Comprehensive Review on Magnetic Resonance Imaging in Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common cause of dementia in the elderly. However, definitive diagnosis of AD is only achievable postmortem and currently relies on clinical neurological evaluation. Magnetic resonance imaging (MRI) can evaluate brain changes typical of AD, including brain atrophy, presence of amyloid β (Aβ) plaques, and functional and biochemical abnormalities. Structural MRI (sMRI) has historically been used to assess the inherent brain atrophy present in AD. However, new techniques have recently emerged that have refined sMRI into a more precise tool to quantify the thickness and volume of AD-sensitive cerebral structures. Aβ plaques, a defining pathology of AD, are widely believed to contribute to the progressive cognitive decline in AD, but accurate assessment is only possible on autopsy. In vivo MRI of plaques, although currently limited to mouse models of AD, is a very promising technique. Measuring changes in activation and connectivity in AD-specific regions of the brain can be performed with functional MRI (fMRI). To help distinguish AD from diseases with similar symptoms, magnetic resonance spectroscopy (MRS) can be used to look for differing metabolite concentrations in vivo. Together, these MR techniques, evaluating various brain changes typical of AD, may help to provide a more definitive diagnosis and ease the assessment of the disease over time, noninvasively.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".