Quantitative Assessment of Gait and Balance Following Deep Brain Stimulation in Patients with Parkinson’s Disease
Bibliographic record
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by tremor, rigidity, bradykinesia and postural instability. Deep Brain Stimulation (DBS) targeting either the subthalamic nucleus (STN) or globus pallidus interna (GPi) is highly effective for treating the cardinal motor symptoms of PD and motor complications of levodopa (L-DOPA) therapy, but its impact on gait and balance symptoms is not well established. In the advanced stages of PD, gait and balance impairments can limit patient mobility, increase the risk of falls and fall-related injuries, and reduce quality of life. The objective of our study was to investigate the precise impact of DBS on the mechanisms of gait (pace, rhythm, variability, asymmetry and postural control) using a quantitative gait analysis. Eight participants awaiting DBS (prospectively implanted participants) were recruited for our study, as well as five PD participants who had previously received DBS (already implanted participants). Prospectively implanted participants were evaluated pre-operatively and post-operatively at four weeks, three months and six months after the initial DBS programming session. Already implanted participants were evaluated after programming was optimized. All participants were tested in four standard treatment conditions: OFF-medication/OFF-DBS, OFF-medication/ON-DBS, ON-medication/OFF-DBS, and ON-medication/ON-DBS. Participants were instructed to walk on a computerized walkway (GaitRite), which was used to collect objective spatial and temporal parameters. To investigate changes in the five domains of gait, our study measured gait velocity (cm/s), step length (cm), stance time (ms), swing time (ms), stance time ratio (|L/R|), step length ratio (|L/R|), and step length variability (% coefficient of variation). Additional standard tests and clinical scales, including the Timed-Up and Go (TUG), Unified Parkinson’s Disease Rating Scale-III (UPDRS-III), Montreal Cognitive Assessment (MoCA) and Freezing of Gait Questionnaire (FOG-Q), were also analyzed. The ON-medication/ON-DBS condition, otherwise known as the best treatment condition (BTC), produced a significant improvement in UPDRS-III, TUG, gait velocity and step length at four weeks and three months post-programming relative to the OFF-medication/OFF-DBS condition (P<0.05). There was a trend towards further improvement in these parameters at six months post-programming in the BTC, but statistical significance was not achieved, likely due to smaller sample size at this time point. Step length variability was significantly reduced in the BTC at four weeks post-programming (P=0.008) and during the OFF-medication/ON-DBS condition at three months (P=0.02), once DBS programming approached optimization. Step length asymmetry improved in the BTC at three months post-programming (P=0.004). Swing time improved at four weeks during the OFF-medication/ON-DBS state (P=0.002) and at three months during the ON-medication/OFF-DBS state (P<0.05). Stance time, stance time ratio, and stride width did not significantly change for prospectively implanted participants. No statistically significant changes were observed in FOG-Q scores before and after DBS. For already implanted participants, performance during the TUG, along with gait velocity, step length and stride width were significantly improved in the BTC relative to OFF-medication/OFF-DBS (P<0.05). Gait velocity and TUG times were also significantly better in the BTC compared to the ON-medication/OFF-DBS condition (P<0.05). We also compared gait and balance outcomes between STN- and GPi-DBS. Our preliminary findings show GPi-DBS to have a slight advantage for improving pace, select gait asymmetry parameters, and balance-related parameters in the BTC. Taken together, our study shows STN- and GPi-DBS does not seem to worsen axial gait and balance in PD patients without pre-existing FOG. However, further analyses with more participants should be conducted to verify our preliminary findings.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".