Jackson, Lauren

Assessing balance is crucial in neurological rehabilitation, yet while wearable sensors enable real-world monitoring, identifying reliable digital biomarkers remains challenging. This study utilized a high-fidelity instrumented walkway to determine which gait parameters best predict balance impairment, providing robust targets for future wearable applications. We analyzed 49 steady-state gait metrics from 140 individuals with diverse neurological conditions. Using statistical analysis and machine learning, we evaluated these parameters against objective force plate sway scores and clinical fall-history labels. Group analysis identified 16 parameters significantly distinguishing fallers from non-fallers, and a neural network classified fallers with an area under the curve of 0.75. Across all analytical approaches, overall gait variability, e.g., Stride Width S.D. and the Gait Variability Index, emerged as a universal predictor of balance impairment and fall risk. Furthermore, while traditional linear models emphasized spatial postural control, machine learning classification uniquely identified inter-limb asymmetry as a premier driver of fall prediction. These findings indicate that instrumented gait analysis effectively identifies digital biomarkers for balance deficits. Isolating these specific metrics provides a clear blueprint for meaningful metrics required for continuous objective monitoring and future development of personalized, adaptive rehabilitation strategies.

Background: Tremor in multiple sclerosis (MS) is often refractory to medications. Deep brain stimulation (DBS) may help, but possible side effects and disease exacerbation limit its use. Objectives: Evaluate outcomes of DBS for MS-associated tremor. Methods: Retrospective review of 18 patients with MS-associated tremor who underwent DBS, analyzing Expanded Disability Status Scale (EDSS) and Fahn–Tolosa–Marin (FTM) tremor scores. Results: Median age at surgery was 49.5 years (range, 28–75), with postoperative follow-up 2.5 years (range, 0–22). No relapses (0/18) or new lesions (0/8) occurred postoperatively. All had ventral intermediate nucleus leads; other targets were not used in programming. EDSS remained stable (median 6 to 6.25; p = 0.2). FTM scores improved (median 14.5 to 10, p = 0.008). Patients with EDSS 3.5–6 had variable outcomes ( p = 0.02). Conclusions: DBS is a potential treatment for severe MS tremor and did not provoke relapse. Targeting VIM alone may optimize tremor control and maintain magnetic resonance imaging (MRI) access.

AbstractBackgroundThe G2019S leucine‐rich repeat kinase 2 (LRRK2) gene mutation is an important and commonly found genetic determinant of Parkinson's disease (PD). The neuropathological findings associated with this mutation have thus far been varied but are most often associated with Lewy body (LB) pathology.ObjectiveDescribe a case of clinical Parkinson's disease with levodopa responsiveness found to have LRRK2 mutations and the absence of Lewy bodies.MethodWe present an 89‐year‐old man with a 10‐year history of slowly progressive parkinsonism suspected to be secondary to Parkinson's disease.ResultsNeuropathological evaluation revealed nigral degeneration without Lewy bodies or Lewy neurites, but there were frequent tau‐immunopositive neurites and astrocytes in the putamen and substantia nigra, neocortical glial tau positive astrocytes associated with aging‐related tau astrogliopathy (ARTAG), as well as neurofibrillary tangles, beta amyloid plaques, and amyloid angiopathy typical of advanced Alzheimer's disease. G2019S LRRK2 homozygous mutations were found.ConclusionThis case illustrates that levodopa‐responsive clinical PD caused by G2019S LRRK2 mutations can occur without Lewy bodies.