Mapping 12 software for MATLAB. We also performed cognitive testing with the Montreal Cognitive Assessment test and tests to detect hippocampal and executive domain dysfunction. Results. Sixteen patients had mild vascular cognitive impairment. The Median Fazekas scale score was 2 and 2 points. The median intracranial volume fraction occupied by the WMH was 0.07%. It correlated with the executive domain performance but not with cortical thickness. Cortical thickness within several clusters of the prefrontal complex and temporal lobe correlated with performance in cognitive tests. Among the computed MRI markers of the SVD, the occipital lobe cortical thickness had an area under the curve of 70%, and among the cognitive tests, the cued recall measure had an area under the curve of 73.8% to detect mild cognitive impairment. Conclusion. The abovementioned metrics is a valuable tool to objectively estimate white and grey matter state in patients with small vessel disease. Performing those analyses helped to assess SVD properties in the sample further and register new correlations between MRI and cognitive markers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".