Understanding the Role of Dynamics in Brain Networks: Methods, Theory and Application

The brain is inherently a dynamical system whose networks interact at multiple spatial and temporal scales. Understanding the functional role of these dynamic interactions is a fundamental question in neuroscience. In this research, we approach this question through the development of new methods for characterizing brain dynamics from real data and new theories for linking dynamics to function. We perform our study at two scales: macro (at the level of brain regions) and micro (at the level of individual neurons). In the first part of this dissertation, we develop methods to identify the underlying dynamics at macro-scale that govern brain networks during states of health and disease in humans. First, we establish an optimization framework to actively probe connections in brain networks when the underlying network dynamics are changing over time. Then, we extend this framework to develop a data-driven approach for analyzing neurophysiological recordings without active stimulation, to describe the spatiotemporal structure of neural activity at different timescales. The overall goal is to detect how the dynamics of brain networks may change within and between particular cognitive states. We present the efficacy of this approach in characterizing spatiotemporal motifs of correlated neural activity during the transition from wakefulness to general anesthesia in functional magnetic resonance imaging (fMRI) data. Moreover, we demonstrate how such an approach can be utilized to construct an automatic classifier for detecting different levels of coma in electroencephalogram (EEG) data. In the second part, we study how ongoing function can constraint dynamics at micro-scale in recurrent neural networks, with particular application to sensory systems. Specifically, we develop theoretical conditions in a linear recurrent network in the presence of both disturbance and noise for exact and stable recovery of dynamic sparse stimuli applied to the network. We show how network dynamics can affect the decoding performance in such systems. Moreover, we formulate the problem of efficient encoding of an afferent input and its history in a nonlinear recurrent network. We show that a linear neural network architecture with a thresholding activation function is emergent if we assume that neurons optimize their activity based on a particular cost function. Such an architecture can enable the production of lightweight, history-sensitive encoding schemes

Neurocognitive and Neuroplastic Mechanisms of Novel Clinical Signs in CRPS.

Complex regional pain syndrome (CRPS) is a chronic, debilitating pain condition that usually arises after trauma to a limb, but its precise etiology remains elusive. Novel clinical signs based on body perceptual disturbances have been reported, but their pathophysiological mechanisms remain poorly understood. Investigators have used functional neuroimaging techniques (including MEG, EEG, fMRI, and PET) to study changes mainly within the somatosensory and motor cortices. Here, we provide a focused review of the neuroimaging research findings that have generated insights into the potential neurocognitive and neuroplastic mechanisms underlying perceptual disturbances in CRPS. Neuroimaging findings, particularly with regard to somatosensory processing, have been promising but limited by a number of technique-specific factors (such as the complexity of neuroimaging investigations, poor spatial resolution of EEG/MEG, and use of modeling procedures that do not draw causal inferences) and more general factors including small samples sizes and poorly characterized patients. These factors have led to an underappreciation of the potential heterogeneity of pathophysiology that may underlie variable clinical presentation in CRPS. Also, until now, neurological deficits have been predominantly investigated separately from perceptual and cognitive disturbances. Here, we highlight the need to identify neurocognitive phenotypes of patients with CRPS that are underpinned by causal explanations for perceptual disturbances. We suggest that a combination of larger cohorts, patient phenotyping, the use of both high temporal, and spatial resolution neuroimaging methods, and the identification of simplified biomarkers is likely to be the most fruitful approach to identifying neurocognitive phenotypes in CRPS. Based on our review, we explain how such phenotypes could be characterized in terms of hierarchical models of perception and corresponding disturbances in recurrent processing involving the somatosensory, salience and executive brain networks. We also draw attention to complementary neurological factors that may explain some CRPS symptoms, including the possibility of central neuroinflammation and neuronal atrophy, and how these phenomena may overlap but be partially separable from neurocognitive deficits.This is the final version of the article. It first appeared from Frontiers via http://dx.doi.org/10.3389/fnhum.2016.0001

A low computational cost, prioritized, multi-objective optimization procedure for predictive control towards cyber physical systems

Cyber physical systems consist of heterogeneous elements with multiple dynamic features. Consequently, multiple objectives in the optimality of the overall system may be relevant at various times or during certain context conditions. Low cost, efficient implementations of such multi-objective optimization procedures are necessary when dealing with complex systems with interactions. This work proposes a sequential implementation of a multi-objective optimization procedure suitable for industrial settings and cyber physical systems with strong interaction dynamics. The methodology is used in the context of an Extended Prediction self-adaptive Control (EPSAC) strategy with prioritized objectives. The analysis indicates that the proposed algorithm is significantly lighter in terms of computational time. The combination with an input-output formulation for predictive control makes these algorithms suitable for implementation with standardized process control units. Three simulation examples from different application fields indicate the relevance and feasibility of the proposed algorithm

A computationally efficient Hill curve adaptation strategy during continuous monitoring of dose-effect relation in anaesthesia

This paper discusses a possibility to simplify the number of parameters in the Hill curve by exploiting special mathematical functions. This simplification is relevant when adaptation is required for personalized model-based medicine during continuous monitoring of dose-response values. A mathematical framework of the involved physiology and modelling by means of distributed parameter progressions has been employed. Convergence to a unique dynamic response is achieved, allowing simplifying assumptions with guaranteed solution. Discussion on its use and comparison with other adaptation mechanism is provided

Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification

In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants. We will look at how the various characteristics of visual stimulation affect the measured electro-physiological response of the brain and describe the optimal parameters found that elicit a steady-state visually evoked potential (SSVEP) in certain parts of the cerebral cortex where it can be reliably perceived by the electrode of the EEG device. After that, we continue with a description of the advanced machine learning pipeline model that can perform confident classification of the collected EEG data in order to (a) reliably distinguish signal from noise (about 85% validation score) and (b) reliably distinguish between EEG records collected from different human participants (about 80% validation score). Finally, we demonstrate that the proposed method works reliably even with an inexpensive (less than $100) consumer-grade EEG sensing device and with participants who do not have previous experience with EEG technology (EEG illiterate). All this in combination opens up broad prospects for the development of new types of consumer devices, [e.g.] based on virtual reality helmets or augmented reality glasses where EEG sensor can be easily integrated. The proposed method can be used to improve an online user experience by providing [e.g.] password-less user identification for VR / AR applications. It can also find a more advanced application in intensive care units where collected EEG data can be used to classify the level of conscious awareness of patients during anesthesia or to automatically detect hardware failures by classifying the input signal as noise

Placing meta-stable states of consciousness within the predictive coding hierarchy: The deceleration of the accelerated prediction error.

While many studies have linked prediction errors and event related potentials at a single processing level, few consider how these responses interact across levels. In response, we present a factorial analysis of a multi-level oddball task - the local-global task - and we explore it when participants are sedated versus recovered. We found that the local and global levels in fact interact. This is of considerable current interest, since it has recently been argued that the MEEG response evoked by the global effect corresponds to a distinct processing mode that moves beyond predictive coding. This interaction suggests that the two processing modes are not distinct. Additionally, we observed that sedation modulates this interaction, suggesting that conscious awareness may not be completely restricted to a single (global) processing level

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth

The goal of this study was to identify features in mouse electrocorticogram recordings that indicate the depth of anesthesia as approximated by the administered anesthetic dosage. Anesthetic depth in laboratory animals must be precisely monitored and controlled. However, for the most common lab species (mice) few indicators useful for monitoring anesthetic depth have been established. We used electrocorticogram recordings in mice, coupled with peripheral stimulation, in order to identify features of brain activity modulated by isoflurane anesthesia and explored their usefulness in monitoring anesthetic depth through machine learning techniques. Using a gradient boosting regressor framework we identified interhemispheric somatosensory coherence as the most informative and reliable electrocorticogram feature for determining anesthetic depth, yielding good generalization and performance over many subjects. Knowing that interhemispheric somatosensory coherence indicates the effectively administered isoflurane concentration is an important step for establishing better anesthetic monitoring protocols and closed-loop systems for animal surgeries