Interaction between amyloid and neuroinflammation on apathy along the Alzheimer’s disease spectrum
Bibliographic record
Abstract
Abstract Background The Apathy Inventory is a rating scale given to informants in order to assess levels of apathy globally. This neuropsychiatric symptom is the most common noncognitive symptom in Alzheimer’s disease (AD), and it causes a great burden on diseased individuals and their caregivers. Even though it has been closely related to cerebrospinal fluid levels of neuroinflammation and amyloid, the interactive effect of pathologies is still unclear. Here we test the interaction between amyloid and neuroinflammation in the brain with apathy, along the AD spectrum. Method We assessed 59 individuals (38 cognitively unimpaired, 15 mild‐cognitive impairment, 6 AD) with [11C]PBR28‐neuroinflammation positron‐emission tomography (PET) and [18F]AZD4694‐amyloid PET. [11C]PBR28 and [18F]AZD4694 uptake value ratios (SUVRs) used the cerebellum grey matter as the reference region and were calculated 0‐90 min post‐injection and 40‐70 min post‐injection respectively. A voxel‐based regression model evaluated the relationship between the interaction of [18F]AZD4694 and [11C]PBR28 with Apathy scores. The model’s covariates were age, gender, education and diagnoses of the participants. Result We found a strong positive correlation between the interaction of [18F]AZD4694 and [11C]PBR28 with apathy. The most impacted regions were the medialfrontal cortex, in the superior portion and prefrontal cortex, as well as the anterior nucleus of the thalamus. Conclusion These preliminary results corroborate the link between amyloid and neuroinflammation, that potentiating the neuropsychiatric symptom apathy. The regions impacted are involved in proper behavior, such as the mediofrontal cortex. This study supports previous findings showing that amyloid and neuroinflammation are related to apathy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".