Electrical Stimulation publications

Objective. Infrared neural stimulation (INS) has emerged as a potent neuromodulation technology, offering safe and focal stimulation with superior spatial recruitment profiles compared to conventional electrical methods. However, the neural dynamics induced by INS stimulation remain poorly understood. Elucidating these dynamics will help develop new INS stimulation paradigms and advance its clinical application. Approach. In this study, we assessed the local network dynamics of INS entrainment in the auditory thalamocortical circuit using the chronically implanted rat model. Our approach focused on measuring INS energy-based local field potential (LFP) recruitment induced by focal thalamocortical stimulation. We further characterized linear and nonlinear oscillatory LFP activity in response to single-pulse and periodic INS and performed spectral decomposition to uncover specific LFP band entrainment to INS. Finally, we examined spike-field transformations across the thalamocortical synapse using spike-LFP coherence coupling measures. Main results. We found that INS significantly increases LFP amplitude as a log-linear function of INS energy per pulse, primarily entraining to LFP β and γ bands with synchrony extending to 200 Hz in some cases. A subset of neurons demonstrated nonlinear, chaotic oscillations linked to information transfer across cortical circuits. Finally, we utilized spike-field coherences to correlate spike coupling to LFP frequency band activity and suggest an energy-dependent model of network activation resulting from INS stimulation. Significance. We show that INS reliably drives robust network activity and can potently modulate cortical field potentials across a wide range of frequencies in a stimulus parameter-dependent manner. Based on these results, we propose design principles for developing full coverage, all-optical thalamocortical auditory neuroprostheses.

Closed-loop neural control is a powerful tool for both the scientific exploration of neural function and for mitigating deficiencies found in open-loop deep brain stimulation (DBS). Here, we present a protocol for artificial intelligence-guided neural control in rats using deep reinforcement learning (RL) and infrared neural stimulation (INS). We describe steps for integrating RL closed-loop control into neuroscience and neuromodulation studies. We then detail procedures for using and deploying infrared INS in chronic DBS applications. For complete details on the use and execution of this protocol, please refer to Coventry et al.1 and Coventry and Bartlett.2.

Multielectrode arrays for interfacing with neurons are of great interest for a wide range of medical applications. However, current electrodes cause damage over time. Ultra small carbon fibers help to address issues but controlling the electrode site geometry is difficult. Here we propose a methodology to create small, pointed fiber electrodes (SPFe). We compare the SPFe to previously made blowtorched fibers in characterization. The SPFe result in small site sizes [Formula: see text] with consistently sharp points (20.8 ± 7.64°). Additionally, these electrodes were able to record and/or stimulate neurons multiple animal models including rat cortex, mouse retina, Aplysia ganglia and octopus axial cord. In rat cortex, these electrodes recorded significantly higher peak amplitudes than the traditional blowtorched fibers. These SPFe may be applicable to a wide range of applications requiring a highly specific interface with individual neurons.

Background and objective High-frequency alternating current (HFAC) can yield a rapid-acting and reversible nerve conduction block. The present study aimed to demonstrate the successful implementation of HFAC block delivery via regenerative macro-sieve electrodes (MSEs). Methods Dual-electrode assemblies in two configurations [dual macro-sieve electrode-1 (DMSE-I), DMSE-II] were fabricated from pairs of MSEs and implanted in the transected and subsequently repaired sciatic nerves of two male Lewis rats. After four months of postoperative nerve regeneration through the MSEs' transit zones, the efficacy of acute HFAC block was tested for both configurations. Frequencies ranging from 10 kHz to 42 kHz, and stimulus amplitudes with peak-to-peak voltages ranging from 2 V to 20 V were tested. Evoked muscle force measurement was used to quantify the nerve conduction block. Results HFAC stimulation delivered via DMSE assemblies obtained a complete block at frequencies of 14 to 26 kHz and stimulus amplitudes of 12 to 20 V p-p. The threshold voltage for the complete block showed an approximately linear dependence on frequency. The threshold voltage for the partial conduction block was also approximately linear. For those frequencies that displayed both partial and complete block, the partial block thresholds were consistently lower. Conclusion This study provides a proof of concept that regenerative MSEs can achieve complete and reversible conduction block via HFAC stimulation of regenerated nerve tissue. A chronically interfaced DMSE assembly may thereby facilitate the inactivation of targeted nerves in cases wherein pathologic neuronal hyperactivity is involved.

Understanding the neural mechanisms of loss and recovery of consciousness remains an active area of scientific and clinical interest. Recent models and theories of consciousness implicate thalamus or neocortex as the dominant regions that control global state change, but experimentally testing these theories requires parsing the response properties of the complex thalamocortical network. In this work, we explore information processing using chronic recordings of multiunit activity between thalamus and cortex by targeting either the corticothalamic or thalamocortical pathway with direct electrical stimulation under subhypnotic and just-hypnotic states of isoflurane or dexmedetomidine induced unconsciousness. We found that mutual information between the stimulus and response in the corticothalamic circuit decreases with loss of consciousness, and that the change is not reciprocated in the thalamocortical direction. These changes in mutual information were moderately correlated with changes in evoked rate, and a strong effect of isoflurane on spontaneous and evoked rate accounts for a large portion of the change in mutual information. Further, we found cortical synchrony to increase under sub-and just-hypnotic doses of dexmedetomidine and isoflurane, suggesting cortical responses become more causally dependent on thalamocortical input at reduced levels of consciousness, or unconsciousness. We believe these changes suggest loss of consciousness is associated with a decrease in the number of available cortical network states, and may suggest cognitive unbinding from ascending sensory input.

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

Negative hemodynamic response has been widely reported in blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging studies, however its origin is still controversial. Optical intrinsic signal (OIS) imaging can be used to study brain activity by simultaneously recording hemodynamic signals at different wavelengths with high spatial resolution. In this study, we found transcorneal electrical stimulation (TcES) could elicit both positive OIS response (POR) and negative OIS response (NOR) in cats’ visual cortex. We then investigated the property of this negative response to TcES and its relationship with cerebral blood flow (CBF) and neuronal activity. Results from laser speckle contrast imaging showed decreased CBF in the NOR region while increased CBF in the POR region. Both planar and laminar electrophysiological recordings in the middle (500–700 μm) cortical layers demonstrated that decreased and increased neuronal activities were coexisted in the NOR region. Furthermore, decreased neuronal activity was also detected in the deep cortical layers in the NOR region. This work provides evidence that the negative OIS together with the decreased CBF should be explained by mechanisms of both neuronal inhibition and excitation within middle cortical layers. Our results would be important for interpreting neurophysiological mechanisms underlying the negative BOLD signals.