Electrical neuroimaging reveals intensity-dependent activation of human cortical gustatory and somatosensory areas by electric taste.

To analyze the neural basis of electric taste we performed electrical neuroimaging analyses of event-related potentials (ERPs) recorded while participants received electrical pulses to the tongue. Pulses were presented at individual taste threshold to excite gustatory fibers selectively without concomitant excitation of trigeminal fibers and at high intensity evoking a prickling and, thus, activating trigeminal fibers. Sour, salty and metallic tastes were reported at both intensities while clear prickling was reported at high intensity only. ERPs exhibited augmented amplitudes and shorter latencies for high intensity. First activations of gustatory areas (bilateral anterior insula, medial orbitofrontal cortex) were observed at 70-80ms. Common somatosensory regions were more strongly, but not exclusively, activated at high intensity. Our data provide a comprehensive view on the dynamics of cortical processing of the gustatory and trigeminal portions of electric taste and suggest that gustatory and trigeminal afferents project to overlapping cortical areas.

High-density electric source imaging (esi) in focal epilepsy: a prospective study of 150 operated patients

Purpose: EEG is mandatory in the diagnosis of the epilepsy syndrome. However, its potential as imaging tool is still under estimated. In the present study, we aim to determine the prerequisites of maximal benefit of electric source imaging (ESI) to localize the irritative zone in patients with focal epilepsy. Methods: One hundred fifty patients suffering from focal epilepsy and with minimum 1 year postoperative follow-up were studied prospectively and blinded to the underlying diagnosis. We evaluated the influence of two important factors on sensitivity and specificity of ESI: the number of electrodes (low resolution, LR-ESI: <30 versus high resolution, HR-ESI: 128-256 electrodes), and the use of individual MRI (i-MRI) versus template MRI (t-MRI) as the head model. Findings: ESI had a sensitivity of 85% and a specificity of 87% when HR-ESI with i-MRI was used. Using LR-ESI, sensitivity decreased to 68%, or even 57% when only t-MRI was available. The sensitivity of HR-ESI/i-MRI compared favorably with those of MRI (76%), PET (69%) and ictal/interictal SPECT (64%). Interpretation: This study on a large patient group shows excellent sensitivity and specificity of ESI if 128 EEG channels or more are used for ESI and if the results are coregistered to the patient's individual MRI. Localization precision is as high as or even higher than established brain imagery techniques. HR-ESI appears to be a valuable additional imaging tool, given that larger electrode arrays are easily and rapidly applied with modern EEG equipment and that structural MRI is nearly always available for these patients.

Statistical discrimination of steroid profiles in doping control with support vector machines.

Due to their performance enhancing properties, use of anabolic steroids (e.g. testosterone, nandrolone, etc.) is banned in elite sports. Therefore, doping control laboratories accredited by the World Anti-Doping Agency (WADA) screen among others for these prohibited substances in urine. It is particularly challenging to detect misuse with naturally occurring anabolic steroids such as testosterone (T), which is a popular ergogenic agent in sports and society. To screen for misuse with these compounds, drug testing laboratories monitor the urinary concentrations of endogenous steroid metabolites and their ratios, which constitute the steroid profile and compare them with reference ranges to detect unnaturally high values. However, the interpretation of the steroid profile is difficult due to large inter-individual variances, various confounding factors and different endogenous steroids marketed that influence the steroid profile in various ways. A support vector machine (SVM) algorithm was developed to statistically evaluate urinary steroid profiles composed of an extended range of steroid profile metabolites. This model makes the interpretation of the analytical data in the quest for deviating steroid profiles feasible and shows its versatility towards different kinds of misused endogenous steroids. The SVM model outperforms the current biomarkers with respect to detection sensitivity and accuracy, particularly when it is coupled to individual data as stored in the Athlete Biological Passport.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

Molecular machines governing exocytosis of synaptic vesicles.

Calcium-dependent exocytosis of synaptic vesicles mediates the release of neurotransmitters. Important proteins in this process have been identified such as the SNAREs, synaptotagmins, complexins, Munc18 and Munc13. Structural and functional studies have yielded a wealth of information about the physiological role of these proteins. However, it has been surprisingly difficult to arrive at a unified picture of the molecular sequence of events from vesicle docking to calcium-triggered membrane fusion. Using mainly a biochemical and biophysical perspective, we briefly survey the molecular mechanisms in an attempt to functionally integrate the key proteins into the emerging picture of the neuronal fusion machine.