The neural correlates of emotional conflict monitoring as an early manifestation of affective and cognitive declines in persons with Type 2 diabetes
Bibliographic record
Abstract
Patients with Type 2 diabetes are known to be more susceptible to experience dementia and depression/anxiety. The neural circuits of emotional conflict monitoring, as indicated by a Stroop task, might become altered in terms of cognitive and affective impairments in diabetes. This study investigated alterations in the emotional conflict monitoring and associations of corresponding brain activities with metabolic parameters in persons with Type 2 diabetes. Participants with normal cognitive and affective functioning, including 40 persons with Type 2 diabetes and 30 non-diabetes control subjects, underwent a functional MRI paradigm with the face-word emotional Stroop task and detailed cognitive and affective assessments, including the Montreal Cognitive Assessment and Beck Anxiety Inventory. Compared with the controls, people with diabetes exhibited stronger emotional interference, as indicated by differential reaction times between congruent and incongruent trials (Δcon). Δcon was correlated with Montreal Cognitive Assessment test scores and fasting glucose levels. People with diabetes demonstrated altered brain activation and functional connectivity in the neural network for emotional conflict monitoring. The neural network for emotional conflict monitoring mediated the association of pancreatic function with anxiety scores as well as the relationship between Δcon and Montreal Cognitive Assessment scores. Results suggested that alterations in the neural network underlying emotional conflict monitoring might present before clinically measurable cognitive and affective decrements were apparent, thereby bridging the gap between dementia and anxiety/depression in persons with diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".