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Přeruš. str. : il., tab., grafy ; 32 cm
Zhodnotit možnosti kontrastní echokardiografie myokardu po i.v. aplikaci u nemocných se srdečním infarktem. Současné provedení dobutaminového testu, koronarografie a scintigrafie.; Assessment possibilities of myocardial ontrast echocardiography after intravenous application of cintrast agent in evaluation of viable myocardium with simultaneous dobutamine stress test in patient with myocardial infraction.
Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ4 band and low β1 band) demonstrated significant negative linear relationship (pFWE < 0.05) to the frequent stimulus and three patterns (two low δ2 and δ3 bands, and narrow θ1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ4, β1, and θ1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. NEW METHOD: We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during "resting-state", visual oddball and semantic decision paradigms. RESULTS: The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. COMPARISON WITH EXISTING METHOD(S): Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. CONCLUSIONS: This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.
- MeSH
- dospělí MeSH
- elektroencefalografie metody MeSH
- funkční zobrazování neurálních procesů metody MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- mladý dospělý MeSH
- nervová síť diagnostické zobrazování fyziologie MeSH
- neurovaskulární vazba fyziologie MeSH
- psycholingvistika MeSH
- velký mozek diagnostické zobrazování fyziologie MeSH
- zraková percepce fyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.
- MeSH
- algoritmy MeSH
- analýza hlavních komponent MeSH
- elektroencefalografie metody statistika a číselné údaje MeSH
- interpretace obrazu počítačem metody MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- mapování mozku metody MeSH
- mladý dospělý MeSH
- počítačové zpracování signálu MeSH
- psychomotorický výkon fyziologie MeSH
- reprodukovatelnost výsledků MeSH
- rozhodování fyziologie MeSH
- shluková analýza MeSH
- zraková percepce fyziologie MeSH
- Check Tag
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
OBJECTIVE: Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. APPROACH: The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. MAIN RESULTS: We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. SIGNIFICANCE: Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.
- MeSH
- časové faktory MeSH
- dospělí MeSH
- elektroencefalografie metody MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- mladý dospělý MeSH
- mozek diagnostické zobrazování fyziologie MeSH
- nervová síť diagnostické zobrazování fyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND: The paper deals with joint analysis of fMRI and scalp EEG data, simultaneously acquired during event-related oddball experiment. The analysis is based on deriving temporal sequences of EEG powers in individual frequency bands for the selected EEG electrodes and using them as regressors in the general linear model (GLM). NEW METHOD: Given the infrequent use of EEG spectral changes to explore task-related variability, we focused on the aspects of parameter setting during EEG regressor calculation and searched for such parameters that can detect task-related variability in EEG-fMRI data. We proposed a novel method that uses relative EEG power in GLM. RESULTS: Parameter, the type of power value, has a direct impact as to whether task-related variability is detected or not. For relative power, the final results are sensitive to the choice of frequency band of interest. The electrode selection also has certain impact; however, the impact is not crucial. It is insensitive to the choice of EEG power series temporal weighting step. Relative EEG power characterizes the experimental task activity better than the absolute power. Absolute EEG power contains broad spectrum component. Task-related relative power spectral formulas were derived. COMPARISON WITH EXISTING METHODS: For particular set of parameters, our results are consistent with previously published papers. Our work expands current knowledge by new findings in spectral patterns of different brain processes related to the experimental task. CONCLUSIONS: To make analysis to be sensitive to task-related variability, the parameters type of power value and frequency band should be set properly.
- MeSH
- dospělí MeSH
- elektroencefalografie MeSH
- kyslík MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- mapování mozku * MeSH
- mladý dospělý MeSH
- mozek krevní zásobení fyziologie MeSH
- mozkové vlny fyziologie MeSH
- počítačové zpracování obrazu MeSH
- spektrální analýza * MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- MeSH
- biomedicínské inženýrství MeSH
- hemodynamika MeSH
- kyslík krev MeSH
- lidé MeSH
- magnetická rezonanční tomografie * MeSH
- matematické pojmy MeSH
- matematika * MeSH
- mozek krevní zásobení MeSH
- nervové dráhy fyziologie MeSH
- počítačové zpracování obrazu * metody MeSH
- poměr signál - šum MeSH
- software MeSH
- teoretické modely MeSH
- výzkum MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
- MeSH
- diagnostické techniky oftalmologické MeSH
- glaukom * diagnóza MeSH
- lidé MeSH
- matematika MeSH
- nervová vlákna MeSH
- optická koherentní tomografie * metody MeSH
- počítačové zpracování obrazu * MeSH
- poměr signál - šum MeSH
- retina patofyziologie MeSH
- statistika jako téma MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
