Magnetic resonance imaging
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Correlated fluctuations of low-frequency fMRI signal have been suggested to reflect functional connectivity among the involved regions. However, large-scale correlations are especially prone to spurious global modulations induced by coherent physiological noise. ⋯ In this paper, the performances of the denoising approach based on general linear fitting of global signals of noninterest extracted from the functional scans were assessed. Results suggested that this approach is sufficiently accurate for the preprocessing of functional connectivity data.
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Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent brain activity in terms of a reliable anatomical localization and a detailed temporal evolution of neural signals. Simultaneous EEG-fMRI recordings offer the possibility to greatly enrich the significance and the interpretation of the single modality results because the same neural processes are observed from the same brain at the same time. Nonetheless, the different physical nature of the measured signals by the two techniques renders the coupling not always straightforward, especially in cognitive experiments where spatially localized and distributed effects coexist and evolve temporally at different temporal scales. ⋯ We illustrate this framework in a graded-load working-memory simultaneous EEG-fMRI experiment based on the n-back task where sustained load-dependent changes in the blood-oxygenation-level-dependent (BOLD) signals during continuous item memorization co-occur with parametric changes in the EEG theta power induced at each single item. In line with previous studies, we demonstrate on two single-subject cases how the presented approach is capable of colocalizing in midline frontal regions two phenomena simultaneously observed at different temporal scales, such as the sustained negative changes in BOLD activity and the parametric EEG theta synchronization. We discuss the presented approach in relation to modeling and interpretation issues typically arising in simultaneous EEG-fMRI studies.
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Comparative Study
Comparison of pulsed arterial spin labeling encoding schemes and absolute perfusion quantification.
Arterial spin labeling (ASL) using magnetic resonance imaging (MRI) is a powerful noninvasive technique to investigate the physiological status of brain tissue by measuring cerebral blood flow (CBF). ASL assesses the inflow of magnetically labeled arterial blood into an imaging voxel. In the last 2 decades, various ASL sequences have been proposed which differ in their ease of implementation and their sensitivity to artifacts. ⋯ We conclude that, although all quantitative ASL sequences and CBF calibration methods should in principle result in the similar CBF values and image quality, substantial differences in CBF values and SNR were found. Thus, comparing studies using different ASL sequences and analysis algorithms is likely to result in erroneous intra- and intergroup differences. Therefore, (i) the same quantification schemes should consistently be used, and (ii) quantification using local tissue proton density should yield the most accurate CBF values because, although still requiring definitive demonstration in future studies, the proton density of blood is assumed to be very similar to the value of gray matter.
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Despite intense research on the blood oxygenation level-dependent (BOLD) signal underlying functional magnetic resonance imaging, our understanding of its physiological basis is far from complete. In this study, it was investigated whether the so-called poststimulus BOLD signal undershoot is solely a passive vascular effect or actively induced by neural responses. Prolonged static and flickering black-white checkerboard stimulation with isoluminant grey screen as baseline condition were employed on eight human subjects. ⋯ Thus, a passive blood volume effect as the only contributor to the poststimulus undershoot comes short in explaining the BOLD poststimulus undershoot phenomenon for this particular experiment. Rather, an additional active neuronal activation or deactivation can strongly modulate the BOLD poststimulus behavior. In summary, the poststimulus time course of BOLD signal could potentially be used to differentiate neuronal activity patterns that are otherwise indistinguishable using the positive evoked response.