Long-range EEG phase synchronization during an arithmetic task indexes a coherent cortical network simultaneously measured by fMRI

www.elsevier.com/locate/ynimg NeuroImage 27 (2005) 553 – 563

Long-range EEG phase synchronization during an arithmetic task indexes a coherent cortical network simultaneously measured by fMRI Hiroaki Mizuhara,a,* Li-Qun Wang,b Koichiro Kobayashi,a,c,d and Yoko Yamaguchia,d a

Laboratory for Dynamics of Emergent Intelligence, RIKEN Brain Science Institute, 2-1, Hirosawa, Wako-shi, Saitama 351-0198, Japan Research Center of Advanced Technologies, Tokyo Denki University, 2-1200, Muzai-Gakuendai, Inzai-shi, Chiba 270-1382, Japan c Department of Welfare Engineering, Faculty of Engineering, Iwate University, 4-3-5, Ueda, Morioka-shi, Iwate 020-8551, Japan d Core Research for Evolutional Science and Technology, Japan Science and Technology Agency, Japan b

Received 28 September 2004; revised 8 April 2005; accepted 15 April 2005 Available online 25 May 2005

An open question lies in whether or not distributed activities in the distant brain regions are integrated into a coherent ensemble for cognitive information processing. Long-range phase synchronization is often observed by scalp EEG measurements during cognitive tasks and is considered to provide a possible neural principle for the functional integration of distributed neural activities. Synchronization could be reflected at the neuron firing level or at the local field potential and could appear in the scalp EEG under certain conditions on neural spatial and temporal coherence. To examine if phase synchronization is concerned with the integration of distant regions, we proposed a method to extract brain activities associated with task-dependent phase synchronization by combining simultaneous fMRI and EEG. By applying this method in a mental arithmetic task, we found a dominant task-dependent increase of phase synchronization around 14 Hz (in beta frequency) across bilateral parietal sites that were associated with both negative and positive BOLD responses. Functional connectivity analyses of these regions demonstrated that an increase in hemispheric beta synchronization was associated with a linking between the crosshemispheric regions (left angular gyrus and right superior parietal gyrus) and also among the anterior – posterior regions (right dorsolateral prefrontal cortex, putamen, and right superior temporal gyrus). These findings indicate that the positive BOLD regions (dorsolateral prefrontal cortex and superior parietal lobule) are linked with other negative BOLD regions. We also discussed the possible importance of beta synchronization in the formation of a working memory network. D 2005 Elsevier Inc. All rights reserved. Keywords: Phase synchronization; fMRI; EEG

* Corresponding author. Fax: +81 48 467 6938. E-mail address: [email protected] (H. Mizuhara). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2005.04.030

Introduction Many studies involving brain imaging and neurophysiological measurements have reported that, during a cognitive task, the distributed brain regions contribute together in the task. However, how those regions are linked for a specific brain function is still unknown. The oscillation synchronizations in neuronal discharges found that around 40 Hz (gamma oscillation) in animal neurophysiological recordings have been considered to integrate a widely distributed set of neurons together into a coherent ensemble underlying a cognitive function (Singer, 1999; Varela et al., 2001). A number of scalp EEG studies have demonstrated that human long-range synchronization is associated with perceptual objects, motor programs, or mnemonic functions in various frequency ranges (Miltner et al., 1999; Rodriguez et al., 1999; Sarnthein et al., 1998; Tallon-Baudry et al., 1997; Varela et al., 2001; von Stein and Sarnthein, 2000; von Stein et al., 1999). However, spatial resolution limits in these studies prevent the uncovering of the distribution of neural sources in the brain. Synchronization could occur at the level of neuron firing or at the local field potential and could only appear in the scalp EEG under certain conditions on neural spatial and temporal coherences. In addition, the blurring effects of the EEG lead field could make the problem of relating synchronized spikes or local field potential activities to the EEG even more complicated, whereas long-range phase synchronization could be crucial for integration. To overcome these limits, simultaneous measurements of EEG and fMRI are under development. The fMRI is used to examine changes in the blood oxygen content and detect the blood oxygen level dependent (BOLD) signal with high spatial resolutions (Ogawa et al., 1990). The analysis of simultaneous fMRI and EEG attempts to combine the spatial resolution of the fMRI and the temporal resolution of the EEG to overcome modality limitations during isolation. Previous studies of simultaneous fMRI and EEG can be classified into three categories: integration through temporal

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prediction, integration through spatial constraints, and integration through the fusion of the two (Kiebel and Friston, 2004). In the category of integration through temporal prediction, the use of parameters derived from the EEG has shown to be useful as experimental variables in the analysis of the fMRI (Benar et al., 2002; Goldman et al., 2002; Martinez-Montes et al., 2004). In our previous paper, this type of multimodality information combination was developed to elucidate task-dependent brain activities during mental arithmetic. Our results successfully demonstrated that cortical networks in the distributed regions emerge in the time window of appearances of a frontal midline theta oscillation (Mizuhara et al., 2004). This strongly suggests that the long-range regional networks are dynamically linked by the synchronization of distant neural activities, while the observation is not directly related to the emergence of long-range synchronization. In the current study, we developed a method of analyzing the brain regions associated with long-range EEG synchronization. This new method is applied to elucidate the relations of EEG phase synchronization and the BOLD response during a blocked experiment that is comprised of the resting state and mental arithmetic. Using the experimental data from our previous study (Mizuhara et al., 2004), we applied our method to a representative pair of scalp EEG electrodes to investigate the regional networks related with phase synchronization.

Materials and methods Subjects Eight male subjects participated in this experiment, and all had written informed consents. Their ages ranged from 21 to 35 years (mean age of 25.4 T 5.4). The RIKEN Ethics Committee approved this experiment. Experimental design

Germany), the raw EEG was sampled at 5 kHz with a 1 Hz high pass and 250 Hz low pass filters. Blood oxygenation sensitive echoplanar images (EPI) were obtained using a 1.5 T MR scanner (Staratis II, Hitachi Medico, Japan) and were taken simultaneously along with the EEG measurements. The subject’s head was immobilized using a vacuum pad during the measurements. The fMRI measurement conditions were the followings: repetition time (TR) = 5 s, echo time (TE) = 47.2 ms, acquisition time (TA) = 3.3 s, slice time (ST) = 94 ms, flip angle (FA) = 90-, field of view (FoV) = 240 mm, matrix size = 64
64, slice thickness = 4 mm, and gap = 0 mm. EEG data analyses Since MR imaging and cardio ballistic artifacts contaminated the EEG measurements (Fig. 1a), the averaged waveforms of the MR and cardio ballistic artifacts were subtracted from the contaminated periods using Brain Vision Analyzer software (Brain Products, Germany) in order to remove the artifacts (Allen et al., 1998, 2000; Fig. 1b). Previous studies reported that both the volume conduction and the reference signals in EEG recordings sometimes incorrectly effect the estimation of phase synchronization measurements (Fein et al., 1988; Lachaux et al., 1999: Zaveri et al., 2000). When studying the EEG, the computation of the scalp current density is often used to reduce the volume conduction and to solve the reference problem. In the current analysis, before the estimation of phase synchronization, we obtained the scalp current density at each electrode position by applying the spherical Laplace operator to the voltage distribution on the surface of the scalp using Brain Vision Analyzer software. This procedure is characterized by the following parameters: the order of the splines, n = 4, and the maximum degree of the Legendre polynomial, n = 10, with a precision of 105 (Perrin et al., 1989). We down-sampled the scalp current density to 500 Hz and exported it into Matlab (Mathworks, Sherborn, MA, USA) software for further analysis.

The experiment consisted of two conditions, one being closedeye resting and the other being a performance of mental arithmetic with closed eyes. Each condition had five states with 30-s durations. Resting and arithmetic were alternatively presented to the subject five times in each trial. Two trials were examined for each subject in the simultaneous fMRI and EEG measurements. During the arithmetic states, the subjects were asked to continuously subtract a single constant digit from 1000. This number was randomly chosen and presented to the subject at the beginning of each arithmetic state. A beeping sound was given at the beginning of each resting to indicate the end of the arithmetic state. Simultaneous measurement of fMRI and EEG Two MR compatible amplifiers (Brain Vision MR, Brain Products, Germany) were used to acquire the EEG and MR measurements. We used a 10/10 standard system electrode cap with sintered Ag/AgCl ring electrodes (Brain Cap, Falk Minow Services, Germany). The electrodes had 61 EEG channels, 2 ECG channels, and 1 EOG channel. An FCz electrode, located between Fz and Cz, was used as the measurement reference, and an AFz electrode, located between FPz and Fz, was used as the measurement ground. Using the Brain Vision Recorder (Brain Products,

Fig. 1. Examples of EEG data simultaneously measured with fMRI. (a) EEG raw data. MR imaging artifacts and cardio ballistic artifacts contaminated the EEG data. (b) Artifacts removed EEG data. MR imaging and cardio ballistic artifacts were removed from raw data by subtracting the waveform of MR imaging and cardio ballistic artifacts.

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To investigate phase synchronization, two methods have often been used to estimate the instantaneous phase of signals in neuroscience. These methods are the analytic concept of Hilbert transformation and convolution with a complex wavelet. A previous study reported that the wavelet transformation is computationally more efficient for scalp EEG, although the differences between the two methods are minor and they are fundamentally equivalent for neuroelectrical signals (Le Van Quyen et al., 2001). In the current study, we applied a complex Morlet’s wavelet transformation to compute the instantaneous phase of each electrode from 1 Hz to 20 Hz in 1-Hz steps. Complex Morlet’s wavelets have a Gaussian shape in the time domain (SD r t) and the frequency domain (SD r f) around its central frequency f (TallonBaudry et al., 1997). The signal of the scalp current density is convoluted by complex Morlet’s wavelet w(t, f):   pffiffiffi1=2  wðt; f Þ ¼ rt p ð1Þ exp  t 2 =2r2t expði2pf t Þ with r f = 1/(2pr t). The wavelet is characterized by a constant ratio (f/r f), and it was set to be 7. At 20 Hz, this constant ratio leads to a wavelet duration (2r t) of 111.4 ms and to a spectral bandwidth (2r f) of 5.71 Hz. At 1 Hz, the constant ratio leads to a duration of 2195 ms and to a bandwidth of 0.29 Hz. We computed the instantaneous phase / n (t,f) of each electrode n using the equation: expði/n ðt; f ÞÞ ¼ wðt; f Þ  sn ðtÞ=jwðt; f Þ  sn ðt Þj;

ð2Þ

where s n (t) is the signal of the scalp current density at electrode n. In order to identify the phase relations between any two electrodes, the instantaneous phase difference D/ lm (t, f) between the lth and mth electrodes was computed for all pairs of electrodes. In order to determine whether or not the phase difference remained unchanged, we defined a phase synchronization index with the following equation for the individual time segment and electrode pairs: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PSIlm ð j; f Þ ¼ Xlm ð j; f Þ2 þ Ylm ð j; f Þ2 ; ð3Þ with PSIlm (j, f) representing the mean vector length of the angular dispersions of the phase differences in each time segment. jN X Xlm ð j; f Þ ¼ cosðD/lm ðk; f ÞÞ=N ; k ¼ ð j  1ÞN þ 1

and Ylm ð j; f Þ ¼

jN X

sinðD/lm ðk; f ÞÞ=N ;

k ¼ ð j  1ÞN þ 1

where j is the number of the time segment with an interval of 1 s and a 500 Hz sampling rate; therefore, N is 500. The set of PSIlm ( j, f) is termed as PSI below. In order to identify the task-dependent modulation of the PSI, we compared the PSIs of the individual subjects that were taken during the arithmetic and resting states in the two trials with a twosample t test. Using the results of the statistical t values for the individual subjects, we applied the one-sample t test to determine the task-dependent modulation of the PSI across the subjects. In this study, we used a wavelet transformation that was both defined and highly correlated over a field of time and frequency. In order to estimate the confidential interval of the statistical t value, we used a bootstrap procedure. This procedure (Efron, 1979, 1986) is often used to handle correlations among statistical comparisons and to generate type I error rate empirical estimations of electrophysio-

555

logical measures (Di Nocera and Ferlazzo, 2000; Sederberg et al., A* 2003). In this study, the virtual PSI data for the arithmetic (u lm (j, R* f)) and resting (u lm (j, f )) states were respectively computed from PSI using the equations A uA4 ¯A ¯ lm ð f Þ; lm ð j; f Þ ¼ ulm ð j; f Þ  u lm ð f Þ þ u

ð4Þ

R u Rlm ð f Þ þ u uR4 ¯ lm ð f Þ; lm ð j; f Þ ¼ ulm ð j; f Þ ¯

ð5Þ

A ( u lm

R u lm (

j, f ) and j, f ) represent the PSI from the where A R ¯ lm ¯ lm arithmetic and resting states, respectively. u ( f ) and u (f) A R denote the means of u lm ( j, f ) and u lm ( j, f ) across segments (j), respectively, and u¯ lm ( f ) denotes the mean of all of the data across the segments from the arithmetic and resting states. Using the 2000 bootstrapped re-samples that were obtained from A* R* ( j, f ) and u lm ( j, f ), the statistical t values were computed u lm with the two-sampled t test for each individual subject. Based on the bootstrapped statistical t values, the one-sampled t test was used to generate a distribution that allowed us to determine the threshold of the t value. In order to decide the frequency of interest (f 0) for further analyses, we counted the number of electrode pairs in which the t value exceeded the threshold (P < 0.05) for each frequency. To identify the relationship between the PSI and the amplitude of the scalp EEG, we computed the time – frequency energy E n (t,f) at the nth electrode by convoluting the complex Morlet’s wavelet w(t, f ) to the signal s n (t): En ðt; f Þ ¼ jwðt; f Þ  sn ðtÞj2 :

ð6Þ

The averaged energy at the jth segment was computed as follows: jN X En ð j; f Þ ¼ E n ðk; f Þ=N : ð7Þ k ¼ ð j  1ÞN þ 1

Using the time series of PSIlm (j, f 0) at the frequency of interest (f 0) as a covariate, a regression analysis was performed to the energy E n (j, f ) for the individual subjects. Based on the results of these regression analyses, we applied the one-sample t test to decide the PSI-related EEG amplitude enhancements across the subjects. fMRI data analyses We used SPM99 software (Wellcome Department of Imaging Neuroscience, UK) for preprocessing and voxel-based statistical analysis. All of the EPIs were transferred into the first image of each trial to correct the subject’s head motion. The slice timing was corrected into the 15th slice, which was the center of each volume, to remove the time delay of scanning the entire brain. The individual EPIs were normalized into a standard brain from the Montreal Neurological Institute. They were then smoothed out with a 10 mm full-width half-maximum Gaussian kernel. Global changes were adjusted by proportional scaling, and low-frequency confounding effects were removed using a high-pass filter with a 120-s cutoff period. The BOLD responses occurred after the proper neuronal activities with a time delay, which was decided by the hemodynamic responses (Logothetis et al., 2001). To identify the local neuronal activities triggered by long-range phase synchronization, we hypothesized an expected BOLD response by convoluting the canonical hemodynamic response function (HRF) to PSIlm (j, f 0) for each subject and trial. The HRF consists of two gamma functions with the following parameters: delay of response = 6 s, delay of

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undershoot = 16 s, dispersion of response = 1, dispersion of undershoot = 1, ratio of response to undershoot = 6, onset of event = 0 s, and length of kernel = 32 s. The expected BOLD response build on PSIlm (j, f 0) that has the task-dependent enhancement necessarily shows state-dependence. In such a case, the regression analyses possibly included the regions that reflected the BOLD association with the state changes (Andrade et al., 1999). To rule out the possibility that the voxels simply reflect the correlation to the state change, we performed our analyses in two steps. The first step consists of the multiple regression analyses with the covariates of the expected BOLD build on the PSIlm (j, f 0) and state-related BOLD. The latter was created by convoluting the HRF to a boxcar design consisting of the 30-s mental arithmetic and the 30-s closed-eye resting periods. The multiple regression analyses were performed on each voxel on an intrasubject basis using the following equation: y ¼b1 X1 þ b2 X2 þ c þ e;

ð8Þ

where y is the M data matrix of the measured BOLD, X1 is a design matrix build on the PSIlm (j, f 0), b1 is the M matrix of a partial correlation coefficient for the covariate X1, X2 is a design matrix build on the boxcar design, b2 is the M matrix of a partial correlation coefficient for the covariate X2, c is an M constant matrix, and e is an M error matrix. The intersubject map was constructed by performing a one-sample t test on the partial correlation coefficient b1 to identify the voxels with significantly large partial correlation coefficients (P < 0.01, 10 voxels extent threshold, uncorrected for multiple comparison by cluster size). The results obtained with X1 inclusively derive from a stateindependent BOLD response. The second step was to rule out the state-independent effect. Epoch analyses were performed on an individual basis using regression analyses with only the state-related BOLD build on the boxcar design as a covariate with the equation: y ¼b3 X3 þ c þ e;

ð9Þ

where X3 is a design matrix build on the boxcar design and b3 is an M matrix of a partial correlation coefficient for the covariate X3. These analyses were performed with the intention of extracting voxels with significantly state-related modulations. The intersubject map for the state-related modulations of BOLD responses was constructed by performing a one-sample t test on the correlation coefficient b3 (P < 0.01, 10 extent voxels, uncorrected). To rule out the possibility that the voxels, which were extracted as PSI-related regions in the multiple regression analyses, do not reflect the staterelated modulations, we extracted the common result regions of PSI-related BOLD responses in the multiple regression analyses and the state-related BOLD responses in the epoch analyses. Thus, the voxels that showed positive and negative BOLD responses are defined by the following conditions: Positive BOLD : Pðb1 > 0Þ < p0 and Pðb3 > 0Þ < p0 ; Negative BOLD : Pðb1 < 0Þ < p0 and Pðb3 < 0Þ < p0 ; where P(x) is a function of probability, p 0 is the probability threshold, and b 1 and b 3 represent partial correlation coefficients at each voxel in the matrices b1 and b3, respectively. To identify the coherent networks of the BOLD responses, we performed a functional connectivity analysis using the regions of interest (ROIs). The functional connectivity between two brain regions is generally defined as the correlation between measurement pairs of the cerebral blood flow or BOLD signals taken from

several scans on the same subject, over several different subjects, or a combination of both (Worsley et al., 1998). In this study, the ROIs were defined as the exceeding of the threshold of probability (P < 0.01) and the extension (10 voxels) in both the multiple regression and epoch analyses. We extracted the BOLD time series in the individual voxels of these ROIs from the preprocessed EPIs for further analysis. The physiological noises, such as the cardiovascular effects, were already removed from the BOLD time series by adjusting the global changes and removing the lowfrequency confounding effects. The BOLD responses in each ROI were classified into two groups according to the arithmetic and resting states. The correlation coefficient between the ROIs during the arithmetic and resting states was respectively computed on an intrasubject basis. To test for functional connectivity across the subjects, we applied a one-sample t test with the correlation coefficients. To test for the modulation of functional connectivity, we compared the correlation coefficients between the arithmetic and resting states with a two-sample t test.

Results Task-dependent EEG phase synchronization To decide the electrode pairs showing the task-dependent PSI changes, we computed the PSI in each pair of electrodes for each segment. To compare the PSI during mental arithmetic to that during closed-eye resting from 1 Hz to 20 Hz with 1 Hz steps, we counted the pairs of electrodes that showed significant PSI modulations between the arithmetic and resting (P < 0.05). One should notice that the statistical threshold for the PSI is uncorrected for the multiple comparison across the electrode pairs, although we applied the bootstrap method against the high correlation of the time and frequency field, which was caused by the wavelet analysis. The results showed that the PSI increases were found in the theta (4 Hz – 8 Hz) and beta (13 Hz – 18 Hz) frequency bands during arithmetic (Fig. 2a) and in the alpha (8 Hz – 12 Hz) frequency band during resting (Fig. 2b). Phase synchronization in the beta range has been reported to be a possible neural entity underlying cognitive processes (Gross et al., 2004; Tallon-Baudry et al., 2001; von Stein et al., 1999). In this study, we focus our attention on the most dominant frequency, 14 Hz, in the beta band. Fig. 2c illustrates the topographical pattern of phase synchronization at 14 Hz (P < 0.01). Long-range phase synchronization was found between the frontal and parietal sites and right and left parietal sites. The maximum significance of the task-dependent increase of phase synchronization was found between the left (P3) and right (CP4) parietal electrodes (Fig. 3a). The phase synchronization of this pair had a peak frequency of task-dependency at 14 Hz (Fig. 3b). In the following analysis, we applied our proposed method to the phase synchronization pairs at 14 Hz to test the feasibility of this method. Fig. 4 shows the phase difference distributions of each segment across all of the subjects. These results indicated that the EEG phase of the P3 electrode on the left hemisphere was advanced by 6.2- to that of the CP4 electrode on the right hemisphere during resting (mean length of vector: r = 0.48, samples: n = 2400, 95% confidence interval A95% = 6.2- T 4.2- tested by the one-sample t test for the mean angle; Zar, 1999) and advanced by 8.8- during arithmetic (r = 0.47, n = 2400, A95% = 8.8- T 4.3-). It should be noticed that the vector length within each segment during

H. Mizuhara et al. / NeuroImage 27 (2005) 553 – 563

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Fig. 2. Task-dependent modulations of the EEG phase synchronization index across all subjects. (a) The number of electrode pairs exhibiting significantly greater PSIs during arithmetic relative to that during resting. The significance threshold for each frequency and pair of electrodes is decided using the bootstrap method. (b) The number of electrode pairs exhibiting greater PSIs during resting relative to those during arithmetic. (c) Task-dependent increases of the EEG PSI at 14 Hz. Lines represent significant increases of PSI during mental arithmetic relative to that during resting (P < 0.001). Drawing is the top view of the scalp. Each white circle signifies an electrode that was used for measurements.

arithmetic was larger than that during resting, even though the mean angles of the vector across all of the segments showed larger uniformity of the mean angle during resting. Phase-synchronization-related BOLD responses To identify the cortical responses with phase synchronization observed in the scalp EEG, we performed voxel-based statistical analyses of the fMRI BOLD images. We firstly applied the random field theory to correct the statistical threshold for multiple comparison across entire volume. The BOLD signal in the left intraparietal sulcus (x = 34, y = 42, z = 42 mm in Talairach coordinate, t 7 = 45.46, P < 0.05 corrected) showed the significant positive increase during the mental arithmetic relative to that during the eye close resting in the analysis using the state-related BOLD build on the boxcar design as a covariate, whereas no responses were found to be significant in the analyses using the PSI-related BOLD. In order to increase the sensitivity for the responses below the corrected statistical threshold and to reduce the risk of Type I error, here we shifted to use the combination of the uncorrected peak-height (P < 0.01 uncorrected) and cluster size thresholds (10 voxels) instead of the corrected peak threshold (P < 0.05) based on the random field theory. Using this statistical threshold, we found significant

responses associated with beta PSIP3 – CP4 (j, 14 Hz) in the frontal, parietal, and temporal cortices (Table 1, Fig. 5). The positive BOLD correlations to this phase synchronization were found in the right superior parietal lobule (BA7) and the right dorsolateral prefrontal cortex (BA9). In the bilateral angular gyri (BA39), the BOLD signals were negatively correlated to the beta phase synchronization. In addition to the negative BOLD responses in the angular gyri, the BOLD signals in the right superior temporal gyrus (BA22), the right putamen, and the posterior cingulate gyrus (BA31) also showed negative correlations to the beta phase synchronization. BOLD functional connectivity In order to demonstrate the functional connectivity of the BOLD signals in relation to beta phase synchronization, we computed a correlation coefficient between each pair of voxels in the ROIs for resting and arithmetic, respectively. Using the correlation coefficient for all trials, the significant correlation coefficients (onesample t test across all subjects, t 7 > 2.37, P < 0.05) and coefficient modulations (two sample t test, t 14 > 2.14, P < 0.05) were identified (Fig. 6). The BOLD in the right superior temporal gyrus showed significant connectivity to the BOLD in the right and left angular

Fig. 3. Electrode pair used for fMRI analysis. (a) Maximum significance of PSI increase during arithmetic relative to that during resting was found between P3 electrode (left hemisphere) and CP4 (right hemisphere) electrode at 14 Hz. (b) t statistical value for the difference in the PSI between mental arithmetic and resting for the pair (P3 – CP4) across subjects. At this pair, the PSI in the 13 Hz – 17 Hz beta band was higher during mental arithmetic with a peak at 14 Hz. Gray region represents the 95% confidence interval constructed using the bootstrap method. (c) Time series of beta PSI pair (P3 – CP4) at 14 Hz for a representative subject. Gray periods represent closed-eye states, and white periods represent mental arithmetic states.

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0.87, P = 0.40). The putamen showed a significant increase in connectivity to the posterior cingulate gyrus during arithmetic relative to resting (t 14 = 2.77, P = 0.015). It also showed a significant connectivity increase to the right dorsolateral prefrontal cortex during arithmetic (t 14 = 3.42, P = 0.004). The right superior parietal lobule showed a significant connectivity increase to the left angular gyrus during arithmetic (t 14 = 3.79, P = 0.002). The pair of ROIs across the hemispheres showed the most significant connectivity increase that was consistent with the bilateral location of phase synchronization. We should notice that positive and negative BOLD regions were negatively correlated with each other. These results of functional connectivities showed that the positive and negative BOLD regions were networking distant brain regions in the prefrontal, putamen, and parietal/ temporal cortices during arithmetic (Fig. 6b), while the network was localized in the parietal/temporal cortices during resting (Fig. 6a). Phase-synchronization-related EEG responses In the parietal and temporal cortices, the BOLD signals exhibited significant responses in association with the beta phase synchronization observed in the scalp EEG. This suggests that long-range phase synchronization accompanied the local neuronal activities in the distant cortical regions. Thus, the EEG activities should be enhanced with long-range phase synchronization, and all or some of the corresponding scalp EEG should be observed in the scalp EEG. In order to identify the EEG activities that corresponded to the BOLD responses in the cortices, we tested them with regression analyses between PSIP3-CP4 (j, 14 Hz) and the EEG energy, which were computed by a wavelet transformation from 1 Hz to 20 Hz with 1-Hz steps. The results demonstrated that the EEG activities at 14 Hz showed significant correlation in the parietal sites to the beta phase synchronization across hemispheres (Fig. 7; P3: t 7 = 3.63, P = 0.0042; CP4: t 7 = 3.93, P = 0.0028). These EEG beta activity sites were consistent with the electrode pair (P3 – CP4) that showed the task-dependent beta phase synchronizations, which we used for the fMRI analyses. The frequency of the enhanced oscillation at 14 Hz was also consistent with that of the synchronization used for the analysis. Thus, the long-range phase

Fig. 4. Phase difference’s mean angle of the pair between P3 and CP4 across subjects. Circular histograms illustrate the mean angles of the phase differences (P3 – CP4) for closed-eye resting (a) and mental arithmetic (b). Mean angles of the phase differences demonstrate that the EEG phase of the P3 electrode on the left hemisphere is rather advanced in phase compared to that of the CP4 electrode on the right hemisphere when the mean angles changed from 0- to 180-. Each count represents the mean angle of one segment for each subject.

gyri during both arithmetic and resting (resting: STG-rAg t 7 = 2.70, P = 0.031, STG-lAG t 7 = 2.91, P = 0.023; arithmetic: STGrAg t 7 = 3.13, P = 0.017, STG-lAG t 7 = 3.05, P = 0.019). Significant connectivities were not found anywhere else during resting. The connectivity of the superior temporal gyrus to the right angular gyrus increased during arithmetic relative to that during resting (t 14 = 2.16, P = 0.048). During arithmetic, the connectivity of the right superior temporal gyrus extended to the right putamen (t 7 = 2.69, P = 0.031), even though the comparison of the coefficient did not show any significant modulation (t 14 =

Table 1 Areas significantly responding to beta phase synchronization across hemispheres around the parietal sites Region (BA)

PSI x

Positive BOLD Right superior parietal lobule (7) Right dorsolateral prefrontal cortex (9) Negative BOLD Left angular gyrus (39) Right angular gyrus (39) Right superior temporal gyrus (22) Right posterior cingulate gyrus (31) Right putamen

Epoch y

z

t7

30 36

50 38

52 32

3.51 4.50

30 38

50 40

50 30

4.85 5.09

42 44 62 10 22

58 62 56 40 15

10 12 6 34 9

4.50 4.40 6.60 4.03 4.31

44 48 60 10 18

60 62 58 40 16

8 12 6 32 10

3.59 9.17 11.8 4.27 4.20

x

y

z

t7

BA: Brodmann area. Coordinates (x, y, z) are expressed in the Talairach space (mm; Talairach and Tournoux, 1988). All regions are P < 0.01, uncorrected for multiple comparison by cluster size in both the results of PSI-related voxels in the multiple regression analyses and state-related voxels in the epoch analyses. Extent threshold is 10 voxels. The results in the PSI related analysis were from the regression analyses between the measured and expected BOLD build on the time series of PSI. The results in the epoch analyses were from the regression analysis between the measured and expected BOLD build on the task design.

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Fig. 5. Brain areas that responded in association with beta phase synchronization that are shown in Fig. 3a. (a) Areas that significantly responded to beta phase synchronization were superimposed onto a standard brain. Brain shows the right lateral view (upper) and the left lateral view (lower). Red regions represent the positive BOLD responses with the beta PSI, and blue regions represent the negative BOLD responses. (b) Negative BOLD responses in the angular and superior temporal gyri are superimposed onto a horizontal slice at z = 10 mm, where the origin is decided by the anterior and posterior commissures. (c) Positive BOLD responses in the right dorsolateral prefrontal cortex and the negative BOLD response in the posterior cingulate gyrus are superimposed onto a horizontal slice at z = 28 mm. (d) Positive BOLD response in the superior parietal lobule is superimposed onto a horizontal slice at z = 52 mm. (e) Negative BOLD in the putamen is superimposed onto an axial slice at y = 16 mm. The coordinates of these slices correspond to the green lines shown in the lateral views (a). The abbreviations represent the regions as follows. SPL: right superior parietal lobule, DLPFC: right dorsolateral prefrontal cortex, lAg: left angular gyrus, rAg: right angular gyrus, STG: right superior temporal gyrus, PCG: posterior cingulate gyrus, and Pu: right putamen.

synchronization observed in the scalp EEG is accompanied by the enhancement of local neuronal activities. This confirms the biophysical reason for the linkage between the scalp EEG and fMRI responses.

Discussion In association with the task-dependent enhancement of beta phase synchronization, we found that the right dorsolateral prefrontal cortex and the right superior parietal lobule were activated, while the left and right angular gyri, the putamen, and the right superior temporal gyrus showed negative BOLD responses. Further analysis of BOLD functional connectivity showed that the network of BOLD regions extended to the prefrontal, putamen, and parietal/temporal cortices during arithmetic, where the network was localized in the parietal/temporal cortices. Furthermore, the most significant task-dependent increase of functional connectivity was found between the right superior parietal lobule and the left angular gyrus. This cross-hemispheric connectivity exhibited reasonable geometrical correspondence to the topographical pattern of the beta phase synchronization that we used in this study. These results indicate that a crosshemispheric phase synchronization of beta oscillation in the parietal cortices emerges along with the enhancement of interactions among the distant brain regions of the positive and negative BOLD responses during mental arithmetic. We discuss

the biophysical implication of these findings and their cognitive functions below. Positive BOLD responses Previous studies have reported that the superior parietal lobule is most consistently involved in spatial working memory tasks (Chafee and Goldman-Rakic, 1998; Leung and Zhang, 2004). The dorsolateral prefrontal cortex is believed to be responsible for stimulus location maintenance (Fletcher and Henson, 2001). The right dorsolateral prefrontal cortex was reported as relating to spatial working memory tasks (Belger et al., 1998). During numerical manipulation, these two regions are also candidates of the network for numerical processing, where the intraparietal sulcus is the prime neural substrate for numerical representation (Dehaene et al., 2003, 2004; Duffau et al., 2002). Since the BOLD effect was first introduced in the early 1990s (Ogawa et al., 1990) in fMRI studies, a localized positive BOLD response between task and no-task intervals or events has been commonly interpreted as evidence for task-related processing in the cortical region. A recent study has reported that the positive BOLD responses resonate the increase in neuronal activities in cortices and are especially well correlated to the local field potential (Logothetis et al., 2001). Recent electorophysiological studies on monkeys have reported that neurons in the lateral prefrontal cortex are activated to encode the quantity of visual field

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Fig. 6. Results of BOLD functional connectivity analysis. (a) Significant correlation coefficient between areas extracted by fMRI analysis across subjects (t 7 > 3.63, P < 0.05). Upper-right columns divided by the orthogonal line represent results during mental arithmetic, and lower-left columns represent results during closed-eye resting. (b) Significant changes of correlation coefficient across subjects (t 14 > 3.93, P < 0.05). Arrangement of columns is the same as in panel (a). (c) Schematic illustration of the functional connectivity of BOLD responses in ROIs during resting. Positive BOLD ROIs are outlined by red squares, and negative BOLD ROIs are outlined by blue squares. Significant correlation coefficients during resting are expressed by solid lines. Abbreviations in the squares represent the regions as follows: DLPFC: right dorsolateral prefrontal cortex, l-Ag: left angular gyrus, r-Ag: right angular gyrus, STG: superior temporal gyrus, PCG: posterior cingulate gyrus, and Pu: right putamen. (d) Schematic illustration of functional connectivity during arithmetic. Squares, solid lines, and abbreviations are the same as in panel (c). Dotted lines represent the increases of correlation coefficients during arithmetic relative to those during resting.

items (Nieder and Miller, 2004; Nieder et al., 2002). Furthermore, a single-unit recording study of posterior parietal neurons has indicated a similarity of neuronal activation to that observed in the dorsolateral prefrontal cortex (Chafee and Goldman-Rakic, 1998). Thus, the positive BOLD response in the superior parietal gyrus and the dorsolateral prefrontal cortex observed in the present study can be interpreted as the neuronal activations that are concerned with the maintenance of quantity representation for mental arithmetic. Negative BOLD responses While most fMRI studies have reported the positive BOLD responses in association with several tasks, a few studies have also noted the associations of negative BOLD responses with certain tasks, stimuli, or events (Shmuel et al., 2002; Smith et al., 2000; Tootell et al., 1998). The biophysical interpretation of the negative BOLD is now a hot topic, and at least two possible hemodynamic effects, Fvascular steal_ and Freduced neuronal activity,_ have been considered to account for that (Wade, 2002). The Fvascular steal_ is interpreted as a draining of blood from neighboring activated areas. In contrast, reduced neuronal activity is supplied with less oxygenated blood and shows a reduced BOLD signal. Some studies provide evidence for the relationship between the negative BOLD and the reduced neuronal activity. Smith et al. (2004) showed that a visual stimulus that motivated the primary visual

cortex in one hemisphere could cause extensive suppression in the other. Their results thus suggested that negative BOLD is not caused by the reduced local blood pressure arising from nearby capillary dilation and that positive and negative BOLD responses cause cross-hemispheric interaction to emerge in the primary visual cortices. Another cross-hemispheric interaction was reported in rat somatosensory cortex as the BOLD response was suppressed (Ogawa et al., 2000). In that study, the evoked potentials and the BOLD responses were simultaneously measured when bilateral somatosensory stimulations were used with time delays. Their results demonstrated that the BOLD response decreased with the suppression of the evoked potential at a proper inter-stimulus interval, which was relative to the BOLD without the suppression of neuronal activity. According to these results, one can expect that the negative BOLD responses reflect the suppression of neuronal activities relative to the reference state. Substantially, the regions showing the negative BOLD observed in this study is included in the regions that were previously reported in the PET study to need high oxygen consumption during resting (Raichle et al., 2001). Several studies of simultaneous fMRI and EEG established that the EEG oscillation in alpha or other frequency ranges associates with the negative BOLD responses (Goldman et al., 2002; Martinez-Montes et al., 2004; Mizuhara et al., 2004). This means that the relative suppression of neuronal activities is not contradictory with activation of neuronal oscillations.

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(McKiernan et al., 2003). Karnath et al. (2001, 2004) recently found that patients of spatial neglect showed damage in the right superior temporal gyrus, and they suggested that the superior temporal gyrus is the critical substrate for spatial attention. Together with the previous studies, both the scalp EEG and fMRI suggest that the beta oscillation observed during mental arithmetic may relate to some attention processes. In our fMRI analyses results, the negative BOLD was also found in the putamen, which consists of the striatum. A previous study reported that beta oscillation was found in the striatums of monkeys (Courtemanche et al., 2003). Our results showed that the putamen forms the network with the superior temporal gyrus and the dorsolateral prefrontal cortex during arithmetic. Because the dorsolateral prefrontal cortex projects to the striatum and caudate nucleus, the striatum is considered to be a necessary component of a working memory network (Levy et al., 1997). Thus, the functional connectivity among the positive and negative BOLD regions seems to be concerned with the attention control for the maintenance of quantity representation in the dorsolateral prefrontal cortex. However, their details are beyond the present study.

Conclusion

Fig. 7. Beta phase-synchronization-related modulation of EEG energy. (a) EEG topography of statistical t value tested by regression analysis at 14 Hz. Positive value represents increase of EEG energy with the beta PSI, and negative value represents decrease of EEG energy. Drawings represent the top view of the scalp. Topographic map of t value was generated with interpolation by spherical splines, which was characterized by these parameters: order of spline, n = 4 and maximum degree of Legendre polynomial, n = 10 with a precision of 105 (Perrin et al., 1989). (b) Modulation of statistical t value with frequency. Solid curve represents result at a left electrode FP3,_ and dotted curve represents results at a right electrode FCP4._

Functions of beta synchronization We will now go to the functional relevance of the cortical network with beta phase synchronization. Beta oscillation around 14 Hz was repeatedly found during higher-level cortical processing, such as semantic processing and spatial tasks (Papanicolaou et al., 1986; Vazquez Marrufo et al., 2001). A recent study reported that the transient long-range phase synchronization in the beta range is concerned with the fronto – parieto – temporal attentional network during an attentional blinking task (Gross et al., 2004). Additionally, beta synchronization across the hemispheres with a topographical pattern similar to our results shown in Fig. 3a was reported during the perception of objects (von Stein et al., 1999) and faces (David et al., 2004). A possible function of beta oscillation in the parietal sites is considered to be an attention process during cognitive tasks (von Stein et al., 1999). The most significant increase of functional connectivity in this study was found between the right superior parietal lobule and the left angular gyrus, which showed geometric well correspondence with the topographical pattern of beta phase synchronization. The right superior parietal lobule was reported during spatial tasks (Chafee and Goldman-Rakic, 1998). A previous brain imaging study also reported that BOLD responses decreased in the angular gyri during several attention-demanding cognitive tasks

In conclusion, our method successfully demonstrated a coherent cortical network that emerges with dominant long-range beta phase synchronization at a pair of electrodes during mental arithmetic. The spatial information of the cortical regions provides a possible function of beta oscillation around the parietal sites in the scalp EEG as an attention control for spatial working memory. By using a simultaneous fMRI and EEG, even a small phase synchronization on the scalp is good enough to extract the functional relevant networking of regions, not only in the superficial, but also deeper brain regions. This method could be feasible for identifying networks that are integrated with phase synchronization.

Acknowledgments We would like to thank Prof. Yoshinori Uchikawa, Mr. Satoshi Sasaki, Ms. Miki Shibuya, and Mr. Tatsuya Ikeda for their support of our data acquisitions. We would also like to express gratitude to Drs. Hironori Nakatani and Fumikazu Miwakeichi for their helpful advice concerning our methods and Dr. Naoyuki Sato for several helpful discussions. Our appreciation is also expressed to the anonymous reviewers for their comments on how to improve our methods.

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