1,721,080 research outputs found
Empirical Markov Chain Monte Carlo Bayesian analysis of fMRI data
No full textIn this work an Empirical Markov Chain Monte Carlo Bayesian approach to analyse fMRI data is proposed. The Bayesian framework is appealing since complex models can be adopted in the analysis both for the image and noise model. Here, the noise autocorrelation is taken into account by adopting an AutoRegressive model of order one and a versatile non-linear model is assumed for the task-related activation. Model parameters include the noise variance and autocorrelation, activation amplitudes and the hemodynamic response function parameters. These are estimated at each voxel from samples of the Posterior Distribution. Prior information is included by means of a 4D spatio-temporal model for the interaction between neighbouring voxels in space and time. The results show that this model can provide smooth estimates from low SNR data while important spatial structures in the data can be preserved. A simulation study is presented in which the accuracy and bias of the estimates are addressed. Furthermore, some results on convergence diagnostic of the adopted algorithm are presented. To validate the proposed approach a comparison of the results with those from a standard GLM analysis, spatial filtering techniques and a Variational Bayes approach is provided. This comparison shows that our approach outperforms the classical analysis and is consistent with other Bayesian techniques. This is investigated further by means of the Bayes Factors and the analysis of the residuals. The proposed approach applied to Blocked Design and Event Related datasets produced reliable maps of activation. © 2008 Elsevier Inc. All rights reserved
A gradiometer for the study of magnetic fileds generated by the human hearth in a magnetically unshielded environment: preliminary results
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A superconducting device coupled to a piezoelectric transducer for the detection of gravitational waves
No full textFrontoparietal cortex controls spatial attention through modulation of anticipatory alpha rhythms
No full textA dorsal frontoparietal network, including regions in intraparietal sulcus (IPS) and frontal eye field (FEF), has been hypothesized to control the allocation of spatial attention to environmental stimuli. One putative mechanism of control is the desynchronization of electroencephalography (EEG) alpha rhythms (∼8 -12 Hz) in visual cortex in anticipation of a visual target. We show that brief interference by repetitive transcranial magnetic stimulation (rTMS) with preparatory activity in right IPS or right FEF while subjects attend to a spatial location impairs identification of target visual stimuli ∼2 s later. This behavioral effect is associated with the disruption of anticipatory (prestimulus) alpha desynchronization and its spatially selective topography in parieto-occipital cortex. Finally, the disruption of anticipatory alpha rhythms in occipital cortex after right IPS- or right FEF-rTMS correlates with deficits of visual identification. These results support the causal role of the dorsal frontoparietal network in the control of visuospatial attention, and suggest that this is partly exerted through the synchronization of occipital visual neurons. Copyright © 2009 Society for Neuroscience
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