Spike-Based Neuromorphic Model of Spasticity for Generation of Affected Neural Activity

Spasticity is a common motor symptom that disrupt muscle contraction and hence movements. Proper management of spasticity requires identification of its origins and reasoning of the therapeutic plans. Challenges arise because spasticity might originate from elevated activity in both the cortical and sub-cortical pathways. No existing models (animal or computational) could cover all possibilities leading to spasticity, especially the peripheral causes such as hyperreflexia. To bridge this gap, this work develops a novel computational, spike-based neuromorphic model of spasticity, named NEUSPA. Rather than relying solely on a monosynaptic spinal loop comprising alpha motoneurons, sensory afferents, synapses, skeletal muscles, and muscle spindles, the NEUSPA model introduces two additional inputs: additive (ADD) and multiplicative (MUL). These inputs generate velocity-dependent EMG responses. The effectiveness of the NEUSPA model is validated using classic experiments from the literature and data collected from two post-stroke patients with affected upper-limb movements. The model is also applied to simulate two real-world scenarios that patients may encounter. Simulation results suggest that hyperreflexia due to extra inputs was sufficient to produce spastic EMG responses. However, EMG onsets were more sensitive to ADD inputs (slope =0.628, p <0.0001,> ${}^{{2}} =0.96$ ) compared to MUL inputs (slope =0.471, p <0.0001,> ${}^{{2}} =0.92$ ). Additionally, simulation of finger-pressing on a deformable object indicated that spasticity could increase the duration from 1.03s to 1.20s compared to a non-impaired condition. These results demonstrate that NEUSPA effectively synthesizes abnormal physiological data, facilitating decision-making and machine learning in neurorehabilitation.