Fadi N. Karameh

Modeling the contribution of lamina 5 neuronal and network dynamics to low frequency EEG phenomena

The Electroencephalogram (EEG) is an important clinical and research tool in neurophysiology. With the advent of recording techniques, new evidence is emerging on the neuronal populations and wiring in the neocortex. A main challenge is to relate the EEG generation mechanisms to the underlying circuitry of the neocortex. In this paper, we look at the principal intrinsic properties of neocortical cells in layer 5 and their network behavior in simplified simulation models to explain the emergence of several important EEG phenomena such as the alpha rhythms, slow-wave sleep oscillations, and a form of cortical seizure. The models also predict the ability of layer 5 cells to produce a resonance-like neuronal recruitment known as the augmenting response. While previous models point to deeper brain structures, such as the thalamus, as the origin of many EEG rhythms (spindles), the current model suggests that the cortical circuitry itself has intrinsic oscillatory dynamics which could account for a wide variety of EEG phenomena.

Automated classification of EEG signals in brain tumor diagnostics

In brain tumor diagnostics, EEG is most relevant in assessing how basic functionality is affected by the lesion and how the brain responds to treatments (e.g. post-operative). This paper focuses on developing an automated system to identify space-occupying lesions in the brain using EEG signals. We discuss major complications in relating EEG to different tumor classes and suggest an approach of feature extraction using wavelet techniques and classification by self-organizing maps. Initial tests show improvement over conventional frequency band features common in the EEG community. The tests also highlight the need to obtain efficient physically-motivated features as to how EEG is affected by various tumors.

A model of nonlinear motor cortical integration and its relation to movement speed profile control

It is recognized that natural point-to-point movements are characterized by bell-shaped speed profiles. However, the neural basis of this smooth, substantially symmetric time course is unknown. Here it is demonstrated via a simplified compartmental model of tufted layer V (TL5) pyramidal neurons, the principal output units of the motor cortex, that nonlinear integration may underlie the bell-shaped profile. Specifically, it is shown that TL5 neuronal output depends upon an approximately multiplicative relationship between inputs to its apical or basal regions (zones A and B, respectively) and those to its central zone (C). This is because the latter facilitate Ca2+ dependent bursting that enhances responsiveness to other inputs. As a result, when part of TL5 output returns to zones A and C via thalamocortical and cerebrocerebellar feedback, TL5 neuronal firing rate initially increases before decreasing, rather than progressively decrease as would the output of a linear integrator. This yields a sigmoidal position vs. time response in the musculoskeletal plant and therefore a bell-shaped speed curve. Because of this mechanism, smooth movements may be triggered and modulated by step-like and tonic inputs to zone C as might be received from SMA or basal ganglia. The model thus gives possible insight into the basis of certain features of motor dysfunction in Parkinsons and cerebellar disease

Intracortical Augmenting Responses in Networks of Reduced Compartmental Models of Tufted Layer 5 Cells

Augmenting responses (ARs) are characteristic recruitment phenomena that can be generated in target neural populations by repetitive intracortical or thalamic stimulation and that may facilitate activity transmission from thalamic nuclei to the cortex or between cortical areas. Experimental evidence suggests a role for cortical layer 5 in initiating at least one form of augmentation. We present a three-compartment model of tufted layer 5 (TL5) cells that faithfully reproduces a wide range of dynamics in these neurons that previously has been achieved only partially and in much more complex models. Using this model, the simplest network exhibiting AR was a single pair of TL5 and inhibitory (IN5) neurons. Intracellularly, AR initiation was controlled by low-threshold Ca2+ current (I(T)), which promoted TL5 rebound firing, whereas AR strength was dictated by inward-rectifying current (I(h)), which regulated TL5 multiple-spike firing and also prevented excessive firing under high-amplitude stimuli. Synaptically, AR was significantly more salient under concurrent stimulus delivery to superficial and deep dendritic zones of TL5 cells than under conventional single-zone stimuli. Moreover, slow GABA-B-mediated inhibition in TL5 cells controlled AR strength and frequency range. Finally, a network model of two cortical populations interacting across functional hierarchy showed that intracortical AR occurred prominently upon exciting superficial cortical layers either directly or via intrinsic connections, with AR frequency dictated by connection strength and background activity. Overall, the investigation supports a central role for a TL5-IN5 skeleton network in low-frequency cortical dynamics in vivo, particularly across functional hierarchies, and presents neuronal models that facilitate accurate large-scale simulations.

A Portable MIDI Controller Using EMG-Based Individual Finger Motion Classification

Classifying the motion of the five fingers of the hand using non-invasive bio-signal readings from the forearm is still an unsolved research challenge. Its solution is relevant to hands-free remote control devices, on-stage live performances, consumer entertainment, the video game industry, and most importantly the design of hand prosthetics for amputees. This paper proposes a solution that uses the continuous wavelet transform (CWT) decompositions of electromyography (EMG) signals from the forearm muscles, and Support Vector Machines (SVM) classification. The resulting design is a low cost, low power and low complexity portable embedded system that is strapped to the arm, where it collects EMG signals, classifies them in real-time, and sends the resulting class labels via Bluetooth to a remote interface. These labels are then converted into musical instrument digital interface (MIDI) commands that can be used to control any MIDI-controllable device. While the design is still at the prototype stage at best, it provides a proof-of-concept of non-invasive finger motion classification solely based on EMG readings from the forearm muscles. Experimental simulation of the expected system achieved 91% accuracy.

Entropy complexity analysis of electroencephalographic signals during pre-ictal, seizure and post-ictal brain events

Epileptic seizures reflect runaway excitation that severely hinders normal brain functions. With EEG recordings reflecting real-time brain activity, it is essential to both predict seizures and improve the classification of seizures in EEG signs. Towards this aim, nonlinear tools are strongly recommended to select the seizure-sensitive features prior to classification. However, the choice of the feature remains challenging. With the multitude of entropy parameters available in literature, and in order to perform a judicious selection of features that are fed to classifiers, this paper presents a comparative study of a host of candidate promising feature extraction techniques. Four entropy features namely Approximate Entropy, Sample Entropy and Renyi entropy of order 2 and Renyi entropy of order 3, were implemented as the standard techniques. Three kernel-based features namely Triangular Entropy, Spherical Entropy and Cauchy entropy were implemented. The former and latter entropies were computed from EEG recordings during induced seizures in three distinct phases: the pre-ictal (pre-seizure) phase, the ictal (seizure) phase, and the post-ictal (post-seizure) phase. Results showed that, among kernel-based methods, Spherical entropy features exhibited the largest parameter sensitivity to (Seizure-Normal) phase changes with the highest normalized relative separation (100%). The sample entropy feature in turn showed the most sensitive to EEG phase changes with the highest relative separation (94.85%), among the studied entropy alternatives.

Modeling of Neuronal Population Activation under Electroconvulsive Therapy

Electroconvulsive therapy (ECT) is a procedure that involves the induction of seizures in the brain of patients with severe psychiatric disorders. The efficacy and therapeutic outcome of electrically-induced seizures is dependent upon both the stimulus intensity and the electrode placement over the scalp, with potentially significant memory loss as side effect. Over the years, ECT modeling aimed to understand current propagation in the head medium with increasingly realistic geometry and conductivity descriptions. The utility of these models remain limited since seizure propagation in the active neural tissue has largely been ignored. Accordingly, a modeling framework that combines head conductivity models with active neural models to describe observed EEG signals under ECT is highly desirable. We present herein a simplified multi-area active neural model that describes (i) the transition from normal to seizure states under external stimuli with particular emphasis on disinhibition and (ii) the initiation and propagation of seizures between multiple connected brain areas. A simulation scenario is shown to qualitatively resemble clinical EEG recordings of ECT. Fitting model param- eters is then performed using modern nonlinear state estimation approaches (cubature Kalman filters). Future work will integrate active models with passive volume conduction approaches to explore seizure induction and propagation.

Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling

OBJECTIVE Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. APPROACH We herein introduce an adaptive framework in which optimal input design is integrated with square root cubature Kalman filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. MAIN RESULTS For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 ms in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. SIGNIFICANCE Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications.

Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics

Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements.

Cascade of nonlinear entropy and statistics to discriminate fetal heart rates

Fetal heart rate discrimination is an evolving field in biomedical engineering with many efforts dedicated to avoid preterm deliveries by way of improving fetus monitoring methods and devices. Entropy analysis is a nonlinear signal analysis technique that has been progressively developed to improve the discriminability of a several physiological signals, with Kernel based entropy parameters (KBEPs) found advantageous over standard techniques. This study is the first to apply KBEPs to analyze fetal heart rates. Specifically, it explores the usability of the cutting-edge nonlinear KBEPs in discriminating between healthy fetuses and fetuses under distress. The database used in this study comprises 50 healthy and 50 distressed fetal heart rate signals with severe intrauterine growth restriction. The Cascade analysis investigates six kernel based entropy measures on fetal heart rates discrimination, and compares them to four standard entropies. The study presents a statistical evaluation of the discrimination power of each parameter (paired t-test statistics and distribution spread). Simulation results showed that the distribution ranges in 80% of the entropy parameters in the distressed heart group are higher than those in the healthy control group. Moreover, the results show that it is advantageous to choose Circular entropy then Cauchy entropy (p <; 0.001) over the standard techniques, in order to discriminate fetal heart rates.