Associations between brain metabolism and financial capacity in Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Individuals with early stages of cognitive decline face a significant stagnation in their financial capacity, leading to a decrease in quality of life. However, whether changes in brain function are associated with financial capacity remains unclear. Here, we evaluate the association between financial capacity and brain glucose metabolism. METHOD: Individuals across the Alzheimer's disease (AD) clinical continuum with complete FCI-SF total scores and FDG-PET imaging data were selected from the ADNI dataset. We performed a Pearson correlation with 79 brain regions of interest, extracted using the ICBM152 atlas, and the total FCI score. A multiple linear regression model was fitted to assess the relationship between FCI and different brain regions, corrected for age, gender, and education. Analyses were adjusted by Bonferroni, with p considered significant if < 0.05. RESULT: A total of 1364 individuals were analyzed in this study (44% female, mean age 73.4). There were 26 brain areas in which glucose metabolism significantly correlated with FCI scores (adjusted p < 0.05, Figure 1A). The highest correlation coefficients were bilaterally located in the Caudate Nucleus and Fornix (r > 0.3, Figure 1A). The regression model demonstrated multiple significant brain regions (adjusted p < 0.05, Figure 1B). The left Fornix (β = 14.8, Figure 1C) and right Fornix (β = 13.7, Figure 1D), as well as the left Caudate Nucleus (β = 14.15, Figure 1E) and right Caudate Nucleus (β = 12.66, Figure 1F), had the highest significance (adjusted p < 0.001). CONCLUSION: We observed positive associations between regional brain glucose metabolism and the FCI-Score. Thus, our findings suggest a relationship between metabolic patterns, serving as an index of brain function, and the financial capacity of individuals.
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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 | 0.001 |
| 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".