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Actions expressed prematurely without regard for their consequences are considered impulsive. Such behaviour is governed by a network of brain regions including the prefrontal cortex (PFC) and nucleus accumbens (NAcb) and is prevalent in disorders including attention deficit hyperactivity disorder (ADHD) and drug addiction. However, little is known of the relationship between neural activity in these regions and specific forms of impulsive behaviour. In the present study we investigated local field potential (LFP) oscillations in distinct sub-regions of the PFC and NAcb on a 5-choice serial reaction time task (5-CSRTT), which measures sustained, spatially-divided visual attention and action restraint. The main findings show that power in gamma frequency (50-60 Hz) LFP oscillations transiently increases in the PFC and NAcb during both the anticipation of a cue signalling the spatial location of a nose-poke response and again following correct responses. Gamma oscillations were coupled to low-frequency delta oscillations in both regions; this coupling strengthened specifically when an error response was made. Theta (7-9 Hz) LFP power in the PFC and NAcb increased during the waiting period and was also related to response outcome. Additionally, both gamma and theta power were significantly affected by upcoming premature responses as rats waited for the visual cue to respond. In a subgroup of rats showing persistently high levels of impulsivity we found that impulsivity was associated with increased error signals following a nose-poke response, as well as reduced signals of previous trial outcome during the waiting period. Collectively, these in-vivo neurophysiological findings further implicate the PFC and NAcb in anticipatory impulsive responses and provide evidence that abnormalities in the encoding of rewarding outcomes may underlie trait-like impulsive behaviour.

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the "usefulness" of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing "good quality signals" or "signals of interest." This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on "signal quality." The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist's appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature "electronic depth control" in contrast to the mechanical repositioning of the electrode shafts in search of "good quality signals" or "signals of interest."

Despite our fine-grain anatomical knowledge of the cerebellar cortex, electrophysiological studies of circuit information processing over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cell types. We approached this problem by assessing the spontaneous activity signatures of identified cerebellar cortical neurones. A range of statistics describing firing frequency and irregularity were then used, individually and in combination, to build Gaussian Process Classifiers (GPC) leading to a probabilistic classification of each neurone type and the computation of equi-probable decision boundaries between cell classes. Firing frequency statistics were useful for separating Purkinje cells from granular layer units, whilst firing irregularity measures proved most useful for distinguishing cells within granular layer cell classes. Considered as single statistics, we achieved classification accuracies of 72.5% and 92.7% for granular layer and molecular layer units respectively. Combining statistics to form twin-variate GPC models substantially improved classification accuracies with the combination of mean spike frequency and log-interval entropy offering classification accuracies of 92.7% and 99.2% for our molecular and granular layer models, respectively. A cross-species comparison was performed, using data drawn from anaesthetised mice and decerebrate cats, where our models offered 80% and 100% classification accuracy. We then used our models to assess non-identified data from awake monkeys and rabbits in order to highlight subsets of neurones with the greatest degree of similarity to identified cell classes. In this way, our GPC-based approach for tentatively identifying neurones from their spontaneous activity signatures, in the absence of an established ground-truth, nonetheless affords the experimenter a statistically robust means of grouping cells with properties matching known cell classes. Our approach therefore may have broad application to a variety of future cerebellar cortical investigations, particularly in awake animals where opportunities for definitive cell identification are limited.

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to select electrodes automatically, hereby allowing to reduce the amount of data and locating those electrodes that are close to neurons. The electrodes are selected according to a new penalized signal-to-noise ratio (PSNR) criterion that demotes electrodes from becoming selected if their signals are redundant with previously selected electrodes. It is shown that, using the PSNR, interneurons generating smaller spikes are also selected. We developed a model that aims to evaluate algorithms for electronic depth control, but also generates benchmark data for testing spike sorting and spike detection algorithms. The model comprises a realistic tufted pyramidal cell, non-tufted pyramidal cells and inhibitory interneurons. All neurons are synaptically activated by hundreds of fibers. This arrangement allows the algorithms to be tested in more realistic conditions, including backgrounds of synaptic potentials, varying spike rates with bursting and spike amplitude attenuation.

The damage caused by corrosion in chemical process installations can lead to unexpected plant shutdowns and the leakage of potentially toxic chemicals into the environment. When subjected to corrosion, structural changes in the material occur, leading to energy releases as acoustic waves. This acoustic activity can in turn be used for corrosion monitoring, and even for predicting the type of corrosion. Here we apply wavelet packet decomposition to extract features from acoustic emission signals. We then use the extracted wavelet packet coefficients for distinguishing between the most important types of corrosion processes in the chemical process industry: uniform corrosion, pitting and stress corrosion cracking. The local discriminant basis selection algorithm can be considered as a standard for the selection of the most discriminative wavelet coefficients. However, it does not take the statistical dependencies between wavelet coefficients into account. We show that, when these dependencies are ignored, a lower accuracy is obtained in predicting the corrosion type. We compare several mutual information filters to take these dependencies into account in order to arrive at a more accurate prediction.

This paper presents the NeuroSelect software for managing the electronic depth control of cerebral CMOS-based microprobes for extracellular in vivo recordings. These microprobes contain up to 500 electronically switchable electrodes which can be appropriately selected with regard to specific neuron locations in the course of a recording experiment. NeuroSelect makes it possible to scan the electrodes electronically and to (re)select those electrodes of best signal quality resulting in a closed-loop design of a neural acquisition system. The signal quality is calculated by the relative power of the spikes compared with the background noise. The spikes are detected by an adaptive threshold using a robust estimator of the standard deviation. Electrodes can be selected in a manual or semi-automatic mode based on the signal quality. This electronic depth control constitutes a significant improvement for multielectrode probes, given that so far the only alternative has been the fine positioning by mechanical probe translation. In addition to managing communication with the hardware controller of the probe array, the software also controls acquisition, processing, display and storage of the neural signals for further analysis.

Objective: Assessing recovery after stroke has been so far a time consuming procedure in which trained clinicians are required. A demand for automated assessment techniques arises due to the increasing number of patients with stroke and the continuous growth of new treatment options. In this study, we investigate the applicability of isometric force and torque measurements in activity of daily living tasks to assess the functional recovery after stroke in an automated way., Methods and Materials: A new hybrid filter-wrapper feature subset technology was developed for a new mechatronic platform with the aim to identify the most important features and sensors that can distinguish normal controls from patients with stroke. We compared 3 different classification algorithms to make the distinction: k-nearest neighbors, kernel density estimation and least-squares support vector machines. Based on isometric force and torque measurements obtained from 16 patients with a first-ever ischemic or haemorrhagic stroke within the middle cerebral artery territory, we computed for each subject the probability to belong to the class of normal subjects. These probabilities were computed during a period of 6 months post-stroke to quantify the level of recovery during this period. The posterior probabilities were validated by means of a correlation study with the Lindmark modified Fugl-Meyer assessment., Results: Patients with stroke and normal controls could be distinguished with an accuracy of 98.25% by means of kernel density estimation. The posterior probability profiles had a correlation of 76.6% and 80.29% with the global score of the Lindmark modified Fugl-Meyer scale and 'part A', the upper extremity subscore, respectively. This degree of correlation was as high as obtained with supervised scoring techniques such as the Barthel index., Conclusion: This study shows that the assessment of recovery after stroke can be automated by means of posterior probability profiles due to their high correlation with the Fugl-Meyer assessment. The posterior probability profiles confirm the importance of a recovery within the first weeks after stroke to obtain a higher recovery plateau compared to later changes in recovery.

Stroke patients have a decreased ability in performing activity of daily living (ADL) tasks such as in 'drinking a glass of water', 'turning a key', 'picking up a spoon', 'lifting a bag', 'reaching a bottle' and 'lifting and carrying a bottle'. These tasks can be quantified by measuring forces and torques exerted on the objects. However, the resulting force and torque time series represent information at a very low level of abstraction and don't inform clinicians what really distinguishes patients from normal controls in performing these tasks. We conduct an extensive quantitative analysis of these tasks and derive interesting features from the time signals that characterize the differences in behavior between patients and normal controls. We show that 'drinking a glass' and 'turning a key' are the most discriminative tasks; furthermore we show that the ability or disability to synchronize the thumb and the middle finger is one of the most important features.

Stroke patients have a decreased ability in performing activity of daily living (ADL) tasks such as in "drinking a glass of water", "lifting a bag", "turning a key" and so on. Sensorimotor force and torque measurements from patients performing these standardized ADL tasks are hypothesized to give quantitative information about the recovery process. Parts of the force/torque measurements contain useful information, when related to the initiation of the movement during ADL tasks. Here we address the challenging problem of automatically extracting the movement initiation from these force/torque measurements. We will adopt a machine learning approach which relies on the statistically rigorous maximal information redundancy (MIR) criterion. This assumes that movement initiation parts of the signals are characterized by an increased redundancy in the signal. A thorough evaluation of the criterion shows that the accuracy of the criterion in movement onset detection is close to that of clinical experts.