Systematic review of rodent studies of deep brain stimulation for the treatment of neurological, developmental and neuropsychiatric disorders
Bibliographic record
Abstract
Deep brain stimulation (DBS) modulates local and widespread connectivity in dysfunctional networks. Positive results are observed in several patient populations; however, the precise mechanisms underlying treatment remain unknown. Translational DBS studies aim to answer these questions and provide knowledge for advancing the field. Here, we systematically review the literature on DBS studies involving models of neurological, developmental and neuropsychiatric disorders to provide a synthesis of the current scientific landscape surrounding this topic. A systematic analysis of the literature was performed following PRISMA guidelines. 407 original articles were included. Data extraction focused on study characteristics, including stimulation protocol, behavioural outcomes, and mechanisms of action. The number of articles published increased over the years, including 16 rat models and 13 mouse models of transgenic or healthy animals exposed to external factors to induce symptoms. Most studies targeted telencephalic structures with varying stimulation settings. Positive behavioural outcomes were reported in 85.8% of the included studies. In models of psychiatric and neurodevelopmental disorders, DBS-induced effects were associated with changes in monoamines and neuronal activity along the mesocorticolimbic circuit. For movement disorders, DBS improves symptoms via modulation of the striatal dopaminergic system. In dementia and epilepsy models, changes to cellular and molecular aspects of the hippocampus were shown to underlie symptom improvement. Despite limitations in translating findings from preclinical to clinical settings, rodent studies have contributed substantially to our current knowledge of the pathophysiology of disease and DBS mechanisms. Direct inhibition/excitation of neural activity, whereby DBS modulates pathological oscillatory activity within brain networks, is among the major theories of its mechanism. However, there remain fundamental questions on mechanisms, optimal targets and parameters that need to be better understood to improve this therapy and provide more individualized treatment according to the patient's predominant symptoms.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".