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

Sevoflurane alters spatiotemporal functional connectivity motifs that link resting-state networks during wakefulness

Background: The spatiotemporal patterns of correlated neural activity during the transition from wakefulness to general anesthesia have not been fully characterized. Correlation analysis of blood-oxygen-level dependent (BOLD) functional magnetic resonance imaging (fMRI) allows segmentation of the brain into resting-state networks (RSNs), with functional connectivity referring to the covarying activity that suggests shared functional specialization. We quantified the persistence of these correlations following the induction of general anesthesia in healthy volunteers and assessed for a dynamic nature over time. Methods: We analyzed human fMRI data acquired at 0 and 1.2% vol sevoflurane. The covariance in the correlated activity among different brain regions was calculated over time using bounded Kalman filtering. These time series were then clustered into eight orthogonal motifs using a K-means algorithm, where the structure of correlated activity throughout the brain at any time is the weighted sum of all motifs. Results: Across time scales and under anesthesia, the reorganization of interactions between RSNs is related to the strength of dynamic connections between member pairs. The covariance of correlated activity between RSNs persists compared to that linking individual member pairs of different RSNs. Conclusions: Accounting for the spatiotemporal structure of correlated BOLD signals, anesthetic-induced loss of consciousness is mainly associated with the disruption of motifs with intermediate strength within and between members of different RSNs. In contrast, motifs with higher strength of connections, predominantly with regions-pairs from within-RSN interactions, are conserved among states of wakefulness and sevoflurane general anesthesia

Recent trend of e-business in Canada

Canadian enterprises are increasingly implementing Internet based e-business technologies such as websites, web-based procurement, and e-commerce to reduce their cost and increase their productivity. While there have been several studies in different countries to locate the determi-nants of e-business adoption, the newness of business could preclude much understanding of the factors, which influence the evolution of e-business within firms. This paper performs a review on recent studies on the implementation of e-business adaptation in Canada. The study covers several studies accomplished on Small and medium enterprises (SMEs) after implementing e-business in their business activities and discusses the barriers and the challenges on enhancing the new technology

THE APPLICATION OF FRICTION STIR PROCESSING TO THE FABRICATION OF MAGNESIUM-BASED FOAMS

In the present paper, friction stir processing (FSP) is used to fabricate magnesium-based nanocomposite foams. The effects of the number of FSP passes, TiH2 to Al2O3 weight ratio, and foaming temperature; on the pore distribution and porosity are described. The results indicate that a minimum TiH2 to Al2O3 weight ratio is necessary to provide the best pore distribution and porosity. Closed-cell porous magnesium with a porosity of about 17.5% was successfully fabricated using 4-pass FSP at 800 rpm, by adding 5 mass% TiH2 and 3.5 mass% Al2O3; at a holding temperature of 858 K, and a holding time of 45 min.111Ysciescopu

Anaerobic co-digestion of oil refinery wastewater and chicken manure to produce biogas, and kinetic parameters determination in batch reactors

ArticleIn order to improve the anaerobic fermentation of oil refinery wastewater (ORWW) via an appropriate nutrients pool for microbial and buffer capacity growth, a study was carried out on related anaerobic co-digestion (AcoD) with a rich organic carbon source, namely chicken manure (CM). The kinetic parameters were investigated (including cumulative biogas production, bio-methane content, retention time, and soluble chemical oxygen demand stabilisation rate) of batch AcoD experiments related to six ORWW:CM-ratio treatments (5:0, 4:1, 3:2, 2:3, 1:4, and 0:5) under mesophilic conditions. The highest soluble chemical oxygen demand removal rate was obtained for the 4:1-ratio treatment. However, the highest biogas production and bio-methane contents were achieved for the 1:4-ratio treatment. When taking into consideration the highest oil refinery wastewater portion in the AcoD mixtures and the statistical test results (LSD0.05) for the kinetic parameters, it can be seen that the 4:1-ratio treatment provided the maximum biogas production levels

Scaling of sensory information in largeneural populations shows signatures ofinformation-limiting correlations

How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations.We would like to thank Alexandre Pouget, Peter Latham, and members of the HMSNeurobiology Department for useful discussions and feedback on the work, and RachelWilson and Richard Born for comments on early versions of the manuscript. The workwas supported by a scholar award from the James S. McDonnell Foundation (grant#220020462 to J.D.), grants from the NIH (R01MH115554 to J.D.; R01MH107620 to C.D.H.; R01NS089521 to C.D.H.; R01NS108410 to C.D.H.; F31EY031562 to A.W.J.), theNSF’s NeuroNex program (DBI-1707398. to R.N.), MINECO (Spain; BFU2017-85936-Pto R.M.-B.), the Howard Hughes Medical Institute (HHMI, ref 55008742 to R.M.-B.), theICREA Academia (2016 to R.M.-B.), the Government of Aragon (Spain; ISAAC lab, codT33 17D to I.A.-R.), the Spanish Ministry of Economy and Competitiveness (TIN2016-80347-R to I.A.-R.), the Gatsby Charitable Foundation (to R.N.), and an NSF GraduateResearch Fellowship (to A.W.J.)

Closed-loop acoustic stimulation during sedation with dexmedetomidine (CLASS-D): Protocol for a within-subject, crossover, controlled, interventional trial with healthy volunteers

Introduction: The relative power of slow-delta oscillations in the electroencephalogram (EEG), termed slow-wave activity (SWA), correlates with level of unconsciousness. Acoustic enhancement of SWA has been reported for sleep states, but it remains unknown if pharmacologically induced SWA can be enhanced using sound. Dexmedetomidine is a sedative whose EEG oscillations resemble those of natural sleep. This pilot study was designed to investigate whether SWA can be enhanced using closed-loop acoustic stimulation during sedation (CLASS) with dexmedetomidine. Methods: Closed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine (CLASS-D) is a within-subject, crossover, controlled, interventional trial with healthy volunteers. Each participant will be sedated with a dexmedetomidine target-controlled infusion (TCI). Participants will undergo three CLASS conditions in a multiple crossover design: in-phase (phase-locked to slow-wave upslopes), anti-phase (phase-locked to slow-wave downslopes) and sham (silence). High-density EEG recordings will assess the effects of CLASS across the scalp. A volitional behavioral task and sequential thermal arousals will assess the anesthetic effects of CLASS. Ambulatory sleep studies will be performed on nights immediately preceding and following the sedation session. EEG effects of CLASS will be assessed using linear mixed-effects models. The impacts of CLASS on behavior and arousal thresholds will be assessed using logistic regression modeling. Parametric modeling will determine differences in sleepiness and measures of sleep homeostasis before and after sedation. Results: The primary outcome of this pilot study is the effect of CLASS on EEG slow waves. Secondary outcomes include the effects of CLASS on the following: performance of a volitional task, arousal thresholds, and subsequent sleep. Discussion: This investigation will elucidate 1) the potential of exogenous sensory stimulation to potentiate SWA during sedation; 2) the physiologic significance of this intervention; and 3) the connection between EEG slow-waves observed during sleep and sedation

Correlating electroconvulsive therapy response to electroencephalographic markers: Study protocol

INTRODUCTION: Electroconvulsive therapy (ECT) is an effective intervention for patients with major depressive disorder (MDD). Despite longstanding use, the underlying mechanisms of ECT are unknown, and there are no objective prognostic biomarkers that are routinely used for ECT response. Two electroencephalographic (EEG) markers, sleep slow waves and sleep spindles, could address these needs. Both sleep microstructure EEG markers are associated with synaptic plasticity, implicated in memory consolidation, and have reduced expression in depressed individuals. We hypothesize that ECT alleviates depression through enhanced expression of sleep slow waves and sleep spindles, thereby facilitating synaptic reconfiguration in pathologic neural circuits. METHODS: Correlating ECT Response to EEG Markers (CET-REM) is a single-center, prospective, observational investigation. Wireless wearable headbands with dry EEG electrodes will be utilized for at-home unattended sleep studies to allow calculation of quantitative measures of sleep slow waves (EEG SWA, 0.5-4 Hz power) and sleep spindles (density in number/minute). High-density EEG data will be acquired during ECT to quantify seizure markers. DISCUSSION: This innovative study focuses on the longitudinal relationships of sleep microstructure and ECT seizure markers over the treatment course. We anticipate that the results from this study will improve our understanding of ECT