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Central-positive complexes (CPCs) are elicited during electroconvulsive therapy (ECT) as generalized high-amplitude waveforms with maximum positive voltage over the vertex. While these complexes have been qualitatively assessed in previous literature, quantitative analyses are lacking. This study aims to characterize CPCs across temporal, spatial, and spectral domains.High-density 64-electrode electroencephalogram (EEG) recordings during 50 seizures acquired from 11 patients undergoing right unilateral ECT allowed for evaluation of spatiotemporal characteristics of CPCs via source localization and spectral analysis.Peak-amplitude CPC scalp topology was consistent across seizures, showing maximal positive polarity over the midline fronto-central region and maximal negative polarity over the suborbital regions. The sources of these peak potentials were localized to the bilateral medial thalamus and cingulate cortical regions. Delta, beta, and gamma oscillations were correlated with the peak amplitude of CPCs during seizures induced during ketamine, whereas delta and gamma oscillations were associated with CPC peaks during etomidate anesthesia (excluding the dose-charge titration).Our findings demonstrate the consistency of CPC presence across participant, stimulus charge, time, and anesthetic agent, with peaks localized to bilateral medial thalamus and cingulate cortical regions and associated with delta, beta, and gamma band oscillations (depending on the anesthetic condition).The consistency and reproducibility of CPCs offers ECT as a new avenue for studying the dynamics of generalized seizure activity and thalamocortical networks.

Periods of low-amplitude electroencephalographic (EEG) signal (quiescence) are present during both anesthetic-induced burst suppression (BS) and postictal generalized electroencephalographic suppression (PGES). PGES following generalized seizures induced by electroconvulsive therapy (ECT) has been previously linked to antidepressant response. The commonality of quiescence during both BS and PGES motivated trials to recapitulate the antidepressant effects of ECT using high doses of anesthetics. However, there have been no direct electrographic comparisons of these quiescent periods to address whether these are distinct entities.We compared periods of EEG quiescence recorded from two human studies: BS induced in 29 healthy adult volunteers by isoflurane general anesthesia and PGES in 11 patients undergoing right unilateral ECT for treatment-resistant depression. An automated algorithm allowed detection of EEG quiescence based on a 10-microvolt amplitude threshold. Spatial, spectral, and temporal analyses compared quiescent epochs during BS and PGES.The median (interquartile range) voltage for quiescent periods during PGES was greater than during BS (1.81 (0.22) microvolts vs 1.22 (0.33) microvolts, p 0.001). Relative power was greater for quiescence during PGES than BS for the 1-4 Hz delta band (p 0.001), at the expense of power in the theta (4-8 Hz, p 0.001), beta (13-30 Hz, p = 0.04) and gamma (30-70 Hz, p = 0.006) frequency bands. Topographic analyses revealed that amplitude across the scalp was consistently higher for quiescent periods during PGES than BS, whose voltage was within the noise floor.Quiescent epochs during PGES and BS have distinct patterns of EEG signals across voltage, frequency, and spatial domains.Quiescent epochs during PGES and BS, important neurophysiological markers for clinical outcomes, are shown to have distinct voltage and frequency characteristics.

Delirium is an acute syndrome characterized by inattention, disorganized thinking, and an altered level of consciousness. A reliable biomarker for tracking delirium does not exist, but oscillations in the electroencephalogram (EEG) could address this need. We evaluated whether the frequencies of EEG oscillations are associated with delirium onset, severity, and recovery in the postoperative period.Twenty-six adults enrolled in the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES; ClinicalTrials.gov NCT02241655) study underwent major surgery requiring general anesthesia, and provided longitudinal postoperative EEG recordings for this prespecified substudy. The presence and severity of delirium were evaluated with the confusion assessment method (CAM) or the CAM-intensive care unit. EEG data obtained during awake eyes-open and eyes-closed states yielded relative power in the delta (1-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) bands. Discriminability for delirium presence was evaluated with c-statistics. To account for correlation among repeated measures within patients, mixed-effects models were generated to assess relationships between: (1) delirium severity and EEG relative power (ordinal), and (2) EEG relative power and time (linear). Slopes of ordinal and linear mixed-effects models are reported as the change in delirium severity score/change in EEG relative power, and the change in EEG relative power/time (days), respectively. Bonferroni correction was applied to confidence intervals (CIs) to account for multiple comparisons.Occipital alpha relative power during eyes-closed states offered moderate discriminability (c-statistic, 0.75; 98% CI, 0.58-0.87), varying inversely with delirium severity (slope, -0.67; 98% CI, -1.36 to -0.01; P = .01) and with severity of inattention (slope, -1.44; 98% CI, -2.30 to -0.58; P = .002). Occipital theta relative power during eyes-open states correlated directly with severity of delirium (slope, 1.28; 98% CI, 0.12-2.44; P = .007), inattention (slope, 2.00; 98% CI, 0.48-3.54; P = .01), and disorganized thinking (slope, 3.15; 98% CI, 0.66-5.65; P = .01). Corresponding frontal EEG measures recapitulated these relationships to varying degrees. Severity of altered level of consciousness correlated with frontal theta relative power during eyes-open states (slope, 11.52; 98% CI, 6.33-16.71; P.001). Frontal theta relative power during eyes-open states correlated inversely with time (slope, -0.05; 98% CI, -0.12 to -0.04; P = .002).Presence, severity, and core features of postoperative delirium covary with spectral features of the EEG. The cost and accessibility of EEG facilitate the translation of these findings to future mechanistic and interventional trials.

Christian S Guay,1 Alyssa K Labonte,1 Michael C Montana,1 Eric C Landsness,2 Brendan P Lucey,2 MohammadMehdi Kafashan,1 Simon Haroutounian,1 Michael S Avidan,1,3 Emery N Brown,4,5 Ben Julian A Palanca1,3,6,7 1Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; 2Department of Neurology, Division of Sleep Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; 3Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; 4Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; 5Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 6Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; 7Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USACorrespondence: Ben Julian A PalancaWashington University School of Medicine in St. Louis, Department of Anesthesiology, 660 S. Euclid Avenue, Box 8054, St. Louis, MO, 63110, USATel +1 314-273-9076Fax +1 314-747-3977Email palancab@wustl.eduIntroduction: 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.Keywords: sleep, anesthesia, consciousness, dexmedetomidine, electroencephalography, acoustic stimulation

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., Information regarding a sensory stimulus is distributed in activity of neuronal populations. Here the authors show stimulus information scales sub-linearly with the number of neurons in mouse visual cortex due to correlated noise and may saturate in far fewer numbers of neurons than the total in V1.

IntroductionDelirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome.Methods and analysisP-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1–2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time.Ethics and disseminationP-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media.Trial registration numberNCT03291626.

Objective Postictal generalized electroencephalographic suppression (PGES) has been defined as electroencephalographic (EEG) activity of less than 10 microvolts following a generalized seizure. PGES is associated with an increased risk of sudden unexplained death in epilepsy, as well as treatment efficacy of electroconvulsive therapy (ECT). We investigated the impact of anesthetic on PGES expression and temporal characteristics. Methods We recorded postictal EEG in 50 ECT sessions in 11 patients with treatment resistant depression (ClinicalTrials.gov NCT02761330). For each participant, repeated sessions included either ketamine or etomidate general anesthesia during ECT. An automated algorithm was employed to detect PGES within 5 minutes after seizure termination. Results PGES was detected in 31/50 recordings, with intermittent epochs recurring up to five minutes after seizure termination. PGES total duration was greater following ketamine than etomidate anesthesia (p = 0.04). PGES expression declined loglinearly as a function of time (r = -0.89, p Conclusions PGES can occur intermittently for several minutes following seizure termination. Anesthetic effects should be considered when correlating PGES duration to clinical outcomes. Significance Prolonged EEG monitoring several minutes following seizure termination may be necessary to fully evaluate the presence and total duration of PGES.

Objective Postictal generalized electroencephalographic suppression (PGES) is a pattern of low-voltage scalp electroencephalographic (EEG) activity following termination of generalized seizures. PGES has been associated with both sudden unexplained death in patients with epilepsy and therapeutic efficacy of electroconvulsive therapy (ECT). Automated detection of PGES epochs may aid in reliable quantification of this phenomenon. Methods We developed a voltage-based algorithm for detecting PGES. This algorithm applies existing criteria to simulate expert epileptologist readings. Validation relied on postictal EEG recording from patients undergoing ECT (NCT02761330), assessing concordance among the algorithm and four clinical epileptologists. Results We observed low-to-moderate concordance among epileptologist ratings of PGES. Despite this, the algorithm displayed high discriminability in comparison to individual epileptologists (c-statistic range: 0.86-0.92). The algorithm displayed high discrimination (c-statistic: 0.91) and substantial peak agreement (Cohen’s Kappa: 0.65) in comparison to a consensus of clinical ratings. Interrater agreement between the algorithm and individual epileptologists was on par with that among expert epileptologists. Conclusions An automated voltage-based algorithm can be used to detect PGES following ECT, with discriminability nearing that of experts. Significance Algorithmic detection may support clinical readings of PGES and improve precision when correlating this marker with clinical outcomes following generalized seizures.

Background Resting wakefulness is not a unitary state, with evidence accumulating that spontaneous reorganization of brain activity can be assayed through functional magnetic resonance imaging (fMRI). The dynamics of correlated fMRI signals among functionally-related brain regions, termed dynamic functional connectivity (dFC), may represent nonstationarity arising from underlying neural processes. However, given the dimensionality and noise inherent in such recordings, seeming fluctuations in dFC could be due to sampling variability or artifacts. New method Here, we highlight key methodological considerations when evaluating dFC in resting-state fMRI data. Comparison with existing method In particular, we demonstrate how dimensionality reduction of fMRI data, a common practice often involving principal component analysis, may give rise to spurious dFC phenomenology due to its effect of decorrelating the underlying time-series. Conclusion We formalize a dFC assessment that avoids dimensionality reduction and use it to show the existence of at least two FC states in the resting-state.

How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, 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 investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into information-limiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations.

A popular approach to characterizing activity in neuronal networks is the use of statistical models that describe neurons in terms of their firing rates (i.e., the number of spikes produced per unit time). The output realization of a statistical model is, in essence, an $n-$ dimensional binary time series, or pattern. While such models are commonly fit to data, they can also be postulated de novo , as a theoretical description of a given spiking network. More generally, they can model any network producing binary events as a function of time. In this paper, we rigorously develop a set of analyses that may be used to assay the controllability of a particular statistical spiking model, the point-process generalized linear model. Our analysis quantifies the ease or difficulty of inducing desired spiking patterns via an extrinsic input signal, thus providing a framework for basic network analysis, as well as for emerging applications such as neurostimulation design.

Introduction Sleep is a fundamental necessity for health and is commonly disrupted in the perioperative period. Technological improvements leveraging dry electroencephalographic (EEG) sensors have opened the door for large-scale quantitative assessments of sleep in relation to perioperative outcomes. Methods Patients utilized the Dreem (Rhythm, New York USA), a wireless EEG headband, to acquire their own preoperative nocturnal sleep records at home. Following cardiac surgery, postoperative recordings were obtained with staff assistance until postoperative night 7. Sleep records were scored as rapid eye movement (REM) and non-rapid eye movement (NREM) stages N1-N3, using modified American Academy of Sleep Medicine guidelines. Results Of 100 patients enrolled for perioperative sleep recordings, 74 patients provided 132 preoperative records; 80% were scorable with a median total sleep time (TST) of 209.8 minutes. TST was distributed as 8.3% N1, 70.6% N2, 2.1% N3 and 19% REM, consistent with expected sleep structure in geriatric populations. EEG markers for staging sleep were evaluated in the scorable records: 92% with sleep spindles, 98% with K-complexes, 69% with slow waves, 92% with sawtooth waves, and 80% with rapid eye movements. Among 26 patients with multiple preoperative sleep recordings, no significant within-subject differences in sleep structure were observed (all p > 0.05, paired Wilcoxon sign-rank test). 270 postoperative nocturnal sleep recordings were obtained from 83 patients, 70% of which were scorable. TST in scorable postoperative records was distributed as 14.9% N1, 78.6% N2, 0.9% N3 and 5.6% REM. Durations of REM and N3 sleep were significantly reduced in postoperative (POD 1-4) overnight recordings compared to preoperative measurements (Skillings–Mack test, p < 0.001 and p = 0.02 for REM and N3, respectively). Conclusion Wireless EEG devices enhance the feasibility of assaying perioperative sleep. A single night of unattended, ambulatory sleep monitoring is sufficient to establish a preoperative baseline. Multiple preoperative and postoperative sleep studies were tolerated by patients, which showed reductions of N3 and REM sleep in the early postoperative period. This study demonstrates the feasibility of using the Dreem for monitoring sleep macro- and microstructural EEG elements in the perioperative setting. Support (if any)

Background Rapidly determining the causes of a depressed level of consciousness (DLOC) including coma is a common clinical challenge. Quantitative analysis of the electroencephalogram (EEG) has the potential to improve DLOC assessment by providing readily deployable, temporally detailed characterization of brain activity in such patients. While used commonly for seizure detection, EEG-based assessment of DLOC etiology is less well-established. As a first step towards etiological diagnosis, we sought to distinguish focal and diffuse causes of DLOC through assessment of temporal dynamics within EEG signals. Methods We retrospectively analyzed EEG recordings from 40 patients with DLOC with consensus focal or diffuse culprit pathology. For each recording, we performed a suite of time-series analyses, then used a statistical framework to identify which analyses (features) could be used to distinguish between focal and diffuse cases. Results Using cross-validation approaches, we identified several spectral and non-spectral EEG features that were significantly different between DLOC patients with focal vs. diffuse etiologies, enabling EEG-based classification with an accuracy of 76%. Conclusions Our findings suggest that DLOC due to focal vs. diffuse injuries differ along several electrophysiological parameters. These results may form the basis of future classification strategies for DLOC and coma that are more etiologically-specific and therefore therapeutically-relevant. Electronic supplementary material The online version of this article (doi: 10.1186/s12883-017-0977-0) contains supplementary material, which is available to authorized users.

In this paper, we investigate the effect of channel state information at the transmitter (CSIT) on the performance of a class of transmission schemes for multi-input–multi-output (MIMO) systems that can be collectively called space modulation (SMod) schemes. Space shift keying (SSK) is a simple example of SMod. In SMod, multiple antennas at the transmitter are employed to spatially modulate the information. The constellation vectors at the receiver are linear combinations of columns of the channel matrix, and therefore, the symbol error rate (SER) performance of the system highly depends on the Euclidean distance between each pair of these vectors. In this paper, we propose two novel methods to design the transmit vectors using CSIT such that the distance between each pair of constellation vectors at the receiver becomes larger, which, in turn, reduces the SER. Whereas in the first method we do not impose any constraint on the structure of the transmit vectors, in the second method, we confine the transmit vectors to have only one nonzero entry, and as such, it can be looked at as a modified SSK (MSSK) transmission scheme. This is to avoid interantenna synchronization (IAS) as only one transmit antenna is active at a time. Compared to SSK, the first method provides a significant performance improvement at the expense of increased complexity at the transmitter due to IAS. The second method provides an appealing SER improvement while keeping the complexity the same as that of SSK. Therefore, one can look at the second method as a modified version of SSK in which instead of transmitting a fixed symbol from one of the transmit antennas, in our proposed method, different transmit powers and phases are assigned to different transmit antennas. As acquiring full CSIT is difficult in some cases, we also consider the case of imperfect CSIT and derive alternative SMod transmission schemes in this case. By simulation, we show that the proposed SMod transmission schemes provide a significant performance improvement in terms of the SER in the presence of full or imperfect CSIT.

A long-standing and influential hypothesis in neural information processing is that early sensory networks adapt themselves to produce efficient codes of afferent inputs. Here, we show how a nonlinear recurrent network provides an optimal solution for the efficient coding of an afferent input and its history. We specifically consider the problem of producing lightweight codes, ones that minimize both l1 and l2 constraints on sparsity and energy, respectively. When embedded in a linear coding paradigm, this problem results in a non-smooth convex optimization problem. We employ a proximal gradient descent technique to develop the solution, showing that the optimal code is realized through a recurrent network endowed with a nonlinear soft thresholding operator. The training of the network connection weights is readily achieved through gradient-based local learning. If such learning is assumed to occur on a slower time-scale than the (faster) recurrent dynamics, then the network as a whole converges to an optimal set of codes and weights via what is, in effect, an alternative minimization procedure. Our results show how the addition of thresholding nonlinearities to a recurrent network may enable the production of lightweight, history-sensitive encoding schemes.

Additional details regarding study population. (DOCX 12Â kb)

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.

In brain networks, neurons communicate through action potentials or spikes. These spikes can be thought of as discrete events that constitute, in essence, a binary, time-varying spatial pattern over the entire network. A general hypothesis in neuroscience is that these patterns encode information, thus enabling function. Consequently, an emerging research direction in experimental neuroscience involves the use of neurostimulation technologies to artificially induce such patterns in a spatiotemporally precise manner - the so-called neurocontrol problem. In this work, we discuss the neurocontrol problem by means of statistical models, which, in contrast to more traditional dynamical-systems models, describe only the probability of spiking as a function of time. Thus, such models aggregate nonlinearity and uncertainty into a more tractable mathematical description. While statistical models are frequently used to describe experimental data, their use as tools for input construction is not as well explored. Here, we formulate an optimal control problem for spiking patterns via a weighted maximum likelihood approach and develop its solution. We demonstrate the design approach for a model network consisting of coupled stochastic integrate-and-fire neurons. Finally, we suggest how this overall framework can be used to develop a class of control analyses for point process models.

In this paper, we study how the dynamics of recurrent networks, formulated as general dynamical systems, mediate the recovery of sparse, time-varying signals. Our formulation resembles the well-described problem of compressed sensing, but in a dynamic setting. We specifically consider the problem of recovering a high-dimensional network input, over time, from observation of only a subset of the network states (i.e., the network output). Our goal is to ascertain how the network dynamics may enable recovery, even if classical methods fail at each time instant. We are particularly interested in understanding performance in scenarios where both the input and output are corrupted by disturbance and noise, respectively. Our main results consist of the development of analytical conditions, including a generalized observability criterion, that ensure exact and stable input recovery in a dynamic, recurrent network setting.

In this paper, we propose a novelty-based metric for quantitative characterization of the controllability of complex networks. This inherently bounded metric describes the average angular separation of an input with respect to the past input history. We use this metric to find the minimally novel input that drives a linear network to a desired state using unit average energy. Specifically, the minimally novel input is defined as the solution of a continuous time, non-convex optimal control problem based on the introduced metric. We provide conditions for existence and uniqueness, and an explicit, closed-form expression for the solution. We support our theoretical results by characterizing the minimally novel inputs for an example of a recurrent neuronal network.

We consider the problem of optimal probing to learn connectivity weights in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to applications in neural medicine and other settings in which the underlying physical network structure is not well-known. We show that the problem of selecting which node to probe amounts to a problem of optimal sensor scheduling. In this case, the solution to the greedy probing strategy has a convenient solution. Furthermore, we show that under certain conditions, the greedy probing strategy is optimal over a finite horizon and, moreover, that it amounts to periodic ‘round-robin’ scheduling.

A persistent question in multivariate neural signal processing is how best to characterize the statistical association between brain regions known as functional connectivity. Of the many metrics available for determining such association, the standard Pearson correlation coefficient (i.e., the zero-lag cross-correlation) remains widely used, particularly in neuroimaging. Generally, the cross-correlation is computed over an entire trial or recording session, with the assumption of within-trial stationarity. Increasingly, however, the length and complexity of neural data requires characterizing transient effects and/or non-stationarity in the temporal evolution of the correlation. That is, to estimate dynamics in the association between brain regions. Here, we present a simple, data-driven Kalman filter-based approach to tracking correlation dynamics. The filter explicitly accounts for the bounded nature of correlation measurements through the inclusion of a Fisher transform in the measurement equation. An output linearization facilitates a straightforward implementation of the standard recursive filter equations, including admittance of covariance identification via an autoregressive least squares method. We demonstrate the efficacy and utility of the approach in an example of multivariate neural functional magnetic resonance imaging data.

In this paper, we propose two lossless compression techniques that represent a two dimensional Run-length Coding which can achieve high compression ratio. This method works by partitioning the block regions of the input image into rectangles instead of working by runs of adjacent pixels, so it is found to be more efficient than ID RLE Run-length Coding for transmitting texts and image. In the first method, length and width of consecutive black and white rectangles are transmitted. The idea of this method is new and it can be very effective for some images which have large blocks of black or white pixels. But in the second method only black rectangles are considered in order to transmit and an intelligent procedure is exploited to encoding the image. The first method is faster and simpler to implement than the second one but its compression ratio is lesser. Our proposed second scheme is more suitable for text compression and has outperformed the existence partitioning methods. Therefore, if we used these partitioning methods instead of previous partitioning methods in well-known lossless compression techniques, we would have a better compression ratio.

In this paper a new iterative method is used to convert analog signals to digital (A/D) using sigma delta modulator (SDM). If intelligent reconstruction technique is used for decoding, either signals with higher bandwidth can be digitized or simpler circuitry can be utilized. An optimal family of SDM has recently been devised in order to improve performance of A/D converters. In this work, we improve performance of A/D converters even more, by combining this optimal family of SDM with iterative methods.

Objective. We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input–output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. Approach. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. Main results. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. Significance. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.