Regional homogeneity changes in social anxiety disorder: A resting-state fMRI study

Psychiatry Research: Neuroimaging 194 (2011) 47–53

Contents lists available at ScienceDirect

Psychiatry Research: Neuroimaging j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / p s yc h r e s n s

Regional homogeneity changes in social anxiety disorder: A resting-state fMRI study Changjian Qiu a,1, Wei Liao b,1, Jurong Ding b, Yuan Feng a, Chunyan Zhu a, Xiaojing Nie a, Wei Zhang a,⁎, Huafu Chen b,⁎⁎, Qiyong Gong c a b c

Mental Health Center, West China Hospital of Sichuan University, Chengdu 610041, China Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China

a r t i c l e

i n f o

Article history: Received 26 May 2010 Received in revised form 4 November 2010 Accepted 13 January 2011 Keywords: Social anxiety disorder Regional homogeneity Resting state fMRI Default mode network

a b s t r a c t The previous task-based or resting perfusion studies in social anxiety disorder (SAD) patients have highlighted specific differences in brain response. Little is known about the changes in the local synchronization of spontaneous functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD) signals that occur in SAD during the resting state. We investigated altered neural activity in the resting state using a regional homogeneity (ReHo) analysis on 20 SAD and 20 healthy controls (HC). Compared with HC, SAD patients exhibited decreased coherence (ReHo) in the bilateral angular gyrus and the left medial prefrontal cortex within the default mode network (DMN), suggesting functional impairment of the perception of socially relevant emotional state and self-related mental representations; and also in the right dorsolateral prefrontal cortex and right inferior parietal gyrus within the central-executive network (CEN), reflecting the deficit of cognitive control of social anxiety. Significantly increased coherence (ReHo) was found in the left middle occipital gyrus, which would be consistent with their hypervigilance and hyperprosexia to the social communication even in the resting state. Our results might supply a novel way to look into neuro-pathophysiological mechanisms in SAD patients. © 2011 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Social anxiety disorder (SAD) is a common and chronic mental disorder (Stein and Stein, 2008), which is thought to involve emotional hyperactivity, cognitive distortions, and ineffective emotion regulation (Goldin et al., 2009b). Results of neurophysiological and neuroimaging studies (Damsa et al., 2009; Engel et al., 2009) showed that patients with SAD exhibited greater activity than healthy subjects in several brain regions related to emotional processing under social fear and anxiety conditions (Tillfors et al., 2001; Stein et al., 2002; Phan et al., 2006; Etkin and Wager, 2007). However, these studies were performed under taskbased conditions. Further understanding of SAD may be achieved during the resting state (Etkin et al., 2009), which may be absent or masked during an activation paradigm (Warwick et al., 2008). Recently, resting-state functional magnetic resonance imaging (fMRI) techniques have been applied to demonstrate abnormalities in various neuropsychiatric disorders (Anand et al., 2005; Garrity et al., 2007; Greicius et al., 2007; Zhang et al., 2009a, 2009b; Liao et al., 2010a, 2010b; Zhang et al., 2010). In particular, these abnormalities mostly relate to alterations in the coherent intrinsic neuronal activity of blood oxygen level-dependent (BOLD) fluctuations observed in resting-state ⁎ Corresponding author. W. Zhang, Fax: + 86 28 85582944. ⁎⁎ Corresponding author. H. Chen, Fax: + 86 28 83208238. E-mail addresses: [email protected] (W. Zhang), [email protected] (H. Chen). 1 Changjian Qiu and Wei Liao contributed equally to this work. 0925-4927/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pscychresns.2011.01.010

fMRI studies. In a previous study, reduced deactivation of the medial prefrontal cortex (MPFC) and increased deactivation of the posterior cingulate cortex (PCC) were observed in anxiety patients during listening to threatening words as compared to a resting condition (Zhao et al., 2007). These two brain regions are known to be critical in the default mode network (DMN) (Buckner et al., 2008; Broyd et al., 2009). In addition, the results of fMRI and single photon emission computed tomography studies consistently implicate alterations in critical regions in the DMN in SAD patients (Warwick et al., 2008; Gentili et al., 2009). More recently, a pioneering resting-state study showed increased connectivity of a frontoparietal network (the centralexecutive network (CEN)) and decreased connectivity of an insulacingulate network (the salience network) in generalized anxiety disorder (Etkin et al., 2009) using functional connectivity analysis. Moreover, our previous study indicated a diffuse impact on widely distributed resting-state networks and selective changes of intrinsic functional connectivity in SAD patients at rest (Liao et al., 2010a). Regional homogeneity (ReHo), a novel method that differs from functional connectivity, has been developed to analyze the local synchronization of spontaneous fMRI BOLD signals (Zang et al., 2004). The ReHo method assumes that the hemodynamic characteristics of every voxel are similar within a functional cluster and that there is dynamic synchronization of voxels within a given cluster (Zang et al., 2004). ReHo may be absent or masked during an activation paradigm and therefore is useful for resting-state fMRI data analysis. In addition, ReHo provides an approach for using fMRI to investigate local connectivity (Zang et al., 2004) and reflects the temporal synchrony of the regional

48

C. Qiu et al. / Psychiatry Research: Neuroimaging 194 (2011) 47–53

fMRI BOLD signal. It may be potentially helpful to understand human brain activity in the resting state and may be useful for revealing the complexity of human brain function (Liu et al., 2008). In contrast, abnormal ReHo is most likely related to changes in the temporal aspects of spontaneous neural activity in the regional brain (Wu et al., 2009; Shukla et al., 2010). It may be speculated that an abnormal ReHo may be a clue to disrupted local functionality (He et al., 2007) and may provide insight into the pathophysiology of psychiatric disorder (Liu et al., 2010). This method has been used to investigate the functional modulations and characterize the pathophysiological changes in the resting state in patients with attention-deficit/hyperactivity disorder (Zhu et al., 2008), Alzheimer's disease (He et al., 2007; Liu et al., 2008), depression (Liu et al., 2010; Wu et al., 2010), Parkinson's disease (Wu et al., 2009) and autism spectrum disorders (Paakki et al., 2010; Shukla et al., 2010). Little is known about the changes in the local synchronization of spontaneous fMRI BOLD signals that occur in SAD during the resting state. We hypothesized that ReHo of resting-state brain activity would be different between patients with SAD and healthy controls, particularly in brain regions that have been implicated in previous task-based fMRI studies. In the present study, we document for the first time the ReHo values for patients with SAD compared to those of HCs. In addition, correlation analyses of the ReHo value for each voxel within one-sample t-test results were carried out in the SAD group to explore whether changes are related to clinical severity as measured by the total Liebowitz Social Anxiety Scale (LSAS). 2. Methods 2.1. Participants The present study was approved by the local Ethics Committee of Huaxi Hospital, Sichuan University, and written informed consents were obtained from all subjects. These subjects have been used in our previous study (Liao et al., 2010a), in which the details of subjects' information were described. A first study group was composed of 20 patients (22.90 ± 2.99 years, all right-handed). Diagnosis of SAD was determined by consensus between the two attending psychiatrists and a trained interviewer using the Structured Clinical Interview for DSM-IV (SCID)-Patient Version. SAD patients did not receive psychotherapy and psychiatric medications. A second group, which was composed of 20 age-, sex-, and education-matched healthy controls (HC) (21.65 ± 3.57 years, all right-handed), was recruited and screened using the SCID-Patient Version to confirm the current absence of psychiatric and neurological illness. All participants of the two groups were evaluated with the Liebowitz Social Anxiety Scale (LSAS), Spielberger State-Trait Anxiety Inventory (STAI) (Spielberger et al., 1988), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Depression Rating Scale (HAMD). More specifically, the STAI questionnaire consists of two components: the STAI-S score, which gives the level of state anxiety at the time of completing the text and the STAI-T score, which measures the inherent trait anxiety level of the subject. The STAI-T questionnaire was measured immediately before and after the MRI scanning (pre-scanning and post-scanning) session (Campbell et al., 2007). 2.2. Image acquisition Experiments were performed on a 3.0-T GE-Signa MRI scanner (EXCITE, General Electric, Milwaukee, USA) in Huaxi MR Research Center. Functional images were acquired using a single-shot, gradientrecalled echo planar imaging sequence (TR = 2000 ms, TE= 30 ms and flip angle = 90°). Thirty transverse slices (FOV = 24 cm, inplane matrix = 64 × 64, slice thickness = 5 mm, without gap, voxel size= 3.75× 3.75 × 5), aligned along the anterior commissure–posterior commissure (AC–PC) line, were acquired. For each subject, a total of 205 volumes were acquired and the first five volumes were discarded to

ensure steady-state longitudinal magnetization. Subjects were instructed simply to rest with their eyes closed, not to think of anything in particular, and not to fall asleep. Subsequently, for spatial normalization and localization, a set of high-resolution T1-weighted anatomical images was acquired in axial orientation using a 3D spoiled gradient recalled (SPGR) sequence (TR = 8.5 ms, TE = 3.4 ms, flip angle= 12°, matrix size = 512 × 512 × 156 and voxel size = 0.47 × 0.47 × 1 mm3) on each subject. 2.3. Data preprocessing Data preprocessing was carried out using SPM8 software (http:// www.fil.ion.ucl.ac.uk/spm). The 200 volumes were first corrected for the temporal difference and head motion. One data set was excluded from the analysis because the translational or rotational parameters in a data set exceeded ±1.5 mm or ±1.5°. The functional images were realigned with the corresponding T1-volume and warped into a standard stereotaxic space at a resolution of 3 × 3 × 3 mm3, using the Montreal Neurological Institute (MNI) echo-planar imaging template in SPM8. Data were temporal band-pass filtered (0.01 b f b0.08 Hz) to reduce the effects of low-frequency drift and physiological highfrequency noise (Biswal et al., 1995), and the linear trend was removed. 2.4. ReHo analysis We used Kendall's coefficient of concordance (KCC) (Kendall and Gibbons, 1990) to measure the similarity of the time series within a functional cluster based on the regional homogeneity hypothesis (Zang et al., 2004). In the current study, 27 nearest neighboring voxels were defined as a cluster and a KCC value was given to the voxel at the center of this cluster (Zang et al., 2004) as follows: W=


2 2 ∑ðRi Þ −n R   1 2 3 K n −n 12

where W is the KCC among given voxels, ranging from 0 to 1; Ri is the sum rank of the ith time point; R = ðn + 1ÞK = 2 is the mean of the Ris; K is the number of time series within a measured cluster; n is the number of ranks (here, n = 200 time points). The individual ReHo map was generated in a voxel-wise fashion with the free REST software (Resting state fMRI data analysis toolkit, http://sourceforge. net/projects/resting-fmri). Then a mask (made from the MNI template to assure matching with the normalization step), in the REST software, was used to remove non-brain tissue, and for standardization purposes, the individual ReHo map was divided by its own mean KCC value within the mask (Wu et al., 2009, 2010). Finally, the ReHo maps were spatially smoothed with a Gaussian filter of 4 mm of full width at half maximum (FWHM). 2.5. Second-level analysis One-sample t-tests were performed within each group to show where in the brain the standardized KCC value was larger than one. The significant threshold was set at P b 0.05 (multiple comparison using the false discovery rate (FDR) criterion (Genovese et al., 2002)). Then, second-level random effect two-sample t-tests were performed to compare the ReHo results between the SAD patients and HC subjects within a mask. This mask was created by combining the voxels in both the SAD and HC groups, which were obtained from one-sample t-test results. The t-map was set at a threshold of P b 0.05 (combined height threshold P b 0.01 and a minimum cluster size of 13 voxels), using the AlphaSim program in the REST software, which applied Monte Carlo simulation to calculate the probability of false positive detection by taking into consideration both the individual voxel probability thresholding and cluster size.

C. Qiu et al. / Psychiatry Research: Neuroimaging 194 (2011) 47–53

49

3.2. Within-group and between-group ReHo analyses

Table 1 Regions of increased/decreased ReHo in SAD patients. Anatomical region

MNI (x,y,z)a

BA

Voxels

tb

ReHo increased regions L MOG R PUT

− 33,−78,15 24,15,3

18/19 –

35 14

3.61 2.83

ReHo decreased regions R DLPFC L AG R AG R ACC L MPFC R IPG R FG

24,30,45 − 39,−63,39 42,−63,48 9,39,21 − 21,45,42 42,−45,51 21,−57,−12

8 39/40 40 32 9 40 19

63 39 38 26 76 36 14

− 4.76 − 4.23 − 3.87 − 3.75 − 3.59 − 3.34 − 3.06

Abbreviations: ReHo, regional homogeneity; SAD, social anxiety disorder; HC, healthy controls; MNI, Montreal Neurological Institute; BA, Brodmann's area; L, left; MOG, middle occipital gyrus; R, right; PUT, putamen; DLPFC, dorsolateral prefrontal cortex; AG, angular gyrus; ACC, anterior cingulate gyrus; MPFC, medial prefrontal cortex; IPG, inferior parietal gyrus; FG, fusiform gyrus. a Coordinates of primary peak locations in the MNI space. b Represents the statistical value of peak voxel showing ReHo differences comparing SAD patients and HC. Positive t value indicates increased ReHo, and negative t value indicates decreased ReHo.

ReHo results for HC and the SAD patient group are shown in Fig. 1 (one-sample t-test; P b 0.05, FDR corrected). Visual inspection indicated that the posterior cingulate cortex (PCC)/precuneus, medial prefrontal cortex (MPFC) and bilateral angular gyrus (AG) had significantly higher ReHo than other brain regions. The ReHo pattern was very similar to the DMN (Raichle et al., 2001). In addition, we also find that other brain regions, including the bilateral supplementary motor area (SMA), dorsolateral prefrontal cortex (DLPFC), insula, inferior parietal gyrus (IPG), medial temporal gyrus (MTG), middle occipital gyrus (MOG), putamen (PUT), anterior cingulate gyrus (ACC), cuneus (CUN) and fusiform gyrus (FG), have higher ReHo values. Here these within-group maps are merely for visualizing ReHo. The results obtained from the two-sample t-test clearly showed significant differences in ReHo between the two groups (P b 0.05, AlphaSim corrected; Fig. 2, Table 1). Compared with HC, the SAD patient group showed significantly decreased ReHo in the left MPFC, the right DLPFC, IPG, ACC and FG and the bilateral AG; and significantly increased ReHo in the left MOG and the right PUT. 3.3. Correlations between ReHo and total LSAS in SAD patients

Finally, to explore whether ReHo correlates with the clinical severity in the patient with SAD, a correlation analysis of ReHo versus the total Liebowitz Social Anxiety Scale (LSAS) was performed at each voxel within a mask obtained from one-sample t-test results on the SAD group. For the correlation analysis, a threshold of P b 0.05 (combined height threshold P b 0.01 and a minimum cluster size of 12 voxels), using the AlphaSim program in the REST software, was chosen as a threshold for statistical significance.

3. Results 3.1. Psychological data and group characteristics The details of psychological scores are shown in Table 1 of our previous study (Liao et al., 2010a). No significant differences were found between SAD patients and HCs in terms of gender, age, educational level and post-scanning STAI-S. Compared with HCs, SAD patients showed significantly higher scores on the LSAS (including total score, fear factor and avoidance factor) assessment social anxiety symptom scales, and higher scores on the HAMD and HAMA, and higher levels of anxiety as assessed by the STAI-T and pre-scanning STAI-S.

Correlation analysis of ReHo against the total LSAS score showed significantly negative correlations for the left putamen and MPFC and the bilateral DLPFC, and significantly positive correlations for the left MOG and CUN and the bilateral IPG in the SAD patients (P b 0.05, AlphaSim corrected; Fig. 3, Table 2). 4. Discussion To the best of our knowledge, this was the first study to examine local BOLD coherence in SAD patients during the resting state. Compared with HC, patients with SAD showed decreased ReHo as follows: the right DLPFC and IPG, which are located in the taskpositive network as described elsewhere (Fox et al., 2005; Fransson, 2005), and the bilateral AG, the left MPFC and the right ACC, which are associated with the task-negative network, namely, the DMN (Fox et al., 2005; Fransson, 2005). Patients with SAD showed increased ReHo in the left MOG and the right PUT, which was not included in these two networks (Van Dijk et al., 2010). It is worthwhile to look at coherent neuronal fluctuations and spontaneous low-frequency fluctuations in the BOLD signal before further interpreting changes in the SAD patient group. Although many functional connectivity studies document that intrinsic coherent

Fig. 1. Results of ReHo shown as a Kendall's coefficient of concordance (KCC) map for HC (top plane) and SAD patients (bottom plane). Threshold was P b 0.05 with FDR corrected. Numbers in the upper left of each image refer to the z-plane coordinates of the Montreal Neurological Institute (MNI) space. Letters L and R correspond to the left and right sides of the brain, respectively.

50

C. Qiu et al. / Psychiatry Research: Neuroimaging 194 (2011) 47–53

Fig. 2. ReHo map of statistically significant differences by two-sample t-test between SAD patients and HC (P b 0.05 [AlphaSim corrected], a combined threshold of P b 0.01, and a minimum cluster size of 13 voxels). Hot and cold colors indicate ReHo increases and decreases, respectively, in the SAD patient group. Numbers in the upper left of each image refer to the z-plane coordinates of the MNI space. Letters L and R correspond to the left and right sides of the brain, respectively. Further details of these regions are presented in Table 1.

Fig. 3. Results of correlation analysis between the total LSAS score and ReHo in the SAD patient group (P b 0.05 [AlphaSim corrected], a combined threshold of P b 0.01, and a minimum cluster size of 12 voxels). Hot and cold colors indicate positive and negative correlations, respectively, between ReHo and total LSAS score. Numbers in the upper left of each image refer to the z- (top), y- (middle), and x- (bottom) plane coordinates of the MNI space. Letters L and R correspond to the left and right sides of the brain, respectively. Further details of these regions are presented in Table 2.

Table 2 Correlation between ReHo and clinical severity in SAD patients. Anatomical region

MNI (x,y,z)a

BA

Voxels

tb

Positive correlation between total LSAS and ReHo in SAD L IPG − 36,−45,39 40 R IPG 36,−51,42 40 L MOG − 12,−93,0 17 L CUN − 9,−75,30 7

26 27 19 29

4.93 3.89 3.18 3.44

Negative correlation between total LSAS and ReHo in SAD L MPFC − 12,45,3 11 L PUT − 21,12,−3 – L DLPFC − 30,45,30 9/10 R DLPFC 42,30,42 8/9

23 56 35 21

− 3.51 − 4.59 − 4.72 − 4.12

Abbreviations: ReHo, regional homogeneity; SAD, social anxiety disorder; HC, healthy controls; MNI, Montreal Neurologic Institute; BA, Brodmann's area; L, left; IPG, inferior parietal gyrus; R, right; MOG, middle occipital gyrus; CUN, cuneus; DLPFC, dorsolateral prefrontal cortex; PUT, putamen; MPFC, medial prefrontal cortex. a Coordinates of primary peak locations in the MNI space. b Represents the statistical value of peak voxel showing correlation between total LSAS score and ReHo in SAD patients. Positive and negative t values indicate positive and negative correlations, respectively.

fluctuation in the resting state is spatially organized in a finite neuroanatomical system (Biswal et al., 1995; Fox et al., 2005), the origin of these coherences and the neurophysiological basis for them remain unclear. Moreover, ReHo was developed to characterize the local synchronization of spontaneous fMRI BOLD signals (Zang et al., 2004). ReHo assumes that voxels within a given brain region are more temporally homogeneous when the brain region is involved in a specific condition (Zang et al., 2004), including the resting state (Long et al., 2008). Thus, ReHo reflects the coherence of spontaneous neuronal activity (Wu et al., 2009). In the current study, we observed that the ReHo of the PCC, MPFC, AG, DLPFC, IPG, and FG is significantly higher than that of other brain regions (Fig. 1). Among these brain regions, the PCC, MPFC, and bilateral AG consistently show increased activity during the resting state, compared with the activity shown during active and passive cognitive tasks, thereby constituting a DMN (Raichle et al., 2001). In contrast, the anticorrelation network shows a negative linear correlation with the DMN in the resting state. Our results also revealed other brain regions with higher ReHo, including the bilateral SMA, DLPFC, insula, IPG and medial temporal cortex, which are located in the task-positive network as described elsewhere (Fox et al., 2005; Fransson, 2005). In addition, the MOG and PUT, which were not included in two anticorrelated networks (the DMN and task-positive network) (Van Dijk et al., 2010), had a higher ReHo.

C. Qiu et al. / Psychiatry Research: Neuroimaging 194 (2011) 47–53

It seems likely that these brain regions would be associated with the task-positive network due to their common activation during external stimuli, but it is evidenced that they show no intrinsic preference for either network (Fox et al., 2005). Aberrant ReHo in the patients with SAD suggests that neural function in specific brain regions for these patients is synchronized to a greater or lesser extent relative to HC. As expected, we found ReHo to be significantly decreased in the MPFC and bilateral AG, which are located in the DMN (Fig. 2, Table 1). The DMPFC, the hub of the DMN, is thought to provide information from prior experiences in the form of memories during the construction of self-relevant mental simulation (Buckner et al., 2008). The MPFC has been increasingly recognized as important for the regulation of emotion in general, and anxiety-related processing in particular (Kelley et al., 2002; Fossati et al., 2004). These data, along with our findings, suggest that the MPFC plays a role in the prominent cognitive behavioral models of SAD. Additionally, the MPFC has been shown to be involved in the inhibition or extinction of excessive corticolimbic activity in anxiety disorders (LeDoux, 1998). Previous studies of SAD show both hyperactivity (Adolphs and Spezio, 2006; Kilts et al., 2006) and hypoactivity (Tillfors et al., 2002; Lorberbaum et al., 2004) to emotional stimuli within the MPFC. In the present study, data collected in the resting state are consistent with a recent prominent model of SAD based on the observation of altered neural information processing in the brain regions related to self-reference (Kelley et al., 2002). The decreased ReHo of the DMN in SAD patients is found not only in the MPFC, but also in the bilateral AG (Fig. 2, Table 1). A vital role has been ascribed to the AG in mental constructs such as thoughts, feelings, and beliefs that relate both to oneself and to others (Frith and Frith, 2003). Recently, a task-based SAD study suggested that the patient reaction of negative self-belief yields greater BOLD responses in the AG (Goldin and Gross, 2010). The findings of the present study provide evidence that SAD is characterized by emotional and attentional biases as well as distorted negative self-beliefs (Goldin et al., 2009a; Goldin and Gross, 2010), even during the resting state, that occur in the absence of external stimuli. In SAD patients, decreased coherences were also observed in the right DLPFC and right IPG, which are located in the central-executive network (CEN) (Etkin et al., 2009) (Fig. 2, Table 1). However, results for the SAD patient group showed significant correlation between the coherence and the total LSAS score in the bilateral DLPFC (Fig. 3, Table 2). Furthermore, the DLPFC and posterior parietal cortices have been identified in many studies of cognitive control over both emotional and nonemotional material (Duncan and Owen, 2000; Miller and Cohen, 2001; Ochsner and Gross, 2005). Taken together, these two brain regions constitute a lateral frontoparietal central executive network (Koechlin and Summerfield, 2007; Seeley et al., 2007). Impairment of the central executive network is also implicated in most neurodegenerative diseases as degeneration spreads beyond the sites of initial injury into widely interconnected supervisory neocortical systems (Seeley et al., 2009). Specifically, Etkin et al. (2009) suggested that amygdalofrontoparietal coupling in anxiety patients reflects the habitual engagement of a cognitive control system to regulate excessive anxiety. In addition, Warwick et al. found that patients with SAD have increased perfusion in the frontal cortex during the resting state compared to HC (Warwick et al., 2008). The results of the current study appear inconsistent with those of Warwick et al. We carefully checked the coordinates of each study and found that the increased perfusion in the frontal cortex refers primarily to the ventral lateral prefrontal cortex. Therefore, the findings of the current study reflect changes in the coherence of the DLPFC, which negatively correlated with the severity of SAD symptoms. The neural substrates underlying executive function, such as that of the DLPFC, modulate the activation of the amygdala and extended limbic cortex (Nomura et al., 2004). This result is in line with other reports, in which decreased activation of the DLPFC was observed during socially relevant tasks, and may correspond to the prevalence of emotions over the cognitive control

51

system (Lorberbaum et al., 2004; Gentili et al., 2008). Thus, the finding may reflect the functional impairment of the central executive network in the cognitive control of social anxiety. Compared with HC, patients in the SAD group showed increased ReHo in the left MOG (Fig. 2, Table 1). In addition, a significant positive correlation between coherence and the total LSAS score in the left MOG was found in the SAD patient group (Fig. 3, Table 2). The MOG cluster is close to the fusiform gyrus, described previously as showing increased BOLD activity in SAD patients' response to emotional facial expressions (Straube et al., 2004; Pujol et al., 2009). However, other studies show that nonenhanced fusiform gyrus activity indicates decreased regional cerebral blood flow in response to a social performance and evaluation task (Kilts et al., 2006) in both anxiety-prone subjects and those with generalized social phobia (Campbell et al., 2007; Stein et al., 2007). The influences of the fusiform gyrus on the amygdala in emotional facial expressions have been explored in SAD (Pujol et al., 2009). Alternatively, modulated visual input can be obtained in the feedback from the amygdala to the occipital lobe. This pathway is sensitive to direct eye contact, especially in a situation involving threatening stimuli (Skuse and Gallagher, 2009). Notably, patients with SAD usually blush rather than make eye contact in social interactions and invariably experience intense emotional or physical symptoms, such as fear and rapid heartbeat (Stein and Stein, 2008). In addition, researchers showed that SAD patients show recognition bias with respect to threatening or critical facial expressions (Foa et al., 2000; Coles and Heimberg, 2005). We, thus, speculate that the increased ReHo in the occipital cortex may be associated with the hypervigilance and hyperprosexia characteristic of social communication in patients with SAD (Mogg et al., 1997; Bogels and Mansell, 2004). Although ReHo is a novel method, it should be mentioned that the relationship among ReHo, task-related brain activations and functional connectivity is uncertain. It is difficult to make a direct comparison of the present findings to those of studies employing other approaches to measuring brain function. First, comparison among studies is impeded by the diverse stimulation tasks used. In addition, the relationship between ReHo and task-related brain activations also remains unclear. More specifically, increased local synchronization in neighboring voxels might be associated with high regional metabolism, but increased metabolism would be the result of any local increased activity (Wu et al., 2010). Functional connectivity calculates temporal correlations between the time courses of distant brain regions. It may reflect a long-distance interregional connectivity (Liao et al., 2010b). However, ReHo does not provide information about the synchronization among remote areas and just targets connectivity at the local level (Zang et al., 2004; Long et al., 2008). 4.1. Conclusions In summary, we show that patterns of neuronal coherence in the resting state were altered in patients with SAD. In patients with SAD, decreased coherence (ReHo) was found in the left MPFC and bilateral AG of the DMN, suggesting functional impairment related to the perception of a socially relevant emotional state and self-related mental representation, and also in the right DLPFC and right IPG of the CEN, reflecting the deficit in cognitive control of social anxiety. Significantly increased coherence was found in the left MOG, which might be associated with the hypervigilance and hyperprosexia found in the social communication in social anxiety, even in the resting state. In conclusion, the present results may provide a novel way of investigating the neuropathophysiological mechanisms of SAD. Acknowledgments This research was supported by the 973 Project (Grant No. 2008CB517407), partly supported by the Natural Science Foundation of China (Grant Nos. 90820006, 61035006 and 30625024).

52

C. Qiu et al. / Psychiatry Research: Neuroimaging 194 (2011) 47–53

References Adolphs, R., Spezio, M., 2006. Role of the amygdala in processing visual social stimuli. Progress in Brain Research 156, 363–378. Anand, A., Li, Y., Wang, Y., Wu, J., Gao, S., Bukhari, L., Mathews, V.P., Kalnin, A., Lowe, M.J., 2005. Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biological Psychiatry 57, 1079–1088. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine 34, 537–541. Bogels, S.M., Mansell, W., 2004. Attention processes in the maintenance and treatment of social phobia: hypervigilance, avoidance and self-focused attention. Clinical Psychology Review 24, 827–856. Broyd, S.J., Demanuele, C., Debener, S., Helps, S.K., James, C.J., Sonuga-Barke, E.J., 2009. Default-mode brain dysfunction in mental disorders: a systematic review. Neuroscience and Biobehavioral Reviews 33, 279–296. Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L., 2008. The brain's default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences 1124, 1–38. Campbell, D.W., Sareen, J., Paulus, M.P., Goldin, P.R., Stein, M.B., Reiss, J.P., 2007. Timevarying amygdala response to emotional faces in generalized social phobia. Biological Psychiatry 62, 455–463. Coles, M.E., Heimberg, R.G., 2005. Recognition bias for critical faces in social phobia: a replication and extension. Behaviour Research and Therapy 43, 109–120. Damsa, C., Kosel, M., Moussally, J., 2009. Current status of brain imaging in anxiety disorders. Current Opinion in Psychiatry 22, 96–110. Duncan, J., Owen, A.M., 2000. Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences 23, 475–483. Engel, K., Bandelow, B., Gruber, O., Wedekind, D., 2009. Neuroimaging in anxiety disorders. Journal of Neural Transmission 116, 703–716. Etkin, A., Wager, T.D., 2007. Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. The American Journal of Psychiatry 164, 1476–1488. Etkin, A., Keller, K.E., Schatzberg, A.F., Menon, V., Greicius, M.D., 2009. Disrupted amygdalar subregion functional connectivity and evidence for a compensatory network in generalized anxiety disorder. Archives of General Psychiatry 66, 1361–1372. Foa, E.B., Gilboa-Schechtman, E., Amir, N., Freshman, M., 2000. Memory bias in generalized social phobia: remembering negative emotional expressions. Journal of Anxiety Disorders 14, 501–519. Fossati, P., Hevenor, S.J., Lepage, M., Graham, S.J., Grady, C., Keightley, M.L., Craik, F., Mayberg, H., 2004. Distributed self in episodic memory: neural correlates of successful retrieval of self-encoded positive and negative personality traits. Neuroimage 22, 1596–1604. Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America 102, 9673–9678. Fransson, P., 2005. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Human Brain Mapping 26, 15–29. Frith, U., Frith, C.D., 2003. Development and neurophysiology of mentalizing. Philosophical Transactions of the Royal Society B: Biological Sciences 358, 459–473. Garrity, A.G., Pearlson, G.D., McKiernan, K., Lloyd, D., Kiehl, K.A., Calhoun, V.D., 2007. Aberrant “default mode” functional connectivity in schizophrenia. The American Journal of Psychiatry 164, 450–457. Genovese, C.R., Lazar, N.A., Nichols, T., 2002. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870–878. Gentili, C., Gobbini, M.I., Ricciardi, E., Vanello, N., Pietrini, P., Haxby, J.V., Guazzelli, M., 2008. Differential modulation of neural activity throughout the distributed neural system for face perception in patients with Social Phobia and healthy subjects. Brain Research Bulletin 77, 286–292. Gentili, C., Ricciardi, E., Gobbini, M.I., Santarelli, M.F., Haxby, J.V., Pietrini, P., Guazzelli, M., 2009. Beyond amygdala: default mode network activity differs between patients with social phobia and healthy controls. Brain Research Bulletin 79, 409–413. Goldin, P.R., Gross, J.J., 2010. Effects of mindfulness-based stress reduction (MBSR) on emotion regulation in social anxiety disorder. Emotion 10, 83–91. Goldin, P.R., Manber-Ball, T., Werner, K., Heimberg, R., Gross, J.J., 2009a. Neural mechanisms of cognitive reappraisal of negative self-beliefs in social anxiety disorder. Biological Psychiatry 66, 1091–1099. Goldin, P.R., Manber, T., Hakimi, S., Canli, T., Gross, J.J., 2009b. Neural bases of social anxiety disorder: emotional reactivity and cognitive regulation during social and physical threat. Archives of General Psychiatry 66, 170–180. Greicius, M.D., Flores, B.H., Menon, V., Glover, G.H., Solvason, H.B., Kenna, H., Reiss, A.L., Schatzberg, A.F., 2007. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry 62, 429–437. He, Y., Wang, L., Zang, Y., Tian, L., Zhang, X., Li, K., Jiang, T., 2007. Regional coherence changes in the early stages of Alzheimer's disease: a combined structural and resting-state functional MRI study. Neuroimage 35, 488–500. Kelley, W.M., Macrae, C.N., Wyland, C.L., Caglar, S., Inati, S., Heatherton, T.F., 2002. Finding the self? An event-related fMRI study. Journal of Cognitive Neuroscience 14, 785–794. Kendall, M., Gibbons, J.D., 1990. Rank Correlation Method. Oxford University Press, Oxford. Kilts, C.D., Kelsey, J.E., Knight, B., Ely, T.D., Bowman, F.D., Gross, R.E., Selvig, A., Gordon, A., Newport, D.J., Nemeroff, C.B., 2006. The neural correlates of social anxiety disorder and response to pharmacotherapy. Neuropsychopharmacology 31, 2243–2253.

Koechlin, E., Summerfield, C., 2007. An information theoretical approach to prefrontal executive function. Trends in Cognitive Sciences 11, 229–235. LeDoux, J., 1998. Fear and the brain: where have we been, and where are we going? Biological Psychiatry 44, 1229–1238. Liao, W., Chen, H., Feng, Y., Mantini, D., Gentili, C., Pan, Z., Ding, J., Duan, X., Qiu, C., Lui, S., Gong, Q., Zhang, W., 2010a. Selective aberrant functional connectivity of resting state networks in social anxiety disorder. Neuroimage 52, 1549–1558. Liao, W., Zhang, Z., Pan, Z., Mantini, D., Ding, J., Duan, X., Luo, C., Lu, G., Chen, H., 2010b. Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS ONE 5, e8525. Liu, Y., Wang, K., Yu, C., He, Y., Zhou, Y., Liang, M., Wang, L., Jiang, T., 2008. Regional homogeneity, functional connectivity and imaging markers of Alzheimer's disease: a review of resting-state fMRI studies. Neuropsychologia 46, 1648–1656. Liu, Z., Xu, C., Xu, Y., Wang, Y., Zhao, B., Lv, Y., Cao, X., Zhang, K., Du, C., 2010. Decreased regional homogeneity in insula and cerebellum: a resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry Research: Neuroimaging 182, 211–215. Long, X.Y., Zuo, X.N., Kiviniemi, V., Yang, Y., Zou, Q.H., Zhu, C.Z., Jiang, T.Z., Yang, H., Gong, Q.Y., Wang, L., Li, K.C., Xie, S., Zang, Y.F., 2008. Default mode network as revealed with multiple methods for resting-state functional MRI analysis. Journal of Neuroscience Methods 171, 349–355. Lorberbaum, J.P., Kose, S., Johnson, M.R., Arana, G.W., Sullivan, L.K., Hamner, M.B., Ballenger, J.C., Lydiard, R.B., Brodrick, P.S., Bohning, D.E., George, M.S., 2004. Neural correlates of speech anticipatory anxiety in generalized social phobia. NeuroReport 15, 2701–2705. Miller, E.K., Cohen, J.D., 2001. An integrative theory of prefrontal cortex function. Annual Review of Neuroscience 24, 167–202. Mogg, K., Bradley, B.P., de Bono, J., Painter, M., 1997. Time course of attentional bias for threat information in non-clinical anxiety. Behaviour Research and Therapy 35, 297–303. Nomura, M., Ohira, H., Haneda, K., Iidaka, T., Sadato, N., Okada, T., Yonekura, Y., 2004. Functional association of the amygdala and ventral prefrontal cortex during cognitive evaluation of facial expressions primed by masked angry faces: an eventrelated fMRI study. Neuroimage 21, 352–363. Ochsner, K.N., Gross, J.J., 2005. The cognitive control of emotion. Trends in Cognitive Sciences 9, 242–249. Paakki, J.J., Rahko, J., Long, X., Moilanen, I., Tervonen, O., Nikkinen, J., Starck, T., Remes, J., Hurtig, T., Haapsamo, H., Jussila, K., Kuusikko-Gauffin, S., Mattila, M.L., Zang, Y., Kiviniemi, V., 2010. Alterations in regional homogeneity of resting-state brain activity in autism spectrum disorders. Brain Research 1321, 169–179. Phan, K.L., Fitzgerald, D.A., Nathan, P.J., Tancer, M.E., 2006. Association between amygdala hyperactivity to harsh faces and severity of social anxiety in generalized social phobia. Biological Psychiatry 59, 424–429. Pujol, J., Harrison, B.J., Ortiz, H., Deus, J., Soriano-Mas, C., Lopez-Sola, M., Yucel, M., Perich, X., Cardoner, N., 2009. Influence of the fusiform gyrus on amygdala response to emotional faces in the non-clinical range of social anxiety. Psychological Medicine 39, 1177–1187. Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L., 2001. A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America 98, 676–682. Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Reiss, A.L., Greicius, M.D., 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience 27, 2349–2356. Seeley, W.W., Crawford, R.K., Zhou, J., Miller, B.L., Greicius, M.D., 2009. Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42–52. Shukla, D.K., Keehn, B., Muller, R.A., 2010. Regional homogeneity of fMRI time series in autism spectrum disorders. Neuroscience Letters 476, 46–51. Skuse, D.H., Gallagher, L., 2009. Dopaminergic–neuropeptide interactions in the social brain. Trends in Cognitive Sciences 13, 27–35. Spielberger, C.D., Gorsuch, E.L., Lushene, R.E., 1988. STAI-Manual for the State Trait Anxiety Inventory, 3rd ed. Consulting Psychologists Press, Palo Alto, CA. Stein, M.B., Stein, D.J., 2008. Social anxiety disorder. Lancet 371, 1115–1125. Stein, M.B., Goldin, P.R., Sareen, J., Zorrilla, L.T., Brown, G.G., 2002. Increased amygdala activation to angry and contemptuous faces in generalized social phobia. Archives of General Psychiatry 59, 1027–1034. Stein, M.B., Simmons, A.N., Feinstein, J.S., Paulus, M.P., 2007. Increased amygdala and insula activation during emotion processing in anxiety-prone subjects. The American Journal of Psychiatry 164, 318–327. Straube, T., Kolassa, I.T., Glauer, M., Mentzel, H.J., Miltner, W.H., 2004. Effect of task conditions on brain responses to threatening faces in social phobics: an event-related functional magnetic resonance imaging study. Biological Psychiatry 56, 921–930. Tillfors, M., Furmark, T., Marteinsdottir, I., Fischer, H., Pissiota, A., Langstrom, B., Fredrikson, M., 2001. Cerebral blood flow in subjects with social phobia during stressful speaking tasks: a PET study. The American Journal of Psychiatry 158, 1220–1226. Tillfors, M., Furmark, T., Marteinsdottir, I., Fredrikson, M., 2002. Cerebral blood flow during anticipation of public speaking in social phobia: a PET study. Biological Psychiatry 52, 1113–1119. Van Dijk, K.R., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., Buckner, R.L., 2010. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of Neurophysiology 103, 297–321. Warwick, J.M., Carey, P., Jordaan, G.P., Dupont, P., Stein, D.J., 2008. Resting brain perfusion in social anxiety disorder: a voxel-wise whole brain comparison with healthy control subjects. Progress in Neuropsychopharmacology & Biological Psychiatry 32, 1251–1256. Wu, T., Long, X., Zang, Y., Wang, L., Hallett, M., Li, K., Chan, P., 2009. Regional homogeneity changes in patients with Parkinson's disease. Human Brain Mapping 30, 1502–1510.

C. Qiu et al. / Psychiatry Research: Neuroimaging 194 (2011) 47–53 Wu, Q.Z., Li, D.M., Kuang, W.H., Zhang, T.J., Lui, S., Huang, X.Q., Chan, R.C., Kemp, G.J., Gong, Q.Y., 2010. Abnormal regional spontaneous neural activity in treatmentrefractory depression revealed by resting-state fMRI. Human Brain Mapping. doi:10.1002/hbm.21108. Zang, Y., Jiang, T., Lu, Y., He, Y., Tian, L., 2004. Regional homogeneity approach to fMRI data analysis. Neuroimage 22, 394–400. Zhang, Z., Lu, G., Zhong, Y., Tan, Q., Liao, W., Chen, Z., Shi, J., Liu, Y., 2009a. Impaired perceptual networks in temporal lobe epilepsy revealed by resting fMRI. Journal of Neurology 256, 1705–1713. Zhang, Z., Lu, G., Zhong, Y., Tan, Q., Yang, Z., Liao, W., Chen, Z., Shi, J., Liu, Y., 2009b. Impaired attention network in temporal lobe epilepsy: a resting FMRI study. Neuroscience Letters 458, 97–101.

53

Zhang, Z., Lu, G., Zhong, Y., Tan, Q., Liao, W., Wang, Z., Wang, Z., LI, K., Chen, H., Liu, Y., 2010. Altered spontaneous neuronal activity of the default-mode network in mesial temporal lobe epilepsy. Brain Research 1323, 152–160. Zhao, X.H., Wang, P.J., Li, C.B., Hu, Z.H., Xi, Q., Wu, W.Y., Tang, X.W., 2007. Altered default mode network activity in patient with anxiety disorders: an fMRI study. European Journal of Radiology 63, 373–378. Zhu, C.Z., Zang, Y.F., Cao, Q.J., Yan, C.G., He, Y., Jiang, T.Z., Sui, M.Q., Wang, Y.F., 2008. Fisher discriminative analysis of resting-state brain function for attention-deficit/ hyperactivity disorder. Neuroimage 40, 110–120.