Functional network topology associated with apathy in Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Apathy, a common neuropsychiatric (NPS) in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), is associated with structural and metabolic brain changes. However, functional connectivity changes across the brain in association with apathy remain unclear. In this study, graph theoretical measures of integration and segregation from resting state functional connectivity in MCI and AD patients with low depression scores, and healthy controls. METHODS: In MCI and AD patients with low depression scores, graph theoretical measures of integration and segregation were derived from resting state functional connectivity in patients, which were compared between those with apathy (NPS_A, n = 21) to those without NPS (NPS_None, n = 28) and those with NPS other than apathy (NPS_NA, n = 38). Additionally, the same measures were compared between AD patients and healthy controls (amyloid uptake below threshold levels). RESULTS: Altered whole brain global efficiency and reduced local efficiency were found in NPS_A compared to NPS_None and NPS_NA. In similar contrasts, apathy was associated with increased participation coefficient in the frontoparietal and cingulo-opercular template-based networks. A study-specific network definition also showed similar results. In comparison, AD patients showed higher modularity compared to controls at the whole brain level and higher participation coefficient in the ventral attention network. LIMITATIONS: The severity and dimensions of apathy were not assessed. CONCLUSIONS: Loss of segregation in the frontoparietal and cingulo-opercular network, which are involved in the control of goal-directed behavior, was associated with apathy in MCI/AD. The results also suggest that network-level changes in AD patients may underlie specific NPS.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".