Zandiyeh, P. | bioRxiv Science

bioRxiv2026

Quantitative Gait Analysis in a Sheep Model of In-Utero Spina Bifida Repair

Objective: To evaluate quantitative gait analysis in a sheep model (Ovis aries) of in-utero spina bifida repair. Method: Using a 3D marker-based motion capture system, we quantified spatiotemporal gait parameters and hindlimb joint angles in a sheep model of in-utero spina bifida repair and normal controls. A linear support vector machine classifier, trained and tested on subject-disjoint partitions with individual gait cycles as the unit of analysis, evaluated the relationship between hindlimb joint angles and clinician-assigned Texas Spinal Cord Injury Scale scores. Linear mixed-effects models compared spatiotemporal parameters, hoof height, and hip drop across 3- to 6-month evaluations and between groups. Results: In treated (n=14) and control (n=3) lambs, front- and hindlimb gait speed differed by timepoint (both p=0.025). Across 1,632 left and 1,661 right hindlimb gait cycles, held-out classification accuracy was 85.9% and 88.6% (Cohen's {kappa}=0.78 and 0.84) for left and right hindlimbs, respectively, against majority-class proportions of 76.5% and 75%. Left hindlimb hip drop differed between treated lambs and controls ({Delta}=0.8 cm, p=0.027). Between-group contrasts were considered exploratory given the small control group. Conclusion: Quantitative gait analysis was feasible in an in-utero spina bifida repair model. Hindlimb kinematic profiles classified clinical ambulation scores above the majority-class baseline.

bioengineering↗

bioRxiv2024

Changes in Walking Function and Neural Control following Pelvic Cancer Surgery with Reconstruction

Surgical planning and custom prosthesis design for pelvic cancer patients is challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. This case study combined comprehensive human movement data collection with personalized neuromusculoskeletal computer modeling to provide a thorough assessment of pre-to post-surgery changes in walking function and neural control for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Extensive walking data (video motion capture, ground reaction, and EMG) were collected from the patient before surgery and after plateau in recovery after surgery. Pre- and post-surgery personalized neuromusculoskeletal computer models of the patient were then constructed using the patients pre- and post-surgery walking data. These models were used to calculate the patients pre- and post-surgery joint motions, joint moments, and muscle synergies. The calculated muscle synergies were described by time-invariant synergy vectors and time-varying synergy activations, were consistent with the patients experimental EMG data, and produced the patients experimental joint moments found via inverse dynamics. The patients post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the patients post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified synergy vectors) with little change in recruitment timing (as evidenced by conserved synergy activations). Furthermore, the patients post-surgery muscle activations were fitted accurately using the patients pre-surgery synergy activations but poorly using the patients pre-surgery synergy vectors. These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patients pre-surgery walking data and the planned surgical decisions and custom prosthesis design.

bioengineering↗