A model for cerebral cortical neuron group electric activity and its implications for cerebral function

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 245-265).The electroencephalogram, or EEG, is a recording of the field potential generated by the electric activity of neuronal populations of the brain. Its utility has long been recognized as a monitor which reflects the vigilance states of the brain, such as arousal, drowsiness, and sleep stages. Moreover, it is used to detect pathological conditions such as seizures, to calibrate drug action during anesthesia, and to understand cognitive task signatures in healthy and abnormal subjects. Being an aggregate measure of neural activity, understanding the neural origins of EEG oscillations has been limited. With the advent of recording techniques, however, and as an influx of experimental evidence on cellular and network properties of the neocortex has become available, a closer look into the neuronal mechanisms for EEG generation is warranted. Accordingly, we introduce an effective neuronal skeleton circuit at a neuronal group level which could reproduce basic EEG-observable slow ( 3mm). The effective circuit makes use of the dynamic properties of the layer 5 network to explain intra-cortically generated augmenting responses, restful alpha, slow wave (< 1Hz) oscillations, and disinhibition-induced seizures. Based on recent cellular evidence, we propose a hierarchical binding mechanism in tufted layer 5 cells which acts as a controlled gate between local cortical activity and inputs arriving from distant cortical areas. This gate is manifested by the switch in output firing patterns in tufted(cont.) layer 5 cells between burst firing and regular spiking, with specific implications on local functional connectivity. This hypothesized mechanism provides an explanation of different alpha band (10Hz) oscillations observed recently under cognitive states. In particular, evoked alpha rhythms, which occur transiently after an input stimulus, could account for initial reogranization of local neural activity based on (mis)match between driving inputs and modulatory feedback of higher order cortical structures, or internal expectations. Emitted alpha rhythms, on the other hand, is an example of extreme attention where dominance of higher order control inputs could drive reorganization of local cortical activity. Finally, the model makes predictions on the role of burst firing patterns in tufted layer 5 cells in redefining local cortical dynamics, based on internal representations, as a prelude to high frequency oscillations observed in various sensory systems during cognition.by Fadi Nabih Karameh.Ph.D

Cascade of Nonlinear Entropy and Statistics to Discriminate Fetal Heart Rates

International audience—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

On-line identification and control algorithm for system model with jump parameters using wavelets

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 73-75).by Fadi Nabih Karameh.M.S

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

Analysis of task-related MEG functional brain networks using dynamic mode decomposition

International audienceObjective.Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks.Approach.In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks.Main results.The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics.Significance.We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks

Cortical model activity for all states at different layers.

<p>The exogenous input is shown in the top left panel for the case where the additive noise is white.</p

Performance of CD-CKF and CKF under white noise.

<p>MSE values averaged over 100 Monte-Carlo runs of CD-CKF and CKF for different SNRs and different sampling rates where the underlying system is perturbed by additive white noise.</p

Probability ratios <i>PI</i>_<i>noise</i>/<i>PI</i>_<i>white</i> for CD-CKF for different sampling intervals and noise structures.

<p>Probability ratios <i>PI</i>_<i>noise</i>/<i>PI</i>_<i>white</i> for CD-CKF for different sampling intervals and noise structures.</p