Wolf, S. L. | Search bioRxiv

bioRxiv2026

Functional Reorganization of Motor Subcircuits in Parkinson's disease

Parkinsons disease disrupts motor control across multiple body parts, yet the neural mechanisms underlying these impairments remain incompletely defined. We compared resting-state functional connectivity in people with mild-to-moderate Parkinsons disease (n = 58) and neurotypical older adults (n = 24), focusing on regions implicated in internally generated (IG) and externally generated (EG) movement pathways. For our analysis, we leveraged the reproducible NeuroMark independent component template and motor effector-specific mapping of primary motor cortex (M1). Our results reveal both increased and decreased connectivity patterns in Parkinsons disease: M1 subregions associated with control of the leg, hand, and larynx showed robust increases in connectivity exclusively with cerebellar territories, particularly Crus II and Lobules VIIIa/VIIIb. The postcentral gyrus (primary somatosensory cortex) showed primarily increased connectivity with cerebellar regions and the insula. In contrast, the caudate nucleus displayed a mixed profile, with increased connectivity to the superior temporal gyrus and decreased connectivity to the superior medial frontal gyrus and cerebellar Crus II. Our motor effector-specific analysis of disease severity scores (MDS-UPDRS) in people with Parkinsons disease revealed mild impairments across all categories (leg, hand, larynx) but disproportionately greater hand-related deficits, suggesting that some of the observed M1 connectivity differences may be influenced by these behavioral asymmetries. These anatomically precise, effector-specific alterations suggest compensatory recruitment of cerebellar circuits in Parkinsons disease and provide a framework for targeting motor subcircuits in rehabilitation, including dance-based interventions.

neuroscience↗

bioRxiv2021

Automated movement assessment in stroke rehabilitation

We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivors movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e. type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e. lower extremity training for neurological accidents).

neuroscience↗