Neuroimaging Biomarkers in Neuropsychiatric Symptom Clusters
Bibliographic record
Abstract
Abstract Background Neuropsychiatric symptoms (NPS) constitute a major challenge for patients with Alzheimer’s disease (AD). We have recently demonstrated that in AD, overall NPS burden is significantly associated with patient function. However, few studies have examined the relationship between specific symptom clusters with neurological biomarkers. Therefore, we identified NPS clusters in AD and explored their association with structural neuroimaging markers. Method Participants with AD (N = 111) were included from the Ontario Neurodegenerative Disease Research Initiative (ONDRI). NPS were assessed using the neuropsychiatric inventory questionnaire (NPI‐Q), and symptom clusters were identified through exploratory factor analysis. The Semi‐Automatic Brain Region Extraction (SABRE) pipeline was used to compute regional cortical thickness and subcortical volumes using participant MRI data. We then evaluated correlations between symptom clusters with subcortical volumes and regional cortical thickness. Result Factor analysis identified four symptom clusters explaining 62% of the variance. These were labeled as “behavioral” (disinhibition, irritability, motor disturbance, and agitation), “psychotic” (hallucinations, delusions, and euphoria), “neurovegetative” (apathy and appetite), and “affective” (depression, anxiety, nighttime behavior) clusters. The psychotic cluster was associated with increased cortical thickness in bilateral frontal regions, the left inferior parietal lobe (r=0.23, p=0.02), and with left anterior cingulate volumes (r=0.21, p=0.03). The neurovegetative cluster was associated with reductions in volume among bilateral frontal and right‐sided temporal and parietal regions. The affective cluster was associated with increased left anterior cingulate volume (r=0.21, p=0.03). Conclusion NPS symptom clusters in AD separate into behavioral, psychotic, neurovegetative, and affective dimensions. These symptom groups demonstrate unique associations with neuroimaging markers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".