Alpha rhythm, described by Hans Berger, is mainly recorded from the occipital cortex (OCC) of relaxed subjects with their eyes closed. Early studies indicated the thalamo-cortical circuit as the origin of alpha rhythm. Recent works suggest an additional relationship between alpha rhythm and the Default Mode Network (DMN). We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals in 36 young males asked to alternately close and open their eyes in 30-s blocks. Using an EEG source channel montage (the recorded signal was interpolated to designated source positions corresponding to certain brain regions) we found an alpha rhythm sub-activity composed of its intrinsic events, called alpha bursting segments (ABS). More ABS were observed on source channels related to the DMN than those located over the OCC. Similarly, both the beamformer source analysis and fMRI indicated that the specific ABS activity detected on the posterior cingulate cortex/precuneus (PCC) source channel was less related to the OCC than to the DMN source channels. The fMRI analysis performed using the PCC-ABS as a general linear model regressor indicated an increased blood oxygenation level-dependent signal change in DMN nodes – precuneus and prefrontal cortex. These results confirm the OCC source of alpha activity and additional specific sources of ABS in the DMN.
Alpha activity in human electroencephalography (EEG) has attracted considerable interest since it was first described by Berger (
In early animal studies, Bishop (
Simultaneous recordings of EEG and functional magnetic resonance imaging (fMRI) signals have allowed for investigation of the neuronal correlates of Berger waves in humans with great temporal and spatial resolution. This approach was first used by Goldman and colleagues (
The classical hypothesis assumes a thalamo-occipital origin of the alpha rhythm (Goldman et al.
Importantly, alpha activity is characterized by segments of stable frequency (Lansky et al.
In the light of the latest findings, we reexamined Berger’s experiment (Berger
Sixty young male adults were recruited for this study. They had no history of neuropsychiatric disorders or head injury. Subjects provided written informed consent prior to participation. The project was approved by the Ethics Committee of the Institute of Physiology and Pathology of Hearing and conformed to the Declaration of Helsinki for Medical Research Involving Human Subjects.
Data from nine subjects were excluded because their head movement exceeds 1.5 mm during the experiment. Additionally, 15 subjects were excluded from further analysis for several reasons based on visual inspection of the data. Some subjects fell asleep during recording (they did not open their eyes during the “eyes-open” condition, indicated by absence of blinking in EEG data), the EEG signal of others was distorted by non-reducible artifacts, and six subjects had no noticeable alpha frequency peak in FFT analysis (for more details see Anokhin et al.
Handedness was assessed using the Edinburgh Handedness Inventory (Oldfield
The study was performed during morning hours between 8 a.m. and noon. Prior to the EEG-fMRI experiment, all subjects were familiarized with the MR scanner environment. Before starting the fMRI session, we verified
Our implementation of the Berger experiment was similar to that described by Ben-Simon and colleagues (
EEG recordings were obtained using the Neuroscan SynAmpS system with a 64-electrode (10/10 system) Maglink cap. Two bipolar electrodes (vertical electro-oc ulogram, VEOG and electrocardiogram, ECG) were used for the purpose of artifact occurrence designation. The skin to electrode (sintered Ag/AgCl) impedance was kept below 15 kΩ for all electrodes. The signal was recorded continuously with a 10-kHz sampling rate. The clock synchronization between the MR scanner hardware and the EEG system was assured by a Neuroscan MRI/EEG Clock Synchronization Unit. During recording, the reference electrode was placed at CPz (recomputed afterwards to an average reference), and the FCz position was set as the ground electrode. We did not find any effect of the EEG equipment on fMRI outcomes during the quality assurance test.
The first step of the EEG signal analysis was removal of electrical artifacts induced by the MRI scanner gradient coils. High-voltage distortions caused by magnetic field fluctuations were removed by subtracting a moving averaged artifact waveform (averaged artifact samples=16) (Allen et al.
The balistocardiogram (BCG) was another source of EEG signal distortions that needed to be minimized (Debener et al.
For the purpose of ABS detection one of the resting state montages, the ‘DMN with Noise Sources’ montage, was used (see Fig.
Workflow of the alpha bursting segements (ABS) detection algorithm. The computation step searching for individual alpha frequency (IAF) is marked with green, the assessment of individual alpha amplitude (IAA) with yellow and the final computation part with blue.
We independently determined alpha frequency of alpha activity for each subject and each source channel following the individual alpha peak frequency method described by Klimesch (
This step was used to complement the limitation of the FFT analysis which only decomposes the EEG signal into a sum of sinusoidal functions that are characterized by frequency, amplitude (or power) and phase. The peak amplitude of a particular frequency is not fully reflected in the spectral domain of a complex signal but is also determined by the neighboring frequency bands. Since ABS is a convolution of two frequencies a precise analysis of the ABS amplitude should be performed, preferably in the time domain. For this purpose, we created a six-period sinusoid template with frequency equal to individual alpha frequency. In cases when the alpha-shaped segment was longer than the template, the algorithm produced several consecutive detections.
For estimation of ABS amplitude the EEG signal was temporally filtered with a band-pass filter (5–15 Hz). A cross-correlation was then computed between the template and the signal. Next, we extracted segments of the EEG signal with the highest correlation coefficients (>0.9). The absolute value of each segment’s extremum was determined, and the 75th percentile of all resulting values was selected as the individual alpha amplitude.
For the computation of the final ABS-like template shape the sinusoidal template that was used for individual alpha amplitude estimation was convolved with a modulation (envelope) function: a sinusoid with a period equal to half of the template length and amplitude equal to individual alpha amplitude.
Raw EEG signals were filtered with a broad band-pass filter (1–20 Hz) and a cross-correlation analysis was performed again between the selected EEG source channel signals and the calculated ABS-like template. Here the correlation coefficient threshold was set at 0.75. The additional detection criterion was applied that the mean value of the amplitude module for each detected ABS is between 50% and 150% of the individual alpha amplitude. The algorithm produced a vector containing the exact starting time points of ABS on a given source channel.
A numerical statistical analysis was performed in IBM SPSS 24 online version (
We also performed a within-subject analysis of correlation between regions assuming that events at two different regions are related if the time shift between them is not higher than ±100 ms (the 180^{o} phase shift for frequency of 10 Hz). The correlation coefficients for each subject were averaged independently for all region pairs.
Additionally, the number of detected ABS within 3 second long bins (an equivalent of the volume acquisition time using fMRI sequence) was averaged across all subjects to show the dynamics of ABS activity.
The multiple source linearly constrained minimum variance beamformer (Gross et al.
Prior to the source analysis a time-frequency analysis was performed. The EEG signal was epoched with respect to ABS start points. Each epoch interval started and ended 600 ms before and after the event marker. The beamformer analysis was performed for each subject and all ABS events obtained for the DMN source channels (PCC, mPFC, LAG, RAG). LOCC_{ABS} and ROCC_{ABS} events were combined because of an insufficient number of occurrences for a reliable beamformer computation. This result is hereinafter referred to as the occipital cortex ABS (OCC_{ABS}).
The ABS time frequency window was defined, covering the frequency range of 7–13 Hz and time range of 50–350 ms. The baseline was defined in an interval from -600 to -300 ms prior to the ABS marker. For source localization, the standardized finite element method (FEM) model, as a realistic approximation for EEG, was used with default conductivity values approximated for adults (0.33 S/m for scalp and brain tissue, 0.0042 S/m for skull tissue, and 1.79 S/m for cerebrospinal fluid).
A group statistical comparison of the beamformer findings was performed in BESA Statistics 2.0 using a one-way ANOVA with repeated measurements. The obtained results were corrected for multiple comparisons using a non-parametric permutation test with alpha cluster equal to 0.05 and 1000 permutations (corrected at
To exclude subjects with brain pathology, standard T1 and T2 sequences were applied prior to functional imaging on a 3T MR scanner (Siemens Magnetom Trio TIM). Then EEG recordings were acquired simultaneously with fMRI data. A standard 12-channel matrix head coil was used for radiofrequency (RF) signal reception. The fMRI data were obtained using a T2*-weighted gradient echo-planar imaging sequence (repetition time 3000 ms, echo time 30 ms, flip angle 90°, image matrix 96 × 96, plane field of view 192 × 192 mm), integrated parallel acquisition techniques factor=2 with 42 ascending slices [slice thickness: 3 mm, no gap] parallel to the axial plane. There were 120 volumes (plus three extra dummy scans for steady-state magnetization) collected in the total scanning time of 6 min 11 s. A trigger signal was delivered by the MR scanner to the EEG recording system at each RF pulse.
The fMRI signal analysis was performed using Statistical Parametric Mapping software (SPM12, Wellcome Trust Centre for Neuroimaging, London, UK) in MATLAB environment. Functional scans were preprocessed in the following steps: slice-time correction, head motion detection and correction, direct normalization to the Montreal Neurological Institute (MNI) space using ICBM152 EPI images, and spatial smoothing with an 8-mm Full Width at Half Maximum Gaussian kernel. The data were also filtered in the temporal domain with a typical high-pass filter (128-s cut-off). The purpose of pre-processing was to remove various kinds of artifacts in order to maximize sensitivity of the following statistical analysis.
Since BOLD signal variance was dominated by fluctuations related to visual stimulation induced by the eyes-open condition, a more complex statistical parametric analysis was needed to reveal the ABS signal. Therefore, a general linear model (GLM) was created on the basis of a block-design paradigm (the eyes-open condition and the eyes-closed condition) and extended with event-related responses using an ABS-based vector (a separate GLM model for each ABS vector independently). ABS occurrences were modeled with the duration of stimuli equal to 0 (stick-function). The entire model was convolved with the canonical hemodynamic response function (HRF)-double gamma functions, as implemented in the SPM12 package, to obtain final regressors for beta value calculation. Thus, our approach to data modeling was similar to that presented by Ben-Simon and colleagues (
The mean number of detected ABSs in consecutive scans (3-s-long periods equal to the repetition time (TR) the fMRI sequence) averaged across all 36 participants. The ABS analysis was performed independently for each source channel. Note the increased number of ABS during the eyes-closed blocks (brown horizontal lines). PCC, posterior cingulate cortex/precuneus; MPFC, medial prefrontal cortex, LAG left angular gyrus; RAG, right an gular gyrus, LOCC, left occipital cortex, ROCC, right occipital cortex.
As this model did not fulfill the contrast orthogonality criterion for statistical parametric mapping an additional verification step was performed – we used the same GLM computation as above, but ABS vectors were randomly assigned to subjects.
The analysis was repeated for each ABS-based vector (PCC_{ABS}, mPFC_{ABS}, LAG_{ABS}, RAG_{ABS}, LOCC_{ABS}, ROCC_{ABS}, and OCC_{ABS}). This produced 7 independent outcomes. As the last step of the fMRI analysis, we performed a paired t-test comparing PCC_{ABS} and OCC_{ABS}. Here only ABS vectors were included in the GLM; the eyes-open and eyes-closed paradigm parameters were excluded to avoid beta deviation during model estimation.
All fMRI data analysis results, except for the paired t-test, were reported with cluster-level correction for multiple comparisons (
ABS segments were mainly detected during the eyes-closed state (Fig.
Difference between mean number (
PCC_{ABS} | mPFC_{ABS} | LAG_{ABS} | RAG_{ABS} | LOCC_{ABS} | ROCC_{ABS} | ||
---|---|---|---|---|---|---|---|
PCC_{ABS} | ( |
||||||
mPFC_{ABS} | ( |
-11.2 | |||||
LAG_{ABS} | ( |
-15.8 | -4.6 | ||||
RAG_{ABS} | ( |
-11.5 | -0.3 | 4.3 | |||
LOCC_{ABS} | ( |
13.8 | 25^{*} | 29.6^{*} | 25.3^{*} | ||
ROCC_{ABS} | ( |
6.6 | 17.9 | 22.4^{+} | 18.1 | -7.1 |
significant result (
trend toward significance (
Within subject correlation coefficients of ABS detection between source channels pairs averaged over 36 subjects. Significant values are colored.
PCC_{ABS} | mPFC_{ABS} | LAG_{ABS} | RAG_{ABS} | LOCC_{ABS} | ROCC_{ABS} | |
---|---|---|---|---|---|---|
PCC_{ABS} | ||||||
mPFC_{ABS} | 0.22 | |||||
LAG_{ABS} | 0.14 | 0.35^{*} | ||||
RAG_{ABS} | 0.12 | 0.28 | 0.55^{**} | |||
LOCC_{ABS} | 0.11 | 0.11 | 0.11 | 0.16 | ||
ROCC_{ABS} | 0.12 | 0.13 | 0.15 | 0.14 | 0.34^{*} |
significant result (
highly significant result (
ABS activity occurred mainly during the eyes-closed state (Fig.
A one-way ANOVA with repeated measurements was used to compare the effect of the source channel on beamformer source analysis (Fig.
Main effect of the ANOVA with repeated measurments analysis of beamformer source localization. The first row presents the clusters of significance (red cluster:
ANOVA post-hoc analysis: PCC_{ABS}
To further assess the neural correlates of ABS, fMRI data were analyzed using a model-driven approach. We performed a GLM analysis of fMRI data using regressors derived from the two study conditions (eyes-open and eyes-closed) and temporal information of ABS occurrence obtained from EEG data (independently for all source channels – six plus one additional for combined LOCC_{ABS} and ROCC_{ABS}). The result for PCC_{ABS} is presented in Fig.
Group results of the GLM analysis of fMRI data for PCC_{ABS} (contrast PCC_{ABS}
Group results of the GLM analysis of fMRI data for OCC_{ABS} (contrast OCC_{ABS}
Regions that exhibited a significant positive correlation (
The result of the analysis where OCC_{ABS} was implemented in GLM showed very similar results in terms of anatomical locations but with some important differences. The occipital cluster as well as both lateral clusters covered larger areas and reached higher absolute values of t-statistics. The revealed frontal cluster was much smaller and the parieto-medial cluster was not present at all. A new fronto-medial cluster was detected, comprising the middle part of the cingulate gyri.
Detailed information on the brain regions presented in Fig.
Finally, we directly compared the PCC_{ABS} with OCC_{ABS} conditions using a paired t-test analysis without introducing any other factors to the GLM analysis (Fig.
Group results of the GLM analysis of fMRI data for a paired t-test comparison between PCC_{ABS} and OCC_{ABS}. The yellow and blue regions represent a positive and a negative difference, respectively. They were significant after AlphaSim cluster-level correction for multiple comparisons (
The present study revealed two subsystems as possible sources of ABS activity: first, related to the mPFC and precuneus/posterior cingulate gyrus, regions typically reported as nodes of the DMN, and the second, associated with the OCC – a part of the thalamo-cortical circuit. The latter circuit was postulated earlier as a foundation of the alpha rhythm (Ben-Simon et al.
When we compared the number of the detected events
A tentative relationship between the resting state alpha rhythm and the DMN has been postulated in previous EEG-fMRI studies (Ben-Simon et al.
Mo and colleagues (
The relationship between alpha rhythm and the DMN was also suggested in a TMS experiment, where single pulse stimulation was applied to the mPFC (Bonnard et al.
This short literature review indicates that a relationship between neuronal correlates of alpha rhythm and DMN is plausible. Our study shows that this association can be particularly strong for the PCC_{ABS} activity. However, the computation of particular alpha rhythm differs in the mentioned papers. Ben-Simon and colleagues (
Our hypothesis is similar to a postulated relationship between alpha activity, self-referential thinking, and the DMN. In their resting state EEG study Knayazev and colleagues (
Both applied methods, the beamformer EEG signal analysis and fMRI GLM analysis revealed an occipital cluster as related to the ABS activity. The regions encompassed by the occipital cluster have been linked to alpha rhythm generation in several previous studies. Schreckenberger and colleagues (
Basic knowledge derived from intracranial animal studies (Contreras and Steriade
The interpretation of fMRI contrast which compared eyes-closed condition with ABS vectors for PCC (Fig.
Interestingly, the beamformer analysis (Figs
We posit that the resting state alpha rhythm (Berger rhythm) consists of two different but co-occurring phenomena: the occipital alpha rhythm supported by the thalamo-occipital circuit and the DMN alpha related to the activity in brain regions typically described as the DMN hubs. We furthermore argue that there is a relationship between the DMN alpha activity driven by PCC_{ABS} and the “spontaneous alpha rhythm” described by Ben-Simon and colleagues (
The two most common approaches to the analysis of temporal changes of continuous EEG signals in the literature are STFT (Allen
The STFT is commonly computed on short (1−2 s) consecutive periods of the signal and therefore considerably reduces temporal resolution of EEG. In general, this resolution is still sufficient with respect to the temporal resolution of fMRI. As ABS can occur within extremely short intervals the precise determination of their onsets and the interval between them impact the final shape of the predicted response during fMRI analysis (Miezin et al.
In comparison to STFT, the microstates analysis provides much better temporal resolution (~150 ms) but it is mainly focused on the spatial distribution of the signal in the form of quasi-stable spectral scalp distributions (“classes”). Although the Berger effect is well-described in terms of scalp distribution, its identification without the frequency characteristic might provide biased results.
We developed an ABS detection algorithm in order to overcome the aforementioned limitations. Following recent publications (Bazanova and Vernon
A significant advantage of our study is the large (n=36) and homogenous (young males) subject pool. This group was selected due to previous reports demonstrating significant sex and age differences with respect to alpha rhythm parameters (Chiang et al.
In keeping with other research (Ben-Simon et al.
Our EEG analysis revealed LAG_{ABS} and RAG_{ABS} as having the highest detection rate from among all the analysed source channels. Nevertheless, our fMRI results showed no activity around these regions. These regions have been proposed as only additional (Raichle et al.
Furthermore, using the mPFC_{ABS} as a GLM regressor provided very similar outcomes when compared to the OCC_{ABS} (Fig.
To conclude we posit that the resting state alpha rhythm should be considered as a complex activity consisting of two components: the occipital alpha rhythm and the more subtle DMN alpha rhythm. In conjunction with existing physiological data, our results suggest that the neuronal correlates of the DMN alpha rhythm might be located in two main nodes of DMN: the medio-parietal (PCC and precueneus) and the frontal (mPFC), whereas the occipital alpha rhythm in the thalamo-occipital circuit.
This study was supported by a grant from the Polish National Science Center no. 2011/01/N/NZ4/04985.
Supplementary materials for this article are available online at
EEG Montage of Default Mode Network with noise sources. The source channel positions are represented by color diamonds. The source channels used for ABS computation are indicated by black circles; the Talairach coordinates are shown in the adjacent table.
ANOVA Post-hoc analysis: PCC_{ABS}
ANOVA post-hoc analysis: PCC_{ABS}
fMRI results for PCC_{ABS} (as shown in Fig.
Cluster | Cluster size [cm^{3}] | Main AAL^{*} region | Talairach coordinates | Max t-value | Area size [cm^{3}] | ||||
---|---|---|---|---|---|---|---|---|---|
X | Y | Z | |||||||
positive t-values | frontal | 6.40 | left superior frontal gyrus | -12 | 40 | 45 | 4.3 | 4.65 | |
right superior frontal gyrus | 15 | 47 | -2 | 4.6 | 1.16 | ||||
parieto-medial | 1.50 | cingulate gyrus, posterior | -9 | -49 | 32 | 4.4 | 0.53 | ||
precuneus | -10 | -50 | 31 | 4.4 | 0.70 | ||||
occipital | 43.31 | left calcarine fissure | -18 | -96 | 1 | 7.0 | 3.20 | ||
right calcarine fissure | 12 | -92 | 4 | 6.4 | 2.94 | ||||
cuneus | 16 | -95 | 19 | 6.2 | 1.42 | ||||
left lingual gyrus | -18 | -85 | -3 | 6.9 | 2.92 | ||||
right lingual gyrus | 8 | -88 | 0 | 6.2 | 3.60 | ||||
left occipital gyrus | -18 | -94 | -1 | 7.1 | 17.14 | ||||
right occipital gyrus | 18 | -95 | 20 | 6.2 | 4.90 | ||||
left fusiform gyrus | -20 | -85 | -3 | 6.9 | 1.06 | ||||
right fusiform gyrus | 27 | -84 | 3 | 4.4 | 0.45 | ||||
negative t-values | lateral | left | 10.33 | left rolandic operculum | -57 | -1 | 8 | -5.2 | 2.49 |
left postcentral gyrus | -59 | -9 | 13 | -4.5 | 2.07 | ||||
left insula | -46 | -5 | -1 | -4.3 | 0.90 | ||||
left temporal gyrus | -62 | -10 | 7 | -5.6 | 3.78 | ||||
right | 3.06 | right rolandic operculum | -61 | 5 | 4 | -4.5 | 0.68 | ||
right temporal gyrus | 61 | 1 | 1 | -5.1 | 2.18 |
The merged AAL (Automated Anatomical Labeling) was used, as introduced by PMOD (
fMRI results for OCC_{ABS} (as shown in Fig.
Cluster | Cluster size [cm^{3}] | Main AAL^{*} region | Talairach coordinates | Max z-score | Area size [cm^{3}] | ||||
---|---|---|---|---|---|---|---|---|---|
X | Y | Z | |||||||
positive t-values | occipital | 56.43 | left calcarine fissure | -18 | -94 | 2 | 9.9 | 4.86 | |
right calcarine fissure | 18 | -92 | 9 | 8.7 | 3.84 | ||||
cuneus | 16 | -94 | 11 | 8.3 | 1.57 | ||||
left lingual gyrus | -15 | -89 | 4 | 8.3 | 4.17 | ||||
right lingual gyrus | 20 | -94 | -1 | 7.7 | 5.09 | ||||
left occipital gyrus | -18 | -93 | 3 | 9.9 | 18.19 | ||||
right occipital gyrus | 19 | -92 | 10 | 8.7 | 10.28 | ||||
left fusiform gyrus | -18 | -85 | -2 | 7.3 | 2.15 | ||||
right fusiform gyrus | 26 | -84 | -6 | 5.7 | 1.39 | ||||
cerebellum | 22 | -85 | -12 | 6.2 | 2.14 | ||||
frontal | 3.26 | left superior frontal gyrus | -14 | 62 | 16 | 4.9 | 3.20 | ||
fronto-medial | 2.60 | cingulate gyrus, middle part | 4 | 3 | 38 | 5.8 | 2.26 | ||
negative t-values | lateral | left | 21.69 | left rolandic operculum | -55 | -2 | 7 | 6.4 | 4.14 |
left precentral gyrus | -54 | 1 | 19 | 5.7 | 1.24 | ||||
left postcentral gyrus | -51 | -19 | 16 | 5.8 | 4.01 | ||||
left insula | -46 | 3 | -3 | 4.9 | 2.04 | ||||
left temporal gyrus | -53 | -7 | 5 | 6.5 | 6.39 | ||||
left supramarginal gyrus | -51 | -22 | 15 | 5.8 | 1.49 | ||||
left Heshl gyrus | -52 | -10 | 8 | 5.7 | 1.24 | ||||
right | 1.34 | right Rolandic operculum | 61 | 0 | 10 | 6.1 | 2.80 | ||
right postcentral gyrus | 65 | -8 | 14 | 5.9 | 1.50 | ||||
right temporal gyrus | 61 | -1 | 3 | 5.9 | 3.70 | ||||
right Heshl gyrus | 61 | -1 | 6 | 5.7 | 0.70 |
The merged AAL (Automated Anatomical Labeling) was used, as introduced by PMOD (
Paired t-test fMRI analysis of PCC_{ABS}
Cluster | Cluster size [cm^{3}] | Main AAL^{*} region | Talairach coordinates | Max z-score | Area size [cm^{3}] | ||||
---|---|---|---|---|---|---|---|---|---|
X | Y | Z | |||||||
positive t-values | lateral | left | 4.41 | left postcentral gyrus | -53 | 0 | 18 | 4.3 | 1.00 |
left temporal gyrus | -63 | -23 | 17 | 4.6 | 1.36 | ||||
left supramarginal gyrus | -64 | -21 | 19 | 4.7 | 1.85 | ||||
right | 4.36 | right Rolandic operculum | 61 | -2 | 8 | 3.7 | 1.05 | ||
right supramarginal gyrus | 65 | -15 | 22 | 4.1 | 0.97 | ||||
right temporal gyrus | 53 | -14 | 7 | 4.0 | 1.79 | ||||
right Heshl gyrus | 51 | -13 | 8 | 3.7 | 0.54 | ||||
negative t-values | occipital | 22.92 | left calcarine fissure | 2 | -94 | 1 | 3.6 | 3.96 | |
right calcarine fissure | 9 | -63 | 6 | 3.4 | 2.06 | ||||
cuneus | 16 | -95 | 19 | 3.3 | 1.12 | ||||
left lingual gyrus | -14 | -77 | 0 | 3.6 | 3.38 | ||||
right lingual gyrus | 18 | -85 | -7 | 3.5 | 2.31 | ||||
left occipital gyrus | -29 | -95 | 13 | 3.5 | 3.88 | ||||
light occipital gyrus | 14 | -95 | 20 | 3.3 | 0.67 | ||||
reft fusiform gyrus | -29 | -75 | -11 | 3.5 | 1.21 | ||||
right fusiform gyrus | 34 | -62 | -11 | 3.4 | 2.50 | ||||
cerebellum | -14 | -38 | -41 | 3.6 | 0.54 |
The merged AAL (Automated Anatomical Labeling) was used, as introduced by PMOD (
Group results of the GLM analysis of fMRI data for mPFC_{ABS} (contrast mPFC_{ABS}
Group results of the GLM analysis of fMRI data for LAG_{ABS} (contrast LAG_{ABS}
Group results of the GLM analysis of fMRI data for RAG_{ABS} (contrast RAG_{ABS}
Group results of the GLM analysis of fMRI data for LOCC_{ABS} (contrast LOCC_{ABS}
Group results of the GLM analysis of fMRI data for ROCC_{ABS} (contrast ROCC_{ABS}
Group results of the GLM analysis of fMRI data for PCC_{ABS} (contrast PCC_{ABS}
Short-time Fourier transformation (STFT) of EEG data for single subject (top row) and group average (bottom row). First column: the sTFT analysis results for PCC regional source; Second column: the STFT analysis results for averaged occipital regional sources: LOCC and ROCC (OCC). The time when eyes should remain closed is indicated with brown color lines. STFT was performed in 2s bin windows.