Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes

Efficient neural spike sorting using data subdivision and unification

Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.

Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

Deciphering useful information from electrophysiological data recorded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately. The spike analysis mechanisms are heavily reliant on the clustering algorithms that enable separation of spike trends based on their spatio-temporal behaviors. Literature review report several clustering algorithms over decades focused on different applications. Although spike analysis algorithms employ only a small subset of clustering algorithms, however, not much work has been reported on the compliance and suitability of such clustering algorithms for spike analysis. In our study, we have attempted to comment on the suitability of available clustering algorithms and performance capacity when exposed to spike analysis. In this regard, the study reports a compatibility evaluation on algorithms previously employed in spike sorting as well as the algorithms yet to be investigated for application in sorting neural spikes. The performance of the algorithms is compared in terms of their accuracy, confusion matrix and accepted validation indices. Three data sets comprising of easy, difficult, and real spike similarity with known ground-truth are chosen for assessment, ensuring a uniform testbed. The procedure also employs two feature-sets, principal component analysis and wavelets. The report also presents a statistical score scheme to evaluate the performance individually and overall. The open nature of the data sets, the clustering algorithms and the evaluation criteria make the proposed evaluation framework widely accessible to the research community. We believe that the study presents a reference guide for emerging neuroscientists to select the most suitable algorithms for their spike analysis requirements.

Electrophysiological evidence of RML12 mosquito cell line towards neuronal differentiation by 20-hydroxyecdysdone

Continuous cell lines from insect larval tissues are widely used in different research domains, such as virology, insect immunity, gene expression, and bio pharmacology. Previous study showed that introduction of 20-hydroxyecdysone to Spodoptera cell line induced a neuron-like morphology with neurite extensions. Despite some results suggesting potential presence of neuro-receptors, no study so far has shown that these neuron-induced cells were functional. Here, using microelectrode arrays, we showed that the mosquito cell line, RML12, differentiated with 20-hydroxyecdysone, displays spontaneous electrophysiological activity. Results showed that these cells can be stimulated by GABAergic antagonist as well as nicotinic agonist. These results provide new evidence of neuron-like functionality of 20-hydroxyecdysone induced differentiated mosquito cell line. Finally, we used this new model to test the effects of two insecticides, temephos and permethrin. Our analysis revealed significant changes in the spiking activity after the introduction of these insecticides with prolonged effect on the neuronal activity. We believe that this differentiated mosquito neuronal cell model can be used for high-throughput screening of new pesticides on insect nervous system instead of primary neurons or in vivo studies.

Zika virus-induced hyper excitation precedes death of mouse primary neuron

BACKGROUND

Zika virus infection in new born is linked to congenital syndromes, especially microcephaly. Studies have shown that these neuropathies are the result of significant death of neuronal progenitor cells in the central nervous system of the embryo, targeted by the virus. Although cell death via apoptosis is well acknowledged, little is known about possible pathogenic cellular mechanisms triggering cell death in neurons.

METHODS

We used in vitro embryonic mouse primary neuron cultures to study possible upstream cellular mechanisms of cell death. Neuronal networks were grown on microelectrode array and electrical activity was recorded at different times post Zika virus infection. In addition to this method, we used confocal microscopy and Q-PCR techniques to observe morphological and molecular changes after infection.

RESULTS

Zika virus infection of mouse primary neurons triggers an early spiking excitation of neuron cultures, followed by dramatic loss of this activity. Using NMDA receptor antagonist, we show that this excitotoxicity mechanism, likely via glutamate, could also contribute to the observed nervous system defects in human embryos and could open new perspective regarding the causes of adult neuropathies.

CONCLUSIONS

This model of excitotoxicity, in the context of neurotropic virus infection, highlights the significance of neuronal activity recording with microelectrode array and possibility of more than one lethal mechanism after Zika virus infection in the nervous system.

Neurotropism and behavioral changes associated with Zika infection in the vector Aedes aegypti

Understanding Zika virus infection dynamics is essential, as its recent emergence revealed possible devastating neuropathologies in humans, thus causing a major threat to public health worldwide. Recent research allowed breakthrough in our understanding of the virus and host pathogenesis; however, little is known on its impact on its main vector, Aedes aegypti. Here we show how Zika virus targets Aedes aegypti’s neurons and induces changes in its behavior. Results are compared to dengue virus, another flavivirus, which triggers a different pattern of behavioral changes. We used microelectrode array technology to record electrical spiking activity of mosquito primary neurons post infections and discovered that only Zika virus causes an increase in spiking activity of the neuronal network. Confocal microscopy also revealed an increase in synapse connections for Zika virus-infected neuronal networks. Interestingly, the results also showed that mosquito responds to infection by overexpressing glutamate regulatory genes while maintaining virus levels. This neuro-excitation, possibly via glutamate, could contribute to the observed behavioral changes in Zika virus-infected Aedes aegypti females. This study reveals the importance of virus-vector interaction in arbovirus neurotropism, in humans and vector. However, it appears that the consequences differ in the two hosts, with neuropathology in human host, while behavioral changes in the mosquito vector that may be advantageous to the virus.

A review on cluster estimation methods and their application to neural spike data

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons-'spike sorting'-is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.