In vivo normative atlas of the hippocampal subfields using multi‐echo susceptibility imaging at 7 Tesla
Bibliographic record
Abstract
OBJECTIVES: To generate a high-resolution atlas of the hippocampal subfields using images acquired from 7 T, multi-echo, gradient-echo MRI for the evaluation of epilepsy and neurodegenerative disorders as well as investigating R2* (apparent transverse relaxation rate) and quantitative volume magnetic susceptibility (QS) of the subfields. EXPERIMENTAL DESIGN: Healthy control subjects (n=17) were scanned at 7 T using a multi-echo gradient-echo sequence and susceptibility-weighted magnitude images, R2* and QS maps were reconstructed. We defined a hippocampal subfield labeling protocol for the magnitude image produced from the average of all echoes and assessed reproducibility through volume and shape metrics. A group-wise diffeomorphic registration procedure was used to generate an average atlas of the subfields for the whole subject cohort. The quantitative MRI maps and subfield labels were then warped to the average atlas space and used to measure mean values of R2* and QS characterizing each subfield. PRINCIPAL OBSERVATIONS: We were able to reliably label hippocampal subfields on the multi-echo susceptibility images. The group-averaged atlas accurately aligns these structures to produce a high-resolution depiction of the subfields, allowing assessment of both quantitative susceptibility and R2* across subjects. Our analysis of variance demonstrates that there are more apparent differences between the subfields on these quantitative maps than the normalized magnitude images. CONCLUSION: We constructed a high-resolution atlas of the hippocampal subfields for use in voxel-based studies and demonstrated in vivo quantification of susceptibility and R2* in the subfields. This work is the first in vivo quantification of susceptibility values within the hippocampal subfields at 7 T.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".