scholarly journals NeuroRoots, a bio-inspired, seamless Brain Machine Interface device for long-term recording

2018 ◽  
Author(s):  
Marc D. Ferro ◽  
Christopher M. Proctor ◽  
Alexander Gonzalez ◽  
Eric Zhao ◽  
Andrea Slezia ◽  
...  
Keyword(s):  
Neurological Diseases ◽  
Signal Amplitude ◽  
Brain Machine Interface ◽  
Behavioral Experiments ◽  
Adult Rats ◽  
Integrative Level ◽  
Machine Interface ◽  
Freely Moving ◽  
The Brain

AbstractMinimally invasive electrodes of cellular scale that approach a bio-integrative level of neural recording could enable the development of scalable brain machine interfaces that stably interface with the same neural populations over long period of time.In this paper, we designed and created NeuroRoots, a bio-mimetic multi-channel implant sharing similar dimension (10µm wide, 1.5µm thick), mechanical flexibility and spatial distribution as axon bundles in the brain. A simple approach of delivery is reported based on the assembly and controllable immobilization of the electrode onto a 35µm microwire shuttle by using capillarity and surface-tension in aqueous solution. Once implanted into targeted regions of the brain, the microwire was retracted leaving NeuroRoots in the biological tissue with minimal surgical footprint and perturbation of existing neural architectures within the tissue. NeuroRoots was implanted using a platform compatible with commercially available electrophysiology rigs and with measurements of interests in behavioral experiments in adult rats freely moving into maze. We demonstrated that NeuroRoots electrodes reliably detected action potentials for at least 7 weeks and the signal amplitude and shape remained relatively constant during long-term implantation.This research represents a step forward in the direction of developing the next generation of seamless brain-machine interface to study and modulate the activities of specific sub-populations of neurons, and to develop therapies for a plethora of neurological diseases.

scholarly journals The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on “An Integrated Brain-Machine Interface Platform With Thousands of Channels” (Preprint)

2019 ◽  
Author(s):  
Robert F Kirsch ◽  
A Bolu Ajiboye ◽  
Jonathan P Miller
Keyword(s):  
Neurological Disorders ◽  
Brain Machine Interface ◽  
Loss Of Function ◽  
Brain Machine Interfaces ◽  
Electrode Interface ◽  
Machine Interface ◽  
Almost All ◽  
Neuroprosthetic Devices ◽  
The Brain

UNSTRUCTURED Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life (potentially due to issues with long-term biocompatibility), as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed.

scholarly journals The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on “An Integrated Brain-Machine Interface Platform With Thousands of Channels”

10.2196/16339 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e16339 ◽  
Author(s):  
Robert F Kirsch ◽  
A Bolu Ajiboye ◽  
Jonathan P Miller
Keyword(s):  
Neurological Disorders ◽  
Brain Machine Interface ◽  
Loss Of Function ◽  
Brain Machine Interfaces ◽  
Electrode Interface ◽  
Machine Interface ◽  
Almost All ◽  
Neuroprosthetic Devices ◽  
The Brain

Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life (potentially due to issues with long-term biocompatibility), as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed.

scholarly journals Analysis of neural signals recorded from substantia nigra pars compacta for brain machine interface in freely moving rat

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S414
Author(s):  
Hae-Yong Park ◽  
Seongjin Her ◽  
Chin Su Koh ◽  
Hwan Gon Lee ◽  
In Seok Seo ◽  
...  
Keyword(s):  
Substantia Nigra ◽  
Substantia Nigra Pars Compacta ◽  
Brain Machine Interface ◽  
Neural Signals ◽  
Machine Interface ◽  
Freely Moving

Spinal and Supraspinal Effects of Long-Term Stimulation of Sensorimotor Cortex in Rats

2007 ◽  
Vol 98 (2) ◽  
pp. 878-887 ◽  
Author(s):  
Xiang Yang Chen ◽  
Shreejith Pillai ◽  
Yi Chen ◽  
Yu Wang ◽  
Lu Chen ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Operant Conditioning ◽  
Sensorimotor Cortex ◽  
H Reflex ◽  
Freely Moving ◽  
Activity Dependent ◽  
Spinal Cord Function ◽  
The Brain ◽  
Stimulation Of

Sensorimotor cortex (SMC) modifies spinal cord reflex function throughout life and is essential for operant conditioning of the H-reflex. To further explore this long-term SMC influence over spinal cord function and its possible clinical uses, we assessed the effect of long-term SMC stimulation on the soleus H-reflex. In freely moving rats, the soleus H-reflex was measured 24 h/day for 12 wk. The soleus background EMG and M response associated with H-reflex elicitation were kept stable throughout. SMC stimulation was delivered in a 20-day-on/20-day-off/20-day-on protocol in which a train of biphasic 1-ms pulses at 25 Hz for 1 s was delivered every 10 s for the on-days. The SMC stimulus was automatically adjusted to maintain a constant descending volley. H-reflex size gradually increased during the 20 on-days, stayed high during the 20 off-days, and rose further during the next 20 on-days. In addition, the SMC stimulus needed to maintain a stable descending volley rose steadily over days. It fell during the 20 off-days and rose again when stimulation resumed. These results suggest that SMC stimulation, like H-reflex operant conditioning, induces activity-dependent plasticity in both the brain and the spinal cord and that the plasticity responsible for the H-reflex increase persists longer after the end of SMC stimulation than that underlying the change in the SMC response to stimulation.

scholarly journals Neuronal activity and learning in local cortical networks are modulated by the action-perception state

2019 ◽  
Author(s):  
Ben Engelhard ◽  
Ran Darshan ◽  
Nofar Ozeri-Engelhard ◽  
Zvi Israel ◽  
Uri Werner-Reiss ◽  
...  
Keyword(s):  
Firing Rate ◽  
Chaotic Regime ◽  
Learning Activity ◽  
Brain Machine Interface ◽  
Action Perception ◽  
Current Action ◽  
Target Neuron ◽  
Machine Interface ◽  
Neuronal Patterns ◽  
The Brain

SummaryDuring sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

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