Cluster tendency assessment in neuronal spike data

Automatical Spike Sorting With Low-Rank and Sparse Representation

Spikesorting is crucial in studying neural individually and synergistically encoding and decoding behaviors. However, existent spike sorting algorithms perform unsatisfactorily in real scenarios where heavy noises and overlapping samples are commonly in the spikes, and the spikes from different neurons are similar. To address such challenging scenarios, we propose an automatic spike sporting method in this paper, which integrally combines low-rank and sparse representation (LRSR) into a unified model. In particular, LRSR models spikes through low-rank optimization, uncovering global data structure for handling similar and overlapped samples. To eliminate the influence of the embedded noises, LRSR uses a sparse constraint, effectively separating spikes from noise. The optimization is solved using alternate augmented Lagrange multipliers methods. Moreover, we conclude with an automatic spike-sorting framework that employs the spectral clustering theorem to estimate the number of neurons. Extensive experiments over various simulated and real-world datasets demonstrate that our proposed method, LRSR, can handle spike sorting effectively and efficiently.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.

A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting

Spike sorting technologies support neuroscientists to access the neural activity with single-neuron or single-action-potential resolutions. However, conventional spike sorting technologies perform the feature extraction and the clustering separately after the spikes are well detected. It not only induces many redundant processes, but it also yields a lower accuracy and an unstable result especially when noises and/or overlapping spikes exist in the dataset. To address these issues, this paper proposes a unified optimization model integrating the feature extraction and the clustering for spike sorting. Unlike the widely used combination strategies, i.e., performing the principal component analysis (PCA) for spike feature extraction and the K-means (KM) for clustering in sequence, interestingly, this paper finds the solution of the proposed unified model by iteratively performing PCA and KM-like procedures. Subsequently, by embedding the K-means++ strategy in KM-like initializing and a comparison updating rule in the solving process, the proposed model can well handle the noises and overlapping interference as well as enjoy a high accuracy and a low computational complexity. Finally, an automatic spike sorting method is derived after taking the best of the clustering validity indices into the proposed model. The extensive numerical simulation results on both synthetic and real-world datasets confirm that our proposed method outperforms the related state-of-the-art approaches.

Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy

Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.

An effective assessment of cluster tendency through sampling based multi-viewpoints visual method

Social networks are the rich sources to people for sharing the knowledge on health-related issues. Nowadays, Twitter is one of the great significant social platforms to the people for a discussion on topics. Analyzing the clusters for the tweets concerning terms is a complex process due to the sparsity problem. Topic models are useful or avoiding this problem with derivations of topic clusters. Finding pre-cluster tendency is the major problem in many clustering methods. Existing methods, such as visual access tendency (VAT), cosine-based VAT (cVAT), multi viewpoints-based cosine similarity VAT (MVS-VAT) majorly used to access the prior information about clusters tendency problem. Solution of cluster tendency indicates the tractable number of clusters. The MVS-VAT enables the cluster tendency for the tweet documents effectively than other visual methods. However, it takes a higher number of viewpoints, thus requiring more computational time for the clustering of tweets data. Therefore, sampling-based visual methods are proposed to overcome the computational problem. Several standard health keywords are used for the extraction of health tweets to illustrate the effectiveness of proposed work in the experimental study.

Features of Action Potentials from Identified Thalamic Nuclei in Anesthetized Patients

Our objective was to describe the electrophysiological properties of the extracellular action potential (AP) picked up through microelectrode recordings (MERs). Five patients were operated under general anesthesia for centromedian deep brain stimulation (DBS). APs from the same cell were pooled to obtain a mean AP (mAP). The amplitudes and durations for all 2/3 phases were computed from the mAP, together with the maximum (dV_max) and minimum (dV_min) values of the first derivative, as well as the slopes of different phases during repolarization. The mAPs are denominated according to the phase polarity (P/N for positive/negative). We obtained a total of 1109 mAPs, most of the positive (98.47%) and triphasic (93.69%) with a small P/N deflection (V_phase1) before depolarization. The percentage of the different types of mAPs was different for the nuclei addressed. The relationship between dV_max and the depolarizing phase is specific. The descending phase of the first derivative identified different phases during the repolarizing period. We observed a high correlation between V_phase1 and the amplitudes of either depolarization or repolarization phases. Human thalamic nuclei differ in their electrophysiological properties of APs, even under general anesthesia. Capacitive current, which is probably responsible for V_phase1, is very common in thalamic APs. Moreover, subtle differences during repolarization are neuron-specific.