High resolution imaging of the hippocampus at 7T
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is a powerful medical imaging technique that provides detailed visualization of internal structures in vivo, making it an indispensable tool in diagnosing and monitoring brain-related diseases and pathologies. In this thesis, we focus on the hippocampus, a critical brain region for memory and learning, and investigate its susceptibility, volume, and vascularization using high-resolution MRI at 7T. Firstly, we estimate the reproducibility of hippocampal subfield susceptibility and volume in healthy participants to determine the precision required for longitudinal studies aimed at tracking changes in the hippocampus in Alzheimer's disease. Secondly, we compare the susceptibility and volume of hippocampal subfields between healthy controls and Alzheimer's disease patients to investigate whether they can be used as biomarkers for early detection of AD. We also examine the relationship between hippocampal subfield measurements, the abnormal protein Amyloidβ1-42 in cerebrospinal fluid (CSF), and cognitive performance measured by the Montreal Cognitive Assessment (MoCA). Thirdly, we investigate the non-invasive visualization of hippocampal vasculature to better understand its pattern, which is critical for surgical planning. We aim to map the vascularity of the hippocampus in vivo without contrast agents to gain insight into the relationship between vascular and degenerative pathology of the hippocampus. Our findings have implications for the diagnosis, monitoring, and treatment of Alzheimer's disease and provide insights into the structure and function of the hippocampus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".