P1‐137: Development of an <i>in vivo</i> magnetic resonance imaging method to evaluate hippocampus volume in APP and PS1 transgenic mice
Bibliographic record
Abstract
One of the hallmark pathological features of Alzheimer's disease is atrophy of the brain and the hippocampus, which could be used as an early biomarker of the disease. MRI can be used as a means to detect and monitor this early biomarker. Imaging transgenic mouse models of Alzheimer's disease is valuable to understand better the structural changes that occur in the brain and could provide a means to test pharmacological treatments. Unfortunately no clearly described repeatable method for hippocampus volume measurements using MRI in live mice exists which examines in detail the accuracy of the measurements. This work successfully created a method to measure hippocampus volumes in live mice and serves as a first step toward understanding a possible biomarker for the disease. An in-vivo T 2 -weighted imaging method was developed with optimal contrast and resolution to allow for straightforward segmentation and volume determination of the hippocampus in mice. 7-month old, single transgenic mice, expressing either a chimeric mouse/human amyloid precursor protein (APP) mutation or a mutant human presenilin 1 (PS1) were imaged multiple times using 3D T 2 -weighted imaging on a 7T magnet (Figure 1) to establish the method and determine its accuracy. To calculate the volume, the hippocampus of each mouse was both manually segmented by three individuals and segmented with a semi-automated method (Figure 2). The images were also registered using an affine transformation to determine shape changes. Representative in-vivo T2-weighted image of a mouse brain (hippocampus region) A manually segmented hippocampus slice A scatter plot of manual method versus the semi-automated method of volume determination of the hippocampus expressed as fractional volume change The hippocampus volumes ranged from 18 mm 3 to 23 mm 3 when done by the manual segmentation method. The semi-automated method gave 30% larger volumes as compared to the manual method (Figure 3). The kappa index, a measure of overlap between manually and automatically segmented brain structures, had values of > 0.86 for each compared image. The registration data showed that there were no significant shape changes for any of the hippocampi. A reliable method able to detect 1 mm 3 volume measurements when performed by a single individual was developed. The semi-automated segmentation was unable to detect differences. These results suggest that manual segmentation is still considered the most reliable segmentation method for small structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".