Association of cortical morphology, white matter hyperintensity, and glymphatic function in frontotemporal dementia variants
Bibliographic record
Abstract
INTRODUCTION: Frontotemporal dementia (FTD) can be phenotypically divided into behavioral variant FTD (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA). However, the neural underpinnings of this phenotypic heterogeneity remain elusive. METHODS: Cortical morphology, white matter hyperintensities (WMH), diffusion tensor image analysis along the perivascular space (DTI-ALPS), and their interrelationships were assessed in subtypes of FTD. Neuroimaging-transcriptional analyses on the regional cortical morphological deviances among subtypes were also performed. RESULTS: Changes in cortical thickness, surface area, gyrification, WMH, and DTI-ALPS were subtype-specific in FTD. The three morphologic indices are related to whole-brain WMH volume and cognitive performance, while cortical thickness is related to DTI-ALPS. Neuroimaging-transcriptional analyses identified key biological pathways linked to the formation and/or spread of TDP-43/tau pathologies. DISCUSSION: We found subtype-specific changes in cortical morphology, WMH, and glymphatic function in FTD. Our findings have the potential to contribute to the development of personalized predictions and treatment strategies for this disorder. HIGHLIGHTS: Cortical morphologic changes, white matter hyperintensities (WMH), and glymphatic dysfunction are subtype-specific. Cortical morphologic changes, WMH, and glymphatic dysfunction are inter-correlated. Cortical morphologic changes and WMH burden contribute to cognitive impairments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".