Biology subjects

Dominici, N.

Publications and source records attributed to Dominici, N..

3 recordsLinked to original sources

bioRxiv2026

Cortico-muscular control of postural balance in older adults - effects of training on functional connectivity

BackgroundBalance training in older adults can lead to reduced centre of mass accelerations and reduced angular momenta after perturbations of unipedal stance, reflecting an enhanced ability to recover balance. It has been suggested that the co-occurring changes in muscle synergies indicated strategy-specific adaptations in feedback control. MethodsWe investigated the cortical involvement in such adaptations by focusing on the interaction between muscle synergies and cortical activity after perturbations. Twenty older adults (>65 years) underwent short-term and three-week long-term balance training, and we assessed their recovery from unpredictable mediolateral perturbations during unipedal stance. We measured high-density EEG and activation of leg and trunk muscles. The representations of the balance-related muscle synergies were localised in the cortex using coherence-based beamformers in the {beta}-frequency band. ResultsBalance performance was accompanied by task-specific {beta}-band activation in the somatotopic representation of the lower extremities in the primary motor cortex. The {beta}-power significantly dropped during the response to perturbations, while the coherence with the activation of muscle synergies significantly increased, especially for synergies active in the early stage of balance recovery. The task-related changes in cortico-synergy coherence, especially during the later phase of balance recovery, were significantly affected by short-term training. ConclusionRefinements of feedback control seem to underlie balance improvements in older adults. The significant changes in the cortico-synergy interaction after balance training suggest cortical involvement in these refinements.

neuroscience↗

bioRxiv2026

Speed-driven transitions between discrete and rhythmic dynamics in walking revealed by kinematic smoothness and muscle synergies

Humans control movement through motor primitives that generate discrete and rhythmic actions. We investigated whether and how speed may drive a transition between discrete and rhythmic organization in walking, and whether muscle synergy changes are associated with kinematic shifts. Eighteen healthy adults walked on a treadmill during incremental and decremental trials (0.5-5 km/h in 0.5 km/h steps). Kinematics and bilateral lower-limb EMG were recorded. Smoothness was quantified using log dimensionless jerk (LDJ) and spectral arc length (SPARC). Both metrics indicated lower smoothness at low speeds and progressively stabilized as speed increased, with a transition region around 3-3.5 km/h showing inter-individual variability. In parallel, EMG synergies showed speed-dependent increases in dimensionality (2[->]3[->]4), consistent with module merging at slower speeds. Overall, these findings reveal coordinated kinematic and neuromuscular shifts with speed, indicating a transition from a discrete-dominated regime at low speeds toward a more stable rhythmic pattern at higher speeds.

neuroscience↗

bioRxiv2022

Predicting vertical ground reaction forces from 3D accelerometery using reservoir computers leads to accurate gait event detection

Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the golden standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.

neuroscience↗