brain research 1543 (2014) 235–243
Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
The contribution of different frequency bands of fMRI data to the correlation with EEG alpha rhythm Zhichao Zhana,b, Lele Xuc, Tian Zuoc, Dongliang Xieb, Jiacai Zhangc, Li Yaoa,c, Xia Wua,b,c,n a
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875 Beijing, PR China State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 100088 Beijing, PR China c College of Information Science and Technology, Beijing Normal University, 100875 Beijing, PR China b
art i cle i nfo
ab st rac t
Article history:
Alpha rhythm is a prominent EEG rhythm observed during resting state and is thought to
Accepted 14 November 2013
be related to multiple cognitive processes. Previous simultaneous electroencephalography
Available online 22 November 2013
(EEG)/functional magnetic resonance imaging (fMRI) studies have demonstrated that alpha
Keywords:
rhythm is associated with blood oxygen level dependent (BOLD) signals in several different
Functional magnetic resonance
functional networks. How these networks influence alpha rhythm respectively is unclear.
imaging (fMRI)
The low-frequency oscillations (LFO) in spontaneous BOLD activity are thought to
Electroencephalography (EEG)
contribute to the local correlations in resting state. Recent studies suggested that either
Alpha rhythm
LFO or other components of fMRI can be further divided into sub-components on different
Correlation
frequency bands. We hypothesized that those BOLD sub-components characterized the
Filtering
contributions of different brain networks to alpha rhythm. To test this hypothesis, EEG and
Low-frequency oscillations (LFO)
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). & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Alpha rhythm is a prominent EEG rhythm when human is in resting state (Berger, 1929) and has been intensively studied. It is thought to be related with several different cognitive processes such as vision, attention, and vigilance (Buzsaki, 2009). Simultaneous electroencephalography and functional
magnetic resonance imaging recording (EEG/fMRI) has been used to study the correlation between brain's EEG alpha rhythm and the blood oxygen level-dependent (BOLD) signals, which are usually associated with physiological and pathological EEG events (Laufs et al., 2003). The results may be important for understanding the neural substrates underpinning the alpha rhythm (Laufs et al., 2003). Goldman et al.
n Corresponding author at: College of Information Science and Technology, Beijing Normal University, 100875 Beijing, PR China. Fax: þ86 10 58800029. E-mail address: [email protected] (X. Wu).
0006-8993/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2013.11.016
236
brain research 1543 (2014) 235–243
(2002) and Moosmann et al. (2003) convolved the alpha rhythm with a hemodynamic response function (HRF) and then correlated with BOLD time courses all over the brain to yield brain regions that are related with the alpha rhythm. They found that the posterior scalp EEG alpha oscillations are positively correlated with the BOLD activities in thalamus while negatively correlated with those in the occipital–parietal areas. Laufs et al. (2003) suggested that the alpha synchronization may be associated with a frontal–parietal network which had been independently established as an attention control system (Corbetta and Shulman, 2002). Mantini et al. (2007) demonstrated that alpha rhythm correlated with several resting-state networks. Although alpha rhythm has been associated with multiple neural networks, the neural substrates of the alpha rhythm remain to be investigated. In BOLD signal, sub-components oscillating at different frequencies may indicate different coherence brain patterns and cognitive processes (Zuo et al., 2010). In resting state, spontaneous BOLD fluctuations in different brain regions have been found to be correlated both locally and globally (Fox and Raichle, 2007). The low-frequency oscillations (LFO) in 0.01–0.08 Hz frequency band were suggested to be the major contributor to the correlation during resting state (Fox et al., 2005). A recent study further suggested that other subdivided frequency bands, including slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz), and slow-2 (0.198–0.25 Hz), may have different meanings (Zuo et al., 2010). Different BOLD frequencies may correlate with alpha rhythm in different spatial patterns which are informative to interpreting the neural substrates of the alpha rhythm. In this study, fMRI data was band-pass filtered into two different frequency bands (0.01–0.08 Hz and 0.08–0.25 Hz) and four different sub-components (0.01–0.027 Hz, 0.027–0.073 Hz, 0.073–0.198 Hz and 0.198–0.25 Hz) according to Zuo et al., and the correlations between each frequency band and the EEG alpha rhythm were assessed respectively all over the brain. Spatial patterns regarding the relation between different BOLD frequency bands and the EEG alpha rhythm are demonstrated.
2.
Results
The fMRI data filtered with the 0.01–0.08 Hz frequency bands negatively correlated with the alpha power (Fig. 1, Table 1), predominantly in the occipital lobe, which involved cuneus, precuneus, and middle parts of the middle occipital lobe (BA 18, 19). Left and right superior parietal lobule (BA 7) and left middle frontal gyrus (BA 8) were also involved. The fMRI data filtered with the 0.08–0.25 Hz frequency bands negatively correlated with the alpha power (Fig. 2, Table 2), mainly in the frontal lobe, including middle frontal gyrus (BA 10), left and right inferior frontal gyrus (BA 47) and parts of left and right superior temporal gyrus (BA 22), and middle temporal gyrus (BA 21). The correlation pattern (Fig. 3, Table 3) of the 0.01–0.027 Hz was very similar with that of the 0.01–0.08 Hz band which contained left and right middle frontal gyrus (BA 7), left and right inferior frontal gyrus (BA 7), left and right superior
parietal lobule (BA 17), left and right inferior parietal lobule (BA 8) and particular occipital areas in the precuneus, cuneus and middle occipital gyrus (BA 18). The correlation pattern differed for the fMRI data filtered with the 0.027–0.073 Hz frequency band. No voxel was clustered under the FDR correction. Only a few voxels were identified at a low significance level (po0.001, uncorrected) where the activity negatively correlated with alpha power (Fig. 4). These voxels were located in regions such as the left superior temporal gyrus, left and right inferior frontal gyrus, superior occipital gyrus, middle occipital gyrus, left precentral gyrus and left postcentral gyrus. One of the 0.073–0.198 Hz bands was very similar with that of 0.08–0.25 Hz band, which included the frontal lobe (BA 7), middle frontal gyrus (BA 10), left and right inferior frontal gyrus (BA 47), superior temporal gyrus (BA 22), and middle temporal gyrus (BA 21) with FDR corrected at po0.01 (Fig. 5, Table 4). The 0.198–0.25 Hz band rarely correlated with the alpha rhythm power, even in the uncorrected significance po0.001 (Fig. 6).
3.
Discussion
The alpha rhythm has been related to several different cognitive processes. In resting state alpha rhythm was found to be correlated with the BOLD activities in several different neural networks (Mantini et al., 2007). To examine whether the BOLD frequency bands were related to those correlations, we filtered the fMRI data into the following 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. Each BOLD frequency band signal was then correlated with the alpha rhythm power. A correlation pattern was obtained between the filtered fMRI data in the 0.01–0.08 Hz frequency band and the EEG alpha rhythm (Fig. 1, Table 1). This pattern outlined the frontal–parietal network and the occipital vision regions. The frontal–parietal network is consistent with the results identified by several previous studies (Laufs et al., 2003; Mantini et al., 2007; Mo et al., 2013) and has been independently recognized as an attention system (Corbetta and Shulman, 2002; Laufs et al., 2003, 2006). The occipital areas are similar to those reported previously (Goldman et al., 2002; Mantini et al., 2007; Moosmann et al., 2003). These results suggested that the correlation between the alpha rhythm and the BOLD signals in the visual cortex and the attention network are most prominent on low frequency band. The correlation pattern (Fig. 2) obtained from the 0.08–0.25 Hz band included part of the self-referential network (Mantini et al., 2007) and part of the attention network (Laufs et al., 2003, 2006). This correlation suggested that the alpha rhythm may also be modulated by the frontal network which is related to self-referential network in higher frequencies. The BOLD signals fluctuate spontaneously at a very low frequency range, which used to be considered as mainly containing noise (Fox and Raichle, 2007). But some studies showed that these slow fluctuations may contain meaningful information of the brain (Buzsáki and Draguhn, 2004; Penttonen, 2003; Wise, 2004). Our study demonstrated that
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brain research 1543 (2014) 235–243
Fig. 1 – Correlation map for the EEG alpha power data and the 0.01–0.08 Hz filtered. fMRI data (p¼ 0.01, FDR corrected, cluster size Z30). Only the negative intensity is displayed, which is colored from red to yellow (as in Figs. 2–6). Table 1 – Regions in the correlation map for the 0.01–0.08 Hz fMRI data (one sample t-test, FDR, p¼ 0.01). Brain region
Occipital lobe Right superior parietal lobule Left superior parietal lobule Left middle frontal gyrus
BA
18 7 7 8
T value
4.32 4.2 2.4 2.32
MNI coordinate
Volume
x
y
z
12.28 26.50 32.97 31.67
75.48 67.73 53.52 17.52
18.65 49.68 49.68 49.68
8776 1290 987 875
BA: Brodmann area.
different frequency components of BOLD signals may have different spatial patterns of correlation with EEG alpha rhythm, suggesting different aspects of the functions of EEG alpha rhythm.
BOLD signal fluctuations can also be subdivided into subtle components (Buzsáki and Draguhn, 2004; Penttonen, 2003). In this study, the BOLD signals were subdivided into 4 different sub-frequency bands, which are 0.01–0.027 Hz,
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brain research 1543 (2014) 235–243
Fig. 2 – Correlation map for the EEG alpha power data and the 0.08–0.25 Hz filtered. fMRI data (p¼0.01, FDR corrected, cluster size Z 30).
Table 2 – Regions in the correlation map for the 0.08–0.25 Hz fMRI data (one sample t-test, FDR, p ¼0.01). Brain region
Middle frontal gyrus Middle temporal gyrus Superior temporal gyrus Inferior frontal gyrus
BA
10 N/A 22 N/A
T value
6.62 4.38 5.32 4.28
MNI coordinate
Volume
x
y
z
36 58.83 67.88 52.36
3 43.19 45.77 31.73
56 1.84 4.42 2.04
1264 124 126 54
BA: Brodmann area.
0.027–0.073 Hz, 0.073–0.198 Hz, and 0.198–0.25 Hz. The results showed that the 0.01–0.027 Hz band and 0.01–0.08 Hz shared a similar pattern, while the 0.073–0.198 Hz bands and the
0.08–0.25 Hz band shared another similar pattern. The signals on the 0.027–0.073 Hz band and the 0.198–0.25 Hz band were rarely correlated with the alpha rhythm. Those results
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brain research 1543 (2014) 235–243
Fig. 3 – Correlation map for the EEG alpha power data and the 0.01–0.027 Hz filtered. fMRI data (p¼ 0.01, FDR corrected, cluster size Z30). Table 3 – Regions in the correlation map for the 0.01–0.027 Hz filtered fMRI data (one sample t-test, FDR, p¼ 0.01). Brain region
Occipital lobe Right superior parietal lobule Left superior parietal lobule Left middle frontal gyrus
BA
18 7 7 8
T value
4.65 4.32 2.52 2.18
MNI coordinate
Volume
x
y
z
13.57 26.50 32.97 31.67
83.23 67.73 53.52 17.52
18.65 49.68 49.68 49.68
8789 1290 987 875
BA: Brodmann area.
indicated that the contribution to the correlation may mainly come from the lower frequency band. Also, different bands may play different roles in spontaneous BOLD signal.
Previous studies have demonstrated that alpha rhythm are differentially associated with visual network, attention network and self-reference network (Mantini et al., 2007).
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brain research 1543 (2014) 235–243
Fig. 4 – Correlation map for the EEG alpha power data and the 0.027–0.073 Hz filtered. fMRI data (p ¼0.001, uncorrected, cluster size Z 10). Our results suggested that these networks modulated the alpha rhythm in different BOLD frequency bands. The patterns that visual and attention networks correlated with the alpha rhythm at very low frequencies and frontal network at higher frequencies, may suggest that different networks influenced the alpha rhythm in different frequency ranges.
healthy volunteers (8 females and 9 males, aged between 19 and 27 years (Mean7SD: 22.173.24 years) participated in the study after providing written informed consent. The subjects were instructed to lay still inside the scanner with their eyes closed and not fall asleep. Each subject had a 10 min EEGfMRI scan.
4.
Experimental procedures
4.2.
4.1.
Subjects
An MRI-compatible EEG system (Brain Amp MR plus, Brain Products, Munich Germany) was used to collect the EEG data. The amplifier and a recharged battery package were placed in a stable position immediately outside the magnetic bore. A computer outside the scanner room received the signal from the amplifier via fiber optic cables. A syntax box coordinated the clocks in the amplifier and the fMRI scanner.
The study was approved by the Institutional Review Board of Beijing Normal University (BNU) Imaging Center for Brain Research, National Key Laboratory of Cognitive Neuroscience and Learning. All subjects gave written informed consent. We recruited participants through the campus website. 17
EEG acquisition
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brain research 1543 (2014) 235–243
Fig. 5 – Correlation map for the EEG alpha power data and the 0.073–0.198 Hz filtered. fMRI data (p ¼0.01, FDR corrected, cluster size Z30).
Table 4 – Regions in the correlation map for the 0.73–0.198 Hz filtered fMRI data (one sample t-test, FDR, p¼ 0.01). Brain region
Middle frontal gyrus Middle temporal gyrus Superior temporal gyrus Inferior frontal gyrus
BA
10 N/A 22 N/A
T value
7.52 4.65 5.63 4.25
MNI coordinate
Volume
x
y
z
36 58.83 67.88 52.36
3 43.19 45.77 31.73
56 1.84 4.42 2.04
1543 165 123 57
BA: Brodmann area.
An elastic cap (EasyCaps, Falk Minnow Services, Herrsching, Germany) with 64 sintered Ag/AgCl ring electrodes arranged in an extended 10-10 system was utilized.
An additional electrode recorded the ECG from a bilateral montage below the heart on the back. All the electrodes required extra series resistors to avoid saturation (5 kΩ for
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brain research 1543 (2014) 235–243
Fig. 6 – Correlation map for the EEG alpha power data and the 0.027–0.073 Hz filtered. fMRI data (p ¼0.001, uncorrected, cluster size Z 10). EEG and 15 kΩ for ECG). On the EEG cap, FCz was chosen as the reference. The sampling rate of EEG data was set at 5000 Hz, and then the data was band-pass filtered with a 0.1– 250 Hz filter to reduce ultra-noise which was induced by the fMRI scan. We adjusted the impedance to below 20 kΩ and placed the amplifier and a rechargeable battery package in a stable position outside the bore. The amplifier signal was transmitted to a computer outside the scanner room via fiber optic cables.
following parameters: repeat time (TR)¼ 2000 ms, echo time (TE) ¼30 ms, 32 slices, matrix size¼ 64 64, acquisition voxel size¼ 3.125 3.125 3.84 mm3, flip angle (FA)¼ 901, and field of view (FOV)¼ 200 cm. Three hundred TR data sets were collected. In addition, a high resolution, three-dimensional T1-weighted structural image was acquired (TR¼2530 ms, TE¼ 3.39 ms, 128 slices, FA¼ 71, matrix size¼ 256 256 and resolution¼1 1 1.33 mm3).
4.4. 4.3.
Data preprocessing
fMRI acquisition
The fMRI data were acquired with a 3T whole body scanner (Siemens Magnetom TrioTIM, Erlange, Germany) equipped with a standard birdcage head coil at the Brain Imaging Center of BNU. Functional images were collected with the
The EEG data was preprocessed using Brain Vision Analyzer 2.0 (Brain products, Germany). At the first step, the gradient artifacts were removed using a sliding average (Allen et al., 2000) of 21 averages. Subsequently, the EEG was down sampled to 500 Hz and low-pass filtered with an infinite
brain research 1543 (2014) 235–243
impulse response (IIR) filter with a cut-off frequency of 70 Hz. Then, the ballistocardiogram (BCG) artifact was removed by first using a sliding average procedure with 11 averages (Allen et al., 2000). And at last so-cleaned EEG data was band-pass filtered between 1 and 40 Hz and changed the reference to average. For each participant, the functional images from the first 10 time points were discarded to allow for equilibration of the magnetic field. The remaining 290 images were preprocessed using the Statistical Parametric Mapping package (SPM8, http://www.fil.ion.ucl.ac.uk/spm) to realign, normalize, and smoothen the data. Images were re-sampled into 3 3 4 mm3 and smoothened with an 8-mm full width at half maximum Gaussian kernel. The six head motion parameters obtained in the realigning step was saved. All the six head motion parameters were regress out using REST.
4.5.
fMRI data filtering
The preprocessed fMRI data were filtered with the 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 frequency bands using the Data Processing Assistant for Resting-State fMRI (DPARSF, http://www.restfmri.net/ forum/DPARSF).
4.6.
Correlation computing
After pre-processing, the O1 and O2 channel were chosen and used to calculate the alpha rhythm which is 8–12 Hz. We remove the first 10 point to maintain consistence with fMRI data. The average of these two channel's alpha rhythm power series was convolved with a classical HRF in SPM8 software (Wellcome Department of Imaging Neuroscience, London, UK). For each subject, correlations were evaluated between EEG alpha power and each frequency band using the RestingState fMRI Data Analysis Toolkit (REST, http://resting-fmri. sourceforge.net/). One sample t-tests were performed to determine the correlation maps. Z-shift transfer was used to make sure the normality of the data, and a FDR correction was used to correct the multi-comparison problem.
Acknowledgment This work was supported by the Key Program of National Natural Science Foundation of China (6121001), General Program of National Natural Science Foundation of China (61222113), Program for New Century Excellent Talents in University (NCET-12–0056) and Open Project Funding of the
243
State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) (SKLNST-2013-1-03).
r e f e r e nc e s
Allen, P.J., et al., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12 (2), 230–239. ¨ ber das elektrenkephalogramm des menschen. Berger, H., 1929. U Arch. Psychiatr. Nervenkr. 87, 527–570. Buzsa´ki, G., Draguhn, A., 2004. Neuronal oscillations in cortical networks. Science 304, 1926–1929. Buzsaki, G., 2009. Rhythms of the Brain. Oxford University Press, USA. Corbetta, M., Shulman, G.L., 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3 (3), 201–215. Fox, M.D., et al., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U S A 102 (27), 9673–9678. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711. Goldman, R.I., Stern, J.M., Engel Jr., J., Cohen, M.S., 2002. Simultaneous EEG and fMRI of the alpha rhythm. NeuroReport 13 (18), 2487–2492. Laufs, H., Holt, J.L., Elfont, R., Krams, M., Paul, J.S., Krakow, K., Kleinschmidt, A., 2006. Where the BOLD signal goes when alpha EEG leaves. NeuroImage 31 (4), 1408–1418. Laufs, H., Kleinschmidt, A., Beyerle, A., Eger, E., Salek-Haddadi, A., Preibisch, C., Krakow, K., 2003. EEG-correlated fMRI of human alpha activity. NeuroImage 19 (4), 1463–1476. Mantini, D., Perrucci, M.G., Del Gratta, C., Romani, G.L., Corbetta, M., 2007. Electrophysiological signatures of resting state networks in the human brain. Proc. Natl. Acad. Sci. USA 104 (32), 13170–13175. Mo, J., Liu, Y., Huang, H., Ding, M., 2013. Coupling between visual alpha oscillations and default mode activity. NeuroImage 68, 112–118. Moosmann, M., Ritter, P., Krastel, I., Brink, A., Thees, S., Blankenburg, F., Taskin, B., Obrig, H., Villringer, A, 2003. Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. NeuroImage 20 (1), 145–158. Penttonen, M., 2003. Natural logarithmic relationship between brain oscillators. Thal. Relat. Syst. 2, 145–152. Wise, R.G., Ide, K., Poulin, M.J, Tracey, I., 2004. Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. NeuroImage 21 (4), 1652–1664. Zuo, X.N., Martino, A.D., Kelly, C., Shehzad, Z.E., Gee, D.G., Klein, D.F., Castellanos, F.X., Biswal, B.B., Milham, M.P., 2010. The oscillating brain: complex and reliable. NeuroImage 49, 1432–1445.
Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
The contribution of different frequency bands of fMRI data to the correlation with EEG alpha rhythm Zhichao Zhana,b, Lele Xuc, Tian Zuoc, Dongliang Xieb, Jiacai Zhangc, Li Yaoa,c, Xia Wua,b,c,n a
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875 Beijing, PR China State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 100088 Beijing, PR China c College of Information Science and Technology, Beijing Normal University, 100875 Beijing, PR China b
art i cle i nfo
ab st rac t
Article history:
Alpha rhythm is a prominent EEG rhythm observed during resting state and is thought to
Accepted 14 November 2013
be related to multiple cognitive processes. Previous simultaneous electroencephalography
Available online 22 November 2013
(EEG)/functional magnetic resonance imaging (fMRI) studies have demonstrated that alpha
Keywords:
rhythm is associated with blood oxygen level dependent (BOLD) signals in several different
Functional magnetic resonance
functional networks. How these networks influence alpha rhythm respectively is unclear.
imaging (fMRI)
The low-frequency oscillations (LFO) in spontaneous BOLD activity are thought to
Electroencephalography (EEG)
contribute to the local correlations in resting state. Recent studies suggested that either
Alpha rhythm
LFO or other components of fMRI can be further divided into sub-components on different
Correlation
frequency bands. We hypothesized that those BOLD sub-components characterized the
Filtering
contributions of different brain networks to alpha rhythm. To test this hypothesis, EEG and
Low-frequency oscillations (LFO)
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). & 2013 Elsevier B.V. All rights reserved.
1.
Introduction
Alpha rhythm is a prominent EEG rhythm when human is in resting state (Berger, 1929) and has been intensively studied. It is thought to be related with several different cognitive processes such as vision, attention, and vigilance (Buzsaki, 2009). Simultaneous electroencephalography and functional
magnetic resonance imaging recording (EEG/fMRI) has been used to study the correlation between brain's EEG alpha rhythm and the blood oxygen level-dependent (BOLD) signals, which are usually associated with physiological and pathological EEG events (Laufs et al., 2003). The results may be important for understanding the neural substrates underpinning the alpha rhythm (Laufs et al., 2003). Goldman et al.
n Corresponding author at: College of Information Science and Technology, Beijing Normal University, 100875 Beijing, PR China. Fax: þ86 10 58800029. E-mail address: [email protected] (X. Wu).
0006-8993/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2013.11.016
236
brain research 1543 (2014) 235–243
(2002) and Moosmann et al. (2003) convolved the alpha rhythm with a hemodynamic response function (HRF) and then correlated with BOLD time courses all over the brain to yield brain regions that are related with the alpha rhythm. They found that the posterior scalp EEG alpha oscillations are positively correlated with the BOLD activities in thalamus while negatively correlated with those in the occipital–parietal areas. Laufs et al. (2003) suggested that the alpha synchronization may be associated with a frontal–parietal network which had been independently established as an attention control system (Corbetta and Shulman, 2002). Mantini et al. (2007) demonstrated that alpha rhythm correlated with several resting-state networks. Although alpha rhythm has been associated with multiple neural networks, the neural substrates of the alpha rhythm remain to be investigated. In BOLD signal, sub-components oscillating at different frequencies may indicate different coherence brain patterns and cognitive processes (Zuo et al., 2010). In resting state, spontaneous BOLD fluctuations in different brain regions have been found to be correlated both locally and globally (Fox and Raichle, 2007). The low-frequency oscillations (LFO) in 0.01–0.08 Hz frequency band were suggested to be the major contributor to the correlation during resting state (Fox et al., 2005). A recent study further suggested that other subdivided frequency bands, including slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz), and slow-2 (0.198–0.25 Hz), may have different meanings (Zuo et al., 2010). Different BOLD frequencies may correlate with alpha rhythm in different spatial patterns which are informative to interpreting the neural substrates of the alpha rhythm. In this study, fMRI data was band-pass filtered into two different frequency bands (0.01–0.08 Hz and 0.08–0.25 Hz) and four different sub-components (0.01–0.027 Hz, 0.027–0.073 Hz, 0.073–0.198 Hz and 0.198–0.25 Hz) according to Zuo et al., and the correlations between each frequency band and the EEG alpha rhythm were assessed respectively all over the brain. Spatial patterns regarding the relation between different BOLD frequency bands and the EEG alpha rhythm are demonstrated.
2.
Results
The fMRI data filtered with the 0.01–0.08 Hz frequency bands negatively correlated with the alpha power (Fig. 1, Table 1), predominantly in the occipital lobe, which involved cuneus, precuneus, and middle parts of the middle occipital lobe (BA 18, 19). Left and right superior parietal lobule (BA 7) and left middle frontal gyrus (BA 8) were also involved. The fMRI data filtered with the 0.08–0.25 Hz frequency bands negatively correlated with the alpha power (Fig. 2, Table 2), mainly in the frontal lobe, including middle frontal gyrus (BA 10), left and right inferior frontal gyrus (BA 47) and parts of left and right superior temporal gyrus (BA 22), and middle temporal gyrus (BA 21). The correlation pattern (Fig. 3, Table 3) of the 0.01–0.027 Hz was very similar with that of the 0.01–0.08 Hz band which contained left and right middle frontal gyrus (BA 7), left and right inferior frontal gyrus (BA 7), left and right superior
parietal lobule (BA 17), left and right inferior parietal lobule (BA 8) and particular occipital areas in the precuneus, cuneus and middle occipital gyrus (BA 18). The correlation pattern differed for the fMRI data filtered with the 0.027–0.073 Hz frequency band. No voxel was clustered under the FDR correction. Only a few voxels were identified at a low significance level (po0.001, uncorrected) where the activity negatively correlated with alpha power (Fig. 4). These voxels were located in regions such as the left superior temporal gyrus, left and right inferior frontal gyrus, superior occipital gyrus, middle occipital gyrus, left precentral gyrus and left postcentral gyrus. One of the 0.073–0.198 Hz bands was very similar with that of 0.08–0.25 Hz band, which included the frontal lobe (BA 7), middle frontal gyrus (BA 10), left and right inferior frontal gyrus (BA 47), superior temporal gyrus (BA 22), and middle temporal gyrus (BA 21) with FDR corrected at po0.01 (Fig. 5, Table 4). The 0.198–0.25 Hz band rarely correlated with the alpha rhythm power, even in the uncorrected significance po0.001 (Fig. 6).
3.
Discussion
The alpha rhythm has been related to several different cognitive processes. In resting state alpha rhythm was found to be correlated with the BOLD activities in several different neural networks (Mantini et al., 2007). To examine whether the BOLD frequency bands were related to those correlations, we filtered the fMRI data into the following 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. Each BOLD frequency band signal was then correlated with the alpha rhythm power. A correlation pattern was obtained between the filtered fMRI data in the 0.01–0.08 Hz frequency band and the EEG alpha rhythm (Fig. 1, Table 1). This pattern outlined the frontal–parietal network and the occipital vision regions. The frontal–parietal network is consistent with the results identified by several previous studies (Laufs et al., 2003; Mantini et al., 2007; Mo et al., 2013) and has been independently recognized as an attention system (Corbetta and Shulman, 2002; Laufs et al., 2003, 2006). The occipital areas are similar to those reported previously (Goldman et al., 2002; Mantini et al., 2007; Moosmann et al., 2003). These results suggested that the correlation between the alpha rhythm and the BOLD signals in the visual cortex and the attention network are most prominent on low frequency band. The correlation pattern (Fig. 2) obtained from the 0.08–0.25 Hz band included part of the self-referential network (Mantini et al., 2007) and part of the attention network (Laufs et al., 2003, 2006). This correlation suggested that the alpha rhythm may also be modulated by the frontal network which is related to self-referential network in higher frequencies. The BOLD signals fluctuate spontaneously at a very low frequency range, which used to be considered as mainly containing noise (Fox and Raichle, 2007). But some studies showed that these slow fluctuations may contain meaningful information of the brain (Buzsáki and Draguhn, 2004; Penttonen, 2003; Wise, 2004). Our study demonstrated that
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brain research 1543 (2014) 235–243
Fig. 1 – Correlation map for the EEG alpha power data and the 0.01–0.08 Hz filtered. fMRI data (p¼ 0.01, FDR corrected, cluster size Z30). Only the negative intensity is displayed, which is colored from red to yellow (as in Figs. 2–6). Table 1 – Regions in the correlation map for the 0.01–0.08 Hz fMRI data (one sample t-test, FDR, p¼ 0.01). Brain region
Occipital lobe Right superior parietal lobule Left superior parietal lobule Left middle frontal gyrus
BA
18 7 7 8
T value
4.32 4.2 2.4 2.32
MNI coordinate
Volume
x
y
z
12.28 26.50 32.97 31.67
75.48 67.73 53.52 17.52
18.65 49.68 49.68 49.68
8776 1290 987 875
BA: Brodmann area.
different frequency components of BOLD signals may have different spatial patterns of correlation with EEG alpha rhythm, suggesting different aspects of the functions of EEG alpha rhythm.
BOLD signal fluctuations can also be subdivided into subtle components (Buzsáki and Draguhn, 2004; Penttonen, 2003). In this study, the BOLD signals were subdivided into 4 different sub-frequency bands, which are 0.01–0.027 Hz,
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Fig. 2 – Correlation map for the EEG alpha power data and the 0.08–0.25 Hz filtered. fMRI data (p¼0.01, FDR corrected, cluster size Z 30).
Table 2 – Regions in the correlation map for the 0.08–0.25 Hz fMRI data (one sample t-test, FDR, p ¼0.01). Brain region
Middle frontal gyrus Middle temporal gyrus Superior temporal gyrus Inferior frontal gyrus
BA
10 N/A 22 N/A
T value
6.62 4.38 5.32 4.28
MNI coordinate
Volume
x
y
z
36 58.83 67.88 52.36
3 43.19 45.77 31.73
56 1.84 4.42 2.04
1264 124 126 54
BA: Brodmann area.
0.027–0.073 Hz, 0.073–0.198 Hz, and 0.198–0.25 Hz. The results showed that the 0.01–0.027 Hz band and 0.01–0.08 Hz shared a similar pattern, while the 0.073–0.198 Hz bands and the
0.08–0.25 Hz band shared another similar pattern. The signals on the 0.027–0.073 Hz band and the 0.198–0.25 Hz band were rarely correlated with the alpha rhythm. Those results
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Fig. 3 – Correlation map for the EEG alpha power data and the 0.01–0.027 Hz filtered. fMRI data (p¼ 0.01, FDR corrected, cluster size Z30). Table 3 – Regions in the correlation map for the 0.01–0.027 Hz filtered fMRI data (one sample t-test, FDR, p¼ 0.01). Brain region
Occipital lobe Right superior parietal lobule Left superior parietal lobule Left middle frontal gyrus
BA
18 7 7 8
T value
4.65 4.32 2.52 2.18
MNI coordinate
Volume
x
y
z
13.57 26.50 32.97 31.67
83.23 67.73 53.52 17.52
18.65 49.68 49.68 49.68
8789 1290 987 875
BA: Brodmann area.
indicated that the contribution to the correlation may mainly come from the lower frequency band. Also, different bands may play different roles in spontaneous BOLD signal.
Previous studies have demonstrated that alpha rhythm are differentially associated with visual network, attention network and self-reference network (Mantini et al., 2007).
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Fig. 4 – Correlation map for the EEG alpha power data and the 0.027–0.073 Hz filtered. fMRI data (p ¼0.001, uncorrected, cluster size Z 10). Our results suggested that these networks modulated the alpha rhythm in different BOLD frequency bands. The patterns that visual and attention networks correlated with the alpha rhythm at very low frequencies and frontal network at higher frequencies, may suggest that different networks influenced the alpha rhythm in different frequency ranges.
healthy volunteers (8 females and 9 males, aged between 19 and 27 years (Mean7SD: 22.173.24 years) participated in the study after providing written informed consent. The subjects were instructed to lay still inside the scanner with their eyes closed and not fall asleep. Each subject had a 10 min EEGfMRI scan.
4.
Experimental procedures
4.2.
4.1.
Subjects
An MRI-compatible EEG system (Brain Amp MR plus, Brain Products, Munich Germany) was used to collect the EEG data. The amplifier and a recharged battery package were placed in a stable position immediately outside the magnetic bore. A computer outside the scanner room received the signal from the amplifier via fiber optic cables. A syntax box coordinated the clocks in the amplifier and the fMRI scanner.
The study was approved by the Institutional Review Board of Beijing Normal University (BNU) Imaging Center for Brain Research, National Key Laboratory of Cognitive Neuroscience and Learning. All subjects gave written informed consent. We recruited participants through the campus website. 17
EEG acquisition
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Fig. 5 – Correlation map for the EEG alpha power data and the 0.073–0.198 Hz filtered. fMRI data (p ¼0.01, FDR corrected, cluster size Z30).
Table 4 – Regions in the correlation map for the 0.73–0.198 Hz filtered fMRI data (one sample t-test, FDR, p¼ 0.01). Brain region
Middle frontal gyrus Middle temporal gyrus Superior temporal gyrus Inferior frontal gyrus
BA
10 N/A 22 N/A
T value
7.52 4.65 5.63 4.25
MNI coordinate
Volume
x
y
z
36 58.83 67.88 52.36
3 43.19 45.77 31.73
56 1.84 4.42 2.04
1543 165 123 57
BA: Brodmann area.
An elastic cap (EasyCaps, Falk Minnow Services, Herrsching, Germany) with 64 sintered Ag/AgCl ring electrodes arranged in an extended 10-10 system was utilized.
An additional electrode recorded the ECG from a bilateral montage below the heart on the back. All the electrodes required extra series resistors to avoid saturation (5 kΩ for
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Fig. 6 – Correlation map for the EEG alpha power data and the 0.027–0.073 Hz filtered. fMRI data (p ¼0.001, uncorrected, cluster size Z 10). EEG and 15 kΩ for ECG). On the EEG cap, FCz was chosen as the reference. The sampling rate of EEG data was set at 5000 Hz, and then the data was band-pass filtered with a 0.1– 250 Hz filter to reduce ultra-noise which was induced by the fMRI scan. We adjusted the impedance to below 20 kΩ and placed the amplifier and a rechargeable battery package in a stable position outside the bore. The amplifier signal was transmitted to a computer outside the scanner room via fiber optic cables.
following parameters: repeat time (TR)¼ 2000 ms, echo time (TE) ¼30 ms, 32 slices, matrix size¼ 64 64, acquisition voxel size¼ 3.125 3.125 3.84 mm3, flip angle (FA)¼ 901, and field of view (FOV)¼ 200 cm. Three hundred TR data sets were collected. In addition, a high resolution, three-dimensional T1-weighted structural image was acquired (TR¼2530 ms, TE¼ 3.39 ms, 128 slices, FA¼ 71, matrix size¼ 256 256 and resolution¼1 1 1.33 mm3).
4.4. 4.3.
Data preprocessing
fMRI acquisition
The fMRI data were acquired with a 3T whole body scanner (Siemens Magnetom TrioTIM, Erlange, Germany) equipped with a standard birdcage head coil at the Brain Imaging Center of BNU. Functional images were collected with the
The EEG data was preprocessed using Brain Vision Analyzer 2.0 (Brain products, Germany). At the first step, the gradient artifacts were removed using a sliding average (Allen et al., 2000) of 21 averages. Subsequently, the EEG was down sampled to 500 Hz and low-pass filtered with an infinite
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impulse response (IIR) filter with a cut-off frequency of 70 Hz. Then, the ballistocardiogram (BCG) artifact was removed by first using a sliding average procedure with 11 averages (Allen et al., 2000). And at last so-cleaned EEG data was band-pass filtered between 1 and 40 Hz and changed the reference to average. For each participant, the functional images from the first 10 time points were discarded to allow for equilibration of the magnetic field. The remaining 290 images were preprocessed using the Statistical Parametric Mapping package (SPM8, http://www.fil.ion.ucl.ac.uk/spm) to realign, normalize, and smoothen the data. Images were re-sampled into 3 3 4 mm3 and smoothened with an 8-mm full width at half maximum Gaussian kernel. The six head motion parameters obtained in the realigning step was saved. All the six head motion parameters were regress out using REST.
4.5.
fMRI data filtering
The preprocessed fMRI data were filtered with the 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 frequency bands using the Data Processing Assistant for Resting-State fMRI (DPARSF, http://www.restfmri.net/ forum/DPARSF).
4.6.
Correlation computing
After pre-processing, the O1 and O2 channel were chosen and used to calculate the alpha rhythm which is 8–12 Hz. We remove the first 10 point to maintain consistence with fMRI data. The average of these two channel's alpha rhythm power series was convolved with a classical HRF in SPM8 software (Wellcome Department of Imaging Neuroscience, London, UK). For each subject, correlations were evaluated between EEG alpha power and each frequency band using the RestingState fMRI Data Analysis Toolkit (REST, http://resting-fmri. sourceforge.net/). One sample t-tests were performed to determine the correlation maps. Z-shift transfer was used to make sure the normality of the data, and a FDR correction was used to correct the multi-comparison problem.
Acknowledgment This work was supported by the Key Program of National Natural Science Foundation of China (6121001), General Program of National Natural Science Foundation of China (61222113), Program for New Century Excellent Talents in University (NCET-12–0056) and Open Project Funding of the
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State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) (SKLNST-2013-1-03).
r e f e r e nc e s
Allen, P.J., et al., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12 (2), 230–239. ¨ ber das elektrenkephalogramm des menschen. Berger, H., 1929. U Arch. Psychiatr. Nervenkr. 87, 527–570. Buzsa´ki, G., Draguhn, A., 2004. Neuronal oscillations in cortical networks. Science 304, 1926–1929. Buzsaki, G., 2009. Rhythms of the Brain. Oxford University Press, USA. Corbetta, M., Shulman, G.L., 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3 (3), 201–215. Fox, M.D., et al., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U S A 102 (27), 9673–9678. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711. Goldman, R.I., Stern, J.M., Engel Jr., J., Cohen, M.S., 2002. Simultaneous EEG and fMRI of the alpha rhythm. NeuroReport 13 (18), 2487–2492. Laufs, H., Holt, J.L., Elfont, R., Krams, M., Paul, J.S., Krakow, K., Kleinschmidt, A., 2006. Where the BOLD signal goes when alpha EEG leaves. NeuroImage 31 (4), 1408–1418. Laufs, H., Kleinschmidt, A., Beyerle, A., Eger, E., Salek-Haddadi, A., Preibisch, C., Krakow, K., 2003. EEG-correlated fMRI of human alpha activity. NeuroImage 19 (4), 1463–1476. Mantini, D., Perrucci, M.G., Del Gratta, C., Romani, G.L., Corbetta, M., 2007. Electrophysiological signatures of resting state networks in the human brain. Proc. Natl. Acad. Sci. USA 104 (32), 13170–13175. Mo, J., Liu, Y., Huang, H., Ding, M., 2013. Coupling between visual alpha oscillations and default mode activity. NeuroImage 68, 112–118. Moosmann, M., Ritter, P., Krastel, I., Brink, A., Thees, S., Blankenburg, F., Taskin, B., Obrig, H., Villringer, A, 2003. Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. NeuroImage 20 (1), 145–158. Penttonen, M., 2003. Natural logarithmic relationship between brain oscillators. Thal. Relat. Syst. 2, 145–152. Wise, R.G., Ide, K., Poulin, M.J, Tracey, I., 2004. Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. NeuroImage 21 (4), 1652–1664. Zuo, X.N., Martino, A.D., Kelly, C., Shehzad, Z.E., Gee, D.G., Klein, D.F., Castellanos, F.X., Biswal, B.B., Milham, M.P., 2010. The oscillating brain: complex and reliable. NeuroImage 49, 1432–1445.
