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Abstract: Previous studies using functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) of the brain have found that a distributed parietal–frontal neuronal network is activated in normals during both auditory and visual oddball tasks. The common cortical regions in this network are inferior parietal lobule (IPL)/supramarginal gyrus (SMG), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). It is not clear whether the same network is activated by oddball tasks during somatosensory stimulation. The present study addressed this question by testing healthy adults as they performed a novel median-nerve oddball paradigm while undergoing magnetoencephalography (MEG). An automated multiple dipole analysis technique, the Multi-Start Spatio-Temporal (MSST) algorithm, localized multiple neuronal generators, and identified their time-courses. IPL/SMG, ACC, and DLPFC were reliably localized in the MEG median-nerve oddball responses, with IPL/SMG activation significantly preceding ACC and DLPFC activation. Thus, the same parietal–frontal neuronal network that shows activation during auditory and visual oddball tests is activated in a median-nerve oddball paradigm. Regions uniquely related to somatosensory oddball responses (e.g., primary and secondary somatosensory, dorsal premotor, primary motor, and supplementary motor areas) were also localized. Since the parietal–frontal network supports attentional allocation during performance of the task, this study may provide a novel method, as well as normative baseline data, for examining attention-related deficits in the somatosensory system of patients with neurological or psychiatric disorders. [Copyright &y& Elsevier]

A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network (WPNN) classifier can account for partial volume effects, which exist commonly in MRI, not only in the final result stage, but also in the modeling process. It adopts the self-organizing map (SOM) neural network to overly segment the input MR image, and yield reference vectors necessary for probabilistic density function (pdf) estimation. A supervised "soft" labeling mechanism based on Bayesian rule is developed, so that weighting factors can be generated along with corresponding SOM reference vectors. Tissue classification results from various algorithms are compared, and the effectiveness and robustness of the proposed approach are demonstrated. [ABSTRACT FROM AUTHOR]

The orienting of attention to different locations in space is fundamental to most organisms and occurs in all sensory modalities. Orienting has been extensively studied in vision, but to date, few studies have investigated neuronal networks underlying automatic orienting of attention and inhibition of return to auditory signals. In the current experiment, functional magnetic resonance imaging and behavioral data were collected while healthy volunteers performed an auditory orienting task in which a monaurally presented tone pip (cue) correctly or incorrectly cued the location of a target tone pip. The stimulus onset asynchrony (SOA) between the cue and target was 100 or 800 msec. Behavioral results were consistent with previous studies showing that valid auditory cues produced facilitation at the short SOA and inhibition of return at the long SOA. Functional results indicated that the reorienting of attention (100 msec SOA) and inhibition of return (800 msec SOA) were mediated by both common and distinct neuronal structures. Both attention mechanisms commonly activated a network consisting of fronto-oculomotor areas, the left postcentral gyrus, right premotor area, and bilateral tonsil of the cerebellum. Several distinct areas of frontal and parietal activation were identified for the reorienting condition, whereas the right inferior parietal lobule was the only structure uniquely associated with inhibition of return. [ABSTRACT FROM AUTHOR]

A novel hybrid algorithm for the tissue segmentation of brain magnetic resonance images is proposed. The core of the algorithm is a probabilistic neural network (PNN) in which weighting factors are added to the summation layer, such that partial volume effects can be taken into account in the modeling process. The mean vectors for the probability density function estimation and the corresponding weighting factors are generated by a hierarchical scheme involving a self-organizing map neural network and an expectation maximization algorithm. Unlike conventional PNN, this approach circumvents the need for training sets. Tissue segmentation results from various algorithms are compared and the effectiveness and robustness of the proposed approach are demonstrated. [ABSTRACT FROM AUTHOR]

A novel hybrid algorithm for the tissue segmentation of brain magnetic resonance images is proposed. The core of the algorithm is a probabilistic neural network (PNN) in which weighting factors are added to the summation layer, such that partial volume effects can be taken into account in the modeling process. The mean vectors for the probability density function estimation and the corresponding weighting factors are generated by a hierarchical scheme involving a self-organizing map neural network and an expectation maximization algorithm. Unlike conventional PNN, this approach circumvents the need for training sets. Tissue segmentation results from various algorithms are compared and the effectiveness and robustness of the proposed approach are demonstrated. [ABSTRACT FROM AUTHOR]

Our perception of time depends on multiple psychological processes that allow us to anticipate events. In this study, we used event-related functional magnetic resonance imaging (fMRI) to differentiate neural systems involved in formulating representations of time from processes associated with making decisions about their duration. A time perception task consisting of two randomly presented standard intervals was used to ensure that intervals were encoded on each trial and to enhance memory requirements. During the encoding phase of a trial, activation was observed in the right caudate nucleus, right inferior parietal cortex and left cerebellum. Activation in these regions correlated with timing sensitivity (coefficient of variation). In contrast, encoding-related activity in the right parahippocampus and hippocampus correlated with the bisection point and right precuneus activation was associated with a measure of memory distortion. Decision processes were studied by examining brain activation during the decision phase of a trial that was associated with the difficulty of interval discriminations. Activation in the right parahippocampus was greater for easier than harder discriminations. In contrast, activation was greater for harder than easier discriminations in systems involved in working memory (left middle-frontal and parietal cortex) and auditory rehearsal (left inferior-frontal and superior-temporal cortex). Activity in the auditory rehearsal network correlated with memory distortion. Our results support the independence of systems that mediate interval encoding and decision processes. The results also suggest that distortions in memory for time may be due to strategic processing in cortical systems involved in either encoding or rehearsal. [Copyright &y& Elsevier]