Machine Learning

AM Spomer, RZ Yan, MH Schwartz, KM Steele (2023) “Motor control complexity can be dynamically simplified during gait pattern exploration using motor control-based biofeedback”

Katie Landwehr JournalArticle, MotorControl, News, Projects, Publications , , , , , ,

Journal Article in Journal of Neurophysiology

Understanding how the central nervous system coordinates diverse motor outputs has been a topic of extensive investigation. Although it is generally accepted that a small set of synergies underlies many common activities, such as walking, whether synergies are equally robust across a broader array of gait patterns or can be flexibly modified remains unclear.

Schematic of the custom biofeedback system. A) Motor control biofeedback used to encourage pattern exploration. B) Individuals significantly modified motor control complexity using biofeedback. C) Distal gait mechanics were associated with changes in control complexity.Aim: The aim of this study was to characterize the robustness of synergies to changing biomechanical constraints during walking. Specifically, we evaluated the extent to which nondisabled individuals could modulate both synergy structure and complexity while using motor control biofeedback to drive broad gait pattern exploration.

Methods: We evaluated the extent to which synergies changed as nondisabled adults (n = 14) explored gait patterns using custom biofeedback. Secondarily, we used Bayesian additive regression trees to identify factors that were associated with synergy modulation.

Results: Participants explored 41.1 ± 8.0 gait patterns using biofeedback, during which synergy recruitment changed depending on the type and magnitude of gait pattern modification. Specifically, a consistent set of synergies was recruited to accommodate small deviations from baseline, but additional synergies emerged for larger gait changes. Synergy complexity was similarly modulated; complexity decreased for 82.6% of the attempted gait patterns, but distal gait mechanics were strongly associated with these changes. In particular, greater ankle dorsiflexion moments and knee flexion through stance, as well as greater knee extension moments at initial contact, corresponded to a reduction in synergy complexity.

Interpretation: Taken together, these results suggest that the central nervous system preferentially adopts a low-dimensional, largely invariant control strategy but can modify that strategy to produce diverse gait patterns. Beyond improving understanding of how synergies are recruited during gait, study outcomes may also help identify parameters that can be targeted with interventions to alter synergies and improve motor control after neurological injury.

New & Noteworthy: We used a motor control-based biofeedback system and machine learning to characterize the extent to which nondisabled adults can modulate synergies during gait pattern exploration. Results revealed that a small library of synergies underlies an array of gait patterns but that recruitment from this library changes as a function of the imposed biomechanical constraints. Our findings enhance understanding of the neural control of gait and may inform biofeedback strategies to improve synergy recruitment after neurological injury.

KM Steele, MH Schwartz (2022) “Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach”

Katie Landwehr JournalArticle, MotorControl, News, Publications , , , , , ,

Journal Article in Frontiers in Human Neuroscience

Altered motor control is common in cerebral palsy (CP). Understanding how altered motor control affects movement and treatment outcomes is important but challenging due to complex interactions with other neuromuscular impairments. While regression can be used to examine associations between impairments and movement, causal modeling provides a mathematical framework to specify assumed causal relationships, identify covariates that may introduce bias, and test model plausibility.

FIGURE 1 Directed Acyclic Graph (DAG) describing the assumed causal relationships between SEMLS (exposure) and 1GDI (outcome). The causal relationship between SEMLS and 1GDI is mediated by changes in impairments (1Imp). Baseline GDI (GDIpre) and 1GDI are related by measurement methods and other, unmeasured factors. Baseline impairment (Imppre), surgical history (Hx), and Age are also included as causal factors. The DAG also includes unmeasured factors related to general CP severity, which impact baseline impairment and surgical history. The step-by-step process and rationale for this DAG are available in the Supplementary Material and an interactive version is available on dagitty (http://dagitty.net/mUCSPWo).Aim: The goal of this research was to quantify the causal effects of altered motor control and other impairments on gait, before and after single-event multi-level orthopedic surgery (SEMLS).

Methods: We evaluated the impact of SEMLS on change in Gait Deviation Index (ΔGDI) between gait analyses. We constructed our causal model with a Directed Acyclic Graph that included the assumed causal relationships between SEMLS, ΔGDI, baseline GDI (GDIpre), baseline neurologic and orthopedic impairments (Imppre), age, and surgical history. We identified the adjustment set to evaluate the causal effect of SEMLS on ΔGDI and the impact of Imppre on ΔGDI and GDIpre. We used Bayesian Additive Regression Trees (BART) and accumulated local effects to assess relative effects.

Results: We prospectively recruited a cohort of children with bilateral CP undergoing SEMLS (N = 55, 35 males, age: 10.5 ± 3.1 years) and identified a control cohort with bilateral CP who did not undergo SEMLS (N = 55, 30 males, age: 10.0 ± 3.4 years). There was a small positive causal effect of SEMLS on ΔGDI (1.70 GDI points). Altered motor control (i.e., dynamic and static motor control) and strength had strong effects on GDIpre, but minimal effects on ΔGDI. Spasticity and orthopedic impairments had minimal effects on GDIpre or ΔGDI.

Interpretation: Altered motor control did have a strong effect on GDIpre, indicating that these impairments do have a causal effect on a child’s gait pattern, but minimal effect on expected changes in GDI after SEMLS. Heterogeneity in outcomes suggests there are other factors contributing to changes in gait. Identifying these factors and employing causal methods to examine the complex relationships between impairments and movement will be required to advance our understanding and care of children with CP.

EC Kuska, N Mehrabi, MH Schwartz, KM Steele (2022) “Number of synergies impacts sensitivity of gait to weakness and contracture”

Katie Landwehr JournalArticle, MotorControl, News, Projects, Publications , , , , , ,

Journal Article in Journal of Biomechanics

Muscle activity during gait can be described by a small set of synergies, weighted groups of muscles, that are theorized to reflect underlying neural control. For people with neurologic injuries, like cerebral palsy or stroke, even fewer synergies are required to explain muscle activity during gait. This reduction in synergies is thought to reflect altered control and is associated with impairment severity and treatment outcomes. Individuals with neurologic injuries also develop secondary musculoskeletal impairments, like weakness or contracture, that can impact gait. Yet, the combined impacts of altered control and musculoskeletal impairments on gait remains unclear.

A two-dimensional sagittal plane musculoskeletal model and synergy simulation framework tracked unimpaired gait kinematics. The model had nine degrees of freedom, including right and left leg hip, knee, and ankle flexion, actuated by eight muscles per leg. Fixed sets of synergies constrained control, forcing the direct collocation algorithm to solve for synergy activations. The objective function minimized deviations from unimpaired kinematics and the sum of muscle activations squared (neural demand). Weakness, simulated by a reduction in maximum isometric force, and contracture, simulated by a reduction in tendon slack length, were progressively increased for each muscle or muscle group until the simulation failed to replicate unimpaired gait. Kinematic deviations and convergence determined the success of the simulation. The primary outcomes were (1) musculoskeletal impairment thresholds, defined by the amount of weakness or contracture before failure, and (2) neural demand of each gait cycle.Aim: In this study, we use a two-dimensional musculoskeletal model constrained to synergy control to simulate unimpaired gait.

Methods: We vary the number of synergies, while simulating muscle weakness and contracture to examine how altered control impacts sensitivity to musculoskeletal impairment while tracking unimpaired gait.

Results: Results demonstrate that reducing the number of synergies increases sensitivity to weakness and contracture for specific muscle groups. For example, simulations using five-synergy control tolerated 40% and 51% more knee extensor weakness than those using four- or three-synergy control, respectively. Furthermore, when constrained to four- or three-synergy control, the model was increasingly sensitive to contracture and weakness of proximal muscles, such as the hamstring and hip flexors. Contrastingly, neither the amount of generalized nor plantarflexor weakness tolerated was affected by the number of synergies.

Interpretation: These findings highlight the interactions between altered control and musculoskeletal impairments, emphasizing the importance of measuring and incorporating both in future simulation and experimental studies.