Magnetic resonance imaging
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The "direct detection" of neuronal activity by MRI could offer improved spatial and temporal resolution compared to the blood oxygenation level-dependent (BOLD) effect. Here we describe initial attempts to use MRI to detect directly the neuronal currents resulting from spontaneous alpha wave activity, which have previously been shown to generate the largest extracranial magnetic fields. Experiments were successfully carried out on four subjects at 3 T. ⋯ It was conservatively assumed that if oscillations occurred at the same frequency in the magnitude signal from the same region or at the same frequency in the phase or magnitude signal from other regions overlying large vessels or cerebrospinal fluid (CSF), then the phase changes were not due to neuronal activity related to alpha waves. Using these criteria the data obtained were consistent with direct detection of alpha wave activity in three of the four volunteers. No significant MR signal fluctuations due to evoked activity were identified.
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Simultaneous EEG-fMRI is a powerful tool to study spontaneous and evoked brain activity because of the complementary advantages of the two techniques in terms of temporal and spatial resolution. In recent years, a significant number of scientific works have been published on this subject. However, many technical problems related to the intrinsic incompatibility of EEG and MRI methods are still not fully solved. ⋯ Thus, timing issue must be carefully considered in order to avoid significant methodological errors. The aim of the present work is to highlight and discuss some of technical and methodological open issues associated with the combined use of EEG and fMRI. These issues are presented in the context of preliminary data regarding simultaneous acquisition of event-related evoked potentials and BOLD images during a visual odd-ball paradigm.
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Noninvasive functional studies on human spinal cord by means of magnetic resonance imaging (MRI) are gaining attention because of the promising applications in the study of healthy and injured central nervous system. The findings obtained are generally consistent with the anatomic knowledge based on invasive methods, but the origin and specificity of functional contrast is still debated. In this paper, a review of current knowledge and major issues about functional MRI (fMRI) in the human spinal cord is presented, with emphasis on the main methodological and technical problems and on forthcoming applications as clinical tool.
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Different brain imaging devices are presently available to provide images of the human functional cortical activity, based on hemodynamic, metabolic or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions are interconnected. The concept of brain connectivity plays a central role in the neuroscience, and different definitions of connectivity, functional and effective, have been adopted in literature. ⋯ The statistical analysis returned that during simulations, both SEM and DTF estimators were able to correctly estimate the imposed connectivity patterns under reasonable operative conditions, that is, when data exhibit an SNR of at least 3 and a LENGTH of at least 75 s of nonconsecutive EEG recordings at 64 Hz of sampling rate. Hence, effective and functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in any practical EEG recordings, by combining high-resolution EEG techniques and linear inverse estimation with SEM or DTF methods. We conclude that the estimation of cortical connectivity can be performed not only with hemodynamic measurements, but also with EEG signals treated with advanced computational techniques.
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Comparative Study
Cortex-based independent component analysis of fMRI time series.
The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies. Since only about 20% of the voxels of a typical fMRI data set lie within the cortex, statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. While such restriction does not influence conventional univariate statistical tests, it may have a substantial effect on the performance of multivariate methods. ⋯ A comparison between cbICA and conventional hypothesis-driven statistical methods shows that cortical surface maps and component TCs blindly obtained with cbICA reliably reflect task-related spatiotemporal activation patterns. Furthermore, the advantages of using cbICA when the specification of a temporal model of the expected hemodynamic response is not straightforward are illustrated and discussed. A comparison between cbICA and anatomically unconstrained ICA reveals that--beside reducing computational demand--the cortex-based approach improves the fitting of the ICA model in the gray matter voxels, the separation of cortical components and the estimation of their TCs, particularly in the case of fMRI data sets with a complex spatiotemporal statistical structure.