Bibliographic record
Abstract
There is a growing need for biomarkers of Alzheimer's Disease (AD) to aid in early detection, tracking of treatment response, and drug development. Functional magnetic resonance imaging (fMRI) has been a focus of efforts, offering a dynamic view of the affected organ. We first examined the existing fMRI literature in AD research using a quantitative meta-analysis of episodic memory studies comparing AD patients and healthy older adults. We found a consistent loss of medial temporal lobe (MTL) activation in patients, but both reduced and increased cortical activation outside of the MTL, including areas of increased activation likelihood in the ventral lateral prefrontal cortex (VL-PFC). These findings suggest some evidence for compensatory hyperactivation in individuals with AD. We next collected both task- and resting-state fMRI data in 16 individuals with mild AD and 13 healthy older adults, and examined the default mode network (DMN) dysfunction in order to determine whether DMN abnormalities vary depending on how they are measured (i.e., rest vs. task). Patients showed resting state deficits in the multiple regions but none during task completion. The change in DMN connectivity in the posterior cingulate between rest and task was predictive of cognitive status in patients, while measures at rest or task alone were not. This suggests that a measurement of change in DMN connectivity may provide unique clinical information unavailable to a single state scan. In chapter 4 we examined the sensitivity of a simple visual task and resting state DMN measures to pharmacological treatment with a cholinesterase inhibitor (ChEI) in AD. Twenty-three patients with AD and 13 healthy matched controls were scanned twice, an average of 7 months apart, with patients receiving ChEI treatment after scan 1. ChEI treatment was associated with increases in visual, parietal and VL-PFC activation in patients, which persisted after controlling for perfusion in individuals with perfusion MRI data. DMN connectivity was disrupted at baseline within the right MTL and showed increased left MTL coactivation with treatment. Controls showed no changes over time. These results suggest that long term ChEI treatment is associated with changes in task-relevant cortical activation and MTL-DMN connectivity, but these changes were not associated with measures of clinical status.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".