Subcortical deformation is uniquely related to cortical thickness among FTLD mutation carriers
Bibliographic record
Abstract
Abstract Background Frontotemporal lobar degeneration (FTLD) due to familial mutations results in heterogenous clinical phenotypes for which there are no specific biomarkers. Here, we test whether subcortical deformation differs in FTLD genetic mutation carriers, and if deformation was uniquely related to cortical thickness. Method 317 participants from ALLFTD with familial FTLD (C9orf72 (n = 139), GRN (n = 77), and MAPT (n = 101)) and 27 controls without mutations (NCC) were evaluated. MRI data underwent FreeSurfer processing to estimate cortical thickness (CT). High‐dimensional, large‐deformation diffeomorphic metric mapping was used to model surfaces of the caudate and putamen. Regression analyses investigated the effect of mutation on CT and subcortical surface deformation, after controlling for age, sex, and education. Finally, linear models were constructed in each mutation group to explore the association between areas of significant surface deformity and CT, corrected for age and multiple comparisons. Result Figure 1 shows significant (corrected p<.05) T‐scores where mutation carrier groups differ in CT from NCC (red = greater atrophy in mutation). Figure 2 shows greater outward local deformation in the caudate body among C9 and MAPT compared to NCC. Significant relationships between deformation and CT (corrected p<.05) were observed in bilateral temporoparietal, frontal and cingulate cortex in both C9 and MAPT carriers. C9 and MAPT mutation carriers have greater outward deformation in postero‐lateral putamen. Deformation was associated with CT in C9 within the left temporal and prefrontal lobes, and in the right temporoparietal, occipital and cingulate cortices for MAPT (Figure 3). No significant deformation was observed in GRN compared to NCC. Conclusion Familial FTLD mutations are associated with subtle subcortical shape variations that show unique relationships to cortical atrophy.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".