Functional integration in the cortical neuronal network of conscious and anesthetized animals

General anesthesia consists of amnesia, analgesia, areflexia and unconsciousness. How anesthetics suppress consciousness has been a mystery for more than one and a half centuries. The overall goal of my research has been to determine the neural correlates of anesthetic-induced loss of consciousness. I hypothesized that anesthetics induce unconsciousness by interfering with the functional connectivity of neuronal networks of the brain and consequently, reducing the brain\u27s capacity for information processing. To test this hypothesis, I performed experiments in which neuronal spiking activity was measured with chronically implanted microelectrode arrays in the visual cortex of freely-moving rats during wakefulness and at graded levels of anesthesia produced by the inhalational anesthetic agent desflurane. I then applied linear and non-parametric information-theoretic analyses to quantify the concentration-dependent effect of general anesthetics on spontaneous and visually evoked spike firing activity in rat primary visual cortex. Results suggest that desflurane anesthesia disrupts cortical neuronal integration as measured by monosynaptic connectivity, spike burst coherence and information capacity. This research furthers our understanding of the mechanisms involved with the anesthetic-induced LOC which may facilitate in the development of better anesthetic monitoring devices and the creation of effective anesthetic agents that will be free of unwanted side effects

Characteristics and coupling of cardiac and locomotor rhythms during treadmill walking tasks

Studying the variability of physiological subsystems (e.g., cardiac and locomotor control systems) has been insightful in understanding how functional and dysfunctional patterns emerge within their behaviors. The coupling of these subsystems (termed cardiolocomotor coupling) is believed to be important to maintain healthy functioning in the diverse conditions in which individuals must operate. Aging and pathology result in alterations to both the patterns of individual systems, as well as to how those systems couple to each other. By examining cardiac and locomotor rhythms concurrently during treadmill walking, it is possible to ascertain how these two rhythms relate to each other in different populations (i.e., younger and older adults) and with varying task constraints (i.e., a gait synchronization task or fast walking task). The purpose of this research was to simultaneously document the characteristics of cardiac and gait rhythms in younger (18-35 yrs) and older (63-80 yrs) healthy adults while walking and to establish the extent to which changes in these systems are coupled when gait is constrained. This study consisted of two repeated-measures experiments that participants completed on two separate days. Both experiments consisted of three 15-minute phases. During the first (baseline) and third (retention) phases of both experiments, participants walked with no cues or specific instructions at their preferred walking speed. During the second phase, participants were asked to synchronize their step falls to the timing of a visual cue (experiment 1) or to walk at 125% of their preferred walking speed (experiment 2). Fifty-one healthy adults (26 older, 67.67±4.70 yrs, 1.72±0.09 m, 70.13±14.30 kg; 25 younger, 24.57±4.29 yrs, 1.76±0.09 m, 73.34±15.35 kg) participated in this study. Participants’ cardiac rhythms (R-R interval time series) and locomotor rhythms (stride interval, step width, and step length time series) were measured while walking on a treadmill. Characteristics of variability in cardiac and locomotor rhythms and the coupling between cardiac and gait rhythms were measured. Results revealed that younger and older healthy adults alter gait patterns similarly when presented with a gait synchronization or fast walking task and that these tasks also alter cardiac patterns. Likewise, both groups exhibited enhanced cardiolocomotor coupling when tasked with a step timing constraint or increased speed during treadmill walking. Combined, these findings suggest that walking tasks likely alter both locomotor and cardiac dynamics and the coupling of physiological subsystems could be insightful in understanding the diverse effects aging and pathology have on individuals

Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based Brain Network.

Although the spatiotemporal complexity and network connectivity are clarified to be disrupted during the general anesthesia (GA) induced unconsciousness, it remains to be difficult to exactly monitor the fluctuation of consciousness clinically. In this study, to track the loss of consciousness (LOC) induced by GA, we first developed the multi-channel cross fuzzy entropy method to construct the time-varying networks, whose temporal fluctuations were then explored and quantitatively evaluated. Thereafter, an algorithm was further proposed to detect the time onset at which patients lost their consciousness. The results clarified during the resting state, relatively stable fuzzy fluctuations in multi-channel network architectures and properties were found; by contrast, during the LOC period, the disrupted frontal-occipital connectivity occurred at the early stage, while at the later stage, the inner-frontal connectivity was identified. When specifically exploring the early LOC stage, the uphill of the clustering coefficients and the downhill of the characteristic path length were found, which might help resolve the propofol-induced consciousness fluctuation in patients. Moreover, the developed detection algorithm was validated to have great capacity in exactly capturing the time point (in seconds) at which patients lost consciousness. The findings demonstrated that the time-varying cross-fuzzy networks help decode the GA and are of great significance for developing anesthesia depth monitoring technology clinically

An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page