IC‐P‐068: Declines in entorhinal cortex structural connectivity in amnestic mild cognitive impairment
Bibliographic record
Abstract
Amnestic mild cognitive impairment (aMCI) is defined by objective memory deficits in non-demented individuals. Nearly 80% of these cases are carriers of Alzheimer's pathology. Since the entorhinal cortex (EC) is first affected by AD pathology, the integrity of white matter (WM) connections between the EC and the hippocampus is of interest. In this study, we intend to evaluate EC structural connectivity using probabilistic fiber tracking in order to expand on traditional diffusion measurements such as fractional anisotropy (FA). T1 (Siemens 3T; ADNI protocol), and diffusion (99 directions, b = 1000; 10 b = 0) images were sequentially acquired. FA maps were generated using FSL-FDT. EC-to-brain probabilistic connectivity maps were generated using an in-house pipeline based on FSL-FDT. In brief, (1) at each voxel, a probability distribution of fiber direction was generated; (2) single voxel level probabilistic maps were generated for every voxel of the seed region; (3) a regional probabilistic map is computed via max function of the single voxel level maps; (4) regional maps were resampled to the MNI152 space nonlinearly and blurred. Voxel-based Wilcoxon group statistics were obtained by comparing controls to individuals with aMCI. 19 aMCI and 15 age-gender matched controls completed the protocol. Figure A-C shows average EC probabilistic maps obtained in controls. These maps reproduced the well-known EC connectivity from gold-standard anatomical studies. T-statistical group differences of FA (hot-color) and Wilcoxon group difference of EC probabilistic fiber tracking method (spectral-color) are depicted in figures D-H. The left EC seed region reveal a cluster in the left inferior longitudinal fascicle (D,E). The right EC seed region revealed a statistical cluster in the angular bundle (G,H). FA group differences were prominent in the inferior longitudinal fascicle but did not overlap with the EC group differences.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".