Seif Eldawlatly

Identifying functional connectivity in large-scale neural ensemble recordings: A multiscale data mining approach

Identifying functional connectivity between neuronal elements is an essential first step toward understanding how the brain orchestrates information processing at the single-cell and population levels to carry out biological computations. This letter suggests a new approach to identify functional connectivity between neuronal elements from their simultaneously recorded spike trains. In particular, we identify clusters of neurons that exhibit functional interdependency over variable spatial and temporal patterns of interaction. We represent neurons as objects in a graph and connect them using arbitrarily defined similarity measures calculated across multiple timescales. We then use a probabilistic spectral clustering algorithm to cluster the neurons in the graph by solving a minimum graph cut optimization problem. Using point process theory to model population activity, we demonstrate the robustness of the approach in tracking a broad spectrum of neuronal interaction, from synchrony to rate co-modulation, by systematically varying the length of the firing history interval and the strength of the connecting synapses that govern the discharge pattern of each neuron. We also demonstrate how activity-dependent plasticity can be tracked and quantified in multiple network topologies built to mimic distinct behavioral contexts. We compare the performance to classical approaches to illustrate the substantial gain in performance.

On the use of dynamic bayesian networks in reconstructing functional neuronal networks from spike train ensembles

Coordination among cortical neurons is believed to be a key element in mediating many high-level cortical processes such as perception, attention, learning, and memory formation. Inferring the structure of the neural circuitry underlying this coordination is important to characterize the highly nonlinear, time-varying interactions between cortical neurons in the presence of complex stimuli. In this work, we investigate the applicability of dynamic Bayesian networks (DBNs) in inferring the effective connectivity between spiking cortical neurons from their observed spike trains. We demonstrate that DBNs can infer the underlying nonlinear and time-varying causal interactions between these neurons and can discriminate between mono- and polysynaptic links between them under certain constraints governing their putative connectivity. We analyzed conditionally Poisson spike train data mimicking spiking activity of cortical networks of small and moderately large size. The performance was assessed and compared to other methods under systematic variations of the network structure to mimic a wide range of responses typically observed in the cortex. Results demonstrate the utility of DBN in inferring the effective connectivity in cortical networks.

NeuroQuest: A comprehensive analysis tool for extracellular neural ensemble recordings

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.

Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers

The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

NeuroQuest: A comprehensive tool for large scale neural data processing and analysis

Analysis of neural data recorded with implantable microelectrode arrays poses a significant challenge to the neuroscience and the neural engineering communities. The numerous signal processing and analysis steps need to be performed in order to extract the affluent amount of information in these data to understand their correlation with observed behavior. This paper summarizes our most recent effort to develop a comprehensive neural signal processing and data analysis software that incorporates standard analysis tools in addition to our in-house advanced tools. The software, referred to herein as NeuroQuest®, is implemented using MATLAB. It has been extensively tested on simulated and experimental neural data and will be disseminated to the community in the short term.

Graphical Models of Functional and Effective Neuronal Connectivity

Publisher Summary Cortical neurons are known to mediate many complex brain functions through their coordinated interaction along numerous parallel and sequential pathways with highly intricate network structures. The massive size of these networks suggests the vastly complex information processing mechanism that underlies their operation. System identification techniques aimed at characterizing widely distributed functional cortical networks require simultaneous monitoring of neural constituents while subjects carry out certain functions. Many functional neuroimaging studies have demonstrated selective coupling between distinct cortical areas during behavioral tasks. Techniques for simultaneous recording of multiple single-unit activities seem to hold the promise of increased resolution, although they are still limited to local areas and shallow cortical depths. Two-photon calcium imaging techniques or chronically implanted high-density microelectrodes have been used in the last few years to simultaneously monitor ensembles of spiking neurons with the hope of deciphering their population-coding properties. There is increasing evidence that fine-scale connectivity between neurons exists in local circuits and may span multiple adjacent cortical layers consistent with segregated subcolumnar cortical structures.

Enhanced SVM versus Several Approaches in SAR Target Recognition

This paper presents a comparative study between different automatic target recognition (ATR) approaches in the application of synthetic aperture radar (SAR) target recognition. Four different categories of approaches are investigated and compared. The first is distribution-based where a statistical data model is assumed for the SAR image data. The second category contains one approach that is based upon principal component analysis (PCA). The third category employs different neural network architectures. The last category utilizes support vector machines (SVM). It contains the classical SVM implementation and an enhanced implementation proposed elsewhere by the authors in which the traditional Euclidean kernel is replaced by a new one that is more suitable for the application in question. Experimental results are presented. It is shown that the enhanced SVM approach outperforms all other investigated approaches in both the classification performance and the confuser rejection

A principal component analysis ensemble classifier for P300 speller applications

Recent advances in developing Brain-Computer Interfaces (BCIs) have opened up a new realm for designing efficient systems that could enable disabled people to communicate. The P300 speller is one important BCI application that allows the selection of characters on a virtual keyboard by analyzing recorded electroencephalography (EEG) activity. In this work, we propose an ensemble classifier that uses Principal Component Analysis (PCA) features to identify evoked P300 signals from EEG recordings. We examine the performance of the proposed method, using different linear classifiers, on the datasets provided by the BCI competition III. Results demonstrate a classification accuracy of 91% using the proposed method. In addition, our results indicate a significant improvement in classification accuracy compared to traditional feature extraction and classification approaches. The proposed method results in low across-subjects variability compared to other methods with minimal parameter tuning required which could be useful in mobile platform P300 applications.

Reconstructing functional neuronal circuits using dynamic Bayesian networks

Identifying functional connectivity from simultaneously recorded spike trains is important in understanding how the brain processes information and instructs the body to perform complex tasks. We investigate the applicability of dynamic Bayesian networks (DBN) to infer the structure of neural circuits from observed spike trains. A probabilistic point process model was used to assess the performance. Results confirm the utility of DBNs in inferring functional connectivity as well as directions of signal flow in cortical network models. Results also demonstrate that DBN outperforms the Granger causality when applied to populations with highly non-linear synaptic integration mechanisms.

A Kalman-based encoder for electrical stimulation modulation in a thalamic network model

Restoring vision is no longer impossible as a result of recent advances in neural interfaces. Successful demonstrations of retinal implants motivate the development of more effective visual prostheses. The thalamic Lateral Geniculate Nucleus (LGN) is one potential deep-brain interfacing site for visual prostheses. A main challenge in developing thalamic as well as other visual prostheses is optimizing the parameters of electrical stimulation. This paper introduces a Kalman-based optimal encoder whose function is to determine the optimal electrical stimulation parameters required to induce a certain visual sensation. The performance of the proposed approach is demonstrated using a probabilistic model of LGN neurons. Results demonstrate a significant similarity between neuronal responses obtained using electrical stimulation and the responses obtained using the corresponding visual stimuli with a mean correlation of 0.62 (P <; 0.01, n = 54). These results indicate the efficacy of the proposed optimal encoder in driving LGN neurons to induce visual sensations.