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In this paper, we present a novel Bayesian adaptive dual controller (ADC) for autonomously programming deep brain stimulation devices. We evaluated the Bayesian ADC’s performance in the context of reducing beta power in a computational model of Parkinson’s disease, in which it was tasked with finding the set of stimulation parameters which optimally reduced beta power as fast as possible. Here, the Bayesian ADC has dual goals: (a) to minimize beta power by exploiting the best parameters found so far, and (b) to explore the space to find better parameters, thus allowing for better control in the future. The Bayesian ADC is composed of two parts: an inner parameterized feedback stimulator and an outer parameter adjustment loop. The inner loop operates on a short time scale, delivering stimulus based upon the phase and power of the beta oscillation. The outer loop operates on a long time scale, observing the effects of the stimulation parameters and using Bayesian optimization to intelligently select new parameters to minimize the beta power. We show that the Bayesian ADC can efficiently optimize stimulation parameters, and is superior to other optimization algorithms. The Bayesian ADC provides a robust and general framework for tuning stimulation parameters, can be adapted to use any feedback signal, and is applicable across diseases and stimulator designs. [ABSTRACT FROM AUTHOR]

ABSTRACT Image denoising is an integral component of many practical medical systems. Non-local means (NLM) is an effective method for image denoising which exploits the inherent structural redundancy present in images. Improved adaptive non-local means (IANLM) is an improved variant of classical NLM based on a robust threshold criterion. In this paper, we have proposed an enhanced non-local means (ENLM) algorithm, for application to brain MRI, by introducing several extensions to the IANLM algorithm. First, a Rician bias correction method is applied for adapting the IANLM algorithm to Rician noise in MR images. Second, a selective median filtering procedure based on fuzzy c-means algorithm is proposed as a postprocessing step, in order to further improve the quality of IANLM-filtered image. Third, different parameters of the proposed ENLM algorithm are optimized for application to brain MR images. Different variants of the proposed algorithm have been presented in order to investigate the influence of the proposed modifications. The proposed variants have been validated on both T1-weighted (T1-w) and T2-weighted (T2-w) simulated and real brain MRI. Compared with other denoising methods, superior quantitative and qualitative denoising results have been obtained for the proposed algorithm. Additionally, the proposed algorithm has been applied to T2-weighted brain MRI with multiple sclerosis lesion to show its superior capability of preserving pathologically significant information. Finally, impact of the proposed algorithm has been tested on segmentation of brain MRI. Quantitative and qualitative segmentation results verify that the proposed algorithm based segmentation is better compared with segmentation produced by other contemporary techniques. [ABSTRACT FROM AUTHOR]

This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings. Section Analysis methods. Classifications Source localization: inverse problem; Source localization: other. [ABSTRACT FROM AUTHOR]

Abstract: Decoding perceptual or cognitive states based on brain activity measured using functional magnetic resonance imaging (fMRI) can be achieved using machine learning algorithms to train classifiers of specific stimuli. However, the high dimensionality and intrinsically low signal to noise ratio (SNR) of fMRI data poses great challenges to such techniques. The problem is aggravated in the case of multiple subject experiments because of the high inter-subject variability in brain function. To address these difficulties, the majority of current approaches uses a single classifier. Since, in many cases, different stimuli activate different brain areas, it makes sense to use a set of classifiers each specialized in a different stimulus. Therefore, we propose in this paper using an ensemble of classifiers for decoding fMRI data. Each classifier in the ensemble has a favorite class or stimulus and uses an optimized feature set for that particular stimulus. The output for each individual stimulus is therefore obtained from the corresponding classifier and the final classification is achieved by simply selecting the best score. The method was applied to three empirical fMRI datasets from multiple subjects performing visual tasks with four classes of stimuli. Ensembles of GNB and k-NN base classifiers were tested. The ensemble of classifiers systematically outperformed a single classifier for the two most challenging datasets. In the remaining dataset, a ceiling effect was observed which probably precluded a clear distinction between the two classification approaches. Our results may be explained by the fact that different visual stimuli elicit specific patterns of brain activation and indicate that an ensemble of classifiers provides an advantageous alternative to commonly used single classifiers, particularly when decoding stimuli associated with specific brain areas. [Copyright &y& Elsevier]

Abstract: A number of methods have been developed to assist surgeons at various stages of deep brain stimulation (DBS) therapy. These include construction of anatomical atlases, functional databases, and electrophysiological atlases and maps. But, a complete system that can be integrated into the clinical workflow has not been developed. In this paper we present a system designed to assist physicians in pre-operative target planning, intra-operative target refinement and implantation, and post-operative DBS lead programming. The purpose of this system is to centralize the data acquired a the various stages of the procedure, reduce the amount of time needed at each stage of the therapy, and maximize the efficiency of the entire process. The system consists of a central repository (CranialVault), of a suite of software modules called CRAnialVault Explorer (CRAVE) that permit data entry and data visualization at each stage of the therapy, and of a series of algorithms that permit the automatic processing of the data. The central repository contains image data for more than 400 patients with the related pre-operative plans and position of the final implants and about 10,550 electrophysiological data points (micro-electrode recordings or responses to stimulations) recorded from 222 of these patients. The system has reached the stage of a clinical prototype that is being evaluated clinically at our institution. A preliminary quantitative validation of the planning component of the system performed on 80 patients who underwent the procedure between January 2009 and December 2009 shows that the system provides both timely and valuable information. [Copyright &y& Elsevier]

Abstract: The automatic real-time detection of spike-wave discharges (SWDs), the electroencephalographic hallmark of absence seizures, would provide a complementary tool for rapid interference with electrical deep brain stimulation in both patients and animal models. This paper describes a real-time detection algorithm for SWDs based on continuous wavelet analyses in rodents. It has been implemented in a commercially available data acquisition system and its performance experimentally verified. ECoG recordings lasting 5–8h from rats (n =8) of the WAG/Rij strain were analyzed using the real-time SWD detection system. The results indicate that the algorithm is able to detect SWDs within 1s with 100% sensitivity and with a precision of 96.6% for the number of SWDs. Similar results are achieved for 24-h ECoG recordings of two rats. The dependence of accuracy and speed of detection on program settings and attributes of ECoG are discussed. It is concluded that the wavelet based real-time detecting algorithm is well suited for automatic, real-time detection of SWDs in rodents. [ABSTRACT FROM AUTHOR]

Abstract: Recently, different methods for obtaining sparse representations of a signal using dictionaries of waveforms have been studied. They are often motivated by the way the brain seems to process certain sensory signals. Algorithms have been developed using a specific criterion to choose the waveforms occurring in the representation. The waveforms are choosen from a fixed dictionary and some algorithms also construct them as a part of the method. In the case of speech signals, most approaches do not take into consideration the important temporal correlations that are exhibited. It is known that these correlations are well approximated by linear models. Incorporating this a priori knowledge of the signal can facilitate the search for a suitable representation solution and also can help with its interpretation. Lewicki proposed a method to solve the noisy and overcomplete independent component analysis problem. In the present paper we propose a modification of this statistical technique for obtaining a sparse representation using a generative parametric model. The representations obtained with the method proposed here and other techniques are applied to artificial data and real speech signals, and compared using different coding costs and sparsity measures. The results show that the proposed method achieves more efficient representations of these signals compared to the others. A qualitative analysis of these results is also presented, which suggests that the restriction imposed by the parametric model is helpful in discovering meaningful characteristics of the signals. [Copyright &y& Elsevier]

Interaction of an active electronic implant such as a deep brain stimulation (DBS) system and magnetic resonance imaging (MRI) radiofrequency (RF) fields can induce excessive tissue heating, limiting MRI accessibility. Efforts to quantify RF heating mostly rely on electromagnetic (EM) simulations to assess individualized specific absorption rate (SAR), but such simulations require extensive computational resources. Here, we investigate if a predictive model using machine learning (ML) can predict the local SAR in the tissue around tips of implanted leads from the distribution of the tangential component of the MRI incident electric field, Etan. A dataset of 260 unique patient-derived and artificial DBS lead trajectories was constructed, and the 1 g-averaged SAR, 1 g SARmax, at the lead-tip during 1.5 T MRI was determined by EM simulations. Etan values along each lead's trajectory and the simulated SAR values were used to train and test the ML algorithm. The resulting predictions of the ML algorithm indicated that the distribution of Etan could effectively predict 1 g SARmax at the DBS lead-tip (R = 0.82). Our results indicate that ML has the potential to provide a fast method for predicting MR-induced power absorption in the tissue around tips of implanted leads such as those in active electronic medical devices. [ABSTRACT FROM AUTHOR]

Oscillatory neuronal activity is implicated in many cognitive functions, and its phase coupling between sensors may reflect networks of communicating neuronal populations. Oscillatory activity is often studied using extracranial recordings and compared between experimental conditions. This is challenging, because there is overlap between sensor-level activity generated by different sources, and this can obscure differential experimental modulations of these sources. Additionally, in extracranial data, sensor-level phase coupling not only reflects communicating populations, but can also be generated by a current dipole, whose sensor-level phase coupling does not reflect source-level interactions. We present a novel method, which is capable of separating and characterizing sources on the basis of their phase coupling patterns as a function of space, frequency and time (trials). Importantly, this method depends on a plausible model of a neurobiological rhythm. We present this model and an accompanying analysis pipeline. Next, we demonstrate our approach, using magnetoencephalographic (MEG) recordings during a cued tactile detection task as a case study. We show that the extracted components have overlapping spatial maps and frequency content, which are difficult to resolve using conventional pairwise measures. Because our decomposition also provides trial loadings, components can be readily contrasted between experimental conditions. Strikingly, we observed heterogeneity in alpha and beta sources with respect to whether their activity was suppressed or enhanced as a function of attention and performance, and this happened both in task relevant and irrelevant regions. This heterogeneity contrasts with the common view that alpha and beta amplitude over sensory areas are always negatively related to attention and performance. [ABSTRACT FROM AUTHOR]

Neurons in the brain represent external stimuli via neural codes. These codes often arise from stereotyped stimulus-response maps, associating to each neuron a convex receptive field. An important problem confronted by the brain is to infer properties of a represented stimulus space without knowledge of the receptive fields, using only the intrinsic structure of the neural code. How does the brain do this? To address this question, it is important to determine what stimulus space features can—in principle—be extracted from neural codes. This motivates us to define the neural ring and a related neural ideal, algebraic objects that encode the full combinatorial data of a neural code. Our main finding is that these objects can be expressed in a “canonical form” that directly translates to a minimal description of the receptive field structure intrinsic to the code. We also find connections to Stanley–Reisner rings, and use ideas similar to those in the theory of monomial ideals to obtain an algorithm for computing the primary decomposition of pseudo-monomial ideals. This allows us to algorithmically extract the canonical form associated to any neural code, providing the groundwork for inferring stimulus space features from neural activity alone. [ABSTRACT FROM AUTHOR]

The article presents a study about control strategies for functional electrical simulation (FES) cycling. The study focused on the propagation of stimulation intensities for multiple muscles. It developed the control system by copying the biological neuronal control system and used genetic algorithm with cost function of cadence error and energy consumption. The study evaluated the proposed control system through computer simulation. By eliminating the experimental determination of the stimulation patterns, the proposed method is expected to relieve patients and improve the FES cycling performance.

In the present study, we explore suitable spatio-temporal filters for inverse estimation of an equivalent dipole-layer distribution from the scalp electroencephalogram (EEG) for imaging of brain electric sources. We propose a time-varying parametric projection filter. (tPPF) for the spatio-temporal EEG analysis. The performance of this tPPF algorithm was evaluated by computer simulation studies. An inhomogeneous three-concentric-spheres model was used in the present simulation study to represent the head volume conductor. An equivalent dipole layer was used to represent equivalently brain electric sources and estimated from the scalp potentials. The tPPF filter was tested to remove time-varying noise such as instantaneous artifacts caused by eyes-blink. The present simulation results indicate that the proposed time-variant tPPF method provides enhanced performance in rejecting time-varying noise, as compared with the time-invariant parametric projection filter. [ABSTRACT FROM AUTHOR]