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Differentiation in additive and multiplicative inputs to motoneuron pool as origins of spasticity - a neuromorphic modeling study.
Article in En | MEDLINE | ID: mdl-38083678
ABSTRACT
Spasticity is characterized by a velocity-dependent increase in the tonic stretch reflex. Evidence suggests that spasticity originates from hyperactivity in the descending tract or reflex loop. To pinpoint the source of hyperactivity, however, is difficult due to lack of human data in-vivo. Thus, we implemented a neuromorphic model to revive the neurodynamics with spiking neuronal activity. Two types of input were modeled (1) the additive condition (ADD) to apply tonic synaptic inputs directly into the reflex loop; (2) the multiplicative (MUL) condition to adjust the loop gains within the reflex loop. Results show that both conditions produced antagonist EMG responses resembling patient data. The timing of spasticity is more sensitive to the ADD condition, whereas the amplitude of spastic EMG is more sensitive to the MUL condition. In conclusion, our model shows that both additive and multiplicative hyperactivities suffice to elicit velocity-dependent spastic electromyographic signals (EMG), but with different sensitivities. This simulation study suggests that spasticity caused by different origins may be discernable by the progression of severity, which may help individualized goalsetting and parameter-selection in rehabilitation.Clinical Relevance-Potential application of neuromorphic modeling on spasticity includes selection of parameters for therapeutic plans, such as movement range, repetition, and load.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Motor Neurons / Muscle Spasticity Limits: Humans Language: En Journal: Annu Int Conf IEEE Eng Med Biol Soc Year: 2023 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Motor Neurons / Muscle Spasticity Limits: Humans Language: En Journal: Annu Int Conf IEEE Eng Med Biol Soc Year: 2023 Document type: Article