Action Potential Waveform Variability Limits Multi-Unit Separation in Freely Behaving Rats

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm3). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution

ELECTROPHYSIOLOGY OF BASAL GANGLIA (BG) CIRCUITRY AND DYSTONIA AS A MODEL OF MOTOR CONTROL DYSFUNCTION

The basal ganglia (BG) is a complex set of heavily interconnected nuclei located in the central part of the brain that receives inputs from the several areas of the cortex and projects via the thalamus back to the prefrontal and motor cortical areas. Despite playing a significant part in multiple brain functions, the physiology of the BG and associated disorders like dystonia remain poorly understood. Dystonia is a devastating condition characterized by ineffective, twisting movements, prolonged co-contractions and contorted postures. Evidences suggest that it occurs due to abnormal discharge patterning in BG-thalamocortocal (BGTC) circuitry. The central purpose of this study was to understand the electrophysiology of BGTC circuitry and its role in motor control and dystonia. Toward this goal, an advanced multi-target multi-unit recording and analysis system was utilized, which allows simultaneous collection and analysis of multiple neuronal units from multiple brain nuclei. Over the cause of this work, neuronal data from the globus pallidus (GP), subthalamic nucleus (STN), entopenduncular nucleus (EP), pallidal receiving thalamus (VL) and motor cortex (MC) was collected from normal, lesioned and dystonic rats under awake, head restrained conditions. The results have shown that the neuronal population in BG nuclei (GP, STN and EP) were characterized by a dichotomy of firing patterns in normal rats which remains preserved in dystonic rats. Unlike normals, neurons in dystonic rat exhibit reduced mean firing rate, increased irregularity and burstiness at resting state. The chaotic changes that occurs in BG leads to inadequate hyperpolarization levels within the VL thalamic neurons resulting in a shift from the normal bursting mode to an abnormal tonic firing pattern. During movement, the dystonic EP generates abnormally synchronized and elongated burst duration which further corrupts the VL motor signals. It was finally concluded that the loss of specificity and temporal misalignment between motor neurons leads to corrupted signaling to the muscles resulting in dystonic behavior. Furthermore, this study reveals the importance of EP output in controlling firing modes occurring in the VL thalamus

Algorithm and software to automatically identify latency and amplitude features of local field potentials recorded in electrophysiological investigation

A function that is called by main_script.m to compute the onset and the maximum latencies and amplitudes from the signal time-derivative. Also the functions that guarantee the correct running of main_script.m. To test the algorithm, invoking only main_script.m is necessary (all the other functions must be contained in the same folder). (M 1 kb

A framework for the comparative assessment of neuronal spike sorting algorithms towards more accurate off-line and on-line microelectrode arrays data analysis

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis

Heuristic Spike Sorting Tuner for the Determination of an Optimal Parameter Set for a Generic Spike Sorting Algorithm

Extracellular microelectrodes frequently record neural activity from multiple sources in the vicinity of the electrode. Spike sorting generally describes the process of labeling each recorded spike waveform with the identity of its source neuron, which is required to conduct any further analysis of the neuronal spiking patterns. This process for spike sorting or isolating neural activity is often approached from an abstracted mathematical perspective such as calculating the Euclidean distance between spike waveform features in some lower dimensional space or using probability distributions to describe the isolation of neural activity or recorded spikes. However, these approaches ignore neurophysiological realities and result in the loss of important information that could improve the accuracy of these methods. Furthermore, standard algorithms typically require manual selection of at least one free parameter, which can have significant effects on the ultimate quality of the spike sorting and all resulting neurophysiological inferences. We describe a Heuristic Spike Sorting Tuner (HSST) which determines the optimal choice of the set of free parameters for a given spike sorting algorithm. A set of heuristic metrics computes a neurophysiologically-based qualification score of an algorithm’s output across a range of parameters. This qualification score measures unit isolation and signal discrimination, allowing HSST to select the best set of parameters for a sorting algorithm, resulting in high sort quality. We demonstrate the power of this spike sorting framework, by comparing its performance across many existing spike sorting methods while using HSST to set their free parameters. The algorithm is robust over varied data (signal-to-noise ratio, number of units, relative size of units to each other, etc), and importantly; this approach requires no human supervision. With simulated datasets, HSST reliably selects the optimal set of free parameters for many different sorting algorithms, allowing simple clustering techniques (such as K-Means) whose performance is highly dependent on correct parameter settings to outperform more complex algorithms. HSST outperforms expert manual sorters and is more robust at parameter estimation than other unsupervised algorithms, achieving this without sacrificing speed or stability. Rather than being a spike sorting algorithm in its own right, HSST is a general framework that can incorporate any existing spike sorting algorithm, has an extendable set of heuristics and can be integrated in any existing neural signal processing stream. HSST makes use of known neurophysiological priors while simultaneously taking advantage of the power of abstract mathematical tools. We believe that this approach enables unsupervised spike sorting that exceeds the performance of previous methods, thereby enabling principled processing of large data sets without the significant confound of human intervention

Signal processing for automated EEG quality assessment

An automated signal quality assessment method was proposed for the EEG signals, which will help in testing new BCI algorithms so that the testing can be made on high quality signals only. This research includes the development of novel feature extraction technique and a new clustering algorithm for EEG signals

Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment

Orchestrating appropriate behavioral responses in the face of competing signals that predict either rewards or threats in the environment is crucial for survival. The basolateral nucleus of the amygdala (BLA) and prelimbic (PL) medial prefrontal cortex have been implicated in reward-seeking and fear-related responses, but how information flows between these reciprocally connected structures to coordinate behavior is unknown. We recorded neuronal activity from the BLA and PL while rats performed a task wherein competing shock- and sucrose-predictive cues were simultaneously presented. The correlated firing primarily displayed a BLA→PL directionality during the shock-associated cue. Furthermore, BLA neurons optogenetically identified as projecting to PL more accurately predicted behavioral responses during competition than unidentified BLA neurons. Finally photostimulation of the BLA→PL projection increased freezing, whereas both chemogenetic and optogenetic inhibition reduced freezing. Therefore, the BLA→PL circuit is critical in governing the selection of behavioral responses in the face of competing signals.National Institutes of Health (U.S.) (Award 1R25-MH092912-01)National Institute of Mental Health (U.S.) (Grant R01- MH102441-01)National Institutes of Health (U.S.) (Award DP2- DK-102256-01

Resource efficient on-node spike sorting

Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces

Model Based Automatic and Robust Spike Sorting for Large Volumes of Multi-channel Extracellular Data

abstract: Spike sorting is a critical step for single-unit-based analysis of neural activities extracellularly and simultaneously recorded using multi-channel electrodes. When dealing with recordings from very large numbers of neurons, existing methods, which are mostly semiautomatic in nature, become inadequate. This dissertation aims at automating the spike sorting process. A high performance, automatic and computationally efficient spike detection and clustering system, namely, the M-Sorter2 is presented. The M-Sorter2 employs the modified multiscale correlation of wavelet coefficients (MCWC) for neural spike detection. At the center of the proposed M-Sorter2 are two automatic spike clustering methods. They share a common hierarchical agglomerative modeling (HAM) model search procedure to strategically form a sequence of mixture models, and a new model selection criterion called difference of model evidence (DoME) to automatically determine the number of clusters. The M-Sorter2 employs two methods differing by how they perform clustering to infer model parameters: one uses robust variational Bayes (RVB) and the other uses robust Expectation-Maximization (REM) for Student’s -mixture modeling. The M-Sorter2 is thus a significantly improved approach to sorting as an automatic procedure. M-Sorter2 was evaluated and benchmarked with popular algorithms using simulated, artificial and real data with truth that are openly available to researchers. Simulated datasets with known statistical distributions were first used to illustrate how the clustering algorithms, namely REMHAM and RVBHAM, provide robust clustering results under commonly experienced performance degrading conditions, such as random initialization of parameters, high dimensionality of data, low signal-to-noise ratio (SNR), ambiguous clusters, and asymmetry in cluster sizes. For the artificial dataset from single-channel recordings, the proposed sorter outperformed Wave_Clus, Plexon’s Offline Sorter and Klusta in most of the comparison cases. For the real dataset from multi-channel electrodes, tetrodes and polytrodes, the proposed sorter outperformed all comparison algorithms in terms of false positive and false negative rates. The software package presented in this dissertation is available for open access.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

The Ecology Of Host-Seeking Mosquitoes Within The Red River Valley Of Central North Dakota

Host-seeking mosquitoes are taxing for people and wildlife alike in the Red River Valley (RRV). During the summer massive numbers of mosquitoes swarm the RRV, yet little is known about the ecology and biology of the mosquito species that inhabit this area. This research will help to fill some of those knowledge gaps by studying the ecology of host seeking mosquitoes in the RRV. Host-seeking mosquitoes were collected using CO2-baited MMXTM traps. Trapping was conducted in two very different rural settings within the RRV. One site, a 40-acre hardwood forest with closed canopy, the other a farmstead consisting of open agricultural fields interspersed with forested wind-rows. Trapping was conducted 2-3 times weekly throughout the mosquito season (May through August). Each night\u27s catch was sorted, counted and identified to species. During sorting, all engorged and partially engorged mosquitoes were removed, identified to species and stored at -80°C. DNA was extracted from individual mosquito blood meals and analyzed via polymerase chain reaction (PCR) assays multiple times to determine the host feeding preferences and parasitic infection status of the host. The first round of PCR assays determined the host species from which the blood originated (e.g., deer, dog, human, etc.). Analyzing the host composition of many mosquito blood meals produced information on the preference of host species that were most commonly fed upon by the various mosquito species within their natural environment. The following rounds of PCR assays examined mosquito blood meals for the presence of blood-borne pathogens (e.g., filarial nematodes, avian malaria, etc.). This process, known as xenomonitoring, uses mosquitoes as a sampling tool to acquire blood samples from wildlife without having direct contact with the vertebrate host. Thus, xenomonitoring is an indirect way of estimating the prevalence of infection among vertebrate populations. Mosquito counts from the forest and farm sites along with Grand Forks “Skeeter Meter” counts from the years of 2002-2010 were used to construct predictive models to understand the effects of climate on mosquito population dynamics and abundance throughout the summer. Generalized linear models are used to determine how climate variables play roles on everyday mosquito activity, while cross-correlation maps were used to determine correlation values of preceding weather variables to trap counts. This allowed for the determination of which climate variables can be used to predict how mosquito populations will fluctuate in the future. This research provides a critical foundation by describing the species composition of mosquitoes that inhabit two unique rural study sites within the RRV. Species composition is crucial to the initial component of mosquito-borne vector transmission of diseases, presence of mosquito vectors. Building from the composition, this study provides information describing the population trends of multiple mosquito populations throughout the summers of 2009-2011 at these two rural sites. Because mosquito population trends differed between sites, several meteorological variables were identified as affectors of mosquito abundance and activity. By understanding how these meteorological factors affect mosquito populations, vital data is provided for the future design of predictive models that will allow for focused mosquito control, but also lend information in potential disease risk-assessment map production. To further build on the potential for zoonotic and enzootic pathogen transmission, it is important to understand the feeding habits of local mosquito species. These feeding preferences determine which hosts are more commonly fed upon by given mosquito species and offer a background to determine which vector transmitted diseases are currently present in the RRV as well as potential diseases, that upon introduction to the region, which could be transmitted within the valley