Biology subjects

McMorland, A. J.

Publications and source records attributed to McMorland, A. J..

6 recordsLinked to original sources

bioRxiv2026

CRADLE: A Clinically Robust, Anatomy-Aware Post-Processing Framework for Infant GMA Landmark Tracking in 2D Videos

Accurate pose estimation is central to automated infant General Movements Assessment during the fidgety period, when subtle limb movements, particularly at distal joints inform neurodevelopmental risks. Robust 2D pose tracking from handheld videos remains challenging in real-world settings, where occlusion, rapid motions, and visually ambiguous smaller joints frequently compromise anatomical accuracy. We present CRADLE, a clinically motivated, anatomy-aware post-processing pipeline designed to refine infant 2D movement trajectories across 24-anatomocal landmarks detected by our DeepLabCut-trained model. CRADLE integrates segment-length constraints, velocity-based anomaly detection, anatomically constrained interpolation, and Kalman filtering to correct both large localization failures and subtle persistent joint misplacements without relying primarily on confidence scores. Evaluations against conventional Confidence-Thresholding using Mean Absolute Error (MAE), {Delta}MAE, average Percentage of Correct Keypoints, and net keypoint correction rate showed consistently reduced or preserved error while maintaining accurate trajectories, with the strongest gains achieved at clinically important distal joints. Mean improvements reached up to 5 pixels for some smaller distal landmarks, large-magnitude corrections occurred more often than with Confidence-Thresholding, and well-localised joints remained largely unaffected. Positive net correction rates across metacarpophalangeal and metatarsophalangeal distal-landmarks further confirmed a favourable correction-degradation balance. By improving pose trajectory quality, CRADLE enhances the reliability of downstream movement analysis.

bioengineering↗

bioRxiv2023

Circuits in the Motor Cortex Explain Oscillatory Responses to Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is a popular method used to investigate brain function. Stimulation over the motor cortex evokes muscle contractions known as motor evoked potentials (MEPs) and also high frequency volleys of electrical activity measured in the cervical spinal cord. The physiological mechanisms of these experimentally derived responses remain unclear, but it is thought that the connections between circuits of excitatory and inhibitory neurons play a vital role. Using a spiking neural network model of the motor cortex, we explained the generation of waves of activity, so called I-waves, following cortical stimulation. The model reproduces a number of experimentally known responses including direction of TMS, increased inhibition and changes in strength. Using populations of thousands of neurons in a model of cortical circuitry we showed that the cortex generated transient oscillatory responses without any tuning, and that neuron parameters such as refractory period and delays influenced the pattern and timing of those oscillations. By comparing our network with simpler, previously proposed circuits, we explored the contributions of specific connections and found that recurrent inhibitory connections are vital in producing later waves which significantly impact the production of motor evoked potentials in downstream muscles (Thickbroom, 2011). This model builds on previous work to increase our understanding of how complex circuitry of the cortex is involved in the generation of I-waves.

bioengineering↗

bioRxiv2022

Modelling Spontaneous Firing Activity of the Motor Cortex in a Spiking Neural Network with Random and Local Connectivity

Computational models of cortical activity can provide in-sight into the mechanisms of higher-order processing in the human brain including planning, perception and the control of movement. Activity in the cortex is ongoing even in the absence of sensory input or discernible movements and is thought to be linked to the topology of the underlying cortical circuitry [1]. However, the connectivity and its functional role in the generation of spatio-temporal firing patterns and cortical computations are still unknown. Movement of the body is a key function of the brain, with the motor cortex the main cortical area implicated in the generation of movement. We built a spiking neural network model of the motor cortex which incorporates a laminar structure and circuitry based on a previous cortical model by Potjans and Diesmann [2]. A local connectivity scheme was implemented to introduce more physiological plausibility to the cortex model, and the effect on the rates, distributions and irregularity of neuronal firing was compared to the original random connectivity method and experimental data. Local connectivity increased the distribution of and overall rate of neuronal firing. It also resulted in the irregularity of firing being more similar to those observed in experimental measurements, and a reduction in the variability in power spectrum measures. The larger variability in dynamical behaviour of the local connectivity model suggests that the topological structure of the connections in neuronal population plays a significant role in firing patterns during spontaneous activity. This model took steps towards replicating the macroscopic network of the motor cortex, replicating realistic spatiotemporal firing to shed light on information coding in the cortex. Large scale computational models such as this one can capture how structure and function relate to observable neuronal firing behaviour, and investigates the underlying computational mechanisms of the brain.

neuroscience↗

bioRxiv2019

Muscle synergies are associated with intermuscular coherence in an isometric upper limb task.

To elucidate the underlying physiological mechanism of muscle synergies, we investigated the functional corticomuscular and intermuscular binding during an isometric upper limb task in 14 healthy participants. Cortical activity was recorded using 32-channel encephalography (EEG) and muscle activity using 16-channel electromyography (EMG). Using non-negative matrix factorization (NMF), we calculated muscle synergies from two different tasks. A preliminary multidirectional task was used to identify synergy preferred directions. A subsequent coherence task, consisting of generating forces isometrically in the synergy PDs, was used to assess the functional connectivity properties of synergies. Functional connectivity was estimated using corticomuscular coherence (CMC) and intermuscular coherence (IMC). Overall, we were able to extract four different synergies from the multidirectional task. A significant alpha band IMC was present consistently in all extracted synergies. Moreover, alpha band IMC was higher between muscles with higher weights within a synergy. In contrast, no significant CMC was found between the motor cortex area and synergy muscles. In addition, there is a relationship between a synergy muscle weight and the level of IMC. Our findings suggest the existence of a consistent shared input between muscles of each synergy. Finally, the existence of a shared input onto synergistic muscles within a synergy supports the idea of neurally-derived muscle synergies that build human movement.

neuroscience↗

bioRxiv2019

Effects of arm weight support on neuromuscular activation during reaching in chronic stroke patients

To better understand how arm weight support (WS) can be used to alleviate upper limb impairment after stroke, we investigated the effects of WS on muscle activity, muscle synergy expression, and corticomotor excitability (CME) in 13 chronic stroke patients and 6 age-similar healthy controls. For patients, lesion location and corticospinal tract integrity were assessed using magnetic resonance imaging. Upper limb impairment was assessed using the Fugl-Meyer upper extremity assessment with patients categorised as either mild or moderate-severe. Three levels of WS were examined: low=0, medium=50 and high=100 % of full support. Surface EMG was recorded from 8 upper limb muscles, and muscle synergies were decomposed using non-negative matrix factorisation from data obtained during reaching movements to an array of 14 targets using the paretic or dominant arm. Interactions between impairment level and WS were found for the number of targets hit, and EMG measures. Overall, greater WS resulted in lower EMG levels, although the degree of modulation between WS levels was less for patients with moderate-severe compared to mild impairment. Healthy controls expressed more synergies than patients with moderate-severe impairment. Healthy controls and patients with mild impairment showed more synergies with high compared to low weight support. Transcranial magnetic stimulation was used to elicit motor-evoked potentials (MEPs) to which stimulus-response curves were fitted as a measure of corticomotor excitability (CME). The effect of WS on CME varied between muscles and across impairment level. These preliminary findings demonstrate that WS has direct and indirect effects on muscle activity, synergies, and CME and warrants further study in order to reduce upper limb impairment after stroke.

neuroscience↗

bioRxiv2018

Fatigue influences the recruitment, but not structure, of muscle synergies

The development of fatigue elicits multiple adaptations from the neuromuscular system. Muscle synergies are common patterns of neuromuscular activation that have been proposed as the building blocks of human movement. We wanted to identify possible adaptations of muscle synergies to the development of fatigue in the upper limb. Recent studies have reported that synergy structure remains invariant during the development of fatigue, but these studies did not examine isolated synergies. We propose a novel approach to characterize synergy adaptations to fatigue by taking advantage of the spatial tuning of synergies. This approach allows improved identification of changes to individual synergies that might otherwise be confounded by changing contributions of overlapping synergies. To analyse upper limb synergies we applied non-negative matrix factorization to 14 EMG signals from muscles of 11 participants performing isometric contractions. A preliminary multidirectional task was used to identify synergy directional tuning. A subsequent fatiguing task was designed to fatigue the participants in their synergies preferred directions. Both tasks provided virtual reality feedback of the applied force direction and magnitude, and were performed at 40% of each participants maximal voluntary force. Five epochs were analysed throughout the fatiguing task to identify progressive changes of EMG amplitude, median frequency, synergy structure, and activation coefficients. Three to four synergies were sufficient to account for the variability contained in the original data. Synergy structure was conserved with fatigue, but interestingly synergy activation coefficients decreased on average by 24.5% with fatigue development. EMG amplitude did not change systematically with fatigue, whereas EMG median frequency consistently decreased across all muscles. These results support the notion of a neuromuscular modular organization as the building blocks of human movement, with adaptations to synergy recruitment occurring with fatigue. When synergy tuning properties are considered, the reduction of activation of muscle synergies may be a reliable marker to identify fatigue.

neuroscience↗