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SigMate: A Comprehensive Automated Tool for Processing and Analysis of Extracellular Brain Signals Recorded by Neuronal Probes
Full text linkThe 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.Sommario
1.1 Motivazioni
I segnali neurali registrati con sonde neurali invasive o non invasive richiedono un’elaborazione e un’analisi rigorosa per arrivare a comprendere l’attività generata dalla sottostante rete neurale in risposta a degli stimoli.
Nel corso degli ultimi due decenni, il rapido sviluppo della microelettronica e della tecnologia del microelettrodo ha permesso agli scienziati di registrare contemporaneamente segnali provenienti da centinaia di neuroni usando numerosi canali. L’ottenimento di risultati significativi attraverso l’elaborazione e analisi di questa enorme quantità di dati registrati in condizioni sperimentali non ottimali rappresenta una grande sfida per le neuroscienze e la comunità della neuroingegneria.
Anche se sono già disponibili singoli software per eseguire l’analisi, ad esempio, di un treno di spike, il sorting e rilevamento del picco dello spike, non sono però ancora stati sviluppati strumenti software che integrino tutti gli step necessari per il processing del segnale EEG, degli spike neurali, e il calcolo dei potenziali di campo (local field potential – LFPs). Pertanto, la comunità della neuroingegneria sente più che mai necessario lo sviluppo di un unico pacchetto software in grado di eseguire tutto il processing e l’analisi standard dei segnali neurali registrati. Questa tesi presenta come risultato finale un pacchetto software, “SigMate”, costruito integrando assieme vari moduli per permettere l’elaborazione e l’analisi di LFP e di segnali EEG per il brain-machine-interface (BMI), la simulazione di un singolo neurone, e la rilevazione, l’ordinamento e l’analisi di un treno di spike.
1.2 Scopi e Obiettivi
Il pacchetto software SigMate è sviluppato allo scopo di essere completo, adattabile, robusto e open-source. Per raggiungere questi obiettivi sono stati integrati metodi già disponibili, presenti nella letteratura scientifica del settore e già affermati all’interno di essa, con altri metodi che sono stati sviluppati durante lo svolgimento della tesi. Le capacità di analisi di SigMate permettono di elaborare nello stesso ambiente segnali EEG, spikes, e calcolare LFP. In particolare:
• Algoritmi adattabili e robusti: gli algoritmi per l’analisi di segnali neurali registrati usando sonde neurali multicanali devono essere: (i) adattabili per tener conto del numero sempre crescente di siti e canali di registrazione, e (ii), robusti ossia capaci di elaborare calcoli su grandi moli di dati, in modo accurato e veloce, quindi evitando lunghe attese al suo utilizzatore.
• Performance: per verificare la performance, l’accuratezza dei risultati, e la giusta integrazione dei moduli, sono stati usati segnali neurali registrati dalla corteccia di topo (in particolare da quella parte sottile della corteccia somatosensoriale (SI) che corrisponde ad una mappatura uno-a-uno dei baffi del naso del ratto) usando tre metodi diversi: (i) con micropipette standard, (ii) con Electrolyte–Oxide–Semiconductor Field Effect Transistor (EOSFET) messi su chip, e (iii) con EOSFET impiantabili.
• Open–source: il pacchetto software sarà distribuito come open-source attraverso una GNU–General Public License (GPL) e per questa ragione Matlab è stato selezionato come ambiente di sviluppo. L’utilizzatore è libero di operare proprie modifiche adattando il software alle proprie esigenze.
1.3 Overview della tesi
La tesi è organizzata in 5 capitoli. Il primo capitolo contiene l’introduzione, il secondo fornisce gli elementi di base che servono alla comprensione dei vari problemi affrontati e presenta anche una review della letteratura. Il capitolo 3 descrive i metodi per il setup del sistema e l’acquisizione dei segnali. I capitoli 4 e 5 descrivono la ricerca sviluppata durante lo svolgimento della tesi, mentre il capitolo 6 contiene un sommario e un overview sui possibili sviluppi futuri di questo lavoro
SigMate: a comprehensive software package for processing and analysis of neuronal signals
No full textScopo della presente invenzione è di conseguenza quello di fornire un nuovo metodo per l’analisi di segnali neuronali completo, automatico e in grado di analizzare i segnali provenienti da una pluralità di sorgenti di acquisizione.
In particolare, il metodo della presente invenzione consente un’efficace analisi di segnali del tipo noto nella tecnica con l’acronimo LFP (“Local Field Potential”),integrandola con l’analisi degli altri tipi di segnali neuronali, ovvero segnali elettroencefalografici, noti nella tecnica con l’acronimo EEG e gli “spike” neurali. In accordo con l'invenzione il suddetto problema tecnico viene risolto tramite un metodo di analisi di segnali neuronali comprendente almeno le fasi di acquisire uno o più segnali neuronali di tipo LFP associati a una regione cerebrale,
analizzare detti segnali LFP per identificare l’attivazione e/o la disattivazione di reti neuronali di detta regione cerebrale e di preparare detti segnali neuronali, detta fase di analisi di detti segnali LFP comprendendo, una prima sottofase di caratterizzazione della forma del segnale per riconoscere e classificare detti segnali LFP, una seconda sottofase di calcolo della densità della sorgente di corrente in funzione di LFP misurati a diverse profondità nel tessuto nervoso, una terza sottofase di calcolo della latenza di detti segnali LFP, una quarta sottofase
di settaggio direzionale per associare a detti segnali LFP rispettive direzioni di movimento di un organo, detta fase di preparazione del segnale e dette prima, seconda, terza e quarta sottofase essendo selettivamente attivabili durante l’esecuzione di detto metodo. La fase di preparazione del segnale consente di svolgere alcune operazioni di pulizia sul segnale, ad esempio mediante rimozione degli artefatti, lenti e veloci.
Le quattro sottofasi del metodo rispettivamente consentono di:
- caratterizzare la forma di singoli segnali LFP per facilitare lo studio della rete neuronale che ha generato tali segnali;
- determinare la topologia della rete neuronale e localizzazione della sorgente di corrente che determina il segnale LFP;
- analizzare la propagazione del segnale LFP nel tessuto nervoso;
- analizzare la sensibilità direzionale di una rete neuronale sottoposta a uno stimolo corrispondente al movimento di un organo in una prefissata direzione. Le fase di prepaquattro sottofasi del metodo sono attivabili indipendentemente una
dall’altra e selezionabili durante l’esecuzione del metodo in funzione delle specifiche esigenze di analisi. Ciò consente di ottenere un metodo al tempo stesso completo e versatile
EEG Based Brain-Machine Interface for Navigation of Robotic Device
No full textThe highly parallel neurophysiological recordings
and the increasing number of signal processing tools open up
new avenues for connecting technologies directly to neuronal
processes. As the understanding of the neuronal signals is
taking a better shape, lot more work to perform is coming up to
properly interpret and use these signals for brain-machine
interfaces. A simple brain-machine interface may be able to
reestablish the broken loop of the persons with motor
dysfunction. With time the brain-machine interfacing is
growing more complex due to the increased availability of
instruments and processes for implementation. In this work,
the author proposes a brain-machine interface model through a
few simple processes for automated navigation and control of
robotic device using the extracted features from the EEG
signals based on saccadic eye movement tasks
A brain-machine interface model based on EEG for automated navigation of mobile robotic device
No full textThe highly parallel neurophysiological recordings and the increasing number of signal processing tools open up new avenues for connecting technologies directly to neuronal processes. As the understanding of the neuronal signals is taking a better shape, lot more work to perform is coming up to properly interpret and use these signals for brain-machine interfaces. A simple brain-machine interface may be able to reestablish the broken loop of the persons with motor dysfunction. With time the brain-machine interfacing is growing more complex due to the increased availability of instruments and processes for implementation. In this work, the author proposes a brain-machine interface model through a few simple processes for automated navigation and control of robotic device using the extracted features from the EEG signals based on saccadic eye movement tasks
Bistability of Sodium channels increases the safety-factor at the Neuromuscular Junction
No full text
Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-art and Challenges
Full text linkIn recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are to be faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data
Trends and Challenges in Neuroengineering: Toward “Intelligent” Neuroprostheses through Brain-“Brain Inspired Systems” Communication
Full text linkFuture technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term ‘neurobiohybrids’ indicating all those systems where such interaction is established. We argue that achieving a ‘high-level’ communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include ‘living robots’ but will also embrace ‘intelligent’ neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and, more specifically, on how deeply it should be allowed that brain processing is affected by implanted ‘intelligent’ artificial systems.Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a ‘community building’ perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes
A High Resolution Bi-Directional Communication through a Brain-Chip Interface.
No full textExisting brain-machine interfacing techniques allow
either high precision recordings from one or a few single
neurons, or low spatial resolution recordings with a sparse
sampling within the networks. Through our app-roach an
efficient simultaneous bidirectional communication to the
brain is realized using capacitively coupled recording and
stimulation sites arranged in a large 2D multi-transistor array
(MTA) with 1000 elements, integrated to a planar chip at high
resolution (10μm pitch and below). The aim of the present
work is to evaluate the reliability of a simple-generation silicon
micro-device in recording neuronal signals from rat brain.
Simultaneous recording of signals using this chip from the
somatosensory cortex (S1) of living rat, are compared to
standard in vivo recordings with a glass micropipette. We show
that the two types of signals are identical, indicating the
possibility to record signals at the same time from different
sites and to perform a real-time electrical imaging of the brain
cortex in vivo
High Resolution Cortical Imaging Using Electrolyte-(Metal)-Oxide-Semiconductor Field Effect Transistors
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