Functional connectivity gradients and neurotransmitter maps among patients with mild cognitive impairment and depression symptoms
Bibliographic record
Abstract
Background: Both depressive symptoms and neurotransmitter changes affect the characteristics of functional brain networks in clinical patients. We sought to explore how brain functional grading is organized among patients with mild cognitive impairment and depressive symptoms (D-MCI) and whether changes in brain organization are related to neurotransmitter distribution. Methods: Using 3 T magnetic resonance imaging (MRI) we acquired functional MRI (fMRI) data from patients with D-MCI, patients with mild cognitive impairment without depression (nD-MCI), and healthy controls. We used resting-state fMRI and diffusion embedding to examine the pattern of functional connectivity gradients. We used analysis of covariance and post hoc t tests to compare the difference in functional connectivity gradients among the 3 groups. We examined the correlation between variations in functional connectivity gradients and neurotransmitter maps using the JuSpace toolbox. Results: We included 105 participants, including 31 patients with D-MCI, 40 patients with nD-MCI, and 34 healthy controls. Compared with healthy controls, both the nD-MCI and D-MCI groups showed abnormalities in the principal unimodal–transmodal gradient pattern. Compared with controls, the D-MCI group showed an increased secondary gradient in the default mode network. Differences in the functional connectivity gradients between the D-MCI and nD-MCI groups were significantly correlated with the distribution of 5-hydroxytryptamine receptor subtype 1A. Limitations: The small sample size affects the generalizability of the results, and the neurotransmitter template is based on healthy participants, not patients. Conclusion: Our results suggest that depressive symptoms cause abnormalities in the hierarchical segregation of functional brain organization among patients with MCI. Such abnormal changes may be related to the distribution of neurotransmitters.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".