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. 2012 Feb 15;204(1):189-201.
doi: 10.1016/j.jneumeth.2011.10.027. Epub 2011 Nov 10.

NeuroQuest: a comprehensive analysis tool for extracellular neural ensemble recordings

Ki Yong Kwon  1 , Seif Eldawlatly  1 , Karim Oweiss  2
Affiliations

NeuroQuest: a comprehensive analysis tool for extracellular neural ensemble recordings

Ki Yong Kwon et al. J Neurosci Methods. .

Abstract

Analyzing the massive amounts of neural data collected using microelectrodes to extract biologically relevant information is a major challenge. Many scientific findings rest on the ability to overcome these challenges and to standardize experimental analysis across labs. This can be facilitated in part through comprehensive, efficient and practical software tools disseminated to the community at large. We have developed a comprehensive, MATLAB-based software package - entitled NeuroQuest - that bundles together a number of advanced neural signal processing algorithms in a user-friendly environment. Results demonstrate the efficiency and reliability of the software compared to other software packages, and versatility over a wide range of experimental conditions.

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Figures

Figure 1
Figure 1. NeuroQuest v1.0 architecture and Flowchart
(a) Architechture: A total of 6 processing modules (colored blocks) provided in the software are classified into two groups: Processing group and Analysis group. Individual modules are connected through the main GUI which has an access to the input and output data. Spike sorting modules require the raw or preprocessed extracellular recordings, while spike analysis tools handle single or multiple spike trains. Once the input data is loaded, the corresponding group of modules becomes available in the main menu. Each module contains sub-modules that assist to yield more accurate analysis results. (b) Flowchart of NeuroQuest: After the extracellular recording data is loaded, spike sorting tools are activated for further processing. The first stage is to denoise the data to enhance the neural yield. After denoising, spikes are detected and subsequently sent to the spike sorting algorithm to obtain the spike trains. These are further analyzed using the primary spike train analysis tools such as Interspike Interval Histogram (ISIH), Peristimulus Time Histogram (PSTH), Joint Peristimulus Time Histogram (JPSTH), Cross-Correlogram (CC)(Oweiss and Anderson, 2002b; Oweiss, 2010), and the ensemble analysis tools such as functional and effective connectivity estimation.
Figure 1
Figure 1. NeuroQuest v1.0 architecture and Flowchart
(a) Architechture: A total of 6 processing modules (colored blocks) provided in the software are classified into two groups: Processing group and Analysis group. Individual modules are connected through the main GUI which has an access to the input and output data. Spike sorting modules require the raw or preprocessed extracellular recordings, while spike analysis tools handle single or multiple spike trains. Once the input data is loaded, the corresponding group of modules becomes available in the main menu. Each module contains sub-modules that assist to yield more accurate analysis results. (b) Flowchart of NeuroQuest: After the extracellular recording data is loaded, spike sorting tools are activated for further processing. The first stage is to denoise the data to enhance the neural yield. After denoising, spikes are detected and subsequently sent to the spike sorting algorithm to obtain the spike trains. These are further analyzed using the primary spike train analysis tools such as Interspike Interval Histogram (ISIH), Peristimulus Time Histogram (PSTH), Joint Peristimulus Time Histogram (JPSTH), Cross-Correlogram (CC)(Oweiss and Anderson, 2002b; Oweiss, 2010), and the ensemble analysis tools such as functional and effective connectivity estimation.
Figure 2
Figure 2
Steps of the artifact removal process
Figure 3
Figure 3. Flowchart of the spike sorting algorithms implemented in NeuroQuest
Two spike sorting algorithms are available: single-channel and multi-channel. The Multi-channel mode uses spatial information about the distribution of the spike events across channels for sorting in addition to the temporal and spectral information used in the single-channel mode. MASSIT: Multiresolution Analysis of Signal Subspace Invariance Technique identifies the number of signal sources in overlapping spikes observed when two or more neurons fire nearly simultaneously (Oweiss and Anderson, 2002c; Oweiss, 2010).
Figure 4
Figure 4. Sub-Clustering and cluster merging
(a) Example of a sub-clustering process (b) Example of cluster merging process Classified clusters in feature space, spike templates, and ISIH plots are displayed. Color indicates the corresponding clusters and their spike templates and ISIH. Spike template displays an average of a cluster with a centerline and variance of a cluster with a shaded area.
Figure 4
Figure 4. Sub-Clustering and cluster merging
(a) Example of a sub-clustering process (b) Example of cluster merging process Classified clusters in feature space, spike templates, and ISIH plots are displayed. Color indicates the corresponding clusters and their spike templates and ISIH. Spike template displays an average of a cluster with a centerline and variance of a cluster with a shaded area.
Figure 5
Figure 5. Sample screen shot of the spike train analysis tools
(a) Single Unit Analysis Tools (b) Multi Unit Analysis Tools (c) Functional connectivity estimation GUI (d) Effective connectivity estimation GUI
Figure 6
Figure 6. Comparison of spike detection performance in NeuroQuest, wave_clus, and OSort
Sim1 dataset contains three neurons with different noise levels (1, 2, 3, and 4) that is an average SNR of 1.2, 2.2, 3.4, and 6.7 dB, respectively. Sim2 dataset contains three neurons with an average SNR of 1.3, 1.7, 2.6 and 5.2 dB, respectively. Sim3 dataset contains five neurons, some of which have very similar waveforms, with an average SNR of 1.2, 1.6, 2.3, and 4.7 dB, respectively.
Figure 7
Figure 7. Separability comparison between three feature extraction methods
A total of 12 datasets generated using a renewal Poisson process with a refractory period of 3 ms and a fixed firing rate 5Hz were examined. Each dataset has different noise levels and spike waveforms that were randomly selected from a database of 131 average spike waveforms obtained from spontaneous activity recorded in the primary motor cortex of an anesthetized rat.
Figure 8
Figure 8. Sample Clustering result of detected spikes in a temporal PCA feature space with different alignment methods
(a) Negative Peak Alignment (b) Absolute Peak Alignment. (c) Positive Peak Alignment
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