Inter-subject variability of resting state brain activity explored using a data and model-driven approach in combination with EEG-FMRI (original) (raw)
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Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: Inter-subject variability
Neuroimage, 2006
Simultaneous recording of electroencephalogram/functional magnetic resonance images (EEG/fMRI) was applied to identify blood oxygenation level-dependent (BOLD) changes associated with spontaneous variations of the alpha rhythm, which is considered the hallmark of the brain resting state. The analysis was focused on inter-subject variability associated with the resting state. Data from 7 normal subjects are presented. Confirming earlier findings, three subjects showed a negative correlation between the BOLD signal and the average power time series within the alpha band (8 -12 Hz) in extensive areas of the occipital, parietal and frontal lobes. In small thalamic areas, the BOLD signal was positively correlated with the alpha power. For subjects 3 and 4, who displayed two different states during the data acquisition time, it was shown that the corresponding correlation patterns were different, thus demonstrating the state dependency of the results. In subject 5, the changes in BOLD were observed mainly in the frontal and temporal lobes. Subject 6 only showed positive correlations, thus contradicting the negative BOLD alpha power cortical correlations that were found in most subjects.
The contribution of different frequency bands of fMRI data to the correlation with EEG alpha rhythm
Brain Research, 2014
Alpha rhythm is a prominent EEG rhythm observed during resting state and is thought to be related to multiple cognitive processes. Previous simultaneous electroencephalography (EEG)/functional magnetic resonance imaging (fMRI) studies have demonstrated that alpha rhythm is associated with blood oxygen level dependent (BOLD) signals in several different functional networks. How these networks influence alpha rhythm respectively is unclear. The low-frequency oscillations (LFO) in spontaneous BOLD activity are thought to contribute to the local correlations in resting state. Recent studies suggested that either LFO or other components of fMRI can be further divided into sub-components on different frequency bands. We hypothesized that those BOLD sub-components characterized the contributions of different brain networks to alpha rhythm. To test this hypothesis, EEG and fMRI data were simultaneously recorded from 17 human subjects performing an eyes-close resting state experiment. EEG alpha rhythm was correlated with the filtered fMRI time courses at different frequency bands (0.01-0.08 Hz, 0.08-0.25 Hz, 0.01-0.027 Hz, 0.027-0.073 Hz, 0.073-0.198 Hz, and 0.198-0.25 Hz). The results demonstrated significant relations between alpha rhythm and the BOLD signals in the visual network and in the attention network at LFO band, especially at the very low frequency band (0.01-0.027 Hz).
Simultaneous EEG and fMRI of the alpha rhythm
NeuroReport, 2002
The alpha rhythm in the EEG is 8-12 Hz activity present when a subject is awake with eyes closed. In this study, we used simultaneous EEG and fMRI to make maps of regions whose MRI signal changed reliably with modulation in posterior alpha activity. We scanned 11 subjects as they rested with eyes closed. We found that increased alpha power was correlated with decreased MRI signal in multiple regions of occipital, superior temporal, inferior frontal, and cingulate cortex, and with increased signal in the thalamus and insula. These results are consistent with animal experiments and point to the alpha rhythm as an index of cortical inactivity that may be generated in part by the thalamus. These results also may have important implications for interpretation of resting baseline in fMRI studies.
BOLD correlates of Alpha and Beta EEG-rhythm during a motor task
2011
In this study, simultaneously acquired EEG and fMRI data from a motor experiment are analyzed. The motor task consists in moving the right hand and is performed by a group of healthy volunteers. The objective is to find the most adequate way to model the movement-related blood oxygen level-dependent (BOLD) response present in the fMRI data. The analysis of the fMRI data is performed using Statistical Parametric Mapping (SPM) and estimating two different models. In the first one (motor event model), the BOLD response is modeled following the time instants of the motor events. The second one (brain wave model) incorporates the dynamics of the 5 canonical EEG rhythms (α, β, γ, δ, θ) to describe the BOLD response. From the results, it can be concluded that the motor event model better describes the BOLD response related to the movement itself, but that the brain wave model is better suited to characterize the BOLD response of complementary brain processes.
Topographic Electrophysiological Signatures of fMRI Resting State Networks
PLoS ONE, 2010
Background: fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG.
Cluster analysis of resting-state fMRI time series
Functional MRI (fMRI) has become one of the leading methods for brain mapping in neuroscience. Recent advances in fMRI analysis were used to define the default state of brain activity, functional connectivity and basal activity. Basal activity measured with fMRI raised tremendous interest among neuroscientists since synchronized brain activity pattern could be retrieved while the subject rests (resting state fMRI). During recent years, a few signal processing schemes have been suggested to analyze the resting state blood oxygenation level dependent (BOLD) signal from simple correlations to spectral decomposition. In most of these analysis schemes, the question asked was which brain areas " behave " in the time domain similarly to a pre-specified ROI. In this work we applied short time frequency analysis and clustering to study the spatial signal characteristics of resting state fMRI time series. Such analysis revealed that clusters of similar BOLD fluctuations are found in the cortex but also in the white matter and sub-cortical gray matter regions (thalamus). We found high similarities between the BOLD clusters and the neuro-anatomical appearance of brain regions. Additional analysis of the BOLD time series revealed a strong correlation between head movements and clustering quality. Experiments performed with T1-weighted time series also provided similar quality of clustering. These observations led us to the conclusion that non-functional contributions to the BOLD time series can also account for symmetric appearance of signal fluctuations. These contributions may include head motions, the underling microvasculature anatomy and cellular morphology.
Human Brain Mapping, 2009
Similar to the posterior alpha rhythm, pericentral (Rolandic) EEG rhythms in the alpha and beta frequency range are referred to as ''idle rhythms'' indicating a ''resting state'' of the respective system. The precise function of these rhythms is not clear. We used simultaneous EEG-fMRI during a bimanual motor task to localize brain areas involved in Rolandic alpha and beta EEG rhythms. The identification of these rhythms in the MR environment was achieved by a blind source separation algorithm. Rhythm ''strength'', i.e. spectral power determined by wavelet analysis, inversely correlated most strongly with the fMRI-BOLD signal in the postcentral cortex for the Rolandic alpha (mu) rhythm and in the precentral cortex for the Rolandic beta rhythm. FMRI correlates of Rolandic alpha and beta rhythms were distinct from those associated with the posterior ''classical'' alpha rhythm, which correlated inversely with the BOLD signal in the occipital cortex. An inverse correlation with the BOLD signal in the respective sensory area seems to be a general feature of ''idle rhythms ''. Hum Brain Mapp 30:1168-1187, 2009. V V C 2008 Wiley-Liss, Inc. in Wiley InterScience (www. interscience.wiley.com). V V C 2008 Wiley-Liss, Inc. r Human Brain Mapping 30:1168-1187 (2009) r r fMRI of Rolandic Alpha and Beta EEG Rhythms r r 1169 r r fMRI of Rolandic Alpha and Beta EEG Rhythms r r 1171 r r Ritter et al. r r 1172 r
EEG-correlated fMRI of human alpha activity
NeuroImage, 2003
Electroencephalography-correlated functional magnetic resonance imaging (EEG/fMRI) can be used to identify blood oxygen leveldependent (BOLD) signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to spontaneous power fluctuations in the alpha rhythm (8 -12 Hz), the dominant EEG pattern during relaxed wakefulness. Thirty-two channels of EEG were recorded in 10 subjects during eyes-closed rest inside a 1.5-T magnet resonance (MR) scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 4 s. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. The average alpha power over 1-s epochs was derived at several electrode positions using a Fast Fourier Transform. The power time course was then convolved with a canonical hemodynamic response function, down-sampled, and used for statistical parametric mapping of associated signal changes in the image time series. At all electrode positions studied, a strong negative correlation of parietal and frontal cortical activity with alpha power was found. Conversely, only sparse and nonsystematic positive correlation was detected. The relevance of these findings is discussed in view of the current theories on the generation and significance of the alpha rhythm and the related functional neuroimaging findings.
Brain Sciences
Alpha is the predominant rhythm of the human electroencephalogram, but its function, multiple generators and functional coupling patterns are still relatively unknown. In this regard, alpha connectivity patterns can change between different cortical generators depending on the status of the brain. Therefore, in the light of the communication through coherence framework, an alpha functional network depends on the functional coupling patterns in a determined state. This notion has a relevance for brain-state dependent EEG-TMS because, beyond the local state, a network connectivity overview at rest could provide further and more comprehensive information for the definition of ‘instantaneous state’ at the stimulation moment, rather than just the local state around the stimulation site. For this reason, we studied functional coupling at rest in 203 healthy subjects with MEG data. Sensor signals were source localized and connectivity was studied at the Individual Alpha Frequency (IAF) bet...
NeuroImage, 2010
Similar to the occipital alpha rhythm, electroencephalographic (EEG) signals in the alpha- and beta-frequency bands can be suppressed by movement or motor imagery and have thus been thought to represent the "idling state" of the sensorimotor cortex. A negative correlation between spontaneous alpha EEG and blood-oxygen-level-dependent (BOLD) signals has been reported in combined EEG and fMRI (functional Magnetic Resonance Imaging) experiments when subjects stayed at the resting state or alternated between the resting state and a task. However, the precise nature of the task-induced alpha modulation remains elusive. It was not clear whether alpha/beta rhythm suppressions may co-vary with BOLD when conducting tasks involving varying activations of the cortex. Here, we quantified the task-evoked responses of BOLD and alpha/beta-band power of EEG directly in the cortical source domain, by using source imaging technology, and examined their covariation across task conditions in ...