Cognition and motion dysfunction‐associated brain functional network disruption in diabetic peripheral neuropathy
Bibliographic record
Abstract
Abstract Neuroimaging studies have demonstrated extensive brain functional alterations in cognitive and motor functional areas in Type 2 diabetes mellitus (T2DM) with diabetic peripheral neuropathy (DPN), suggesting potential alterations in large‐scale brain networks related to DPN and associated cognition and motor dysfunction. In this study, using resting‐state functional connectivity (FC) and graph theory computational approaches, we investigated the topological disruptions of brain functional networks in 28 DPN, 43 T2DM without DPN (NDPN), and 32 healthy controls (HCs) and examined the correlations between altered network topological metrics and cognitive/motor function parameters in T2DM. For global topology, NDPN exhibited a significantly decreased shortest path length compared with HCs, suggesting increased efficient global integration. For regional topology, DPN and NDPN had separated topological reorganization of functional hubs compared with HCs. In addition, DPN showed significantly decreased nodal efficiency (Enodal), mainly in the bilateral superior occipital gyrus (SOG), right cuneus, middle temporal gyrus (MTG), and left inferior parietal gyrus (IPL), compared with NDPN, whereas NDPN showed significantly increased Enodal compared with HCs. Intriguingly, in T2DM patients, the Enodal of the right SOG was significantly negatively correlated with Toronto Clinical Scoring System scores, while the Enodal of the right postcentral gyrus (PoCG) and MTG were significantly positively correlated with Montreal Cognitive Assessment scores. Conclusively, DPN and NDPN patients had segregated disruptions in the brain functional network, which were related to cognition and motion dysfunctions. Our findings provide a theoretical basis for understanding the neurophysiological mechanism of DPN and its effective prevention and treatment in T2DM.
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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 | 0.001 |
| 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".