Domain-specific brain regions are associated with cognitive impairment in progressive supranuclear palsy
Bibliographic record
Abstract
Background: Cognitive impairment significantly contributes to the disease burden of progressive supranuclear palsy (PSP), however, the underlying pathophysiologiy is not well understood. Objectives: To gain a better understanding of the pathophysiology, we identified the brain regions associated with individual domains of impaired cognition. Methods: We analyzed MRI data from a cohort of 31 patients with PSP (age 71.0 +-7.0 years, range 58-87; 15 females; disease duration 2.9 +- 1.8 years). Cerebral microstructure was approximated with Diffusion Microstructure Imaging and cognitive performance was measured using the Frontal Assessment Battery (FAB) and Montreal Cognitive Assessment (MoCA). To reveal the underlying affected brain regions, whole-brain voxel-wise associations were employed to test the microstructural metrics regarding their correlation with the FAB as well as the individual cognitive domains 'Attention', 'Execution', 'Language', 'Memory', 'Orientation', and 'Visuoconstruction' derived from MoCA. Results: MoCA performance was impaired in 87.5% of patients (20.2 +- 5.4 points, range 8-28; cut-off value: <26/30). In the voxel-wise analyses, we noted significant associations of cerebral microstructure and FAB in the right-sided frontal and temporopolar white matter, deficits in 'Memory' with hippocampal and temporomesial regions, in reduced 'Orientation' with wide spread white-matter areas with a parietal accentuation, whereas deficits in 'Attention' correlated with frontal and prefrontal structures. Conclusions: Diffusion Microstructure Imaging revealed domain-specific regions of neurodegenerative alterations in PSP. The regions identified in this approach integrate well in existing disease concepts. They might therefore be a possible biomarker for cognitive impairment, as well as amonitoring parameter for future disease modifying therapeutics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".