Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.     

Fibrin Sealants in Dura Sealing: A Systematic Literature Review

BackgroundFibrin sealants are widely used in neurosurgery to seal the suture line, provide watertight closure, and prevent cerebrospinal fluid leaks. The aim of this systematic review is to summarize the current efficacy and safety literature of fibrin sealants in dura sealing and the prevention/treatment of cerebrospinal fluid leaks.MethodsA comprehensive electronic literature search was run in the following databases: Cochrane Database of Systematic Reviews, Cochrane Central Resister of Controlled Trials, clinicaltrials.gov, MEDLINE/PubMed, and EMBASE. Titles and abstracts of potential articles of interest were reviewed independently by 3 of the authors.ResultsA total of 1006 database records and additional records were identified. After screening for duplicates and relevance, a total of 78 articles were assessed by the investigators for eligibility. Thirty-eight were excluded and the full-text of 40 articles were included in the qualitative synthesis. Seven of these included only safety data and were included in the safety assessment. The remaining 33 articles included findings from 32 studies that enrolled a total of 2935 patients who were exposed to fibrin sealant. Among these 33 studies there were only 3 randomized controlled trials, with the remaining being prospective cohort analysis, case controlled studies, prospective or retrospective case series. One randomized controlled trial, with 89 patients exposed to fibrin sealant, found a greater rate of intraoperative watertight dura closure in the fibrin sealant group than the control group (92.1% versus 38.0%, p<0.001); however, post-operative cerebrospinal fluid leakage occurred in more fibrin sealant than control patients (6.7% versus 2.0%, p>0.05). Other clinical trials evaluated the effect of fibrin sealant in the postoperative prevention of cerebrospinal fluid leaks. These were generally lower level evidence studies (ie, not prospective, randomized, controlled trials) that were not designed or powered to demonstrate a significant advantage to fibrin sealant use. Two small case series studies evaluated the effect of fibrin sealants in persistent cerebrospinal fluid leak treatment, but did not establish firm efficacy conclusions. Specific adverse reports where fibrin sealants were used for dura sealing were limited, with only 8 cases reported in neurosurgical procedures since 1987 and most reporting only a speculative relationship/association with fibrin sealant exposure.ConclusionsA major finding of this systematic literature review is that there is a paucity of randomized studies that have evaluated the effectiveness and safety of fibrin sealants in providing intraoperative watertight dura closure and post-operative cerebrospinal fluid leakage. Among the limited studies available, evidence from a single randomized, controlled trial indicates that fibrin sealants provide a higher rate of intraoperative watertight closure of the dura suture line than control, albeit with a higher rate of postoperative cerebrospinal fluid leakage. Evidence from non-randomized, controlled trials suggests that fibrin sealants may be effective in preventing cerebrospinal fluid leaks with an acceptable safety profile. There is a substantial need for randomized, controlled clinical trials or well-designed prospective observational trials where the conduct of a randomized trial is not feasible to fully assess the impact of fibrin sealant utilization on the rates of intraoperative dura closure, postoperative cerebrospinal leakage, and safety.     

A wavelet-based ECG delineation algorithm for 32-bit integer online processing

Background  

Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding.     

Unbiased and efficient log-likelihood estimation with inverse binomial sampling

The fate of scientific hypotheses often relies on the ability of a computational model to explain the data, quantified in modern statistical approaches by the likelihood function. The log-likelihood is the key element for parameter estimation and model evaluation. However, the log-likelihood of complex models in fields such as computational biology and neuroscience is often intractable to compute analytically or numerically. In those cases, researchers can often only estimate the log-likelihood by comparing observed data with synthetic observations generated by model simulations. Standard techniques to approximate the likelihood via simulation either use summary statistics of the data or are at risk of producing substantial biases in the estimate. Here, we explore another method, inverse binomial sampling (IBS), which can estimate the log-likelihood of an entire data set efficiently and without bias. For each observation, IBS draws samples from the simulator model until one matches the observation. The log-likelihood estimate is then a function of the number of samples drawn. The variance of this estimator is uniformly bounded, achieves the minimum variance for an unbiased estimator, and we can compute calibrated estimates of the variance. We provide theoretical arguments in favor of IBS and an empirical assessment of the method for maximum-likelihood estimation with simulation-based models. As case studies, we take three model-fitting problems of increasing complexity from computational and cognitive neuroscience. In all problems, IBS generally produces lower error in the estimated parameters and maximum log-likelihood values than alternative sampling methods with the same average number of samples. Our results demonstrate the potential of IBS as a practical, robust, and easy to implement method for log-likelihood evaluation when exact techniques are not available.