Brain and Body: A Review of Central Nervous System Contributions to Movement Impairments in Diabetes
Bibliographic record
Abstract
Diabetes is associated with a loss of somatosensory and motor function, leading to impairments in gait, balance, and manual dexterity. Data-driven neuroimaging studies frequently report a negative impact of diabetes on sensorimotor regions in the brain; however, relationships with sensorimotor behavior are rarely considered. The goal of this review is to consider existing diabetes neuroimaging evidence through the lens of sensorimotor neuroscience. We review evidence for diabetes-related disruptions to three critical circuits for movement control: the cerebral cortex, the cerebellum, and the basal ganglia. In addition, we discuss how central nervous system (CNS) degeneration might interact with the loss of sensory feedback from the limbs due to peripheral neuropathy to result in motor impairments in individuals with diabetes. We argue that our understanding of movement impairments in individuals with diabetes is incomplete without the consideration of disease complications in both the central and peripheral nervous systems. Neuroimaging evidence for disrupted central sensorimotor circuitry suggests that there may be unrecognized behavioral impairments in individuals with diabetes. Applying knowledge from the existing literature on CNS contributions to motor control and motor learning in healthy individuals provides a framework for hypothesis generation for future research on this topic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".