Biology subjects

Mulla, D. M.

Publications and source records attributed to Mulla, D. M..

3 recordsLinked to original sources

bioRxiv2026

Musculoskeletal design of the human shoulder: implications for neuromuscular control

The shoulder complex is a unique musculoskeletal structure capable of versatile motor behaviour yet requiring delicate control. The purpose of our work was to better understand the nature of musculoskeletal redundancy at the shoulder accounting for biomechanical demands and motor control strategies. Using a biomechanical model of the shoulder, we simulated a series of static exertions. Joint moment results from inverse dynamics were combined with an iterative sampling method to survey the landscape of feasible muscle activity patterns. By repeating the sampling process across different numbers of degrees of freedom at the shoulder, we demonstrate how emergent solutions are shaped by the biomechanical demands at each of the shoulder joints. Furthermore, we observed that the degree of musculoskeletal redundancy appears to be higher among the scapulohumeral muscles than the thoracohumeral and thoracoscapular muscles. Finally, we found that many of the muscle activity patterns requiring similar effort costs as the minimal effort solution have similar activation profiles, but there can be a wide range of possibilities especially at greater task intensities. Altogether, the simulations provide insight into neuromuscular control and musculoskeletal model decision-making process for the shoulder.

neuroscience↗

bioRxiv2025

ATHENA: Automatically Tracking Hands Expertly with No Annotations

Studying naturalistic hand behaviours is challenging due to the limitations of conventional marker-based motion capture, which can be costly, time-consuming, and encumber participants. While markerless pose estimation exists - an accurate, off-the-shelf solution validated for hand-object manipulation is needed. We present ATHENA (Automatically Tracking Hands Expertly with No Annotations), an open-source, Python-based toolbox for 3D markerless hand tracking. To validate ATHENA, we concurrently recorded hand kinematics using ATHENA and an industry-standard optoelectronic marker-based system (OptiTrack). Participants performed unimanual, bimanual, and naturalistic object manipulation and we compared common kinematic variables like grip aperture, wrist velocity, index metacarpophalangeal flexion, and bimanual span. Our results demonstrated high spatiotemporal agreement between ATHENA and OptiTrack. This was evidenced by extremely high matches (R2 > 0.90 across the majority of tasks) and low root mean square differences (< 1 cm for grip aperture, < 4 cm/s for wrist velocity, and < 5-10{degrees} for index metacarpophalangeal flexion). ATHENA reliably preserved trial-to-trial variability in kinematics, offering identical scientific conclusions to marker-based approaches, but with significantly reduced financial and time costs and no participant encumbrance. In conclusion, ATHENA is an accurate, automated, and easy-to-use platform for 3D markerless hand tracking that enables more ecologically valid motor control and learning studies of naturalistic hand behaviours, enhancing our understanding of human dexterity.

neuroscience↗

bioRxiv2025

Unravelling neuromechanical constraints to finger independence

Intentional use of a single finger results in involuntary forces and movements among other fingers. Constraints to finger independence are attributed to both neural and mechanical factors, but the contribution of these factors is debated. We hypothesized that neural factors primarily constrain finger independence during isometric exertions whereas mechanical factors impose larger constraints during movements. We investigated changes in finger independence following a ring finger fatigue protocol. We assumed that with fatigue, the ability to actively transmit forces across fingers through neural pathways will be reduced but force transmission passively through mechanical pathways will remain unaffected. Participants performed isometric finger contractions and flexion-extension movements at baseline and following a ring finger fatigue protocol. At baseline, involuntary ring finger forces ranged from 7.3-16.5% MVC. Consistent with our predictions, involuntary ring finger forces decreased by 2.5-8.9% MVC following fatigue. In contrast, involuntary ring finger movement did not change or surprisingly in several cases, increased by greater than 10-20{degrees} following fatigue relative to baseline across movement tasks. Our findings demonstrate that the neuromechanical control of finger force versus motion are distinct from each other and can alter the constraints to finger independence in a task-dependent way.

neuroscience↗