Short-wave Infrared Neural Stimulation Drives Graded Sciatic Nerve Activation Across A Continuum of Wavelengths

Focal Infrared Neural Stimulation Propagates Dynamical Transformations in Auditory Cortex

Significance

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.

Aim

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.

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.

Conclusions

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.

Field-Programmable Gate Array-Based Ultra-Low Power Discrete Fourier Transforms for Closed-Loop Neural Sensing

Digital implementations of discrete Fourier transforms (DFT) are a mainstay in feature assessment of recorded biopotentials, particularly in the quantification of biomarkers of neurological disease state for adaptive deep brain stimulation. Fast Fourier transform (FFT) algorithms and architectures present a substantial power demand from onboard batteries in implantable medical devices, necessitating the development of ultra-low power Fourier transform methods in resource-constrained environments. Numerous FFT architectures aim to optimize power and resource demand through computational efficiency; however, prioritizing the reduction of logic complexity at the cost of additional computations can be equally or more effective. This paper introduces a minimal architecture single-delay feedback discrete Fourier transform (mSDF-DFT) for use in ultra-low-power field programmable gate array applications and shows energy and power improvements over state-of-the-art FFT methods. We observe a 33% reduction in dynamic power and 4% reduction in resource utilization in a neural sensing application when compared to state-of-the-art FFT algorithms. While designed for use in closed-loop deep brain stimulation and medical device implementations, the mSDF-DFT is also easily extendable to any ultra-low power embedded application.

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Epidural combined optical and electrical stimulation induces high-specificity activation of target muscles in spinal cord injured rats

Introduction

Epidural electrical stimulation (EES) has been shown to improve motor dysfunction after spinal cord injury (SCI) by activating residual locomotor neural networks. However, the stimulation current often spreads excessively, leading to activation of non-target muscles and reducing the accuracy of stimulation regulation.

Objectives

Near-infrared nerve stimulation (nINS) was combined with EES to explore its regulatory effect on lower limb muscle activity in spinal-cord-transected rats.

Methods

In this study, stimulation electrodes were implanted into the rats' L3-L6 spinal cord segment with T8 cord transected. Firstly, a series of EES parameters (0.2-0.6 mA and 20-60 Hz) were tested to determine those that specifically regulate the tibialis anterior (TA) and medial gastrocnemius (MG). Subsequently, to determine the effect of combined optical and electrical stimulation, near-infrared laser with a wavelength of 808 nm was used to irradiate the L3-L6 spinal cord segment while EES was performed. The amplitude of electromyography (EMG), the specific activation intensity of the target muscle, and the minimum stimulus current intensity to induce joint movement (motor threshold) under a series of optical stimulation parameters (power: 0.0-2.0 W; pulse width: 0-10 ms) were investigated and analyzed.

Results

EES stimulation with 40 Hz at the L3 and L6 spinal cord segments specifically activated TA and MG, respectively. High stimulation intensity (>2 × motor threshold) activated non-target muscles, while low stimulation frequency (<20 Hz) produced intermittent contraction. Compared to electrical stimulation alone (0.577 ± 0.081 mV), the combined stimulation strategy could induce stronger EMG amplitude of MG (1.426 ± 0.365 mV) after spinal cord injury (p < 0.01). The combined application of nINS effectively decreased the EES-induced motor threshold of MG (from 0.237 ± 0.001 mA to 0.166 ± 0.028 mA, p < 0.001). Additionally, the pulse width (PW) of nINS had a slight impact on the regulation of muscle activity. The EMG amplitude of MG only increased by ~70% (from 3.978 ± 0.240 mV to 6.753 ± 0.263 mV) when the PW increased by 10-fold (from 1 to 10 ms).

Conclusion

The study demonstrates the feasibility of epidural combined electrical and optical stimulation for highly specific regulation of muscle activity after SCI, and provides a new strategy for improving motor dysfunction caused by SCI.

Spatially specific, closed-loop infrared thalamocortical deep brain stimulation

Deep brain stimulation (DBS) is a powerful tool for the treatment of circuitopathy-related neurological and psychiatric diseases and disorders such as Parkinson's disease and obsessive-compulsive disorder, as well as a critical research tool for perturbing neural circuits and exploring neuroprostheses. Electrically-mediated DBS, however, is limited by the spread of stimulus currents into tissue unrelated to disease course and treatment, potentially causing undesirable patient side effects. In this work, we utilize infrared neural stimulation (INS), an optical neuromodulation technique that uses near to mid-infrared light to drive graded excitatory and inhibitory responses in nerves and neurons, to facilitate an optical and spatially constrained DBS paradigm. INS has been shown to provide spatially constrained responses in cortical neurons and, unlike other optical techniques, does not require genetic modification of the neural target. We show that INS produces graded, biophysically relevant single-unit responses with robust information transfer in thalamocortical circuits. Importantly, we show that cortical spread of activation from thalamic INS produces more spatially constrained response profiles than conventional electrical stimulation. Owing to observed spatial precision of INS, we used deep reinforcement learning for closed-loop control of thalamocortical circuits, creating real-time representations of stimulus-response dynamics while driving cortical neurons to precise firing patterns. Our data suggest that INS can serve as a targeted and dynamic stimulation paradigm for both open and closed-loop DBS.

Infrared neurostimulation inex-vivorat sciatic nerve using 1470 nm wavelength

Objective.To design and implement a setup forex-vivooptical stimulation for exploring the effect of several key parameters (optical power and pulse duration), activation features (threshold, spatial selectivity) and recovery characteristics (repeated stimuli) in peripheral nerves.Approach.A nerve chamber allowing ex-vivo electrical and optical stimulation was designed and built. A 1470 nm light source was chosen to stimulate the nerve. A photodiode module was implemented for synchronization of the electrical and optical channels.Main results. Compound neural action potentials (CNAPs) were successfully generated with infrared light pulses of 200-2000µs duration and power in the range of 3-10 W. These parameters determine a radiant exposure for stimulation in the range 1.59-4.78 J cm^-2. Recruitment curves were obtained by increasing durations at a constant power level. Neural activation threshold is reached at a mean radiant exposure of 3.16 ± 0.68 J cm^-2and mean pulse energy of 3.79 ± 0.72 mJ. Repetition rates of 2-10 Hz have been explored. In eight out of ten sciatic nerves (SNs), repeated light stimuli induced a sensitization effect in that the CNAP amplitude progressively grows, representing an increasing number of recruited fibres. In two out of ten SNs, CNAPs were composed of a succession of peaks corresponding to different conduction velocities.Significance.The reported sensitization effect could shed light on the mechanism underlying infrared neurostimulation. Our results suggest that, in sharp contrast with electrical stimuli, optical pulses could recruit slow fibres early on. This more physiological order of recruitment opens the perspective for specific neuromodulation of fibre population who remained poorly accessible until now. Short high-power light pulses at wavelengths below 1.5µm offer interesting perspectives for neurostimulation.