1,721,020 research outputs found
Understanding brain connectivity from EEG data by identifying systems composed of interacting sources
No full textIn understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the superimposition of underlying brain source activities volume conducted through the head. The effects of
volume conduction produce spurious interactions in the measured signals. It is fundamental to separate true
source interactions from noise and to unmix the contribution of different systems composed by interacting sources in order to understand interference mechanisms. As a prerequisite, we consider the problem of unmixing the contribution of uncorrelated sources to a measured field. This problem is equivalent to the problem of unmixing the contribution of different uncorrelated compound systems composed by interacting sources. To this end, we develop a principal
component analysis-based method, namely, the source principal component analysis (sPCA), which exploits the underlying assumption of orthogonality for sources, estimated from linear inverse methods, for the extraction of essential features in signal space. We then consider the problem of demixing the contribution of correlated sources that comprise each of the
compound systems identified by using sPCA. While the sPCA orthogonality assumption is sufficient to separate uncorrelated systems, it cannot separate the individual components within each system. To address that problem, we introduce the Minimum Overlap Component Analysis (MOCA), employing a pure spatial criterion to unmix pairs of correlates (or coherent) sources. The proposed methods are tested in simulations
and applied to EEG data from human μ and α rhythms
Cortical brain responses during passive nonpainful median nerve stimulation at low frequencies (0.5-4 Hz). An fMRI study
No full textTrigeminal activation using chemical, electrical, and mechanical stimuli
No full textTactile, proprioceptive, and nociceptive information, including also chemosensory functions are expressed in the trigeminal nerve sensory response. To study differences in the processing of different stimulus qualities, we performed a study based on functional magnetic resonance imaging. The first trigeminal branch (ophthalmic nerve) was activated by (a) intranasal chemical stimulation with gaseous CO2 which produces stinging and burning sensations, but is virtually odorless, (b) painful, but not nociceptive specific cutaneous electrical stimulation, and (c) cutaneous mechanical stimulation using air puffs. Eighteen healthy subjects participated (eight men, 10 women, mean age 31 years). Painful stimuli produced patterns of activation similar to what has been reported for other noxious stimuli, namely activation in the primary and secondary somatosensory cortices, anterior cingulate cortex, insular cortex, and thalamus. In addition, analyses indicated intensity-related activation in the prefrontal cortex which was specifically involved in the evaluation of stimulus intensity. Importantly, the results also indicated similarities between activation patterns after
intranasal chemosensory trigeminal stimulation and patterns usually found following intranasal odorous stimulation, indicating the intimate connection between these two systems in the processing of sensory information
A frontal but not parietal neural correlate of auditory consciousness
Full text linkHemodynamic correlates of consciousness were investigated in humans during the presentation of a dichotic sequence inducing illusory auditory percepts with features analogous to visual multistability. The sequence consisted of a variation of the original stimulation eliciting the Deutsch's octave illusion, created to maintain a stable illusory percept long enough to allow the detection of the underlying hemodynamic activity using functional magnetic resonance imaging (fMRI). Two specular 500 ms dichotic stimuli (400 and 800 Hz) presented in alternation by means of earphones cause an illusory segregation of pitch and ear of origin which can yield up to four different auditory percepts per dichotic stimulus. Such percepts are maintained stable when one of the two dichotic stimuli is presented repeatedly for 6 s, immediately after the alternation. We observed hemodynamic activity specifically accompanying conscious experience of pitch in a bilateral network including the superior frontal gyrus (SFG, BA9 and BA10), medial frontal gyrus (BA6 and BA9), insula (BA13), and posterior lateral nucleus of the thalamus. Conscious experience of side (ear of origin) was instead specifically accompanied by bilateral activity in the MFG (BA6), STG (BA41), parahippocampal gyrus (BA28), and insula (BA13). These results suggest that the neural substrate of auditory consciousness, differently from that of visual consciousness, may rest upon a fronto-temporal rather than upon a fronto-parietal network. Moreover, they indicate that the neural correlates of consciousness depend on the specific features of the stimulus and suggest the SFG-MFG and the insula as important cortical nodes for auditory conscious experience
Visual learning induces changes in resting-state fMRI multivariate pattern of information
Full text linkWhen measured with functional magnetic resonance imaging (fMRI) in the resting state (R-fMRI), spontaneous activity is correlated between brain regions that are anatomically and functionally related. Learning and/or task performance can induce modulation of the resting synchronization between brain regions. Moreover, at the neuronal level spontaneous brain activity can replay patterns evoked by a previously presented stimulus. Here we test whether visual learning/task performance can induce a change in the patterns of coded information in R-fMRI signals consistent with a role of spontaneous activity in representing task-relevant information. Human subjects underwent R-fMRI before and after perceptual learning on a novel visual shape orientation discrimination task. Task-evoked fMRI patterns to trained versus novel stimuli were recorded after learning was completed, and before the second R-fMRI session. Using multivariate pattern analysis on task-evoked signals, we found patterns in several cortical regions, as follows: visual cortex, V3/V3A/V7; within the default mode network, precuneus, and inferior parietal lobule; and, within the dorsal attention network, intraparietal sulcus, which discriminated between trained and novel visual stimuli. The accuracy of classification was strongly correlated with behavioral performance. Next, we measured multivariate patterns in R-fMRI signals before and after learning. The frequency and similarity of resting states representing the task/visual stimuli states increased post-learning in the same cortical regions recruited by the task. These findings support a representational role of spontaneous brain activity
EEG-fMRI Bayesian framework for neural activity estimation: A simulation study
No full textObjective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics
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