SigMate: A Comprehensive Automated Tool for Processing and Analysis of Extracellular Brain Signals Recorded by Neuronal Probes

The ionic gating across the neuron membrane generates neuronal activity in the brain. During the last two decades rapid advances in microelectronics and microelectrode technology have provided scientists with many devices enabling them to record extracellularly the transmembrane potentials near the electrode in the brain. These devices that are implanted invasively without causing too much tissue damage, can record from hundreds of neurons, and also simultaneously from a number of channels generating a huge amount of data. Inferring meaningful conclusions by analyzing this massive amount of data often recorded from noisy experimental conditions is a big challenge for the neuroscience and neuroengineering community and sophisticated signal processing and analysis tools are required. But, relatively little work has been done on development of comprehensive signal processing tools operable on different software platforms and that can be easily diffused to the scientific community. Though individual tools are available for signal visualization, spike detection and sorting, spike train analysis, yet analysis of local field potentials (LFPs) are still done manually. Most of these tools are developed by laboratories for their own requirements. Moreover, no software tools are available to date integrating all the signal processing steps under a single platform. This thesis aims at developing a comprehensive tool called ‘SigMate’ for processing and analysis of extracellular potentials; capable of performing operations ranging from signal visualization and basic operations to single sweep analysis and simulation of neuronal activity. The software package is designed to avoid file-type based incompatibility among different acquisition software and works with the neuronal data files in ASCII format. The functionalities of SigMate is described briefly below. • Signal visualization (2D and 3D) and basic operations: This is the starting and home module of the software package that provides connectivity to other functionalities. With signal visualization it includes basic operations like signal averaging, noise estimation, +/- averaging, mean square and root mean square noise estimation. In every module a visualization pane is provided with zooming, panning and data cursor options. • Basic file operations: Usually, incompatibility between acquisition and analysis tools poses a barrier in quick analysis of the recorded signals. However, most of the acquisition tools provide a way to convert the recorded files into ASCII format files and most of the analysis tools require specifically formatted files. To meet this need, the module includes operations like file splitting, file concatenating, and file column rearranging. • Artifact removal: Stimulus artifacts very often obscure the real neuronal response in signals. This module performs artifact removal for both slow and fast stimulus artifacts with an optional baseline correction operation. • Noise characterization: Invasive neuronal recording setups involve sophisticated electronic devices. Due to the wide variety of neural probes used by different labs a unique method for noise analysis is required. This module measures the quality of the recorded signals through noise estimation using detection of steady states. • Latency estimation: Very often neuroscientists use latency information to understand the signal propagation in the brain. This module calculates latency and automatically determines cortical layer activation order using LFPs as well as current source density data by applying current source density analysis on the LFPs. • Spike detection and spike train analysis tool: Neuronal spikes are most widely studied signals. Many tools address spike detection and spike train analysis in the existing literature and this module adapts 'Wave_Clus', a popular tool among them. • Single sweep LFP clustering: LFPs represent cumulative response of neuronal populations around the recording electrode and are studied as an average of many single sweeps. Single sweep LFPs contain response of a neuronal population at a particular time instance and shows a range of shapes. As the shape of an LFP is considered as a fingerprint of the underlying neuronal network generating it, a shape based clustering system is presented in this module to facilitate the study of neuronal circuit activation. • Interface with EEG based robotic system: This module contains an interface with the ‘Simulink’ based EEG acquiring system developed by g.tec medical engineering GmbH. Using this module, it is possible to establish communication with a robotic device for navigation. • Simulations: Neuronal simulations for optimization of stimulation protocol and simulation of calcium based model for flicking-based short-term plasticity. Except the spike detection and spike train analysis tool, the rest of the features are in-house developed algorithms which are tested rigorously with datasets recorded using standard micropipette, implantable and planar EOSFETs from anesthetized rats upon different stimulations. In conclusion, with the growth of neuronal probes, amount of acquired data are increasing and the need of one single software package performing all necessary processing and analysis on the data has become crucial. This thesis is the first step towards meeting that need. As the software has been extensively tested with three possible sources of data, we believe that once it is disseminated to the community (which will happen in the near future), it will serve a good deal in processing and analyzing extracellularly recorded neurophysiological signals.