Hippocampal subfield deformation shows unique patterns associated with amyloid‐beta, TDP‐43, and PHF‐tau burden
Bibliographic record
Abstract
Abstract Background Alzheimer's dementia (AD) is the most common form of dementia in adults over the age of 65, however, current diagnostic tools need to be improved. The relationships between clinical syndromes and pathological causes are complex, which makes accurate diagnosis difficult. The goals are to develop an in vivo hippocampal surface atlas from structural MRI that is predictive of postmortem β‐amyloid, paired helical filament (PHF‐tau) neurofibrillary tangles (NFTs) and transactive response DNA‐binding protein‐43 (TDP‐43) neuropathologies. Method Using a sample of 101 older adults from two longitudinal cohort studies conducted by the Rush Alzheimer’s Disease Center, we utilized hippocampal shape analysis of ante‐mortem T1‐weighted sMRI to generate surfaces for the whole hippocampus and zones approximating the underlying subfields using a previously developed automated image‐segmentation pipeline (Freesurfer‐Initiated Large Deformation Diffeomorphic Metric Mapping; FSLDDMM). Multivariate linear regression models were constructed to examine the relationship between shape and pathology measures while accounting for covariates which include co‐existing pathologies and other neuropathological variables (hippocampal sclerosis, Lewy bodies, gross infarcts, atherosclerosis, arteriosclerosis, and cerebral amyloid angiopathy). These relationships were mapped onto hippocampal surface locations. In a previous sample of 42 subjects from the same cohort, univariate models were not able to be examined due to low power. Result A significant and unique pattern of deformation for each neuropathology when accounting for covariates were seen. Specifically, β‐amyloid was associated with a significant inward deformation in zones approximating the subiculum, where PHF‐tau NFTs were associated with a significant inward deformation along the left hippocampal tail within the subiculum. TDP‐43 inclusions were associated with a significant inward deformation in the body of the hippocampus along the CA1/subiculum border. Results were corrected for multiple comparisons using the random field theory (RFT) with a family‐wise error rate (FWER) < 0.05. Conclusion These results indicate a unique pattern of deformation due to individual neuropathology, after accounting for covariates. With the presented increased sample size, a significant TDP‐43 signature arose. Results indicate that hippocampal deformation may be used to represent a biomarker of individual post mortem disease which could allow for the early and accurate diagnosis of disease and aid in the selection for clinical trials.
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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 | 0.000 |
| 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 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".