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The GOES-R Series Extreme Ultraviolet and X-ray Irradiance Sensors (EXIS) measures solar irradiance at the very short wavelengths where the Sun’s output varies the most. EXIS has two primary components, the X-ray Sensor (XRS) and the Extreme Ultraviolet Sensor (EUVS). The first level of processed data has cadences of 1–30 s. XRS data products include X-ray fluxes in two wavelength bands, flare event detection, and flare locations. EUVS products include irradiances for seven solar lines representative of coronal, transition region, and chromospheric variability, the Magnesium II index, a modeled solar spectrum from 5 to 127 nm, and event detection.

The Geostationary Operational Environmental Satellite R (GOES-R) series of four satellites are the next generation NOAA GOES satellites. Once on orbit and commissioned, they are renamed GOES 16-19, making critical terrestrial and space weather measurements through 2035. GOES 16 and 17 are currently on orbit, having been launched in 2016 and 2018, respectively. The GOES-R satellites include the EUV and X-ray Irradiance Sensors (EXIS) instrument suite, which measures calibrated solar irradiance in 8 lines or bands between 25 and 285 nm with the Extreme Ultraviolet Sensors (EUVS) instrument. EXIS also includes the X-Ray Sensor (XRS) instrument, which measures solar soft X-ray irradiance at the legacy GOES bands. The EUVS measurements are used as inputs to the EUVS Model, a solar spectral irradiance model for space weather operations that predicts irradiance in twenty-two 5 nm wide intervals from 5 nm to 115 nm, and one 10 nm wide interval from 117 to 127 nm at 30 second cadence. Once fully operational, NOAA will distribute the EUVS Model irradiance with 1 minute latency as a primary space weather data product, ushering in a new era of rapid dissemination and measurement continuity of EUV irradiance spectra. This paper describes the EUVS Model algorithms, data sources, calibration methods and associated uncertainties. Typical model (relative) uncertainties are less than $\sim$5\% for variability at time-scales longer than 6 hours, and are $\sim$25\% for solar flare induced variability. The absolute uncertainties, originating from the instruments used to calibrate the EUVS Model, are $\sim$10\%. Examples of model results are presented at both sub-daily and multi-year timescales to demonstrate the model's capabilities and limitations. Example solar flare irradiances are also modeled., Accepted by Journal of Space Weather and Space Climate on 20 November 2019

In the hippocampus, replay sequences are temporally compressed patterns of neural spiking that resemble patterns that occur when the animal is moving through the environment. Because replay sequences typically occur when the animal is at rest, replay is hypothesized to be part of an internal cognitive process that enables the retrieval of past spatial memories and the planning of future movement. Traditionally, replay sequences have been discovered by identifying sharp wave ripples (SWRs)—high frequency oscillations that occur in association with replay—and then looking within SWRs for spatially continuous patterns of neural spiking. This does not fully account for the content or timing of replay sequences, however. Replay sequences do not always co-occur with sharp wave ripples, have more complex dynamics than spatially continuous movement, have different temporal ordering than during movement, and change based on task. In this work, we introduce a hybrid state space framework to describe the richness of replay sequences. We show how defining discrete latent states associated with continuous latent dynamics and point process observations allows us to identify when non-local replay sequences occur, categorize the type of sequence based on their inferred continuous dynamics, and decode the spatial trajectory corresponding to the replay sequence.

The ability to regulate emotions in the service of meeting ongoing goals and task demands is a key aspect of adaptive human behavior in our volatile social world. Consequently, difficulties in processing and responding to emotional stimuli underlie many psychiatric diseases ranging from depression to anxiety, the common thread being effects on behavior. Behavior, which is made up of shifting, difficult to measure hidden states such as attention and emotion reactivity, is a product of integrating external input and latent mental processes. Directly measuring, and differentiating, separable hidden cognitive, emotional, and attentional states contributing to emotion conflict resolution, however, is challenging, particularly when only using task-relevant behavioral measures such as reaction time. State-space representations are a powerful method for investigating hidden states underlying complex systems. Using state-space modeling of behavior, we identified relevant hidden cognitive states and predicted behavior in a standardized emotion regulation task. After identifying and validating models which best fit the behavior and narrowing our focus to one model, we used targeted intracranial stimulation of the emotion regulation-relevant neurocircuitry, including prefrontal structures and the amygdala, to causally modulate separable states. Finally, we focused on this one validated state-space model to perform real-time, bidirectional closed-loop adaptive stimulation in a subset of participants. These approaches enable an improved understanding of how to sample and understand emotional processing in a way which could be leveraged in neuromodulatory therapy for disorders of emotional regulation.

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

Background and objective New large bore aspiration catheters are being used as first line therapy for mechanical thrombectomy in patients with emergent large vessel occlusions. We present the largest combined experience with the new Stryker Neurovascular Vecta 071/074 aspiration catheters. Vecta 074 represents the largest available aspiration catheter in the U.S. We sought to assess the safety, efficacy, direct aspiration first pass success, need for rescue with stent retriever, and clinical improvement. Methods Multi-institution retrospective review of all Vecta aspiration catheter cases performed at 2 comprehensive stroke centers between January 1, 2019 and March 25, 2019 yielded 26 patients. Key metrics were recorded including pre-procedure ASPECTS score, presenting NIHSS score, last seen normal to groin access, groin access to first device deployment, time to recanalization, revascularization TICI grading, thrombectomy technique (successful direct aspiration, need for rescue stent retriever and number of passes), and outcomes (NIHSS at discharge, rate of symptomatic intracranial hemorrhage sICH, and death prior to discharge). Results Eighteen 071 and six 074 Vecta aspiration catheters were used (figures.1/2). The average pre-procedure ASPECTS, NIHSS, and time from last seen normal were 8, 17.7, and 5.3 hours, respectively. Time from vascular access to first device delivery was 25.9 minutes. Direct aspiration was attempted as first line technique in all cases and was successful in 72% of cases. 28% of cases required use of a stent retriever to deliver the aspiration catheter (grappling hook technique-figure.3), or for rescue thrombectomy (direct aspiration unsuccessful or to target residual small, distal emboli). TICI 3 revascularization was achieved in 46%, TICI 2C in 23%, TICI 2B in 26%, and 5% with unsuccessful TICI 2A revascularization. The average NIHSS on discharge was 6.1 with two patients expiring prior to discharge (withdrawal of care in two patients with basilar artery occlusion). Complications included one intracranial/extradural non-flow limiting dissection and one sICH. Limitations included: Small sample size, retrospective review, variation in data collection due to differences in institutional records and short time to follow up. Conclusion Initial experience with the new Stryker Neurovascular Vecta 071/074 aspiration catheters suggests that high TICI 2C/3 reperfusion rates can safely be obtained using direct aspiration in a majority of cases. Stent retrievers can be reserved for cases where grappling hook technique is necessary to deliver the aspiration catheter to the clot or for rescue thrombectomy. Disclosures S. Satti: 2; C; Stryker Neurovascular, Medtronic Neurovascular, Penumbra Neurovascular, Cerenovus Neurovascular, Terumo. T. Sivapatham: 2; C; Penumbra Neurovascular. E. Almallouhi: None. T. Eden: None. A. Spiotta: 2; C; Minnetronix Neurovascular, Cerenovus Neurovascular, Penumbura Neurovascular.

Characterizing coordinated brain dynamics present in high-density neural recordings is critical for understanding the neurophysiology of healthy and pathological brain states and to develop principled strategies for therapeutic interventions. In this research, we propose a new modeling framework called State Space Global Coherence (SSGC), which allows us to estimate neural synchrony across distributed brain activity with fine temporal resolution. In this modeling framework, the cross-spectral matrix of neural activity at a specific frequency is defined as a function of a dynamical state variable representing a measure of Global Coherence (GC); we then combine filter-smoother and Expectation-Maximization (EM) algorithms to estimate GC and the model parameters. We demonstrate a SSGC analysis in a 64-channel EEG recording of a human subject under general anesthesia and compare the modeling result with empirical measures of GC. We show that SSGC not only attains a finer time resolution but also provides more accurate estimation of GC.

Purpose Retrospective review of all Medtronic Pipeline Flex flow diverting stent placement at single center with attention to guiding catheter selection, technical success, intracranial complications, access site complications and length of procedure. Summarize technical advantages and limitations of each access strategy and present a simplified 6 French triaxial system for flow diversion stent placement via a transradial/trans-ulnar approach. We also present some tips and tricks to optimize procedural success including transulnar access and direct radial access. Materials and methods Single Center retrograde suspect of review of all Medtronic Pipeline Embolization Flex procedures performed between October 2015-March 2019 via arm access (radial or ulnar). Three different 6F constructs were used for arm access: Direct radial access with long sheath–Terumo destination slender Benchmark radial access Codman Envoy DA radial access Primary endpoints include time from radial accessed to target lesion, time from radial access to pipeline embolization deployment, technical failure requiring alternative access, intracranial complications, radial access site complications, occlusion rates at longest available follow-up were delayed neurologic complications. Results A total of 23 patients were included in this retrospective analysis. Average patient age and male gender were 60.4 years and 22% respectively. A total of 31 devices were used to treat a total of 30 aneurysms. Average aneurysm size was 7 mm. Primary placement of a flow diverting stent was performed in a majority of patients with adjunctive coil placement was performed in 13.1% procedures. Post stent placement balloon angioplasty was performed in 34% procedures. No intracranial complications including dissection, intracranial hemorrhage, symptomatic embolic stroke were identified. Major access site complications and symptomatic delayed radial artery occlusion were not identified in any patients undergoing arm access. Furthest available follow-up was on an average of 9.8 months. Complete occlusion was achieved in 70% patients (we anticipate further occlusion by one year). Additional pipeline embolization devices were required in 1 patient, at 18 months. Foley catheter and arterial line placement, 35% and 22%, respectively. Limitations Single center retrospective review, short interval follow-up (less than 1 year), and some patients lost to follow-up. Conclusion 6 French Triaxial Trans-radial (or ulnar access) can be used in placement of a Pipeline Embolization Flex device with low associated complications and high technical success. Disclosures S. Satti: 2; C; Stryker Neurovascular, Medtronic Neurovascular, Penumbra Neurovascular, Cerenovus Neurovascular, Terumo. T. Eden: None.

Behavioral outcomes in many cognitive tasks are often recorded in a trial structure at discrete times. To adapt to this structure, neural encoder and decoder models have been built to take into account the trial organization to characterize the connection between brain dynamics and behavior, e.g. through latent dynamical models. The challenge of these models is that they are limited to discrete trial times while neural data is continuous. Here, we propose a marked-point process framework to characterize multivariate behavioral outcomes recorded during a trial-structured cognitive task, to build an estimation of cognitive state at a fine time resolution. We propose a state-space marked-point process modeling framework to characterize the relationship between observed behavior and underlying dynamical cognitive processes. We define the framework for a class of behavioral readouts by a response time and a discrete mark signifying an observed binary decision, and develop the state estimation and system identification steps. We define the filter and smoother for the marked-point process observation and develop an EM algorithm to estimate the model's free parameters. We demonstrate this modeling approach in a behavioral readout captured while participants perform an emotional conflict resolution task (ECR) and show that we can estimate underlying cognitive processes at a fine temporal resolution beyond the trial by trial approach.

Benign epilepsy with centrotemporal spikes is a common childhood epilepsy syndrome that predominantly affects boys, characterized by self-limited focal seizures arising from the perirolandic cortex and fine motor abnormalities. Concurrent with the age-specific presentation of this syndrome, the brain undergoes a developmentally choreographed sequence of white matter microstructural changes, including maturation of association u-fibres abutting the cortex. These short fibres mediate local cortico-cortical communication and provide an age-sensitive structural substrate that could support a focal disease process. To test this hypothesis, we evaluated the microstructural properties of superficial white matter in regions corresponding to u-fibres underlying the perirolandic seizure onset zone in children with this epilepsy syndrome compared with healthy controls. To verify the spatial specificity of these features, we characterized global superficial and deep white matter properties. We further evaluated the characteristics of the perirolandic white matter in relation to performance on a fine motor task, gender and abnormalities observed on EEG. Children with benign epilepsy with centrotemporal spikes (n = 20) and healthy controls (n = 14) underwent multimodal testing with high-resolution MRI including diffusion tensor imaging sequences, sleep EEG recordings and fine motor assessment. We compared white matter microstructural characteristics (axial, radial and mean diffusivity, and fractional anisotropy) between groups in each region. We found distinct abnormalities corresponding to the perirolandic u-fibre region, with increased axial, radial and mean diffusivity and fractional anisotropy values in children with epilepsy (P = 0.039, P = 0.035, P = 0.042 and P = 0.017, respectively). Increased fractional anisotropy in this region, consistent with decreased integrity of crossing sensorimotor u-fibres, correlated with inferior fine motor performance (P = 0.029). There were gender-specific differences in white matter microstructure in the perirolandic region; males and females with epilepsy and healthy males had higher diffusion and fractional anisotropy values than healthy females (P ≤ 0.035 for all measures), suggesting that typical patterns of white matter development disproportionately predispose boys to this developmental epilepsy syndrome. Perirolandic white matter microstructure showed no relationship to epilepsy duration, duration seizure free, or epileptiform burden. There were no group differences in diffusivity or fractional anisotropy in superficial white matter outside of the perirolandic region. Children with epilepsy had increased radial diffusivity (P = 0.022) and decreased fractional anisotropy (P = 0.027) in deep white matter, consistent with a global delay in white matter maturation. These data provide evidence that atypical maturation of white matter microstructure is a basic feature in benign epilepsy with centrotemporal spikes and may contribute to the epilepsy, male predisposition and clinical comorbidities observed in this disorder., We found abnormalities in the white matter abutting the seizure onset zone in a common childhood epilepsy syndrome. The degree of abnormality predicted the severity of problems with motor coordination, a common symptom in this disease. Healthy boys have similar white matter findings, which may predispose them to this disease., Graphical Abstract Graphical Abstract

Background The apnea-hypopnea index (AHI) (or one of its derivatives) is the primary clinical metric for characterizing sleep disordered breathing—the value of which with respect to a threshold determines severity of diagnosis and eligibility for treatment reimbursement. The index value, however, is taken as a perfect point estimate, with no measure of statistical uncertainty. Thus, current practice does not robustly account for variability in diagnosis/eligibility due to chance. In this paper, we quantify the statistical uncertainty associated with respiratory event indices for sleep disordered breathing and the effect of uncertainty on treatment eligibility. Methods We develop an empirical estimate of uncertainty using a non-parametric bootstrap on the interevent times, as well as a theoretical Poisson estimate reflecting the current formulation of the AHI. We then apply these methods to estimate AHI uncertainty for 2049 subjects (954/1095 M/F, age: mean 69 ± 9.1) from the Multi-Ethnic Study of Atherosclerosis (MESA). Results and Conclusions The mean 95% empirical confidence interval width was 11.500 ± 6.208 events per hour and the mean 95% theoretical Poisson confidence interval width was 5.998 ± 2.897 events per hour, suggesting that uncertainty is likely a major confounding factor within the current diagnostic framework. Of the 278 subjects in the symptomatic population (ESS>10), 27% (76/278) had uncertain diagnoses given the 95% empirical confidence interval. Of the 2049 subjects in the full population, 43% (880/2049) had uncertain diagnoses given the 95% empirical confidence interval. The inclusion of subjects with uncertain diagnoses increases the number of eligible patients by 21.3% for the symptomatic population and by 84.8% for the full population. The exclusion of subjects with uncertain diagnoses given the 95% empirical confidence interval decreases the number of eligible patients by 12.4% for the symptomatic population and by 34.8% for full population. Additional analyses suggest that it is practically infeasible to gain diagnostic statistical significance through additional testing for a broad range of borderline cases. Overall, these results suggest that AHI uncertainty is a vital additional piece of information that would greatly benefit clinical practice, and that the inclusion of uncertainty in epidemiological analysis might help improve the ability for researchers to robustly link AHI with co-morbidities and long-term outcomes.

Purpose Consulting with current volunteers in World Child Cancer (WCC) projects, to help us to expand our volunteering strategy. WCC’s model has been to support ‘twinning’ partnerships between paediatric oncology institutions in high income countries and LMICs with healthcare volunteers visiting for short periods of time once or twice a year to assist staff training, support and sharing clinical expertise. Methods WCC held a workshop including 8 volunteers with their experience of WCC programmes in Ghana, Myanmar, Malawi, Cameroon and Bangladesh. These volunteers hold diverse expertise including paediatric oncology, paediatric oncology nursing, counselling and patient support. Results What is currently working well: Volunteering with WCC has helped to improve childhood cancer services in LMICs by increasing capacity, professional confidence to care for children with cancer, and increasing survival rates in the hospitals. Involvement is sustainable using a team approach with volunteers returning repeatedly and inducting new volunteers as well. Volunteers noted WCC’s flexibility and support for implementing new ways of working. Following visits, volunteers remain connected to WCC’s work and placements are mutually beneficial as volunteers bring new knowledge/skills back to their roles at home. Steps to improve impact of volunteers: Expand beyond current paediatric oncology units to improve healthcare systems by involving volunteers with a wider range of expertise, such as psycho–social support, surgery and pathology, always working with local teams to identify the most beneficial expertise Expand beyond specific twinning institutions to include individual volunteers from other sources Support volunteers on longer–term placements alongside existing twinning partnerships aiming to strengthen and embed knowledge/skills Expand and improve digital volunteering opportunities and facilities Formalise recruitment and assessment processes to ensure high quality volunteers Conclusion WCC’s volunteering strategy has worked well for the past 10 years, building long-lasting relationships and improving the capacity of healthcare professionals in LMICs to care for children with cancer. Based on learning from programmes and the workshop, WCC will expand its approach to involve professionals with wider areas of expertise, include volunteers on longer-term placements, improve digital volunteering opportunities and formalise recruitment and assessment processes.

New recording technologies and the potential for closed-loop experiments have led to an increasing demand for computationally efficient and accurate algorithms to decode population spiking activity in multi-dimensional spaces. Exact point process filters can accurately decode low-dimensional signals, but are computationally intractable for high-dimensional signals. Approximate Gaussian filters are computationally efficient, but are inaccurate when the signals have complex distributions and nonlinear dynamics. Even particle filter methods tend to become inefficient and inaccurate when the filter distribution has multiple peaks. Here, we develop a new point process filter algorithm that combines the computational efficiency of approximate Gaussian methods with a numerical accuracy that exceeds standard particle filters. We use a mixture of Gaussian model for the posterior at each time step, allowing for an analytic solution to the computationally expensive filter integration step. During non-spike intervals, the filter needs only to update the mean, covariance, and mixture weight of each component. At spike times, a sampling procedure is used to update the filtering distribution and find the number of Gaussian mixture components necessary to maintain an accurate approximation. We illustrate the application of this algorithm to the problem of decoding a rat's position and velocity in a maze from hippocampal place cell data using both 2-D and 4-D decoders.

Mental disorders are a leading cause of disability, morbidity, and mortality among civilian and military populations. Most available treatments have limited efficacy, particularly in disorders where symptoms vary over relatively short time scales. Targeted modulation of neural circuits, particularly through open-loop deep brain stimulation (DBS), showed initial promise but has failed in blinded clinical trials. We propose a new approach, based on targeting neural circuits linked to functional domains that cut across diagnoses. Through that framework, which includes measurement of patients using six psychophysical tasks, we seek to develop a closed-loop DBS system that corrects dysfunctional activity in brain circuits underlying those domains. We present convergent preliminary evidence from functional neuroimaging, invasive human electrophysiology, and human brain stimulation experiments suggesting that this approach is feasible. Using the Emotional Conflict Resolution (ECR) task as an example, we show that emotion-related networks can be identified and modulated in individual patients. Invasive and non-invasive methodologies both identify a network between prefrontal cortex, cingulate cortex, insula, and amygdala. Further, stimulation in cingulate and amygdala changes patients' performance in ways that are linked to the task's emotional content. We present preliminary statistical models that predict this change and allow us to track it at a single-trial level. As these diagnostic and modeling strategies are refined and embodied in an implantable device, they offer the prospect of a new approach to psychiatric treatment and its accompanying neuroscience.

Sharp-wave ripple (SWR) events in the hippocampus replay millisecond-timescale patterns of place cell activity related to the past experience of an animal. Interrupting SWR events leads to learning and memory impairments, but how the specific patterns of place cell spiking seen during SWRs contribute to learning and memory remains unclear. A deeper understanding of this issue will require the ability to manipulate SWR events based on their content. Accurate real-time decoding of SWR replay events requires new algorithms that are able to estimate replay content and the associated uncertainty, along with software and hardware that can execute these algorithms for biological interventions on a millisecond timescale. Here we develop an efficient estimation algorithm to categorize the content of replay from multiunit spiking activity. Specifically, we apply real-time decoding methods to each SWR event and then compute the posterior probability of the replay feature. We illustrate this approach by classifying SWR events from data recorded in the hippocampus of a rat performing a spatial memory task into four categories: whether they represent outbound or inbound trajectories and whether the activity is replayed forward or backward in time. We show that our algorithm can classify the majority of SWR events in a recording epoch within 20 ms of the replay onset with high certainty, which makes the algorithm suitable for a real-time implementation with short latencies to incorporate into content-based feedback experiments.

Significance Biases and fallacies can nudge humans in one direction or another as they make decisions. During gambling, bias is often generated by internal factors, including individual preferences, past experience, or emotions, and can move a person toward or away from risky behavior. The neural mechanisms responsible for generating internal bias are largely unknown, limiting the treatment of patients with neurological diseases that impair decision-making. We applied mathematical modeling techniques and high-resolution intracerebral recordings to uncover how a hidden internal bias builds up from past experiences to influence decisions and where this internal bias is encoded in the brain. Our findings suggest that biology exploits a distributed lateralized push–pull neural system to govern counterintuitive and highly variable decision-making in humans., A person’s decisions vary even when options stay the same, like when a gambler changes bets despite constant odds of winning. Internal bias (e.g., emotion) contributes to this variability and is shaped by past outcomes, yet its neurobiology during decision-making is not well understood. To map neural circuits encoding bias, we administered a gambling task to 10 participants implanted with intracerebral depth electrodes in cortical and subcortical structures. We predicted the variability in betting behavior within and across patients by individual bias, which is estimated through a dynamical model of choice. Our analysis further revealed that high-frequency activity increased in the right hemisphere when participants were biased toward risky bets, while it increased in the left hemisphere when participants were biased away from risky bets. Our findings provide electrophysiological evidence that risk-taking bias is a lateralized push–pull neural system governing counterintuitive and highly variable decision-making in humans.

There is an increasing demand for a computationally efficient and accurate point process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the point process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general point- process filter problem when the conditional intensity of a cell’s spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of point process observation called marked point-process, which encompasses both clustered – mainly, called sorted – and clusterless – generally called unsorted or raw – spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional point-process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat’s position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% highest probability coverage area (HPD) performance metrics. Though the marked-point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.

Biophysical models are widely used to characterize temporal dynamics of the brain networks on different topological and spatial scales. In parallel, the state-space modeling framework with point process observations has been successfully applied in characterizing spiking activity of neuronal ensembles in response to different dynamical covariates. Parameter estimation in biophysical models is generally done heuristically, which hampers their applicability and interpretability. Heuristic parameter estimation becomes an intractable problem when the number of model parameters grows. Here, we propose an algorithm for estimating biophysical model parameters using point-process models and a state-space framework. The framework provides methods for parameter estimation as well as model validation. We demonstrate the application of this methodology to the problem of estimating the parameters of a dynamic synapse model. We generate simulation data for the dynamic synapse across a range of parameters values and assess the estimation accuracy of our method using a combination of goodness-of-fit measures. The proposed methodology can be applied broadly to parameter estimation problems across a broad range of biophysical models, including Hodgkin-Huxley models and network models.

Real-time, closed-loop experiments can uncover causal relationships between specific neural activity and behavior. An important advance in realizing this is the marked point process filtering framework which utilizes the “mark” or the waveform features of unsorted spikes, to construct a relationship between these features and behavior, which we call the encoding model. This relationship is not fixed, because learning changes coding properties of individual neurons, and electrodes can physically move during the experiment, changing waveform characteristics. We introduce a sequential, Bayesian encoding model which allows incorporation of new information on the fly to adapt the model in real time. A possible application of this framework is to the decoding of the contents of hippocampal ripples in rats exploring a maze. During physical exploration, we observe the marks and positions at which they occur, to update the encoding model, which is employed to decode contents of ripples when rats stop moving, and switch back to updating the model once the rat starts moving again.

Mathematical modeling of behavior during a psychophysical task, referred to as “computational psychiatry,” could greatly improve our understanding of mental disorders. One barrier to the broader adoption of computational methods, is that they often require advanced statistical modeling and mathematical skills. Biological and behavioral signals often show skewed or non-Gaussian distributions, and very few toolboxes and analytical platforms are capable of processing such signal categories. We developed the Computational Psychiatry Adaptive State-Space (COMPASS) toolbox, an open-source MATLAB-based software package. This toolbox is easy to use and capable of integrating signals with a variety of distributions. COMPASS has the tools to process signals with continuous-valued and binary measurements, or signals with incomplete—missing or censored—measurements, which makes it well-suited for processing those signals captured during a psychophysical task. After specifying a few parameters in a small set of user-friendly functions, COMPASS allows users to efficiently apply a wide range of computational behavioral models. The model output can be analyzed as an experimental outcome or used as a regressor for neural data and can also be tested using the goodness-of-fit measurement. Here, we demonstrate that COMPASS can replicate two computational behavioral analyses from different groups. COMPASS replicates and can slightly improve on the original modeling results. We also demonstrate the use of COMPASS application in a censored-data problem and compare its performance result with naïve estimation methods. This flexible, general-purpose toolkit should accelerate the use of computational modeling in psychiatric neuroscience.

Mathematical modeling of behavior during psychophysical tasks, referred to as “computational psychiatry”, could greatly improve our understanding of mental disorders. One barrier to broader adoption of computational methods is that they often require advanced programming skills. We developed the Computational Psychiatry Adaptive State-Space (COMPASS) toolbox, an open-source MATLAB-based software package. After specifying a few parameters in a small set of user-friendly functions, COMPASS allows the user to efficiently fit of a wide range of computational behavioral models. The model output can be analyzed as an experimental outcome or used as a regressor for neural data, and can be tested using goodness-of-fit methods. Here, we demonstrate that COMPASS can replicate two computational behavior analyses from different groups. COMPASS replicates and, in one case, slightly improves on the original modeling results. This flexible, general-purpose toolkit should accelerate the use of computational modeling in psychiatric neuroscience.

In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges (‘spike ripple events’) are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.

Background How the human brain coordinates network activity to support cognition and behavior remains poorly understood. New high-resolution recording modalities facilitate a more detailed understanding of the human brain network. Several approaches have been proposed to infer functional networks, indicating the transient coordination of activity between brain regions, from neural time series. One category of approach is based on statistical modeling of time series recorded from multiple sensors (e.g., multivariate Granger causality). However, fitting such models remains computationally challenging as the history structure may be long in neural activity, requiring many model parameters to fully capture the dynamics. New Method We develop a method based on Granger causality that makes the assumption that the history dependence varies smoothly. We fit multivariate autoregressive models such that the coefficients of the lagged history terms are smooth functions. We do so by modelling the history terms with a lower dimensional spline basis, which requires many fewer parameters than the standard approach and increases the statistical power of the model. Results We show that this procedure allows accurate estimation of brain dynamics and functional networks in simulations and examples of brain voltage activity recorded from a patient with pharmacoresistant epilepsy. Comparison with Existing Method The proposed method has more statistical power than the Granger method for networks of signals that exhibit extended and smooth history dependencies. Conclusions The proposed tool permits conditional inference of functional networks from many brain regions with extended history dependence, furthering the applicability of Granger causality to brain network science.

Introduction Radial access has been demonstrated to be associated with lower access site complications, greater patient satisfaction and cost savings for coronary angiography. We sought to assess whether radial access for outpatient cerebral angiography would result in lower length of stay (LOS) and, therefore, cost savings over femoral angiography. Incidence of major access complication rates (including transfusion and surgical vascular repair) was additionally reviewed. Methods A single-center retrospective review of 101 consecutive radial access diagnostic cerebral angiogram via electronic database query in patients compared to 417 consecutive femoral access procedures. Using electronic query, LOS, patient demographics, procedure type, and complication rates were determined. Results From January 2014 through February 2018, all patients undergoing out-patient cerebral angiography were identified and divided into femoral (417 pts-70% female) versus radial access (101 pts-75% female). Average age (years): 57 radial verus 56 femoral. Average procedure times: 41.6 min radial versus 54.5 min femoral. Average time in Interventional Suite: 7 min (9%) shorter with Radial. Average length of stay: 3.2 hours shorter LOS for radial access patients Radial: 6.8 hours (1% (1/111) required more than 12 hour stay – 12.8 hours) Femoral: 10.0 hours (6% (26/417) required required more than 12 hour stay-with an average of 32.8 hours in that subgroup) Major Complication Rates: 0% (no patients in either group required a transfusion or surgical vascular repair). Recovery Room Costs: 5% lower with Radial Access. Conclusion Based on our single center retrospective review, incorporation of radial access for outpatient cerebral angiography is associated with shorter patient stays, lower cost, and no major complications. This time savings could increase case volume, while decreasing recovery room staffing. Disclosures S. Satti: 2; C; Stryker Neurovascular, Penumbra Neurovascular. P. Krishna Reddy: None. T. Eden: None. V. Ansar: None.

The emergence of deep learning techniques has provided new tools for the analysis of complex data in the field of neuroscience. In parallel, advanced statistical approaches like point-process modeling provide powerful tools for analyzing the spiking activity of neural populations. How statistical and machine learning techniques compare when applied to neural data remains largely unclear. In this research, we compare the performance of a point-process filter and a long short-term memory (LSTM) network in decoding the 2D movement trajectory of a rat using the neural activity recorded from an ensemble of hippocampal place cells. We compute the least absolute error (LAE), a measure of accuracy of prediction, and the coefficient of determination (R2), a measure of prediction consistency, to compare the performance of these two methods. We show that the LSTM and point-process filter provide comparable accuracy in predicting the position; however, the point-process provides further information about the prediction which is unavailable for LSTM. Though previous results report better performance using deep learning techniques, our results indicate that this is not universally the case. We also investigate how these techniques encode information carried by place cell activity and compare the computational efficiency of the two methods. While the point-process model is built using the receptive field for each place cell, we show that LSTM does not necessarily encode receptive fields, but instead decodes the movement trajectory using other features of neural activity. Although it is less robust, LSTM runs more than 7 times faster than the fastest point-process filter in this research, providing a strong advantage in computational efficiency. Together, these results suggest that the point-process filters and LSTM approaches each provide distinct advantages; the choice of model should be informed by the specific scientific question of interest.

Background There is increasing anxiety worldwide that the World Health Organisation recommended Essential Medicines may not be universally available,accessible,affordable and of good quality. Aims/methods We wished to seek the perceptions of 10 lead paediatricians treating children with cancer in 9 low-middle income countries. We used a 17 point semi-structured questionnaire to gather the information. this study involved doctors in Cameroon,Ghana, Malawi,Tanzania,Zambia, Bangladesh,Myanmar, The Philippines,and Colombia. Collectively the centres were seeing overr 2000 new patients annually of whom a median of 65% (range 5%–90%) received ‘curative intent’ therapy. Results Only Bangladesh produced any but not all the required cytotoxics. All countries were required to import some or all the medicines.In only 3 countries did the Ministry of Health directly oversee procurement/importation of drugs.In 5 delegated pharmacies or a single hospital was responsible. in Bangladesh there was a free market approach for procurement and importation. Inconsistent supplies were cited by all respondents especially of critical drugs including antibiotics,morphine,6-mercaptopurine, methotrexate,cytosine, asparaginase, and vincristine. Reasons cited for no-importation were non/or late renewal of licences to import, reluctance to procure low profit or low volume drugs and anxieties regarding importation of opiates. Medicines were imported from a range of countries most commomly from India (7/9) and China (often via India), argentina,Brazil, South Kores, Cyprus and Malaysia. All respondents expressed anxiety about drug quality imported with no International Quality Certification. High rates of treatment refusal/abandonment (20% median range Conclusions From these perceptions there are major obstacles to be overcome to ensure that all children can receive the medicines they need for any chance of cure certainly those living in low-middle income countries. There are challenges at each stage from production to the bedside and only a truly global effort by all interested parties including the WHO and the Pharmaceutical Industry can resolve these issues.

Background/Aims World Child Cancer has created twinning partnerships with developing oncology services in low-middle income countries (LMICs) to support improvement of services for children with cancer. Central to success for these is the creation of effective shared-care networks not just single centre support.There is a dearth of good literature on network development. Our aim was to create an ideal model. Methods The model was developed through learning from a 3 year UK Government(DFID) funded programme in Ghana and Bangladesh in which new shared care units were created and from lessons shared from other WCC-funded programmes in Myanmar and the Philippines. A 2 dayworkshop was held,focussing on lessons learnt from paediatricians representing networks in different stages of development to identify key elements and steps necessary to optimise planning. Results The over-arching proposed themes for the model were need for; excellent,regular communication between the centres;twinning partnerships and funding.A succesful shared- care network must have a strong hub hospital at its centre with at least one fully trained paediatric oncologist and a committed multi-disciplinary team. The hub (referral) centre must have dedicated space/beds,develop treatment guidelines and protocols and provide training for the staff populating the satellite units.Shared- care centres must, be strategically chosen based on population demography and accessibility, create development plans and service provision to replicate the hub centre as close as resources allow… Collaborative working and good communication, using the same treatment protocols, developing two-way referral systems and sharing successes and any failures are essential. Sustainable development is ensured through a step -by step process, funding support, and ongoing opportunities within the network. Conclusion We hope that this model can be shared to enable others to access it and help iinform their systems development. Whilst the model is not exhaustive and requires further research,it represents a first step,with lessons learnt from paediatricians with actual experience of creating such networks. Hub and spoke service provision better meets the needs of all children no matter where they live in the world.

© 2015 Massachusetts Institute of Technology. Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally thesemethods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision, such as real-time decoding for brain-computer interfaces. Because the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights into clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes's rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat's position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalent to or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain.

Background Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons’ encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics. New method This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test. Results MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples. Comparison with existing methods The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates. Conclusions These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection.

We propose a time-frequency representation based on the ridges of the continuous chirplet transform to identify both fast transients and components with well-defined instantaneous frequency in noisy data. At each chirplet modulation rate, every ridge corresponds to a territory in the time-frequency plane such that the territories form a partition of the time-frequency plane. For many signals containing sparse signal components, ridge length and ridge stability are maximized when the analysis kernel is adapted to the signal content. These properties provide opportunities for enhancing signal in noise and for elementary stream segregation by combining information across multiple analysis chirp rates.

A critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more recently clusterless modeling methods have been used to describe population activity using a single model. Here we develop a generalization of the time-rescaling theorem that enables comprehensive goodness-of-fit analysis for either of these classes of population models. We use the theory of marked point processes to model population spiking activity, and show that under the correct model, each spike can be rescaled individually to generate a uniformly distributed set of events in time and the space of spike marks. After rescaling, multiple well-established goodness-of-fit procedures and statistical tests are available. We demonstrate the application of these methods both to simulated data and real population spiking in rat hippocampus. We have made the MATLAB and Python code used for the analyses in this paper publicly available through our Github repository at https://github.com/Eden-Kramer-Lab/popTRT .

Is it possible to robustly encode information from a population of neurons that rarely provide information? Neurons within the rat hippocampus and entorhinal cortex (EC) have been shown to possess spatial receptive fields that modulate their firing properties under different sets of behavioral or experimental conditions, termed contexts. Recent studies have identified cells in these regions that show changes in trial-to-trial firing rate variability as a function of experimental context. How then, could changes in variability, not observable in a single trial be useful to information processing in the brain? We propose a scenario in which individual neurons with high trial-to-trial variability each provide substantive information over small subsets of trials, which are distributed so that a population of the cells could robustly encode context across all trials. In this chapter, we explore the nature of trial-to-trail variability, and seek to verify our hypothesis by developing a decoding algorithm that predicts context from spiking data using a model characterizing changes in the full distribution of firing rate structure across trials. We compare this algorithm to another decoding procedure that accounts for only changes in mean firing rate. We apply these decoding algorithms to and experimental spiking data, and show that information is combined from many sparsely encoding cells within CA1 and dcMEC ensembles to robustly encode context.

In the past three decades, we have seen a massive expansion in our ability to record neural activity: from many more neurons, from multiple brain areas, and across multiple spatial and temporal scales. As a result, scientific investigation is limited in many cases not by the availability of data but by the availability of statistical and analysis tools. In particular, making use of such complex datasets to understand the mechanisms and effects of neural phenomena requires integration of multiple information sources. The state-space approach, whose application to complex neural phenomena has been pioneered by Emery Brown and his colleagues, provides a natural statistical modeling approach for integrating information across multiple sources and scales, for discovering low-dimensional representations of behavioral and cognitive states, and for expressing confidence about estimates and inferences. In this chapter, we will review the fundamental features of the state-space paradigm, discuss successful applications of the paradigm to various neural data analysis problems, and present a novel extension of these methods to better understand the phenomenon of hippocampal “ripple-replay” events. These events are defined by high-frequency oscillations in the local field potential (LFP), called ripples, during which neural spike sequences correspond to those seen during previous experience, or replay; their analysis therefore requires integration of neural information at multiple scales. Specifically, we will discuss a semi-latent state-space model that combines information from a rat’s movement, LFP, and ensemble hippocampal spiking to simultaneously identify periods of ripple-replay and decode its content in real time.

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm., National Science Foundation (U.S.) (Grant DMS-1127914), National Institute of Mental Health (U.S.) (Grant MH064537)

To understand neural activity, two broad categories of models exist: statistical and dynamical. While statistical models possess rigorous methods for parameter estimation and goodness-of-fit assessment, dynamical models provide mechanistic insight. In general, these two categories of models are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input current. We then fit these spike train data with a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured.

Segregation and integration are distinctive features of large-scale brain activity. Although neuroimaging studies have been unraveling their neural correlates, how integration takes place over segregated modules remains elusive. Central to this problem is the mechanism by which a brain region adjusts its activity according to the influence it receives from other regions. In this study, we explore how dynamic connectivity between two regions affects the neural activity within a participating region. Combining functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in the same group of subjects, we analyzed resting-state data from the core of the default-mode network. We observed directed influence from the posterior cingulate cortex (PCC) to the anterior cingulate cortex (ACC) in the 10-Hz range. This time-varying influence was associated with the power alteration in the ACC: strong influence corresponded with a decrease of power around 13–16 Hz and an increase of power in the lower (1–7 Hz) and higher (30–55 Hz) ends of the spectrum. We also found that the amplitude of the 30- to 55-Hz activity was coupled to the phase of the 3- to 4-Hz activity in the ACC. These results characterized the local spectral changes associated with network interactions. The specific spectral information both highlights the functional roles of PCC–ACC connectivity in the resting state and provides insights into the dynamic relationship between local activity and coupling dynamics of a network.

In 2013, the US National Oceanographic and Atmospheric Agency refined the historical rainfall estimates over the African Continent and produced the African Rainfall Climate version 2.0 (ARC2) estimator. ARC2 offers a nearly complete record of daily rainfall estimates since 1983 at 0.1 x 0.1 degree resolution. Despite short-term anomalies in twice-yearly rainy season intensities in Uganda, we identify an overall decrease in average rainfall of about 12% during the past 34 years. Spatiotemporally, these decreases are greatest in agricultural regions of central and western Uganda, but are also reflective of rainfall decreases in the gorilla habitat within the Bwindi Forest in Southwest Uganda. The findings carry significant implications for agriculture, food security, and wildlife habitat., 19 pages, 5 figures

A number of studies have examined the theta-rhythmic modulation of neuronal firing in the hippocampal circuit. For extracellular recordings, this is often done by examining spectral properties of the spike-time autocorrelogram, most significantly, for validating the presence or absence of theta modulation across species. These techniques can show significant rhythmicity for high firing rate, highly rhythmic neurons; however, they are substantially biased by several factors including the peak firing rate of the neuron, the amount of time spent in the neuron's receptive field, and other temporal properties of the rhythmicity such as cycle-skipping. These limitations make it difficult to examine rhythmic modulation in neurons with low firing rates or when an animal has short dwell times within the firing field and difficult to compare rhythmicity under disparate experimental conditions when these factors frequently differ. Here, we describe in detail the challenges that researchers face when using these techniques and apply our findings to recent recordings from bat entorhinal grid cells, suggesting that they may have lacked enough data to examine theta rhythmicity robustly. We describe a more sensitive and statistically rigorous method using maximum likelihood estimation (MLE) of a parametric model of the lags within the autocorrelation window, which helps to alleviate some of the problems of traditional methods and was also unable to detect rhythmicity in bat grid cells. Using large batteries of simulated data, we explored the boundaries for which the MLE technique and the theta index can detect rhythmicity. The MLE technique is less sensitive to many features of the autocorrelogram and provides a framework for statistical testing to detect rhythmicity as well as changes in rhythmicity in individual sessions providing a substantial improvement over previous methods.

The rat hippocampus and entorhinal cortex have been shown to possess neurons with place fields that modulate their firing properties under different behavioral contexts. Such context-dependent changes in neural activity are commonly studied through electrophysiological experiments in which a rat performs a continuous spatial alternation task on a T-maze. Previous research has analyzed context-based differential firing during this task through the characterization of differences in the mean firing activity between left-turn and right-turn experimental trials. In this paper, we draw upon findings that demonstrate considerable trial-to-trial variability of neural activity in these regions and broaden the definition of differential firing to include context-dependent changes in the stochastic structure of the trial-to-trial rate variability. We develop qualitative and quantitative methods to characterize and compare changes in trial-to-trial variability in the CA1 region of hippocampus and in the dorsocaudal medial entorhinal cortex (dcMEC) between turn direction contexts during a spatial alternation task on a T-maze. We identify a subset of cells with context-dependent changes in firing rate variability. Additionally, we show that dcMEC populations encode turn direction uniformly throughout the T-maze stem, whereas CA1 populations encode context at major waypoints in the spatial trajectory. Our results suggest scenarios in which individual cells that sparsely provide information on turn direction might combine in the aggregate to produce a robust population encoding.

Background Brain voltage activity displays distinct neuronal rhythms spanning a wide frequency range. How rhythms of different frequency interact – and the function of these interactions – remains an active area of research. Many methods have been proposed to assess the interactions between different frequency rhythms, in particular measures that characterize the relationship between the phase of a low frequency rhythm and the amplitude envelope of a high frequency rhythm. However, an optimal analysis method to assess this cross-frequency coupling (CFC) does not yet exist. New method Here we describe a new procedure to assess CFC that utilizes the generalized linear modeling (GLM) framework. Results We illustrate the utility of this procedure in three synthetic examples. The proposed GLM-CFC procedure allows a rapid and principled assessment of CFC with confidence bounds, scales with the intensity of the CFC, and accurately detects biphasic coupling. Comparison with existing methods Compared to existing methods, the proposed GLM-CFC procedure is easily interpretable, possesses confidence intervals that are easy and efficient to compute, and accurately detects biphasic coupling. Conclusions The GLM-CFC statistic provides a method for accurate and statistically rigorous assessment of CFC.

The UK MRC ALL97 trial included a randomised comparison of the efficacy and toxicity of dexamethasone and thioguanine (TG, experimental arms) with prednisolone and mercaptopurine (MP). The cytotoxic effects of MP and TG are mediated, in part, by thioguanine nucleotide (TGN) metabolites. TGNs are formed directly from TG whereas MP produces intermediate metabolites which are substrates for the enzyme thiopurine methyltransferase (TPMT). TPMT is under the control of a common genetic polymorphism. Both drugs are good substrates for TPMT. Both drugs undergo oxidative metabolism, but TG requires prior deamination. Consecutive children with ALL, diagnosed in the United Kingdom and Ireland between April 1997 and June 2002, were randomised to receive either TG or MP in maintenance courses. All patients received TG in intensification courses. Toxicity data was collected by an adverse event reporting system with follow-up questionnaires to seek detailed information on specific toxicities. Red blood cell (RBC) TGN concentrations and TPMT activities were measured by standard techniques in specific sub-groups. Of 1498 randomised patients, 750 were allocated TG and 748 MP. The trial produced a 5 year event-free survival (EFS) of 80%, with no difference in EFS or overall survival between thiopurine arms. However, TG was associated with an acute hepatitis syndrome with features of veno-occlusive disease (VOD) in 95 patients. Eighty-two of these were in the randomised TG arm (11% of TG recipients), the remaining were patients allocated MP who developed VOD during TG intensification courses. On long term follow-up, 43 (5.7%) TG recipients developed persistent splenomegaly and mild to moderate thrombocytopenia due to portal hypertension. TPMT activities were measured in 73 of the 95 VOD children at a standard protocol dose of thiopurine. For comparison, TPMT activities were measured in children who did not develop VOD or splenomegaly; there was no difference in the range of TPMT activities measured at 40mg/m2 TG (n = 83, median 15.4 units/ml RBCs) compared to 75mg/m2 MP (n = 78, median 15.2 units). Median TPMT activities in these 161 children, taking thiopurines at the protocol standard dose, were 15.2 units (range 5.8 to 23) compared to a median of 13.4 units (range 5.8 to 23) in the 73 VOD children (median difference 1.8 units, 95% CI 0.9 to 2.7, p < 0.001). A similar difference in TPMT activity was also seen when comparing persistent splenomegaly cases with controls. There was no difference in accumulation of TGNs at comparable doses. When used for maintenance therapy, TG causes liver toxicity in a significant minority of children with ALL without improving long term survival. An association with polymorphic variations in TPMT levels, a critical enzyme in thiopurine metabolism, implicates inter-individual variations in metabolism of the drug as a mechanism for liver damage, and points to a possible means of identifying at-risk patients. MP should remain the standard thiopurine for maintenance therapy unless longer follow-up reveals a survival advantage in favour of TG, and the mechanisms of liver toxicity are understood to avoid exposure of at risk patients.

Over the past two decades, the increased ability to analyze network relationships among neural structures has provided novel insights into brain function. Most network approaches, however, focus on static representations of the brain's physical or statistical connectivity. Few studies have examined how brain functional networks evolve spontaneously over long epochs of continuous time. To address this, we examine functional connectivity networks deduced from continuous long-term electrocorticogram recordings. For a population of six human patients, we identify a persistent pattern of connections that form a frequency-band-dependent network template, and a set of core connections that appear frequently and together. These structures are robust, emerging from brief time intervals (∼100 s) regardless of cognitive state. These results suggest that a metastable, frequency-band-dependent scaffold of brain connectivity exists from which transient activity emerges and recedes.

We develop a general likelihood-based framework for use in the estimation of neural firing rates, which is designed to choose the temporal smoothing parameters that maximize the likelihood of missing data. This general framework is algorithm-independent and thus can be applied to a multitude of established methods for firing rate or conditional intensity estimation. As a simple example of the use of the general framework, we apply it to the peristimulus time histogram and kernel smoother, the methods most widely used for firing rate estimation in the electrophysiological literature and practice. In doing so, we illustrate how the use of the framework can employ the general point process likelihood as a principled cost function and can provide substantial improvements in estimation accuracy for even the most basic of rate estimation algorithms. In particular, the resultant kernel smoother is simple to implement, efficient to compute, and can accurately determine the bandwidth of a given rate process from individual spike trains. We perform a simulation study to illustrate how the likelihood framework enables the kernel smoother to pick the bandwidth parameter that best predicts missing data, and we show applications to real experimental spike train data. Additionally, we discuss how the general likelihood framework may be used in conjunction with more sophisticated methods for firing rate and conditional intensity estimation and suggest possible applications.

The coherence between neural spike trains and local-field potential recordings, called spike-field coherence, is of key importance in many neuroscience studies. In this work, aside from questions of estimator performance, we demonstrate that theoretical spike-field coherence for a broad class of spiking models depends on the expected rate of spiking. This rate dependence confounds the phase locking of spike events to field-potential oscillations with overall neuron activity and is demonstrated analytically, for a large class of stochastic models, and in simulation. Finally, the relationship between the spike-field coherence and the intensity field coherence is detailed analytically. This latter quantity is independent of neuron firing rate and, under commonly found conditions, is proportional to the probability that a neuron spikes at a specific phase of field oscillation. Hence, intensity field coherence is a rate-independent measure and a candidate on which to base the appropriate statistical inference of spike field synchrony.

The hippocampus is critical to remembering the flow of events in distinct experiences and, in doing so, bridges temporal gaps between discontiguous events. Here we report a robust hippocampal representation of sequence memories, highlighted by “time cells” that encode successive moments during an empty temporal gap between the key events, while at the same times encoding location and ongoing behavior. Furthermore, just as most place cells “remap” when a salient spatial cue is altered, most time cells form qualitatively different representations (“re-time”) when the main temporal parameter is altered. Hippocampal neurons also differentially encode the key events and disambiguate different event sequences to compose unique, temporally organized representations of specific experiences. These findings suggest that hippocampal neural ensembles segment temporally organized memories much the same as they represent locations of important events in spatially defined environments.

Background: World Child Cancer (WCC) has been working in partnership with pediatric oncology programs in low-middle income countries (LMICs) to support improved services for children with cancer. Central to the success of services is the development of effective shared-care networks situated to match population centers. Literature on how to develop shared-care networks in LMICs does not currently exist. Aim: Modeling sustainable national, regional and local health systems based on childhood cancer shared-care networks in LMICs. Methods: The model was developed through learning from a 3 year UK Government (DFID) funded program in Ghana and Bangladesh and lessons shared from WCC-funded programs in Myanmar and the Philippines. A workshop was held focusing on lessons learned from practitioners representing shared-care networks in different stages of development to identify key elements and steps necessary to build a shared-care network. Results: The overarching themes of the model are; good communication, health partnerships (twinning) and funding. A successful shared-care network must have a strong hub hospital at its center which requires a doctor with training and some experience in pediatric oncology, a committed multidisciplinary team, dedicated bed space, provision for training, patient data accurately recorded, essential medicines available and research opportunities accessible. A health partnership with an external developed center is beneficial. A tangible plan, developed treatment guidelines and protocols, measurable outcomes and financial support are needed for development into a center of excellence. Support would ideally be available for patients and families, to include accommodation, treatment costs, food and transport. Each shared-care center needs an interested doctor, a basic multidisciplinary team, some ward space for oncology patients and the support of the hospital administration. Patient data needs to be stored and there must be a close relationship with the hub center. A development plan is outlined and services provided should replicate the hub as well as resources allow. Major challenges include obtaining support from the hospital administration, and even more importantly, government policies and financing for such developments. Collaborative working and good communication are emphasized by using the same treatment protocols, developing two-way referral systems and sharing challenges and successes. The overarching principle of sustainability requires availability of training within the system and funding. Conclusion: This model can be shared to enable others in LMICs to access the information and inform their systems development. While the model is not exhaustive and requires further research, it represents an important first step with lessons learned from practitioners with experience. The inclusion of such practitioners in the process of developing this model is essential for sustainability.

Results from an instrument-level spectral characterization of the 21-channel High Resolution Dynamics Limb Sounder (HIRDLS) flight instrument will be presented. These data were obtained during the prelaunch calibration of HIRDLS at the University of Oxford (fall 2002). A monochromator, equipped with a controllable diffraction grating, was used to produce monochromatic light for these tests. The monochromator was housed, along with HIRDLS, in a large vacuum chamber. The monochromator was also equipped with a polarizer, which allowed data to be acquired at known polarizations for each channel. A calibration detector, with a flat spectral response, was used to measure the output from the monochromator. This paper will document the analysis procedures used to obtain a measured instrument spectral response for each channel, along with the associated error analyses for these measurements.