Abstract

Cerebral white matter tract lesions prevent cortico-spinal descending inputs from effectively activating spinal motoneurons, leading to untreatable muscle paralysis. However, in most cases the damage to cortico-spinal axons is incomplete and the spared connections could be potentiated by neurotechnologies to restore motor function. Here we hypothesized that, by engaging direct excitatory connections to cortico-spinal motoneurons, deep brain stimulation (DBS) of the motor thalamus could facilitate activation of spared cortico-spinal fibers improving movements of the paretic limb. We first identified, in monkeys, optimal stimulation targets and parameters that enhanced motor evoked potentials to arm, hand, and face muscles, as well as grip forces. This potentiation persisted after cerebral white matter lesions. We then translated these results to human subjects by identifying the corresponding optimal thalamic targets (VIM/VOP nuclei) and replicated the results obtained in monkeys. Finally, we designed a DBS protocol that immediately improved voluntary grip force control in a patient with a chronic traumatic brain injury. Our results suggest that targeted DBS of the motor thalamus may become an effective therapy for motor paralysis.

No abstract

Seventy years ago, Hodgkin and Huxley published the first mathematical model to describe action potential generation, laying the foundation for modern computational neuroscience. Since then, the field has evolved enormously, with studies spanning from basic neuroscience to clinical applications for neuromodulation. Computer models of neuromodulation have evolved in complexity and personalization, advancing clinical practice and novel neurostimulation therapies, such as spinal cord stimulation. Spinal cord stimulation is a therapy widely used to treat chronic pain, with rapidly expanding indications, such as restoring motor function. In general, simulations contributed dramatically to improve lead designs, stimulation configurations, waveform parameters and programming procedures and provided insight into potential mechanisms of action of electrical stimulation. Although the implementation of neural models are relentlessly increasing in number and complexity, it is reasonable to ask whether this observed increase in complexity is necessary for improved accuracy and, ultimately, for clinical efficacy. With this aim, we performed a systematic literature review and a qualitative meta‐synthesis of the evolution of computational models, with a focus on complexity, personalization and the use of medical imaging to capture realistic anatomy. Our review showed that increased model complexity and personalization improved both mechanistic and translational studies. More specifically, the use of medical imaging enabled the development of patient‐specific models that can help to transform clinical practice in spinal cord stimulation. Finally, we combined our results to provide clear guidelines for standardization and expansion of computational models for spinal cord stimulation.