A simple, inexpensive method for subcortical stereotactic targeting in nonhuman primates.

BACKGROUND: Many current neuroscience studies in large animal models have focused on recordings from cortical structures. While sufficient for analyzing sensorimotor systems, many processes are modulated by subcortical nuclei. Large animal models, such as nonhuman primates (NHP), provide an optimal model for studying these circuits, but the ability to target subcortical structures has been hampered by lack of a straightforward approach to targeting. NEW METHOD: Here we present a method of subcortical targeting in NHP that uses MRI-compatible titanium screws as fiducials. The in vivo study used a cellular marker for histologic confirmation of accuracy. RESULTS: Histologic results are presented showing a cellular stem cell marker within targeted structures, with mean errors ± standard deviations (SD) of 1.40 ±â€¯1.19 mm in the X-axis and 0.9 ±â€¯0.97 mm in the Z-axis. The Y-axis errors ± SD ranged from 1.5 ±â€¯0.43 to 4.2 ±â€¯1.72 mm. COMPARISON WITH EXISTING METHODS: This method is easy and inexpensive, and requires no fabrication of equipment, keeping in mind the goal of optimizing a technique for implantation or injection into multiple interconnected areas. CONCLUSION: This procedure will enable primate researchers to target deep, subcortical structures more precisely in animals of varying ages and weights.

Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space.

Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance.

OBJECTIVE: For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials ('spikes') requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials. APPROACH: We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naïve Bayes classifier for reaching direction and a linear regression to evaluate hand position. MAIN RESULTS: We found the highest performance for thresholding when placing a threshold between -3 and -4.5 × Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naïve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded. SIGNIFICANCE: For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.