Training with noninvasive brain-machine interface, tactile feedback, and locomotion to enhance neurological recovery in individuals with complete paraplegia: a randomized pilot study.

In recent years, our group and others have reported multiple cases of consistent neurological recovery in people with spinal cord injury (SCI) following a protocol that integrates locomotion training with brain machine interfaces (BMI). The primary objective of this pilot study was to compare the neurological outcomes (motor, tactile, nociception, proprioception, and vibration) in both an intensive assisted locomotion training (LOC) and a neurorehabilitation protocol integrating assisted locomotion with a noninvasive brain-machine interface (L + BMI), virtual reality, and tactile feedback. We also investigated whether individuals with chronic-complete SCI could learn to perform leg motor imagery. We ran a parallel two-arm randomized pilot study; the experiments took place in São Paulo, Brazil. Eight adults sensorimotor-complete (AIS A) (all male) with chronic (> 6 months) traumatic spinal SCI participated in the protocol that was organized in two blocks of 14 weeks of training and an 8-week follow-up. The participants were allocated to either the LOC group (n = 4) or L + BMI group (n = 4) using block randomization (blinded outcome assessment). We show three important results: (i) locomotion training alone can induce some level of neurological recovery in sensorimotor-complete SCI, and (ii) the recovery rate is enhanced when such locomotion training is associated with BMI and tactile feedback (∆Mean Lower Extremity Motor score improvement for LOC = + 2.5, L + B = + 3.5; ∆Pinprick score: LOC = + 3.75, L + B = + 4.75 and ∆Tactile score LOC = + 4.75, L + B = + 9.5). (iii) Furthermore, we report that the BMI classifier accuracy was significantly above the chance level for all participants in L + B group. Our study shows potential for sensory and motor improvement in individuals with chronic complete SCI following a protocol with BMIs and locomotion therapy. We report no dropouts nor adverse events in both subgroups participating in the study, opening the possibility for a more definitive clinical trial with a larger cohort of people with SCI.Trial registration: http://www.ensaiosclinicos.gov.br/ identifier RBR-2pb8gq.

Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface.

Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description-wavelet average coefficients (WAC)-to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

The impact of super-spreader cities, highways, and intensive care availability in the early stages of the COVID-19 epidemic in Brazil.

Although international airports served as main entry points for SARS-CoV-2, the factors driving the uneven geographic spread of COVID-19 cases and deaths in Brazil remain mostly unknown. Here we show that three major factors influenced the early macro-geographical dynamics of COVID-19 in Brazil. Mathematical modeling revealed that the "super-spreading city" of São Paulo initially accounted for more than 85% of the case spread in the entire country. By adding only 16 other spreading cities, we accounted for 98-99% of the cases reported during the first 3 months of the pandemic in Brazil. Moreover, 26 federal highways accounted for about 30% of SARS-CoV-2's case spread. As cases increased in the Brazilian interior, the distribution of COVID-19 deaths began to correlate with the allocation of the country's intensive care units (ICUs), which is heavily weighted towards state capitals. Thus, severely ill patients living in the countryside had to be transported to state capitals to access ICU beds, creating a "boomerang effect" that contributed to skew the distribution of COVID-19 deaths. Therefore, if (i) a lockdown had been imposed earlier on in spreader-capitals, (ii) mandatory road traffic restrictions had been enforced, and (iii) a more equitable geographic distribution of ICU beds existed, the impact of COVID-19 in Brazil would be significantly lower.

Generating artificial sensations with spinal cord stimulation in primates and rodents.

For patients who have lost sensory function due to a neurological injury such as spinal cord injury (SCI), stroke, or amputation, spinal cord stimulation (SCS) may provide a mechanism for restoring somatic sensations via an intuitive, non-visual pathway. Inspired by this vision, here we trained rhesus monkeys and rats to detect and discriminate patterns of epidural SCS. Thereafter, we constructed psychometric curves describing the relationship between different SCS parameters and the animal's ability to detect SCS and/or changes in its characteristics. We found that the stimulus detection threshold decreased with higher frequency, longer pulse-width, and increasing duration of SCS. Moreover, we found that monkeys were able to discriminate temporally- and spatially-varying patterns (i.e. variations in frequency and location) of SCS delivered through multiple electrodes. Additionally, sensory discrimination of SCS-induced sensations in rats obeyed Weber's law of just-noticeable differences. These findings suggest that by varying SCS intensity, temporal pattern, and location different sensory experiences can be evoked. As such, we posit that SCS can provide intuitive sensory feedback in neuroprosthetic devices.

Neuroengineering challenges of fusing robotics and neuroscience.

Advances in neuroscience are inspiring developments in robotics and vice versa.

A Brain to Spine Interface for Transferring Artificial Sensory Information.

Lack of sensory feedback is a major obstacle in the rapid absorption of prosthetic devices by the brain. While electrical stimulation of cortical and subcortical structures provides unique means to deliver sensory information to higher brain structures, these approaches require highly invasive surgery and are dependent on accurate targeting of brain structures. Here, we propose a semi-invasive method, Dorsal Column Stimulation (DCS) as a tool for transferring sensory information to the brain. Using this new approach, we show that rats can learn to discriminate artificial sensations generated by DCS and that DCS-induced learning results in corticostriatal plasticity. We also demonstrate a proof of concept brain-to-spine interface (BTSI), whereby tactile and artificial sensory information are decoded from the brain of an "encoder" rat, transformed into DCS pulses, and delivered to the spinal cord of a second "decoder" rat while the latter performs an analog-to-digital conversion during a sensory discrimination task. These results suggest that DCS can be used as an effective sensory channel to transmit prosthetic information to the brain or between brains, and could be developed as a novel platform for delivering tactile and proprioceptive feedback in clinical applications of brain-machine interfaces.

Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics.

Back in 2012, Churchland and his colleagues proposed that "rotational dynamics", uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland's own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal sequence in peak firing rates exhibited by individual neurons, and (2) this sequence remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland's data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.'s bold assessment that such rotations are related to "an unexpected yet surprisingly simple structure in the population response", which "explains many of the confusing features of individual neural responses". Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

Author Correction: Non-invasive, Brain-controlled Functional Electrical Stimulation for Locomotion Rehabilitation in Individuals with Paraplegia.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Creating a neuroprosthesis for active tactile exploration of textures.

Intracortical microstimulation (ICMS) of the primary somatosensory cortex (S1) can produce percepts that mimic somatic sensation and, thus, has potential as an approach to sensorize prosthetic limbs. However, it is not known whether ICMS could recreate active texture exploration-the ability to infer information about object texture by using one's fingertips to scan a surface. Here, we show that ICMS of S1 can convey information about the spatial frequencies of invisible virtual gratings through a process of active tactile exploration. Two rhesus monkeys scanned pairs of visually identical screen objects with the fingertip of a hand avatar-controlled first via a joystick and later via a brain-machine interface-to find the object with denser virtual gratings. The gratings consisted of evenly spaced ridges that were signaled through individual ICMS pulses generated whenever the avatar's fingertip crossed a ridge. The monkeys learned to interpret these ICMS patterns, evoked by the interplay of their voluntary movements and the virtual textures of each object, to perform a sensory discrimination task. Discrimination accuracy followed Weber's law of just-noticeable differences (JND) across a range of grating densities; a finding that matches normal cutaneous sensation. Moreover, 1 monkey developed an active scanning strategy where avatar velocity was integrated with the ICMS pulses to interpret the texture information. We propose that this approach could equip upper-limb neuroprostheses with direct access to texture features acquired during active exploration of natural objects.

Non-invasive, Brain-controlled Functional Electrical Stimulation for Locomotion Rehabilitation in Individuals with Paraplegia.

Spinal cord injury (SCI) impairs the flow of sensory and motor signals between the brain and the areas of the body located below the lesion level. Here, we describe a neurorehabilitation setup combining several approaches that were shown to have a positive effect in patients with SCI: gait training by means of non-invasive, surface functional electrical stimulation (sFES) of the lower-limbs, proprioceptive and tactile feedback, balance control through overground walking and cue-based decoding of cortical motor commands using a brain-machine interface (BMI). The central component of this new approach was the development of a novel muscle stimulation paradigm for step generation using 16 sFES channels taking all sub-phases of physiological gait into account. We also developed a new BMI protocol to identify left and right leg motor imagery that was used to trigger an sFES-generated step movement. Our system was tested and validated with two patients with chronic paraplegia. These patients were able to walk safely with 65-70% body weight support, accumulating a total of 4,580 steps with this setup. We observed cardiovascular improvements and less dependency on walking assistance, but also partial neurological recovery in both patients, with substantial rates of motor improvement for one of them.

Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network.

Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large (N = 134-402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.

Frequency-specific coupling in fronto-parieto-occipital cortical circuits underlie active tactile discrimination.

Processing of tactile sensory information in rodents is critically dependent on the communication between the primary somatosensory cortex (S1) and higher-order integrative cortical areas. Here, we have simultaneously characterized single-unit activity and local field potential (LFP) dynamics in the S1, primary visual cortex (V1), anterior cingulate cortex (ACC), posterior parietal cortex (PPC), while freely moving rats performed an active tactile discrimination task. Simultaneous single unit recordings from all these cortical regions revealed statistically significant neuronal firing rate modulations during all task phases (anticipatory, discrimination, response, and reward). Meanwhile, phase analysis of pairwise LFP recordings revealed the occurrence of long-range synchronization across the sampled fronto-parieto-occipital cortical areas during tactile sampling. Causal analysis of the same pairwise recorded LFPs demonstrated the occurrence of complex dynamic interactions between cortical areas throughout the fronto-parietal-occipital loop. These interactions changed significantly between cortical regions as a function of frequencies (i.e. beta, theta and gamma) and according to the different phases of the behavioral task. Overall, these findings indicate that active tactile discrimination by rats is characterized by much more widespread and dynamic complex interactions within the fronto-parieto-occipital cortex than previously anticipated.

Training with brain-machine interfaces, visuo-tactile feedback and assisted locomotion improves sensorimotor, visceral, and psychological signs in chronic paraplegic patients.

Spinal cord injury (SCI) induces severe deficiencies in sensory-motor and autonomic functions and has a significant negative impact on patients' quality of life. There is currently no systematic rehabilitation technique assuring recovery of the neurological impairments caused by a complete SCI. Here, we report significant clinical improvement in a group of seven chronic SCI patients (six AIS A, one AIS B) following a 28-month, multi-step protocol that combined training with non-invasive brain-machine interfaces, visuo-tactile feedback and assisted locomotion. All patients recovered significant levels of nociceptive sensation below their original SCI (up to 16 dermatomes, average 11 dermatomes), voluntary motor functions (lower-limbs muscle contractions plus multi-joint movements) and partial sensory function for several modalities (proprioception, tactile, pressure, vibration). Patients also recovered partial intestinal, urinary and sexual functions. By the end of the protocol, all patients had their AIS classification upgraded (six from AIS A to C, one from B to C). These improvements translated into significant changes in the patients' quality of life as measured by standardized psychological instruments. Reexamination of one patient that discontinued the protocol after 12 months of training showed that the 16-month break resulted in neurological stagnation and no reclassification. We suggest that our neurorehabilitation protocol, based uniquely on non-invasive technology (therefore necessitating no surgical operation), can become a promising therapy for patients diagnosed with severe paraplegia (AIS A, B), even at the chronic phase of their lesion.

Interbrain cortical synchronization encodes multiple aspects of social interactions in monkey pairs.

While it is well known that the primate brain evolved to cope with complex social contingencies, the neurophysiological manifestation of social interactions in primates is not well understood. Here, concurrent wireless neuronal ensemble recordings from pairs of monkeys were conducted to measure interbrain cortical synchronization (ICS) during a whole-body navigation task that involved continuous social interaction of two monkeys. One monkey, the passenger, was carried in a robotic wheelchair to a food dispenser, while a second monkey, the observer, remained stationary, watching the passenger. The two monkeys alternated the passenger and the observer roles. Concurrent neuronal ensemble recordings from the monkeys' motor cortex and the premotor dorsal area revealed episodic occurrence of ICS with probability that depended on the wheelchair kinematics, the passenger-observer distance, and the passenger-food distance - the social-interaction factors previously described in behavioral studies. These results suggest that ICS represents specific aspects of primate social interactions.

Cortical Neuroprosthesis Merges Visible and Invisible Light Without Impairing Native Sensory Function.

Adult rats equipped with a sensory prosthesis, which transduced infrared (IR) signals into electrical signals delivered to somatosensory cortex (S1), took approximately 4 d to learn a four-choice IR discrimination task. Here, we show that when such IR signals are projected to the primary visual cortex (V1), rats that are pretrained in a visual-discrimination task typically learn the same IR discrimination task on their first day of training. However, without prior training on a visual discrimination task, the learning rates for S1- and V1-implanted animals converged, suggesting there is no intrinsic difference in learning rate between the two areas. We also discovered that animals were able to integrate IR information into the ongoing visual processing stream in V1, performing a visual-IR integration task in which they had to combine IR and visual information. Furthermore, when the IR prosthesis was implanted in S1, rats showed no impairment in their ability to use their whiskers to perform a tactile discrimination task. Instead, in some rats, this ability was actually enhanced. Cumulatively, these findings suggest that cortical sensory neuroprostheses can rapidly augment the representational scope of primary sensory areas, integrating novel sources of information into ongoing processing while incurring minimal loss of native function.

Cortical neurons multiplex reward-related signals along with sensory and motor information.

Rewards are known to influence neural activity associated with both motor preparation and execution. This influence can be exerted directly upon the primary motor (M1) and somatosensory (S1) cortical areas via the projections from reward-sensitive dopaminergic neurons of the midbrain ventral tegmental areas. However, the neurophysiological manifestation of reward-related signals in M1 and S1 are not well understood. Particularly, it is unclear how the neurons in these cortical areas multiplex their traditional functions related to the control of spatial and temporal characteristics of movements with the representation of rewards. To clarify this issue, we trained rhesus monkeys to perform a center-out task in which arm movement direction, reward timing, and magnitude were manipulated independently. Activity of several hundred cortical neurons was simultaneously recorded using chronically implanted microelectrode arrays. Many neurons (9-27%) in both M1 and S1 exhibited activity related to reward anticipation. Additionally, neurons in these areas responded to a mismatch between the reward amount given to the monkeys and the amount they expected: A lower-than-expected reward caused a transient increase in firing rate in 60-80% of the total neuronal sample, whereas a larger-than-expected reward resulted in a decreased firing rate in 20-35% of the neurons. Moreover, responses of M1 and S1 neurons to reward omission depended on the direction of movements that led to those rewards. These observations suggest that sensorimotor cortical neurons corepresent rewards and movement-related activity, presumably to enable reward-based learning.

Electrical stimulation of the dorsal columns of the spinal cord for Parkinson's disease.

Spinal cord stimulation has been used for the treatment of chronic pain for decades. In 2009, our laboratory proposed, based on studies in rodents, that electrical stimulation of the dorsal columns of the spinal cord could become an effective treatment for motor symptoms associated with Parkinson's disease (PD). Since our initial report in rodents and a more recent study in primates, several clinical studies have now described beneficial effects of dorsal column stimulation in parkinsonian patients. In primates, we have shown that dorsal column stimulation activates multiple structures along the somatosensory pathway and desynchronizes the pathological cortico-striatal oscillations responsible for the manifestation of PD symptoms. Based on recent evidence, we argue that neurological disorders such as PD can be broadly classified as diseases emerging from abnormal neuronal timing, leading to pathological brain states, and that the spinal cord could be used as a "channel" to transmit therapeutic electrical signals to disrupt these abnormalities. © 2017 International Parkinson and Movement Disorder Society.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation.

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.