Cognitive Dysfunction in Type 2 Diabetes Is Not a One-Way Process: Evidence From a Longitudinal Brain Connectivity Study
Bibliographic record
Abstract
Background: Cognitive dysfunction is an important comorbidity of diabetes characterized by brain functional hypo-connectivity. However, our recent study demonstrated an adaptive hyper-connectivity in young type 2 diabetes with cognitive decrements. This longitudinal study aimed to further explore the changes in functional connectivity and cognitive outcomes after regular glycemic control. Methods: At 18 months after recruitment, participants underwent a second cognitive assessment and magnetic resonance imaging. Three enhanced functional connectivities previously identified at baseline were followed up. Linear mixed-effects models were performed to compare the longitudinal changes of cognition and functional connectivity in patients with type 2 diabetes and non-diabetic controls. A linear regression model was used to investigate the association between changes in functional connectivity and changes in cognitive performance. Results: Improvements in multiple cognitive domains were observed in diabetes; however, the enhanced functional connectivity at baseline decreased significantly. Moreover, the decrease in hippocampal connectivity was correlated with an increase in the accuracy of Stroop task and the decrease in posterior cingulate cortex connectivity was correlated with an increase in Montreal Cognitive Assessment in diabetes. Conclusion: This study suggests diabetes-related cognitive dysfunction is not a one-way process and the early-stage enhancement of brain connectivity was a potential "window period" for cognitive reversal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".