Common and distinct brain networks underlying panic and social anxiety disorders

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Common and distinct brain networks underlying panic and social anxiety disorders Yong-Ku Kim, Ho-Kyoung Yoon⁎ Department of Psychiatry, College of Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea

A R T I C L E I N F O

A B S T R A C T

Keywords: Functional connectivity Panic disorder Resting state Social anxiety disorder

Although panic disorder (PD) and phobic disorders are independent anxiety disorders with distinct sets of diagnostic criteria, there is a high level of overlap between them in terms of pathogenesis and neural underpinnings. Functional connectivity research using resting-state functional magnetic resonance imaging (rsfMRI) shows great potential in identifying the similarities and differences between PD and phobias. Understanding common and distinct networks between PD and phobic disorders is critical for identifying both specific and general neural characteristics of these disorders. We review recent rsfMRI studies and explore the clinical relevance of resting-state functional connectivity (rsFC) in PD and phobias. Although findings differ between studies, there are some meaningful, consistent findings. Social anxiety disorder (SAD) and PD share common default mode network alterations. Alterations within the sensorimotor network are observed primarily in PD. Increased connectivity in the salience network is consistently reported in SAD. This review supports hypotheses that PD and phobic disorders share common rsFC abnormalities and that the different clinical phenotypes between the disorders come from distinct brain functional network alterations.

1. Introduction Phobic disorder or phobia is the most common form of anxiety disorder, and is characterized by persistent, marked, and unreasonable fears of an object or situation. People with a phobia avoid specific situations or objects that induce these types of fears. One of the most famous types of phobia is social anxiety disorder (SAD), also called social phobia, which involves an excessive fear of embarrassment in social situations and avoidance of such situations. Panic disorder (PD) involves repeated and spontaneous panic attacks. A panic attack is an extreme form of fear, and is characterized by physical sensations, such as a racing heart, shortness of breath, and chest pain that lasts for a short period of time. Agoraphobia is characterized by a fear of being alone or a fear of being in public places together with avoidance of such situations. The relationship between PD and agoraphobia is particularly complicated. Traditionally, agoraphobia has been viewed as a complication of panic symptoms and has tended to be co-diagnosed with PD.

Currently, agoraphobia is unlinked with PD in the Diagnostic and Statistical Manual of Mental Disorders 5 (2013). However, high comorbidity and the conceptual overlap still pose obstacles to the diagnostic distinction between PD and agoraphobia (Asmundson et al., 2014). While obsessive-compulsive disorder and post-traumatic stress disorder have been split into discrete disorder categories, PD and phobic disorders remain together in the chapter on anxiety disorders. Although PD and phobic disorders are independent anxiety disorders with distinct sets of diagnostic criteria, there is a high level of overlap between them in terms of pathogenesis and neural underpinnings. Epidemiological and translational studies have shown similarities and differences across these disorders. For example, one epidemiological study found high comorbidity among anxiety disorders including PD, SAD, specific phobia (SP), and agoraphobia (Kessler et al., 2005). A twin study reported that PD, agoraphobia, and SP strongly co-aggregated within families, and that common genetic factors explained a moderate to high proportion of variance in these disorders without the

Abbreviations: AI, anterior insular cortex; ACC, anterior cingulate cortex; BOLD, blood-oxygen-level-dependent; CEN, central executive network; dACC, dorsal anterior cingulate cortex; dlPFC, dorsolateral prefrontal cortex; DMN, default mode network; dmPFC, dorsomedial prefrontal cortex; DTI, diffusion tensor imaging; EEG, electroencephalography; FC, functional connectivity; fMRI, functional magnetic resonance imaging; ICA, independent component analysis; mOFC, medial orbitofrontal cortex; mPFC, medial prefrontal cortex; MTL, middle temporal lobe; PCC, post cingulate cortex; OFC, orbitofrontal cortex; PD, panic disorder; pgACC, perigenual anterior cingulate cortex; rACC, rostral anterior cingulate cortex; ROI, region of interest; ReHo, regional homogeneity; rsFC, resting-state functional connectivity; rsfMRI, resting-state functional magnetic resonance imaging; SAD, social anxiety disorder; SMN, sensorimotor network; SN, salience network; SP, specific phobia; SPECT, single-photon emission computed tomography; VMHC, Voxel-mirrored homotopic connectivity; vmPFC, ventromedial prefrontal cortex ⁎ Corresponding author at: Department of Psychiatry, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan-si, Gyeonggi-do 15355, Republic of Korea. E-mail address: [email protected] (H.-K. Yoon). http://dx.doi.org/10.1016/j.pnpbp.2017.06.017 Received 17 February 2017; Received in revised form 14 May 2017; Accepted 18 June 2017 0278-5846/ © 2017 Published by Elsevier Inc.

Please cite this article as: Kim, Y.-K., Progress in Neuropsychopharmacology & Biological Psychiatry (2017), http://dx.doi.org/10.1016/j.pnpbp.2017.06.017

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2. Panic disorder

influence of a common environment (Mosing et al., 2009). On the other hand, many linkage and candidate gene studies, as well as a PD genome-wide association study and other phobic disorders have produced inconclusive results to date (Shimada-Sugimoto et al., 2015). Understanding similarities and differences between PD and phobic disorders is critical for identifying both specific and general neural characteristics of these disorders. Resting-state functional MRI (rsfMRI) has been developed for analyzing large-scale connectivity in brain networks. Resting-state functional connectivity (rsFC) measures the temporal correlation of spontaneous blood-oxygen-level-dependent (BOLD) signals between spatially remote brain regions during times without the performance of an explicit task. Several resting-state networks have been identified and investigated. The default mode network (DMN), one of the canonical resting-state brain networks, is the most studied network (Smith et al., 2009). The DMN, a set of temporally correlated brain regions, is most active during rest and is deactivated during the performance of cognitively demanding goal-directed tasks. This network includes the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC)/precuneus, the ventral/perigenual anterior cingulate cortex (pgACC), and the inferior parietal cortex. In addition to the DMN, many other major canonical resting-state networks are frequently identified in existing literature (Barkhof et al., 2014). These networks include the salience network (SN: dorsal anterior cingulate cortex (dACC) and anterior insular cortex (AI) circuitry), the central executive network (CEN: the dorsolateral prefrontal cortex (dlPFC) and parietal cortex), the dorsal attention network (DAN: intraparietal sulcus, precentral and superior frontal gyrus), the sensorimotor network (SMN: primary sensorimotor cortex, supplementary motor area and secondary somatosensory cortex), the visual network (Striate cortex, occipital pole, and lateral visual areas), and the auditory network (Superior temporal gyrus). This rsfMRI analysis-based method has several specific features for investigating functional alterations of brain networks in psychiatric disorders (Woodward and Cascio, 2015). First, reliable and reproducible results can be obtained through this relatively standard method. Second, because this method does not depend on explicit task performance, it can be evaluated in populations incapable of performing task-based functional MR imaging, such as pediatric subjects and patients with reduced consciousness. Moreover, in comparison to the modular representations of traditional fMRI, functional connectivity provides a broader network representation of the functional architecture of the brain. Proper connection and harmonious interaction between brain areas are crucial for optimal brain functioning. Therefore, this technique may offer a new understanding of the functional integration of brain regions involved in the symptomatology of anxiety and other psychiatric disorders (Peterson et al., 2014). Several different approaches can be used in rsfMRI analysis. There are two widely used rsFC analysis methods, namely, seed-based approaches and independent component analysis (ICA) (Fox and Raichle, 2007; van den Heuvel and Hulshoff Pol, 2010). In seed-based approaches, according to an a priori hypothesis, individual seed voxels are extracted from a predefined brain region and are correlated with the time courses of other voxels in selected seeds of the brain. In contrast, ICA is a multivariate, data-driven method that decomposes fMRI timeseries data throughout the brain into linear mixtures of spatially independent and temporally coherent components. Our comprehensive literature review focuses on PD and SAD because there are, to our knowledge, no resting-state studies in patient samples with SP as a primary diagnosis. Based on a literature review, we elaborate on the common and distinct network alterations between the disorders, explore the clinical relevance of rsFC alterations, and discuss future considerations regarding the usefulness of rsfMRI for biomarkers of psychiatric disorders.

Until recently, the majority of work on brain function in PD has focused on cognitive task-related or conditioned stimuli-related brain activity. There is growing evidence, however, for resting-state networks in PD. Two studies on PD have demonstrated alterations of DMN. The work of Shin et al. (2013) reported that rsFC between the pgACC and the precuneus was increased in patients with PD compared to control subjects. The research also observed that GABA concentration of the pgACC was correlated with functional connectivity between the pgACC and the precuneus. Using voxel-mirrored homotopic connectivity (VMHC) analysis, another rsfMRI study also reported an aberrant rsFC within the DMN (Lai and Wu, 2014). Therein, investigators found decreased inter-hemispheric connectivity of bilateral PCC and the precuneus in PD. The mPFC (including the pgACC) has been associated with cognitive processes, such as mental representation, theory-ofmind, and/or narrative processing (Frith and Frith, 2007; Hartwright et al., 2014; Mano et al., 2009). The pgACC plays a role in monitoring and appraising the external environment and mutually interacting with various regions of the brain to regulate stressor-related autonomic reactions. (Gianaros and Sheu, 2009; Ryan et al., 2011). Though the PCC/ precuneus is not directly connected to the visceral autonomic system, it is involved in a wide spectrum of attentional processes including selfmonitoring, remembering the past, thinking about the future, and assessing the environment (Wagner et al., 2005). The PCC is also implicated in somatosensory processing, evaluation of sensory events, spatial orientation, and memory and memory retrieval (Olson and Musil, 1992). Several studies examining resting-state connectivity in PD reported consistent changes in the SMN. The work of Pannekoek et al. (2013a) examined rsFC using seed regions of interest (ROI) in bilateral amygdala, bilateral dACC, and bilateral PCC. The research found increased rsFC between the right amygdala and bilateral precuneus in patients with PD compared to healthy control subjects. Altered dACC rsFC with frontal, parietal, and occipital areas was also found. Notably, the left dACC demonstrated increased positive connectivity with the postcentral gyrus, known as the somatosensory cortex, with the function of integrating and interpreting most of the sensory information from the body (Northoff et al., 2006). A whole-brain analysis study using a novel functional connectivity metric revealed increased FC between the thalamus and postcentral gyrus in PD patients (Cui et al., 2016). Altered connectivity between the post/precentral gyrus and the thalamus was found to be positively related to the scores on the Spielberger StateTrait Anxiety Inventory and the Body Perception Questionnaire. The postcentral and precentral gyrus are known to be engaged in interoception processing (Critchley et al., 2004; Inoue et al., 2013). Recently, a whole-brain functional connectome study using the new method of network-based statistics revealed limbic-motor-sensory region connectivity alteration in certain subjects (Lai and Wu, 2016). In that study, the precentral gyrus was one of the central hubs for altered functional connectivity network in PD. The findings of that study seemed to make the original “fear network model” more comprehensive in terms of our understanding of the sensory-related symptoms of PD. These sensorimotor region-centered results are quite distinct from the findings of functional connectome alterations in other anxiety disorders, such as posttraumatic stress disorder. To summarize, although contemporary rsfMRI studies on PD are still scarce, emerging evidence consistently suggests that abnormalities of the DMN in PD appear prominent within emotion regulatory networks. Functional connectivity in the DMN has been linked to core processes of human cognition, such as the integration of cognitive and emotional processing, mentalizing, autobiographical memory retrieval, and envisioning the future (Buckner et al., 2008; Greicius et al., 2003). Hyperconnectivity has also been suggested in SMN. This hyperconnectivity of SMN may cause abnormally high interoceptive 2

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Table 1 Resting-state fMRI studies of panic disorder. Study

Sample

Brain regions and/or networks analysed

Main finding

Pannekoek et al. (2013a) Shin et al. (2013)

11 11 11 11

PD HC PD HC

Seed: bilateral amygdala, bilateral dACC, and bilateral PCC Seed: pgACC

Lai and Wu (2014)

30 21 18 21 22 53 54

PD HC PD GAD HC PD HC

Whole brain analysis: VMHC analysis

PD patients showed increased rsFC between the right amygdala and the bilateral precuneus. The dACC demonstrated altered connectivity with frontal, parietal and occipital areas in PD PD patients showed increased FC between pgACC and precuneus compared to controls. The functional connectivity between the pgACC and the precuneus negatively correlated with the GABA concentration of the pgACC The controls had significantly higher VMHC values than PD patients in the PCC and precuneus. The VMHC value in the posterior cingulate cortex was also negatively correlated with panic severity The greater FC between somatosensory cortex and thalamus in PD; the increased FC between hippocampus/parahippocampus and fusiform gyrus in GAD

Cui et al. (2016)

Lai and Wu (2016)

Whole brain analysis

Whole brain analysis: network-based statistics method

PD patients had significant functional alterations in limbic, sensory, and motor regions. The central hubs were the left parahippocampal gyrus and left precentral gyrus

dACC, dorsal anterior cingulate cortex; FC, functional connectivity; HC, healthy controls; PCC, post cingulate cortex; OFC, orbitofrontal cortex; PD, panic disorder; pgACC, perigenual anterior cingulate cortex; rsFC, resting-state functional connectivity; GAD, generalized anxiety disorder; VMHC, voxel-mirrored homotopic connectivity.

Therefore, the further research is necessary to understand the meaning of the measured values from new analysis methods, and care must be taken in interpreting the results. Inter-study differences in sample size, the sociodemographic and clinical characteristics of the participants, such as the various medication status and the incompatible illness duration may also be other reasons for the discrepant results. Studies investigating whole-brain in SAD have provided relatively consistent results. An ICA study of rsfMRI found selective alterations of resting-state networks in SAD patients (Liao et al., 2010a). SAD patients showed decreased FC in the SMN and visual network, increased in the self-referential network, and both increased and decreased FC in the DAN, CEN, DMN and SN. An rsfMRI study using a regional homogeneity (ReHo) analysis showed decreased coherence in the bilateral angular gyri, the left mPFC and the right ACC in SAD (Qiu et al., 2011). A whole-brain analysis using a graph-theory method found decreased connectivity in the bilateral precuneus in SAD patients (Liu et al., 2015b). According to the findings of that research, the rsFC in the precuneus had a negative correlation with illness duration. In fact, the precuneus is a critical hub of DMN, and plays a significant role in selfrelated mental processing (Cavanna and Trimble, 2006). Interestingly, a study by Liu et al. (2015a), using multivariate pattern analysis, explored the potential for rsFC to distinguish SAD patients from normal controls. The consensus functional connections used to distinguish SAD were largely located within or across the DMN, SN, SMN, visual network, and cerebellar regions. Among these regions, the region with the highest weight in SAD diagnosis was the right mOFC. Previous research proposed the potential of the rsFC as a complementary tool for SAD in clinical diagnosis. To summarize, the most consistent findings in SAD are abnormalities in amygdala-frontal, SN, and DMN connectivity. Other intrinsic neural networks have been identified (e.g., CEN), but insufficient literature exists to implicate these neural networks in SAD. Thus, they are not reviewed here. Table 2 shows fMRI studies of resting state networks in SAD.

sensitivity and somatosensory stimulus processing, which underlie the typical somatic symptoms of PD. Table 1 shows fMRI studies of resting state networks in PD. 3. Social anxiety disorder To date, most studies investigating emotional processing using fearful social cues showed altered brain responses in limbic and paralimbic brain areas. Therefore, up until this point, many investigations on SAD have focused on resting-state limbic or SN connectivity (Sripada et al., 2012). Because the amygdala plays significant roles in emotion process, many studies use the amygdala as a seed ROI to analyze rsFC of SAD patients. The work of Liao et al. (2010b) reported increased bidirectional influences between the amygdala and visual regions and between the medial orbitofrontal cortex (mOFC) and amygdala in SAD patients. Decreased effective connectivity from the bilateral inferior temporal gyri to the bilateral amygdalae is also reported in their study. A study by Hahn et al. (2011) investigated the rsFC in patients with SAD. Their research revealed a reduced rsFC between the left amygdala and the mOFC as well as the PCC/precuneus in the patient group. The work of Prater et al. (2013) reported decreased rsFC between the amygdala and pgACC in patients with SAD. The work of Dodhia et al. (2014) reported reduced rsFC from the left and right amygdala to the mPFC/pgACC in generalized SAD patients. The authors of that research found that oxytocin enhanced the reduced amygdala-frontal connectivity in SAD. A recent treatment study showed increased connectivity of the left amygdala with the dorsomedial prefrontal cortex (dmPFC) and the right dACC normalized after CBT in SAD patients (Yuan et al., 2016). They also found that the changes of the connectivity between the left amygdala and the dACC positively correlated with anxiety symptoms in patients. A recent study using bilateral amygdalae, dACC, and PCC as the seed ROIs also showed increased negative right amygdala connectivity and increased positive bilateral dACC connectivity (Pannekoek et al., 2013b). Increased connectivity in the dACC may be due to limbic hyperactivity because the dACC of the SN is directly involved in conflict monitoring and anticipation (Botvinick et al., 2001; Brown and Braver, 2005). The seed-based approach studies using amygdala as a seed region suggest alterations within the amygdalafrontal FC network in SAD. The discrepancy in results between these studies (e.g. increased and decreased amygdala-mOFC connectivity) could be ascribed to differences in analysis methods. Liao et al. (2010b) employed Granger causality analysis (GCA) which estimates directed influences between brain systems using the temporal dynamics in the fMRI data. The definition of effective connectivity in GCA is different from the concept of functional connectivity value of the approaches using correlations with, or between a priori defined regions of interest.

4. Medial temporal lobe subsystem disruption in DMN: a common neural-network Alterations in DMN were consistently observed in brain networks of patients with both PD and SAD (Fig. 1). A large number of studies have now connected particular patterns of resting connectivity between DMN and the psychopathologies of other psychiatric disorders including ADHD, depression, and schizophrenia (Greicius et al., 2003). The DMN is an interconnected set of brain regions showing higher intrinsic activity at rest (Raichle and Snyder, 2007). DMN activity decreases when task performance requires an increase in activity of other specific regions. On the other hand, it is supposed that this default 3

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Table 2 Resting-state fMRI studies of social anxiety disorder. Study

Sample

Brain regions and/or networks analysed

Main finding

Liao et al. (2010b)

22 SAD 21 HC

Seed: bilateral amygdala

Hahn et al. (2011)

7 SAD 1 PD 2 SAD + PD 27 HC 20 SAD 17 HC 18 SAD 18 HC 15 SAD 19 HC 20 SAD 20 HC

Seed: bilateral amygdala

Decreased effective connectivity from ITG to amygdala in SAD patients compared to controls. Increased connectivity between the mOFC and amygdala and between the visual regions and amygdala in SAD patients Reduced rsFC between left amygdala and mOFC as well as PCC/precuneus in SAD patients compared to HC

Pannekoek et al. (2013b)

12 SAD 12 HC

Seed: bilateral amygdala, dACC, PCC

Qiu et al. (2011)

20 20 20 20 20 20

Whole brain analysis: regional homogeneity method Whole brain analysis: multivariate pattern analysis method Whole brain analysis: graph theory method

Prater et al. (2013) Dodhia et al. (2014) Yuan et al. (2016) Liao et al. (2010a)

Liu et al. (2015a) Liu et al. (2015b)

SAD HC SAD HC SAD HC

Seed: bilateral amygdala

SAD patients showed decreased connectivity between amygdala and pgACC

Seed: bilateral amygdala

FC from left and right amygdala to mPFC/pgACC is reduced in SAD. Oxytocin normalized the reduced amygdala-mPFC/pgACC connectivity in SAD SAD Patients showed higher connectivity of left amygdala with dmPFC and dACC compared to controls. Increased amygdala-dACC connectivity normalized after CBT FC was significantly different in DAN, CEN, DMN and core network; decreased FC was found in the somato-motor network and visual network; increased FC was found in the self-referential network in SAD patients Increased negative right amygdala connectivity with the left middle temporal gyrus, left supramarginal gyrus and left lateral occipital cortex in SAD. Increased positive bilateral dACC connectivity with the left precuneus and left lateral occipital cortex in SAD. No group differences in connectivity were found for PCC/precuneus seeds. SAD patients showed decreased coherence in the bilateral angular gyrus, right ACC and the left mPFC within the DMN and in the right dlPFC and right inferior parietal gyrus within the CEN The right mOFC was the region with the highest weight in SAD diagnosis

Seed: bilateral amygdala Eight resting-state networks identified and investigated

SAD patients showed decreased FC in the bilateral precuneus and increased FC in the right fusiform gyrus. A negative correlation was observed between the FC value in the precuneus and the illness duration

ACC, anterior cingulate cortex; CEN, central executive network; dACC, dorsal anterior cingulate cortex; dlPFC, dorsolateral prefrontal cortex; DMN, default mode network; dmPFC, dorsomedial prefrontal cortex; FC, functional connectivity; HC, healthy controls; ITG, inferior temporal gyrus; mOFC, medial orbitofrontal cortex; mPFC, medial prefrontal cortex; PCC, post cingulate cortex; PD, panic disorder; ReHo, regional homogeneity; rsFC, resting-state functional connectivity; SAD, social anxiety disorder.

personally significant life events. Perceiving and inferring the emotional status of others is one of the most important factors in phobia. A recent review on the DMN and social understanding of others by Li et al. (2014) proposed the crucial role of the MTL subsystem in emotion perception processes. It has been postulated that the DMN makes sensory inputs meaningful as “situated conceptualizations” for distinct emotions because the DMN reconstitutes past experiences for use in the present (Lindquist et al., 2012). The vmPFC, as part of the DMN, is understood to receive reinforcement expectancy information through stimulus-reinforcement learning processes (Blair, 2007; Li et al., 2014). Therefore, vmPFC might play a pivotal role in the anticipatory processes of SAD and PD with agoraphobia. Agoraphobia is characterized by a phobic anxiety in situations where escape can be difficult or embarrassing, and is highly comorbid with PD. Because most previous imaging studies lack information on the coincidence of agoraphobia, there is very little evidence for the neural networks specific to agoraphobia (Wittmann et al., 2014). The neural substrate underpinnings of panic disorder with comorbid agoraphobia might be quite different from those underlying panic disorder

system of the human brain might be continuously busy with tasks such as monitoring and evaluating the present and the relevant future by extracting analogies from autobiographical information (Bar, 2009). The work of Andrews-Hanna et al. (2010) suggested that the DMN consisted of two subsystems that interact with a common core system. The first subsystem included the dmPFC, the temporoparietal junction, the lateral temporal cortex, and the temporal pole, and it was selectively activated during instances in which a person was thinking about the thoughts and emotional states of others. The second subsystem, called the medial temporal lobe (MTL) subsystem, included the ventral medial prefrontal cortex (vmPFC), the posterior inferior parietal lobule, the retrosplenial cortex, the parahippocampal cortex, and the hippocampal formation. The MTL subsystem is implicated in autobiographical memory and future simulations. These two subsystems interact with a midline common core system consisting of the anterior medial prefrontal cortex and the PCC, which is typically activated during the processing of information regarding the self. The MTL and the dmPFC subsystems interact and communicate with the midline core system to facilitate the constitution of mental representations of

Fig. 1. Medial and lateral view of the altered brain networks consistently observed in resting-state fMRI studies of in panic disorder (PD) and social anxiety disorder (SAD). Green areas represent the overlap altered network between PD and SAD (default mode network: ventromedial prefrontal cortex, perigenual anterior cingulate cortex, and precuneus/posterior cingulate cortex). Red areas represent the regions of increased connectivity only in SAD (salience network: dorsal anterior cingulate cortex, anterior insula, and amygdala). Blue areas represent the regions of increased connectivity only in PD (sensorimotor network: precentral and postcentral gyrus). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Medial view

Lateral view 4

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increased regional cerebral blood flow in the precentral as well as the postcentral gyrus (Seo et al., 2014). A structural study showed residual gray matter deficits in the precentral gyrus (even following remission) under antidepressant treatment (Lai and Wu, 2013). These findings suggested that precentral alteration might be a trait marker and underlying pathophysiology in PD. Two recent studies using whole-brain rsFC analysis also supported this hypothesis regarding the significance of network alterations (Cui et al., 2016; Lai and Wu, 2016). Motor area responses in PD might have negative feedback for persistently high interoceptive sensitivity. Therefore, an aberrant sensory-motor area network might be a marker of vulnerability for panic attacks, as opposed to the PD itself (Fig. 1).

without phobic avoidance. Thus, this vmPFC alteration could be associated with agoraphobia rather than with the panic attack itself (Na et al., 2013). Although to date there is no existing research on restingstate connectivity in SP, it should be noted that consistent results have been reported regarding the role of the cingulate cortex, the prefrontal, and the orbitofrontal cortices in SP neuroimaging studies. Taken together, these findings provide a basis for the hypothesis that alterations in the MTL subsystem are a hallmark of at least some common forms of PD and phobias. 5. DMN and serotonin Alterations in DMN might be due to dysfunction in various neurotransmitter systems, and the serotonergic system is assumed to be a key system for these alterations. Impaired serotonin (5-HT) synthesis in the central nervous system had been observed in molecular investigations on the pathogenesis of anxiety disorders (Kotting et al., 2013; Lee and Meltzer, 2001). There was a large amount of evidence for a genetic predisposition to PD and SAD (Gottschalk and Domschke, 2016; Yoon et al., 2008). Recent evidence also suggested a relationship between inflammatory and immunological parameters, serotonin levels, and panic symptoms (Kim and Kim, 2016; Zepf and Stewart, 2016). Several 5-HT receptor subtypes showed considerable spatial overlap with the DMN (Saulin et al., 2012). The influence of 5-HT on the DMN was the subject of various previous studies. The core DMN regions are innervated by serotonergic neurons from the midbrain raphe nuclei (Michelsen et al., 2007). The work of Hahn et al. (2012) proposed a key role for serotonin 1-A receptors in DMN modulation. A rsfMRI study reported acute tryptophan depletion-induced mood changes and DMN connectivity alterations in certain subjects (Kunisato et al., 2011). A pharmacological fMRI study showed that escitalopram, which is a serotonin reuptake inhibitor intended to treat generalized anxiety disorder, decreased DMN regional pairwise connectivity. A recent fMRI study demonstrated that platelet maximal 5-HT uptake velocity predicted global DMN activation (Scharinger et al., 2014). This finding suggested a pivotal role for neuronal 5-HT reuptake in DMN regulation. In addition, it has been shown that selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors modulated DMN connectivity (van de Ven et al., 2013; van Wingen et al., 2014). Along with the serotonin, endogenous opioids play a pivotal role in PD and anxiety disorders (Graeff, 2017). Endogenous opioids deficit results in heightened sensitivity to suffocation and separation anxiety in panic patients. Experimental results indicate that serotonin interacts synergistically with endogenous opioids in the dorsal periaqueductal gray through 5-HT1A and μ-opioid receptors to inhibit proximal defense and, supposedly, panic attacks (Roncon et al., 2013). Further study regarding endogenous opioids and resting state brain connectivity can shed lights on the role of opioids in anxiety disorders.

7. Salience network and amygdala-frontal connectivity in social anxiety disorder Besides DMN alterations, SN connectivity and amygdala-frontal alterations are also consistently reported in SAD (Fig. 1). The SN is generally comprised of core regions including the bilateral AI and the dACC, and is readily identified using ICA analysis of rsfMRI data (Seeley et al., 2007). Apart from these core regions, other limbic areas including the amygdala, ventral striatum, dorsomedial thalamus, hypothalamus and ventral tegmental area extending toward the temporal pole are also often reported as part of this network. The SN plays an important role in saliency detection and amplification, as well as in enhancing access to resources once a salient event has been detected. The SN also plays a pivotal role in switching between externally-oriented and internally-oriented brain networks (Sridharan et al., 2008). This set of regions is important for detecting errors, response selection, and conflict monitoring (Botvinick et al., 2004). People with high trait anxiety and anxiety disorders general show a similar pattern of SN dysfunction (Sylvester et al., 2012). Subjects with high trait anxiety may make more errors in trials involving response conflict relative to healthy controls in tasks that use non-emotional stimuli (Basten et al., 2011). Among the core regions of the SN, the dACC is implicated in the affective processing of negative information in SAD patients (Amir et al., 2005). This area also has been associated with self-focused attention (Lemogne et al., 2012). Therefore, we hypothesize that increased connectivity in the SN is necessary for balancing and resolving limbic hyperactivity. Alternatively, increased connectivity in the dACC may be due to repeated and sustained higher vigilance and alertness in SAD patients. Abnormal functional connectivity involving the amygdala and a lower part of the PFC known as the orbitofrontal area has also been consistently observed in SAD. The orbitofrontal cortex (OFC) plays a crucial role in the modulation of fear via the amygdala (Rosenkranz and Grace, 2002). Studies investigating emotional processing using pictorial or conditioned stimuli showed increased amygdala reactivity in anxiety disorder patients (Hariri et al., 2002; Monk et al., 2008). This amygdala hyperactivity has been suggested to be inversely related to orbitofrontal reactivity during the suppression of negative emotions (Phan et al., 2005). White matter deficits of the OFC–amygdala connection were shown in SAD patients (Phan et al., 2006). However, alterations of the OFC might not be specific to SAD considering similar findings in other psychiatric disorders (Jackowski et al., 2012). Structural and functional OFC abnormalities have been reported in PD as well (Bystritsky et al., 2001; Na et al., 2013). Reduced regional cerebral blood flow (rCBF) has been observed in the right OFC during unexpected panic attacks in a panic provocation study (Kent et al., 2005). Therefore, alterations of amygdala-frontal connectivity seem to be a common pathomechanism of phobic disorders, rather than a special characteristic that appears only in SAD. Additional rsfMRI research is needed to understand the role of amygdala-OFC connectivity in anxiety disorders.

6. Sensorimotor network and panic attacks Panic attacks consist of the rapid onset of intense anxiety, with prominent somatic symptoms, such as chest tightness, palpitations, abdominal discomfort, and dizziness. Neural correlates of panic attacks on a state level might be distinct from those of trait anxiety, such as anticipatory anxiety. Sensory-related regions are connected with limbic areas to manage one's oversensitivity to an unknown fear. Inadequate control due to dysfunction of these areas may cause abnormally high interoceptive sensitivity and somatosensory stimulus processing, which may eventually lead to panic attacks. Alterations of the precentral gyrus (a motor region) associated with anxiety and PD have been mentioned in several early studies. In a previous study, a pentagastrin challenge test (anticipated to provoke the symptoms of a panic attack) demonstrated hypoactivity of the precentral gyrus (Boshuisen et al., 2002). A single-photon emission computed tomography (SPECT) study also demonstrated that cognitive behavioral therapy was associated with 5

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8. Future directions and conclusion

connectivity. This type of approach might facilitate increased reproducibility in findings. Graph-based analysis also provides a distinct alternative to seed-based analyses and ICA (Fair et al., 2009; Salvador et al., 2005; van den Heuvel et al., 2008). This article reviewed recent studies on brain connectivity using rsfMRI methods in patients with PD and SAD. The reviewed studies suggest that PD and SAD have both common and distinct neural networks. Common alterations involve aberrant connectivity in the DMN. Among DMN systems, the MTL subsystem is closely related to the common psychopathology of PD and phobias. In contrast, PD is more closely linked to interoceptive pathways involving the sensorimotor cortex. Salience network connectivity is more prominent in SAD. Identifying these common and distinctive aspects of resting-state brain connectivity helps to assemble phenotypically heterogeneous phobic disorders into more meaningful subgroups. This work, in turn, will elucidate underlying mechanisms and biomarkers of existing and novel therapeutic interventions.

This review summarized the current status of research that utilized rsfMRI to examine alterations in rsFC in brain networks of patients with PD and SAD. Although there are concerns about the small sample size of relevant populations within the existing literature, together with an insufficient amount of literature to draw inferences from and replicate these network alterations, emerging studies have yielded some consistent findings. Research on resting-state brain activity using fMRI holds great promise as a method to elucidate the neurobiological underpinnings of psychiatric disorders. Because rsfMRI is easy to acquire and does not depend on cognitive tasks, reliable and stable results can be obtained with this approach. Moreover, the method is well suited for pediatric and cognitively impaired subjects for whom the performance of task-based functional MR imaging is difficult. Accordingly, rsfMRI has become a common technique in clinical research studies and stands to play a vital role in the development of novel imaging biomarkers. However, our understanding of the functional and clinical relevance of aberrant patterns of functional connectivity remains poorly defined. Furthermore, it is unclear how to interpret altered RSNs as a function of disease, recovery, and treatment. Studies based on rsfMRI will be more meaningful with increased understanding of the underlying neurophysiological correlates. Recent investigations, therefore, have begun to combine rsfMRI with other imaging techniques (e.g., diffusion tensor imaging (DTI)) or electrophysiological methods (e.g., electroencephalography (EEG) or magnetoencephalography (MEG)). The work of Kirino et al. (2016) conducted a simultaneous EEG and rsfMRI recording study to evaluate FC within and outside the DMN in schizophrenia patients. The work of Sui et al. (2011) proposed a multimodal canonical correlation analysis and joint ICA model to fuse fMRI and DTI data for classification of schizophrenia and bipolar disorders. It would be useful to combine rsfMRI and DTI methods to investigate both functional and structural connectivity. Emerging imaging-genetics approaches using rsfMRI techniques also stand to be a great complement to existing efforts to reveal the pathogenesis of psychiatric disorders at neuronal and genetic levels. The work of Yang et al. (2010) demonstrated a hybrid machine learning method to combine fMRI and genetics data for automatic recognition of schizophrenia patients relative to healthy controls. More work is needed to determine whether rsfMRI—either alone or in combination with other measures of brain connectivity (e.g. structural connectivity)—meets biomarker standards for diagnosis. Although rsfMRI has the advantage of obtaining stable and reliable results, there are still discrepancies in findings across studies, and even within the existing studies. These discrepancies may be due to diagnostic complexity and methodological differences. It remains unclear how to interpret altered functional connectivity in resting states in PD and SAD, and its involvement in their symptomatology is complex. The phenomenological complexity of categorical approaches of the diagnostic system results in substantial overlap between psychiatric disorders (Insel et al., 2010). Transdiagnostic dimensional approaches (e.g., using measures of trait anxiety) or subphenotype determination of discrete psychiatric disorders will help to identify underlying neural pathomechanisms that are potentially unique. Multicenter studies with larger samples are needed to better clarify the role and the nature of functional connectivity alterations in psychiatric disorders. Furthermore, several different approaches have been used in rsfMRI analysis. The most common methods are seed-based and ICA approaches. Seedbased approaches are relatively simple and are more suitable for research on resting fMRI data in adults because the ROIs are well defined by many investigators. ICA can be used to spatially identify distinct resting-state brain networks. Compared to seed-based methods, ICA has the advantage of requiring few a priori assumptions. A multi-approach analysis, integrating large-scale network connectivity analyses and more localized connectivity methods, should be encouraged with the aim of establishing a comprehensive view of resting-state functional

References American Psychiatric Association. DSM-5 Task Force, 2013. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th ed. American Psychiatric Association, Arlington, VA. Amir, N., Klumpp, H., Elias, J., Bedwell, J.S., Yanasak, N., Miller, L.S., 2005. Increased activation of the anterior cingulate cortex during processing of disgust faces in individuals with social phobia. Biol. Psychiatry 57 (9), 975–981. Andrews-Hanna, J.R., Reidler, J.S., Sepulcre, J., Poulin, R., Buckner, R.L., 2010. Functional-anatomic fractionation of the brain's default network. Neuron 65 (4), 550–562. Asmundson, G.J., Taylor, S., Smits, J.A., 2014. Panic disorder and agoraphobia: an overview and commentary on DSM-5 changes. Depress. Anxiety 31 (6), 480–486. Bar, M., 2009. The proactive brain: memory for predictions. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 364 (1521), 1235–1243. Barkhof, F., Haller, S., Rombouts, S.A., 2014. Resting-state functional MR imaging: a new window to the brain. Radiology 272 (1), 29–49. Basten, U., Stelzel, C., Fiebach, C.J., 2011. Trait anxiety modulates the neural efficiency of inhibitory control. J. Cogn. Neurosci. 23 (10), 3132–3145. Blair, R.J., 2007. The amygdala and ventromedial prefrontal cortex in morality and psychopathy. Trends Cogn. Sci. 11 (9), 387–392. Boshuisen, M.L., Ter Horst, G.J., Paans, A.M., Reinders, A.A., den Boer, J.A., 2002. rCBF differences between panic disorder patients and control subjects during anticipatory anxiety and rest. Biol. Psychiatry 52 (2), 126–135. Botvinick, M.M., Braver, T.S., Barch, D.M., Carter, C.S., Cohen, J.D., 2001. Conflict monitoring and cognitive control. Psychol. Rev. 108 (3), 624–652. Botvinick, M.M., Cohen, J.D., Carter, C.S., 2004. Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn. Sci. 8 (12), 539–546. Brown, J.W., Braver, T.S., 2005. Learned predictions of error likelihood in the anterior cingulate cortex. Science 307 (5712), 1118–1121. Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L., 2008. The brain's default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38. Bystritsky, A., Pontillo, D., Powers, M., Sabb, F.W., Craske, M.G., Bookheimer, S.Y., 2001. Functional MRI changes during panic anticipation and imagery exposure. Neuroreport 12 (18), 3953–3957. Cavanna, A.E., Trimble, M.R., 2006. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129 (Pt 3), 564–583. Critchley, H.D., Wiens, S., Rotshtein, P., Ohman, A., Dolan, R.J., 2004. Neural systems supporting interoceptive awareness. Nat. Neurosci. 7 (2), 189–195. Cui, H., Zhang, J., Liu, Y., Li, Q., Li, H., Zhang, L., Hu, Q., Cheng, W., Luo, Q., Li, J., Li, W., Wang, J., Feng, J., Li, C., Northoff, G., 2016. Differential alterations of resting-state functional connectivity in generalized anxiety disorder and panic disorder. Hum. Brain Mapp. 37 (4), 1459–1473. Dodhia, S., Hosanagar, A., Fitzgerald, D.A., Labuschagne, I., Wood, A.G., Nathan, P.J., Phan, K.L., 2014. Modulation of resting-state amygdala-frontal functional connectivity by oxytocin in generalized social anxiety disorder. Neuropsychopharmacology 39 (9), 2061–2069. Fair, D.A., Cohen, A.L., Power, J.D., Dosenbach, N.U., Church, J.A., Miezin, F.M., Schlaggar, B.L., Petersen, S.E., 2009. Functional brain networks develop from a "local to distributed" organization. PLoS Comput. Biol. 5 (5), e1000381. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8 (9), 700–711. Frith, C.D., Frith, U., 2007. Social cognition in humans. Curr. Biol. 17 (16), R724–R732. Gianaros, P.J., Sheu, L.K., 2009. A review of neuroimaging studies of stressor-evoked blood pressure reactivity: emerging evidence for a brain-body pathway to coronary heart disease risk. NeuroImage 47 (3), 922–936. Gottschalk, M.G., Domschke, K., 2016. Novel developments in genetic and epigenetic mechanisms of anxiety. Curr. Opin. Psychiatry 29 (1), 32–38. Graeff, F.G., 2017. Translational approach to the pathophysiology of panic disorder: Focus on serotonin and endogenous opioids. Neurosci. Biobehav. Rev. 76 (PT A), 48–55. Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the

6

Progress in Neuropsychopharmacology & Biological Psychiatry xxx (xxxx) xxx–xxx

Y.-K. Kim, H.-K. Yoon

Monk, C.S., Telzer, E.H., Mogg, K., Bradley, B.P., Mai, X., Louro, H.M., Chen, G., McClureTone, E.B., Ernst, M., Pine, D.S., 2008. Amygdala and ventrolateral prefrontal cortex activation to masked angry faces in children and adolescents with generalized anxiety disorder. Arch. Gen. Psychiatry 65 (5), 568–576. Mosing, M.A., Gordon, S.D., Medland, S.E., Statham, D.J., Nelson, E.C., Heath, A.C., Martin, N.G., Wray, N.R., 2009. Genetic and environmental influences on the comorbidity between depression, panic disorder, agoraphobia, and social phobia: a twin study. Depress. Anxiety 26 (11), 1004–1011. Na, K.S., Ham, B.J., Lee, M.S., Kim, L., Kim, Y.K., Lee, H.J., Yoon, H.K., 2013. Decreased gray matter volume of the medial orbitofrontal cortex in panic disorder with agoraphobia: a preliminary study. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 45, 195–200. Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., Panksepp, J., 2006. Self-referential processing in our brain—a meta-analysis of imaging studies on the self. NeuroImage 31 (1), 440–457. Olson, C.R., Musil, S.Y., 1992. Posterior cingulate cortex: sensory and oculomotor properties of single neurons in behaving cat. Cereb. Cortex 2 (6), 485–502. Pannekoek, J.N., Veer, I.M., van Tol, M.J., van der Werff, S.J., Demenescu, L.R., Aleman, A., Veltman, D.J., Zitman, F.G., Rombouts, S.A., van der Wee, N.J., 2013a. Aberrant limbic and salience network resting-state functional connectivity in panic disorder without comorbidity. J. Affect. Disord. 145 (1), 29–35. Pannekoek, J.N., Veer, I.M., van Tol, M.J., van der Werff, S.J., Demenescu, L.R., Aleman, A., Veltman, D.J., Zitman, F.G., Rombouts, S.A., van der Wee, N.J., 2013b. Restingstate functional connectivity abnormalities in limbic and salience networks in social anxiety disorder without comorbidity. Eur. Neuropsychopharmacol. 23 (3), 186–195. Peterson, A., Thome, J., Frewen, P., Lanius, R.A., 2014. Resting-state neuroimaging studies: a new way of identifying differences and similarities among the anxiety disorders? Can. J. Psychiatr. 59 (6), 294–300. Phan, K.L., Fitzgerald, D.A., Nathan, P.J., Moore, G.J., Uhde, T.W., Tancer, M.E., 2005. Neural substrates for voluntary suppression of negative affect: a functional magnetic resonance imaging study. Biol. Psychiatry 57 (3), 210–219. 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. Biol. Psychiatry 59 (5), 424–429. Prater, K.E., Hosanagar, A., Klumpp, H., Angstadt, M., Phan, K.L., 2013. Aberrant amygdala-frontal cortex connectivity during perception of fearful faces and at rest in generalized social anxiety disorder. Depress. Anxiety 30 (3), 234–241. Qiu, C., Liao, W., Ding, J., Feng, Y., Zhu, C., Nie, X., Zhang, W., Chen, H., Gong, Q., 2011. Regional homogeneity changes in social anxiety disorder: a resting-state fMRI study. Psychiatry Res. 194 (1), 47–53. Raichle, M.E., Snyder, A.Z., 2007. A default mode of brain function: a brief history of an evolving idea. NeuroImage 37 (4), 1083–1090 (discussion 1097-1089). Roncon, C.M., Biesdorf, C., Coimbra, N.C., Audi, E.A., Zangrossi Jr., H., Graeff, F.G., 2013. Cooperative regulation of anxiety and panic-related defensive behaviors in the rat periaqueductal grey matter by 5-HT1A and mu-receptors. J. Psychopharmacol. 27 (12), 1141–1148. Rosenkranz, J.A., Grace, A.A., 2002. Cellular mechanisms of infralimbic and prelimbic prefrontal cortical inhibition and dopaminergic modulation of basolateral amygdala neurons in vivo. J. Neurosci. 22 (1), 324–337. Ryan, J.P., Sheu, L.K., Gianaros, P.J., 2011. Resting state functional connectivity within the cingulate cortex jointly predicts agreeableness and stressor-evoked cardiovascular reactivity. NeuroImage 55 (1), 363–370. Salvador, R., Suckling, J., Schwarzbauer, C., Bullmore, E., 2005. Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 360 (1457), 937–946. Saulin, A., Savli, M., Lanzenberger, R., 2012. Serotonin and molecular neuroimaging in humans using PET. Amino Acids 42 (6), 2039–2057. Scharinger, C., Rabl, U., Kasess, C.H., Meyer, B.M., Hofmaier, T., Diers, K., Bartova, L., Pail, G., Huf, W., Uzelac, Z., Hartinger, B., Kalcher, K., Perkmann, T., Haslacher, H., Meyer-Lindenberg, A., Kasper, S., Freissmuth, M., Windischberger, C., Willeit, M., Lanzenberger, R., Esterbauer, H., Brocke, B., Moser, E., Sitte, H.H., Pezawas, L., 2014. Platelet serotonin transporter function predicts default-mode network activity. PLoS One 9 (3), e92543. 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. J. Neurosci. 27 (9), 2349–2356. Seo, H.J., Choi, Y.H., Chung, Y.A., Rho, W., Chae, J.H., 2014. Changes in cerebral blood flow after cognitive behavior therapy in patients with panic disorder: a SPECT study. Neuropsychiatr. Dis. Treat. 10, 661–669. Shimada-Sugimoto, M., Otowa, T., Hettema, J.M., 2015. Genetics of anxiety disorders: genetic epidemiological and molecular studies in humans. Psychiatry Clin. Neurosci. 69 (7), 388–401. Shin, Y.W., Dzemidzic, M., Jo, H.J., Long, Z., Medlock, C., Dydak, U., Goddard, A.W., 2013. Increased resting-state functional connectivity between the anterior cingulate cortex and the precuneus in panic disorder: resting-state connectivity in panic disorder. J. Affect. Disord. 150 (3), 1091–1095. Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N., Watkins, K.E., Toro, R., Laird, A.R., Beckmann, C.F., 2009. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. U. S. A. 106 (31), 13040–13045. Sridharan, D., Levitin, D.J., Menon, V., 2008. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl. Acad. Sci. U. S. A. 105 (34), 12569–12574. Sripada, R.K., King, A.P., Welsh, R.C., Garfinkel, S.N., Wang, X., Sripada, C.S., Liberzon, I., 2012. Neural dysregulation in posttraumatic stress disorder: evidence for disrupted equilibrium between salience and default mode brain networks. Psychosom. Med. 74

resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. U. S. A. 100 (1), 253–258. Hahn, A., Stein, P., Windischberger, C., Weissenbacher, A., Spindelegger, C., Moser, E., Kasper, S., Lanzenberger, R., 2011. Reduced resting-state functional connectivity between amygdala and orbitofrontal cortex in social anxiety disorder. NeuroImage 56 (3), 881–889. Hahn, A., Wadsak, W., Windischberger, C., Baldinger, P., Hoflich, A.S., Losak, J., Nics, L., Philippe, C., Kranz, G.S., Kraus, C., Mitterhauser, M., Karanikas, G., Kasper, S., Lanzenberger, R., 2012. Differential modulation of the default mode network via serotonin-1A receptors. Proc. Natl. Acad. Sci. U. S. A. 109 (7), 2619–2624. Hariri, A.R., Tessitore, A., Mattay, V.S., Fera, F., Weinberger, D.R., 2002. The amygdala response to emotional stimuli: a comparison of faces and scenes. NeuroImage 17 (1), 317–323. Hartwright, C.E., Apperly, I.A., Hansen, P.C., 2014. Representation, control, or reasoning? Distinct functions for theory of mind within the medial prefrontal cortex. J. Cogn. Neurosci. 26 (4), 683–698. van den Heuvel, M.P., Hulshoff Pol, H.E., 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20 (8), 519–534. van den Heuvel, M.P., Stam, C.J., Boersma, M., Hulshoff Pol, H.E., 2008. Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. NeuroImage 43 (3), 528–539. Inoue, K., Nakanishi, K., Hadoush, H., Kurumadani, H., Hashizume, A., Sunagawa, T., Ochi, M., 2013. Somatosensory mechanical response and digit somatotopy within cortical areas of the postcentral gyrus in humans: an MEG study. Hum. Brain Mapp. 34 (7), 1559–1567. Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D.S., Quinn, K., Sanislow, C., Wang, P., 2010. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167 (7), 748–751. Jackowski, A.P., Araujo Filho, G.M., Almeida, A.G., Araujo, C.M., Reis, M., Nery, F., Batista, I.R., Silva, I., Lacerda, A.L., 2012. The involvement of the orbitofrontal cortex in psychiatric disorders: an update of neuroimaging findings. Rev. Bras. Psiquiatr. 34 (2), 207–212. Kent, J.M., Coplan, J.D., Mawlawi, O., Martinez, J.M., Browne, S.T., Slifstein, M., Martinez, D., Abi-Dargham, A., Laruelle, M., Gorman, J.M., 2005. Prediction of panic response to a respiratory stimulant by reduced orbitofrontal cerebral blood flow in panic disorder. Am. J. Psychiatry 162 (7), 1379–1381. Kessler, R.C., Chiu, W.T., Demler, O., Merikangas, K.R., Walters, E.E., 2005. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62 (6), 617–627. Kim, H.J., Kim, Y.K., 2016. The G allele in IL-10-1082 G/A may have a role in lowering the susceptibility to panic disorder in female patients. Acta Neuropsychiatry 28 (6), 357–361. Kirino, E., Tanaka, S., Fukuta, M., Inami, R., Arai, H., Inoue, R., Aoki, S., 2016. Simultaneous rsfMRI and EEG recordings of functional connectivity in patients with schizophrenia. Psychiatry Clin. Neurosci. 71 (4), 262–270. Kotting, W.F., Bubenzer, S., Helmbold, K., Eisert, A., Gaber, T.J., Zepf, F.D., 2013. Effects of tryptophan depletion on reactive aggression and aggressive decision-making in young people with ADHD. Acta Psychiatr. Scand. 128 (2), 114–123. Kunisato, Y., Okamoto, Y., Okada, G., Aoyama, S., Demoto, Y., Munakata, A., Nomura, M., Onoda, K., Yamawaki, S., 2011. Modulation of default-mode network activity by acute tryptophan depletion is associated with mood change: a resting state functional magnetic resonance imaging study. Neurosci. Res. 69 (2), 129–134. Lai, C.H., Wu, Y.T., 2013. Changes in gray matter volume of remitted first-episode, drugnaive, panic disorder patients after 6-week antidepressant therapy. J. Psychiatr. Res. 47 (1), 122–127. Lai, C.H., Wu, Y.T., 2014. The alterations in inter-hemispheric functional coordination of patients with panic disorder: the findings in the posterior sub-network of default mode network. J. Affect. Disord. 166, 279–284. Lai, C.H., Wu, Y.T., 2016. The explorative analysis to revise fear network model for panic disorder: functional connectome statistics. Medicine (Baltimore) 95 (18), e3597. Lee, M.A., Meltzer, H.Y., 2001. 5-HT(1A) receptor dysfunction in female patients with schizophrenia. Biol. Psychiatry 50 (10), 758–766. Lemogne, C., Delaveau, P., Freton, M., Guionnet, S., Fossati, P., 2012. Medial prefrontal cortex and the self in major depression. J. Affect. Disord. 136 (1–2), e1–e11. Li, W., Mai, X., Liu, C., 2014. The default mode network and social understanding of others: what do brain connectivity studies tell us. Front. Hum. Neurosci. 8, 74. 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 (4), 1549–1558. Liao, W., Qiu, C., Gentili, C., Walter, M., Pan, Z., Ding, J., Zhang, W., Gong, Q., Chen, H., 2010b. Altered effective connectivity network of the amygdala in social anxiety disorder: a resting-state FMRI study. PLoS One 5 (12), e15238. Lindquist, K.A., Wager, T.D., Kober, H., Bliss-Moreau, E., Barrett, L.F., 2012. The brain basis of emotion: a meta-analytic review. Behav. Brain Sci. 35 (3), 121–143. Liu, F., Guo, W., Fouche, J.P., Wang, Y., Wang, W., Ding, J., Zeng, L., Qiu, C., Gong, Q., Zhang, W., Chen, H., 2015a. Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct. Funct. 220 (1), 101–115. Liu, F., Zhu, C., Wang, Y., Guo, W., Li, M., Wang, W., Long, Z., Meng, Y., Cui, Q., Zeng, L., Gong, Q., Zhang, W., Chen, H., 2015b. Disrupted cortical hubs in functional brain networks in social anxiety disorder. Clin. Neurophysiol. 126 (9), 1711–1716. Mano, Y., Harada, T., Sugiura, M., Saito, D.N., Sadato, N., 2009. Perspective-taking as part of narrative comprehension: a functional MRI study. Neuropsychologia 47 (3), 813–824. Michelsen, K.A., Schmitz, C., Steinbusch, H.W., 2007. The dorsal raphe nucleus—from silver stainings to a role in depression. Brain Res. Rev. 55 (2), 329–342.

7

Progress in Neuropsychopharmacology & Biological Psychiatry xxx (xxxx) xxx–xxx

Y.-K. Kim, H.-K. Yoon

Wittchen, H.U., Arolt, V., Heinz, A., Strohle, A., 2014. Anticipating agoraphobic situations: the neural correlates of panic disorder with agoraphobia. Psychol. Med. 44 (11), 2385–2396. Woodward, N.D., Cascio, C.J., 2015. Resting-state functional connectivity in psychiatric disorders. JAMA Psychiatry 72 (8), 743–744. Yang, H., Liu, J., Sui, J., Pearlson, G., Calhoun, V.D., 2010. A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Front. Hum. Neurosci. 4, 192. Yoon, H.K., Yang, J.C., Lee, H.J., Kim, Y.K., 2008. The association between serotoninrelated gene polymorphisms and panic disorder. J. Anxiety Disord. 22 (8), 1529–1534. Yuan, M., Zhu, H., Qiu, C., Meng, Y., Zhang, Y., Shang, J., Nie, X., Ren, Z., Gong, Q., Zhang, W., Lui, S., 2016. Group cognitive behavioral therapy modulates the restingstate functional connectivity of amygdala-related network in patients with generalized social anxiety disorder. BMC Psychiatry 16, 198. Zepf, F.D., Stewart, R.M., 2016. Inflammation, immunity and suicidality: a potential role for autoantibodies against neurotransmitters and antiphospholipid syndrome? Acta Psychiatr. Scand. 133 (3), 249–250.

(9), 904–911. Sui, J., Pearlson, G., Caprihan, A., Adali, T., Kiehl, K.A., Liu, J., Yamamoto, J., Calhoun, V.D., 2011. Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA + joint ICA model. NeuroImage 57 (3), 839–855. Sylvester, C.M., Corbetta, M., Raichle, M.E., Rodebaugh, T.L., Schlaggar, B.L., Sheline, Y.I., Zorumski, C.F., Lenze, E.J., 2012. Functional network dysfunction in anxiety and anxiety disorders. Trends Neurosci. 35 (9), 527–535. van de Ven, V., Wingen, M., Kuypers, K.P., Ramaekers, J.G., Formisano, E., 2013. Escitalopram decreases cross-regional functional connectivity within the defaultmode network. PLoS One 8 (6), e68355. Wagner, A.D., Shannon, B.J., Kahn, I., Buckner, R.L., 2005. Parietal lobe contributions to episodic memory retrieval. Trends Cogn. Sci. 9 (9), 445–453. van Wingen, G.A., Tendolkar, I., Urner, M., van Marle, H.J., Denys, D., Verkes, R.J., Fernandez, G., 2014. Short-term antidepressant administration reduces default mode and task-positive network connectivity in healthy individuals during rest. NeuroImage 88, 47–53. Wittmann, A., Schlagenhauf, F., Guhn, A., Lueken, U., Gaehlsdorf, C., Stoy, M., Bermpohl, F., Fydrich, T., Pfleiderer, B., Bruhn, H., Gerlach, A.L., Kircher, T., Straube, B.,

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