Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity
Why this work is in the frame
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Bibliographic record
Abstract
Majority of type 2 diabetes mellitus (T2DM) patients are highly susceptible to several forms of cognitive impairments, particularly dementia. However, the underlying neural mechanism of these cognitive impairments remains unclear. We aimed to investigate the correlation between whole brain resting state functional connections (RSFCs) and the cognitive status in 95 patients with T2DM. We constructed an elastic net model to estimate the Montreal Cognitive Assessment (MoCA) scores, which served as an index of the cognitive status of the patients, and to select the RSFCs for further prediction. Subsequently, we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected RSFCs. The estimated and chronological MoCA scores were significantly correlated with R = 0.81 and the mean absolute error (MAE) = 1.20. Additionally, cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54% and the area under the receiver operating characteristic (ROC) curve (AUC) of 0.9737. This connectivity pattern not only included the connections between regions within the default mode network (DMN), but also the functional connectivity between the task-positive networks and the DMN, as well as those within the task-positive networks. The results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it