Voxel‐by‐Voxel regression analysis identifies association between postmortem TDP‐43 and antemortem fractional anisotropy within white matter fibers connected to the hippocampus
Bibliographic record
Abstract
Abstract Background TAR DNA‐binding protein 43 (TDP‐43), has been shown to be involved in various neurodegenerative disorders involving axonal damage including ALS, FTLD, and LATE. Studying the relationships between postmortem TDP‐43 and antemortem white matter (WM) structural integrity can allow for a better understanding of the disease, rather than exploring clinical presentations alone. Method In‐vivo diffusion‐weighted images were gathered on a 1.5T scanner from subjects from the Religious Orders Study and the Rush Memory and Aging Project. Images were processed using TORTOISE to calculate fractional anisotropy (FA). We utilized the IIT probabilistic WM atlas to identify voxels containing fibers that originate or terminate in the hippocampus. A semi‐quantitative rating of TDP‐43 severity was assessed in 5 brain regions. We utilized regression models to relate postmortem disease and antemortem FA within each voxel. Coexisting disease including ß‐amyloid plaques, tau tangles, and cerebrovascular disease were used as covariates, along with demographic variables. Result The 58 subjects were 91.10 (SD = 6.37) years old at death with a median interval from MRI to death of 2.8 years (SD = 1.19). Results revealed a significant negative relationship (p<0.05) between postmortem TDP‐43 and FA within fibers connecting the hippocampus to the parahippocampal and entorhinal cortices, fornix, and lingual gyrus. Preliminary results did not survive multiple comparisons correction. Conclusion Our findings indicate that greater TDP‐43 disease burden is associated with lower white matter integrity within regions of the limbic system. These findings suggest the TDP‐43 disease process may contribute to reduced structural connectivity between the hippocampus and related regions, which may impact larger brain networks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".