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Stop-event-related potentials from intracranial electrodes reveal a key role of premotor and motor cortices in stopping ongoing movements

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      Abstract

      In humans, the ability to withhold manual motor responses seems to rely on a right-lateralized frontal–basal ganglia–thalamic network, including the pre-supplementary motor area and the inferior frontal gyrus (IFG). These areas should drive subthalamic nuclei to implement movement inhibition via the hyperdirect pathway. The output of this network is expected to influence those cortical areas underlying limb movement preparation and initiation, i.e., premotor (PMA) and primary motor (M1) cortices. Electroencephalographic (EEG) studies have shown an enhancement of the N200/P300 complex in the event-related potentials (ERPs) when a planned reaching movement is successfully stopped after the presentation of an infrequent stop-signal. PMA and M1 have been suggested as possible neural sources of this ERP complex but, due to the limited spatial resolution of scalp EEG, it is not yet clear which cortical areas contribute to its generation. To elucidate the role of motor cortices, we recorded epicortical ERPs from the lateral surface of the fronto-temporal lobes of five pharmacoresistant epileptic patients performing a reaching version of the countermanding task while undergoing presurgical monitoring. We consistently found a stereotyped ERP complex on a single-trial level when a movement was successfully cancelled. These ERPs were selectively expressed in M1, PMA, and Brodmann's area (BA) 9 and their onsets preceded the end of the stop process, suggesting a causal involvement in this executive function. Such ERPs also occurred in unsuccessful-stop (US) trials, that is, when subjects moved despite the occurrence of a stop-signal, mostly when they had long reaction times (RTs). These findings support the hypothesis that motor cortices are the final target of the inhibitory command elaborated by the frontal–basal ganglia–thalamic network.

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      Automated Talairach atlas labels for functional brain mapping.

      An automated coordinate-based system to retrieve brain labels from the 1988 Talairach Atlas, called the Talairach Daemon (TD), was previously introduced [Lancaster et al., 1997]. In the present study, the TD system and its 3-D database of labels for the 1988 Talairach atlas were tested for labeling of functional activation foci. TD system labels were compared with author-designated labels of activation coordinates from over 250 published functional brain-mapping studies and with manual atlas-derived labels from an expert group using a subset of these activation coordinates. Automated labeling by the TD system compared well with authors' labels, with a 70% or greater label match averaged over all locations. Author-label matching improved to greater than 90% within a search range of +/-5 mm for most sites. An adaptive grey matter (GM) range-search utility was evaluated using individual activations from the M1 mouth region (30 subjects, 52 sites). It provided an 87% label match to Brodmann area labels (BA 4 & BA 6) within a search range of +/-5 mm. Using the adaptive GM range search, the TD system's overall match with authors' labels (90%) was better than that of the expert group (80%). When used in concert with authors' deeper knowledge of an experiment, the TD system provides consistent and comprehensive labels for brain activation foci. Additional suggested applications of the TD system include interactive labeling, anatomical grouping of activation foci, lesion-deficit analysis, and neuroanatomy education.
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        Fast and robust fixed-point algorithms for independent component analysis.

         A Hyvärinen (1999)
        Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.
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          Transformed up-down methods in psychoacoustics.

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            Author and article information

            Affiliations
            1simpleDepartment of Technologies and Health, Istituto Superiore di Sanità, Viale Regina Elena Rome, Italy
            2simpleIRCCS Neuromed, Via Atinense Pozzilli (IS), Italy
            3simplePhD Program in Neurophysiology, Department of Physiology and Pharmacology, University of Rome La Sapienza Piazzale Aldo Moro Rome, Italy
            4simpleDepartment of Physiology and Pharmacology, University of Rome La Sapienza, Piazzale Aldo Moro Rome, Italy
            Author notes

            Edited by: Laura Ballerini, University of Trieste, Italy

            Reviewed by: Hari S. Sharma, Uppsala University, Sweden; Liang Guo, Massachusetts Institute of Technology, USA

            *Correspondence: G. Mirabella, Department of Physiology and Pharmacology “V. Erspamer”, La Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy. e-mail: giovanni.mirabella@ 123456uniroma1.it
            Journal
            Front Neuroeng
            Front Neuroeng
            Front. Neuroeng.
            Frontiers in Neuroengineering
            Frontiers Media S.A.
            1662-6443
            28 May 2012
            29 June 2012
            2012
            : 5
            3386527
            22754525
            10.3389/fneng.2012.00012
            Copyright © 2012 Mattia, Spadacenta, Pavone, Quarato, Esposito, Sparano, Sebastiano, Di Gennaro, Morace, Cantore and Mirabella.

            This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

            Counts
            Figures: 5, Tables: 2, Equations: 2, References: 73, Pages: 13, Words: 10548
            Categories
            Neuroscience
            Original Research Article

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