Mobile cognition: imaging the human brain in the ‘real world’

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson's disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS. 

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

Insights and opportunities for deep brain stimulation as a brain circuit intervention

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

https://spj.science.org/doi/pdf/10.34133/cbsystems.0034?download=true

Cross-Frequency Coupling and Intelligent Neuromodulation

Cervical spinal cord injury (SCI) leads to permanent impairment of arm and hand functions. Here we conducted a prospective, single-arm, multicenter, open-label, non-significant risk trial that evaluated the safety and efficacy of ARC 

Non-invasive spinal cord electrical stimulation for arm and hand function in chronic tetraplegia: a safety and efficacy trial

People with late-stage Parkinson’s disease (PD) often suffer from debilitating locomotor deficits that are resistant to currently available therapies. To alleviate these deficits, we developed a neuroprosthesis operating in closed loop that targets the dorsal root entry zones innervating lumbosacral segments to reproduce the natural spatiotemporal activation of the lumbosacral spinal cord during walking. We first developed this neuroprosthesis in a non-human primate model that replicates locomotor deficits due to PD. This neuroprosthesis not only alleviated locomotor deficits but also restored skilled walking in this model. We then implanted the neuroprosthesis in a 62-year-old male with a 30-year history of PD who presented with severe gait impairments and frequent falls that were medically refractory to currently available therapies. We found that the neuroprosthesis interacted synergistically with deep brain stimulation of the subthalamic nucleus and dopaminergic replacement therapies to alleviate asymmetry and promote longer steps, improve balance and reduce freezing of gait. This neuroprosthesis opens new perspectives to reduce the severity of locomotor deficits in people with PD. A spinal cord neuroprosthesis targeting leg motor neurons in real time improves walking and reduces freezing of gait in non-human primate models and in one individual with advanced Parkinson’s disease. 

A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease.

Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease. Description The subthalamic nucleus encodes the initiation, termination, and amplitude of muscle activation, supporting decoding of gait in Parkinson’s disease. Decoding gait deficits Individuals with Parkinson’s disease (PD) experience motor symptoms including tremor, stiffness, and gait disturbances. To better understand the complex regulation of lower limb movements during walking, Thenaisie et al. studied local field potentials (LFPs) in the subthalamic nucleus (STN) and leg muscle activation in patients with PD implanted with deep brain stimulation electrodes. Using a neurorobotic platform based on a dynamometric chair and controlled locomotor tasks, the authors found that the STN encodes the initiation, termination, and amplitude of passive and active leg muscle activation and sensory feedback from the legs. They designed algorithms using STN LFPs to distinguish functional from dysfunctional gait that could detect freezing gait in individuals with PD. Results will help inform future treatment strategies for gait impairments.

Principles of gait encoding in the subthalamic nucleus of people with Parkinson’s disease

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A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease.

Principles of gait encoding in the subthalamic nucleus of people with Parkinson’s disease