Amplitude- and frequency-dependent activation of layer II/III neurons by intracortical microstimulation.

Intracortical microstimulation (ICMS) has been used for the development of brain machine interfaces. However, further understanding about the spatiotemporal responses of neurons to different electrical stimulation parameters is necessary to inform the design of optimal therapies. In this study, we employed in vivo electrophysiological recording, two-photon calcium imaging, and electric field simulation to evaluate the acute effect of ICMS on layer II/III neurons. Our results show that stimulation frequency non-linearly modulates neuronal responses, whereas the magnitude of responses is linearly correlated to the electric field strength and stimulation amplitude before reaching a steady state. Temporal dynamics of neurons' responses depends more on stimulation frequency and their distance to the stimulation electrode. In addition, amplitude-dependent post-stimulation suppression was observed within ∼500 µm of the stimulation electrode, as evidenced by both calcium imaging and local field potentials. These findings provide insights for selecting stimulation parameters to achieve desirable spatiotemporal specificity of ICMS.

EEG classification of traumatic brain injury and stroke from a nonspecific population using neural networks.

Traumatic Brain Injury (TBI) and stroke are devastating neurological conditions that affect hundreds of people daily. Unfortunately, detecting TBI and stroke without specific imaging techniques or access to a hospital often proves difficult. Our prior research used machine learning on electroencephalograms (EEGs) to select important features and to classify between normal, TBI, and stroke on an independent dataset from a public repository with an accuracy of 0.71. In this study, we expanded to explore whether featureless and deep learning models can provide better performance in distinguishing between TBI, stroke and normal EEGs by including more comprehensive data extraction tools to drastically increase the size of the training dataset. We compared the performance of models built upon selected features with Linear Discriminative Analysis and ReliefF with several featureless deep learning models. We achieved 0.85 area under the curve (AUC) of the receiver operating characteristic curve (ROC) using feature-based models, and 0.84 AUC with featureless models. In addition, we demonstrated that Gradient-weighted Class Activation Mapping (Grad-CAM) can provide insight into patient-specific EEG classification by highlighting problematic EEG segments during clinical review. Overall, our study suggests that machine learning and deep learning of EEG or its precomputed features can be a useful tool for TBI and stroke detection and classification. Although not surpassing the performance of feature-based models, featureless models reached similar levels without prior computation of a large feature set allowing for faster and cost-efficient deployment, analysis, and classification.

Human in vitro assay for irreversible electroporation cardiac ablation.

Introduction: Pulsed electric field (PEF) cardiac ablation has been recently proposed as a technique to treat drug resistant atrial fibrillation by inducing cell death through irreversible electroporation (IRE). Improper PEF dosing can result in thermal damage or reversible electroporation. The lack of comprehensive and systematic studies to select PEF parameters for safe and effective IRE cardiac treatments hinders device development and regulatory decision-making. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been proposed as an alternative to animal models in the evaluation of cardiac electrophysiology safety. Methods: We developed a novel high-throughput in vitro assay to quantify the electric field threshold (EFT) for electroporation (acute effect) and cell death (long-term effect) in hiPSC-CMs. Monolayers of hiPSC-CMs were cultured in high-throughput format and exposed to clinically relevant biphasic PEF treatments. Electroporation and cell death areas were identified using fluorescent probes and confocal microscopy; electroporation and cell death EFTs were quantified by comparison of fluorescent images with electric field numerical simulations. Results: Study results confirmed that PEF induces electroporation and cell death in hiPSC-CMs, dependent on the number of pulses and the amplitude, duration, and repetition frequency. In addition, PEF-induced temperature increase, absorbed dose, and total treatment time for each PEF parameter combination are reported. Discussion: Upon verification of the translatability of the in vitro results presented here to in vivo models, this novel hiPSC-CM-based assay could be used as an alternative to animal or human studies and can assist in early nonclinical device development, as well as inform regulatory decision-making for cardiac ablation medical devices.

Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.

OBJECTIVES: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used supervised machine learning algorithms in the classification of patients with traumatic brain injury (TBI) history from those with stroke history and/or normal EEG. METHODS: Support vector machine (SVM) and K-nearest neighbors (KNN) models were generated with a diverse feature set from Temple EEG Corpus for both two-class classification of patients with TBI history from normal subjects and three-class classification of TBI, stroke and normal subjects. RESULTS: For two-class classification, an accuracy of 0.94 was achieved in 10-fold cross validation (CV), and 0.76 in independent validation (IV). For three-class classification, 0.85 and 0.71 accuracy were reached in CV and IV respectively. Overall, linear discriminant analysis (LDA) feature selection and SVM models consistently performed well in both CV and IV and for both two-class and three-class classification. Compared to normal control, both TBI and stroke patients showed an overall reduction in coherence and relative PSD in delta frequency, and an increase in higher frequency (alpha, mu, beta and gamma) power. But stroke patients showed a greater degree of change and had additional global decrease in theta power. CONCLUSIONS: Our study suggests that EEG data-driven machine learning can be a useful tool for TBI classification. SIGNIFICANCE: Our study provides preliminary evidence that EEG ML algorithm can potentially provide specificity to separate different neurological conditions.

An Open Resource for Non-human Primate Optogenetics.

Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.

Empirical analysis of phase-amplitude coupling approaches.

Analysis of the coupling between the phases and amplitudes of oscillations within the same continuously sampled signal has provided interesting insights into the physiology of memory and other brain process, and, more recently, the pathophysiology of parkinsonism and other movement disorders. Technical aspects of the analysis have a significant impact on the results. We present an empirical exploration of a variety of analysis design choices that need to be considered when measuring phase-amplitude coupling (PAC). We studied three alternative filtering approaches to the commonly used Kullback-Leibler distance-based method of PAC analysis, including one method that uses wavelets, one that uses constant filter settings, and one in which filtering of the data is optimized for individual frequency bands. Additionally, we introduce a time-dependent PAC analysis technique that takes advantage of the inherent temporality of wavelets. We examined how the duration of the sampled data, the stability of oscillations, or the presence of artifacts affect the value of the "modulation index", a commonly used parameter describing the degree of PAC. We also studied the computational costs associated with calculating modulation indices by the three techniques. We found that wavelet-based PAC performs better with similar or less computational cost than the two other methods while also allowing to examine temporal changes of PAC. We also show that the reliability of PAC measurements strongly depends on the duration and stability of PAC, and the presence (or absence) of artifacts. The best parameters to be used for PAC analyses of long samples of data may differ, depending on data characteristics and analysis objectives. Prior to settling on a specific PAC analysis approach for a given set of data, it may be useful to conduct an initial analysis of the time-dependence of PAC using our time-resolved PAC analysis.

Quantification of movement in normal and parkinsonian macaques using video analysis.

BACKGROUND: Quantification of spontaneous animal movement can be achieved using analysis of video recordings of the animals. Previous reports of video-based methods are based on outdated computer platforms or require the use of specialized equipment. NEW METHOD: We developed a video analysis algorithm to quantify movement based on the commonly used MATLAB programming language. The algorithm is based on pixel differences between frames of video footage acquired with a standard video camera. RESULTS: The new algorithm was validated, analyzing the amount of movements made by monkeys undergoing treatment with the dopaminergic neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) to induce parkinsonism. We compared the movement quantification generated by the new system of analysis with results obtained with a conventional infrared beam break counting system, a parkinsonism rating scale, and accelerometry-based motion quantification in three rhesus macaques. The information provided by our video analysis method was consistent with that obtained with the first two methods, and more detailed than the third. COMPARISON WITH EXISTING METHODS: The new method can replace other methods to quantify movement. Although other video analysis methods have been described, some have since been deprecated, or involve the use of specialized hardware. The new method provides a straightforward and fast approach of analyzing the amount of movement in caged experimental animals, using conventional off-the-shelf equipment and moderate computing resources. CONCLUSIONS: This video analysis method provides an affordable, open access platform to quantify animal movement.

Model and Analysis for the Onset of Parkinsonian Firing Patterns in a Simplified Basal Ganglia.

Parkinson's disease (PD) is a degenerative neurological disease that disrupts the movement cycle in the basal ganglia. As the disease progresses, dopamine depletion leads to changes to how the basal ganglia functions as well as the appearance of abnormal beta oscillations. There is much debate on just exactly how these connection strengths change and just how the oscillations emerge. One leading hypothesis claims that the oscillations develop in the globus pallidus external, subthalamic nucleus, and globus pallidus internal loop. We introduce a mathematical model that calculates the average firing rates of this loop while still accounting for the larger closed loop of the entire basal ganglia system. This model is constructed such that physiologically realistic results can be obtained while not sacrificing the use of analytic methods. Because of this, it is possible to determine how the change in the connection strengths can drive the necessary changes in firing rates seen in recordings and account for the generation of trademark beta oscillations of PD without relying on highly specific time delays, stochastic approaches, or numerical approximations. Additionally, we find that the entire cortico-basal ganglia-thalamo-cortical loop is essential for abnormal oscillations to originate.

Invariance properties for the error function used for multilinear regression.

The connections between the error function used in multilinear regression and the expected, or assumed, properties of the data are investigated. It is shown that two of the most basic properties often required in data analysis, scale and rotational invariance, are incompatible. With this, it is established that multilinear regression using an error function derived from a geometric mean is both scale and reflectively invariant. The resulting error function is also shown to have the property that its minimizer, under certain conditions, is well approximated using the centroid of the error simplex. It is then applied to several multidimensional real world data sets, and compared to other regression methods.

Advances in optogenetic and chemogenetic methods to study brain circuits in non-human primates.

Over the last 10 years, the use of opto- and chemogenetics to modulate neuronal activity in research applications has increased exponentially. Both techniques involve the genetic delivery of artificial proteins (opsins or engineered receptors) that are expressed on a selective population of neurons. The firing of these neurons can then be manipulated using light sources (for opsins) or by systemic administration of exogenous compounds (for chemogenetic receptors). Opto- and chemogenetic tools have enabled many important advances in basal ganglia research in rodent models, yet these techniques have faced a slow progress in non-human primate (NHP) research. In this review, we present a summary of the current state of these techniques in NHP research and outline some of the main challenges associated with the use of these genetic-based approaches in monkeys. We also explore cutting-edge developments that will facilitate the use of opto- and chemogenetics in NHPs, and help advance our understanding of basal ganglia circuits in normal and pathological conditions.