White matter connectivity differences between treatment responders and non-responders in patients with panic disorder

Journal of Affective Disorders 260 (2020) 527–535

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Research paper

White matter connectivity differences between treatment responders and non-responders in patients with panic disorder ⁎

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Se-woong Kim1, Min-Kyoung Kim1, Borah Kim, Tae-Kiu Choi , Sang-Hyuk Lee

Department of Psychiatry, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 13492, Republic of Korea

A R T I C LE I N FO

A B S T R A C T

Keywords: Panic disorder Treatment response predictor White matter connectivity Neuroimaging

Background: Panic disorder (PD) is a prevalent and highly disabling mental condition. However, less is known about relationships between biomarkers that may together predict a better response to pharmacological treatment. The objective of the present study was to compare the brain white matter (WM) connectivity between treatment-responsive patients with panic disorder (RPD) and non-responsive patients with panic disorder (NRPD) after 12 weeks of pharmacotherapy. Methods: Sixty-four patients with PD were enrolled in this study (RPD, n = 37; NRPD, n = 27). All patients were examined by using magnetic resonance imaging at 3 Tesla. The Panic Disorder Severity Scale (PDSS), Albany Panic and Phobia Questionnaire (APPQ), Anxiety Sensitivity Inventory-Revised (ASI-R), Beck Anxiety Inventory (BAI), and Beck Depression Inventory (BDI) were administered at baseline of the study. Fractional anisotropy (FA) data were compared using tract-based spatial statistics (TBSS). Results: TBSS results showed that the FA values of the patients with NRPD were significantly higher than of those with RPD in the WM regions such as the precentral gyrus, parahippocampal gyrus, posterior corona radiata, posterior thalamic radiation, posterior parts of the corpus callosum, and precuneus. Symptom severity scales, such as ASI-R scores, showed significant positive correlations of the FA values with the fronto-temporal WM regions in NRPD. Conclusions: These results suggest that structural changes to areas such as the fronto-limbic regions and the posterior part of default mode network, could influence medication response in PD. Further studies with a larger number of patients should be performed to confirm our findings.

1. Introduction Panic disorder (PD) can be effectively treated with medication, cognitive-behavioral therapy (CBT), or a combination of the two modalities. Most notably, serotonin selective reuptake inhibitors (SSRIs) used for acute treatment of PD demonstrated an average effect size of 0.55 (Cohen's d) relative to placebo and better tolerability relative to previously used antidepressants. Although drug therapy is the accepted first-line treatment for PD, 45% of patients do not respond adequately and continue to exhibit one of the most common symptoms of PD, panic attack (Otto et al., 2001). As several weeks are required before a clinical effect can be expected with SSRIs, predicting non-responsiveness to pharmacotherapy would be very helpful for both patients and clinicians. Demographic and clinical variables such as longer duration of illness, greater symptom severity, and higher rates of comorbid psychiatric disorders have been proposed as potential

predictors of non-responsiveness to PD treatment (Denys and de Geus, 2005). However, less is known about relationships between biomarkers that may together predict a better response to treatment with SSRIs. Accumulating neuroimaging studies have reported neural substrates of anxiety disorders (Fredrikson and Faria, 2013; Maron et al., 2012; Sylvester et al., 2012). A recent review of functional neuroimaging studies indicated that patients with anxiety disorders exhibit increased functioning of the cingulo-opercular (CON, which is important for detecting errors or conflicts) and ventral attention networks (VAN, is associated with the orientation of stimulus-driven attention) as well as decreased functioning of the fronto-parietal (FPN, referred to as the executive control network) and default mode networks (DMN, which is related to self-referential and emotion regulation) during affective stimuli-related tasks compared with HCs (Sylvester et al., 2012). However, this particular pattern of functional network dysfunction is

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Corresponding author. E-mail addresses: [email protected], [email protected] (S.-H. Lee). 1 These authors contributed equally to this work and should be considered co-first authors. https://doi.org/10.1016/j.jad.2019.09.032 Received 21 June 2019; Received in revised form 26 August 2019; Accepted 3 September 2019 Available online 04 September 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.

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abnormalities related to CBT responses in patients with PD, analysis of pharmacotherapy response predictors in PD has been surprisingly scarce to date, with only one previous publication addressing this question to our knowledge. In this study, the authors used a fluorodeoxyglucose–positron emission tomography approach to determine that drug therapy responders showed an increase in metabolic activity in frontal and temporal areas, including the limbic areas and posterior parts of the DMN, such as the precuneus, whereas non-responders did not (Kang et al., 2012). Based on these previous reports, we hypothesized that both WM regions of the modified fear network and the DMN are related to SSRI response in patients with PD. In this study, we compared structural alterations in the WM connectivity and the treatment response to pharmacotherapy in patients with PD based on DTI analysis and explored whether the relationship between the structural WM alterations in these regions are associated with clinical symptom severity.

slightly different according to the diverse group of patients with anxiety disorders and task-dependent activities. Moreover, in the case of structural neuroimaging studies in a large HC sample, those with high trait anxiety showed lower WM connectivity in the amygdala connections of all these four different networks and did not detect WM alterations in the structures of these networks (De Witte and Mueller, 2017). Therefore, searching for the neural substrates of anxiety disorders, particulary PD, may help find biomarkers of treatment response with pharmacotherapy and a stratifed medicine approach. Gorman and colleagues have proposed that individuals susceptible to PD have an abnormally sensitive fear network consisting of the frontal cortex, insula, thalamus, brain stem, and limbic system regions such as the cingulate gyrus, amygdala, hippocampus, and parahippocampal gyrus (Gorman et al., 2000). Although the neural pathophysiology of psychiatric disorders is difficult to assess, diffusion tensor imaging (DTI) is sufficiently sensitive to illustrate alterations in the tissue microstructure (Wakana et al., 2004), such as WM changes, through non-invasive quantification of osmosis characteristics (Basser et al., 1994). Previous structural magnetic resonance imaging (MRI) studies reported altered fractional anisotropy (FA) values in various brain WM regions, including the frontal lobe, superior longitudinal fasciculus, corpus callosum (CC), and cingulate cortex (Han et al., 2008; Kim et al., 2014; Lai and Wu, 2013b), as well as volumetric changes in limbic areas such as the parahippocampal gyrus in patients with PD compared to HCs (Massana et al., 2003b). Functional MRI studies also detected altered activity in multiple frontal cortex and limbic regions, including the parahippocampal area (Ball et al., 2013; Maddock et al., 2003). Altogether, these aforementioned neuroimaging findings support the involvement of the fear network model as a key brain circuit in PD. A recent review suggested that a modified extension of the fear circuit to other regions such as sensory-related regions might be a more compelling candidate region underlying the pathophysiology of PD (Dresler et al., 2013). In addition, patients with PD were found to exhibit alterations in the DMN, which is important for emotion, somatosensation, and self-referential processing function (Pannekoek et al., 2013). Moreover, increased functional connectivity within the DMN was observed in PD patients in the resting state (Shin et al., 2013), while administration of SSRIs to HCs changed connectivity in multiple networks including the DMN, particularly in the precuneus (Klaassens et al., 2015b). Several recent studies have suggested that predictors of antidepressant response in individuals with psychiatric illness can be identified using neuroimaging approaches (Phillips et al., 2015). For example, SSRIs are purported to have a neuroprotective effect that involves increasing brain derived neurotrophic factor (BDNF) levels (Hunsberger et al., 2009). BDNF has been found to influence oligodendrocytes and myelination (Xiao et al., 2010), which could result in changes to the WM microstructure. Furthermore, there is evidence that specific SSRIs, including fluoxetine, stimulate astrocyte glycogenesis, suggesting that SSRIs have the potential to alter diffusivity coefficients such that they could be detected on DTI (Kong et al., 2002). Indeed, subtle changes in fronto-temporal WM integrity were observed after remission, which might reflect neural correlates of antidepressant effects in PD (Lai et al., 2013). A PD neuroimaging study indicated that the CBT response was associated with an inhibitory functional coupling between the anterior cingulate cortex and amygdala (Lueken et al., 2013). These results support the hypothesis that CBT exerts its therapeutic effect by facilitating frontal cortex control over the amygdala (i.e., top-down mechanisms). Gorman et al. suggested that SSRI mechanisms are different from CBT mechanisms for patients with PD (Gorman et al., 2000). SSRIs diminish the activity of brain stem centers that receive input from the central nucleus of the amygdala and control autonomic and neuroendocrine responses during panic attacks (i.e., bottom-up mechanisms). Although previous neuroimaging studies showed functional

2. Materials and methods 2.1. Patients and clinical assessment Between January 2011 and December 2015, 112 patients with PD were recruited from the outpatient units of the Department of Psychiatry of CHA Bundang Medical Center. After screening, 64 patients were enrolled in this study. Although determining power and sample size in neuroimaging studies remains challenging, we consider a group size of at least 20 may be appropriate based on previous studies (Hayasaka et al., 2007; Kang et al., 2012; Poldrack et al., 2017; Shear et al., 1997). All patients were between 18 and 60 years old, of Korean descent, and right-handed. Psychiatric diagnoses were based on the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR) criteria for PD, and were determined using the structured clinical interview to assess DSM-IV-TR Axis I disorders (SCID-I) (First and Gibbon, 2004). Only patients with primary PD were included in the present study. Patients were excluded if they had any current diagnosis of Axis I and II psychiatric disorders other than PD or a lifetime history of psychiatric disorders; such as schizophrenia, other psychotic disorders, major depressive disorder (MDD), bipolar disorder I or II, alcohol/substance abuse or dependence, mental retardation, present or past serious medical or neurological disorders, and contraindication to brain MR scanning, including pregnancy and metal implant status. The first visit was used as the baseline. Patients with PD were assessed for the clinical severity of panic and related symptoms using the panic disorder severity scale (PDSS) (Cronbach's α = 0.88) (Lee et al., 2009; Shear and Maser, 1994), the Albany Panic and Phobia Questionnaire (APPQ) (Cronbach's α = 0.95) (Rapee et al., 1994; Yu, 2004), and the anxiety sensitivity inventory revised (ASI-R) (Cronbach's α = 0.93) (Lim, 2004; Taylor and Cox, 1998). The clinician-administered PDSS is a 7-item scale rated 0–4 that is used to assess multiple dimensions of PD severity. The APPQ is a 27-item scale with rating ranging from “not at all” (0) to “extremely” (8) and is used to assess how much fear patients with panic perceive in a given situation, including induced physical sensations. There are three APPQ subscales: interoceptive avoidance, agoraphobia, and social phobia (Brown et al., 2005). The ASI-R is a 36-item scale with ratings ranging from “very little” (0) to “very much” (4) that is used to assess anxiety sensitivity. The ASI-R is a commonly used measure of anxiety sensitivity that consists of fear of a respiratory symptom, fear of a cardiovascular symptom, fear of a publicly observable anxiety reaction, and fear of cognitive dyscontrol. To measure the clinical severities of the patients’ anxiety and comorbid depressive symptoms, we also administered the Beck Anxiety Inventory (BAI) (Cronbach's α = 0.91) (Beck et al., 1988; Kim, 2016) and the Beck Depression Inventory (BDI) (Cronbach's α = 0.90) (Beck et al., 1961; Suh et al., 2017) in the same time. These 528

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weighted fast spoiled gradient recalled echo (3D T1-FSPGR) images were: 16 ms repetition time (TR), 4.3 ms echo time (TE), 10° flip angle, 1.7 mm slice thickness, 25.6 cm field of view (FOV), 256 × 256 matrix, and isotropic voxel size 1 mm × 1 mm × 1 mm. Diffusion weighted images were obtained using an echo planar imaging (EPI) sequence with the following parameters: 17,000 ms TR, 108 ms TE, 24 cm FOV, 1.7 mm slice thickness, 1.67 mm × 1.67 mm × 1.7 mm voxel size, and 144 × 144 matrix. To reduce eddy current-related distortions, a double echo option was conducted. An 8-channel coil and an array of spatial sensitivity encoding techniques (ASSET, GE Healthcare) with a sensitivity encoding (SENSE) speed-up factor of 2 was used to reduce the impact of EPI spatial distortions. 70 axial slices parallel to the anterior commissure–posterior commissure (AC-PC) line covering the whole brain were acquired in 51 directions with b = 900 s/mm2 and 8 baseline scans with b = 0 s/mm2. Diffusion tensor images were acquired from the diffusion-weighted images by using the least-squares method.

measures were used to examine correlation analysis as a factor and establish baseline. We defined ‘treatment response’ at 12 weeks as PDSS total score reduction of 40% from the baseline PDSS (Barlow et al., 2000; Roy-Byrne et al., 2003; Shear et al., 1997). Then, we divided the patients with PD into a treatment-responsive group (RPD) and a nonresponsive group (NRPD) to compare WM connectivity related to clinical improvement after 12 weeks of pharmacotherapy treatment. After study commencement, every patient began treatment with a minimal dosage of SSRIs (escitalopram equivalence dosage, 5 mg) (Hayasaka et al., 2015), such as escitalopram (n = 19 in RPD, n = 12 in NRPD) or paroxetine (n = 18 in RPD, n = 15 in NRPD). All patients with PD began treatment with the dosage being increased by up to 20 mg escitalopram or its equivalent each day, according to the clinician's judgment. A minimal dosage of benzodiazepines (BZDs) as adjunctive anxiolytics, including alprazolam or clonazepam was prescribed to some patients (RPD 35%; NRPD 37%). Dosing was flexible, but the BZD was the use of PRN (pro re nata) medication within the limit of equivalence of 2 mg lorazepam. To minimize the effects of medication on the brain, we tried to conduct brain MR scans at early stages of treatment. Thus, MRI was performed as soon as possible after enrollment in the study. As a result, brain MR scans of all patients were conducted within 10 days of the initiation of medication (mean = 6.83 days). All study protocols were approved by CHA Bundang Medical Center Institutional Review Board regulations and were prepared in accordance with the ethical standards of the Declaration of Helsinki and the principles of Good Clinical Practice. After an extensive description of the research, written informed consent was obtained from all study participants (Fig. 1).

2.3. Imaging processing and data analysis 2.3.1. TBSS analysis A voxel-wise statistical analysis of the FA data was performed using Tract-Based Spatial Statistics (TBSS) version 1.2, implemented in FMRIB Software Library (FSL version 4.1, Oxford, U.K.) according to the standard method as follows (Smith et al., 2006). To begin with, DTI preprocessing, including skull stripping using the Brain Extraction Tool and eddy current correction, were conducted in FSL. Next, FA images were created by adapting a tensor model to the raw diffusion data (Smith, 2002). Using the FMRIB's Nonlinear Image Registration Tool (FNIRT), all patients’ FA data were aligned into the standard space (Montreal Neurologic Institute 152 standard). All transformed FA images were added and applied to the original FA map, resulting in a standard-space version FA map. All transformed FA images were standardized to create a mean FA image and they were skeletonized to create a mean FA skeleton, taking only the centers of WM tracts. The

2.2. MRI procedures Diffusion MR scans were performed using a GE Signa HDxt 3.0T scanner (GE Healthcare). The parameters for three-dimensional T1-

Fig. 1. Schematic representation of the study: MR, Magnetic Resonance; PDSS, Panic Disorder Severity Scale; BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory; ASI-R, Anxiety Sensitivity Inventory-Revised; APPQ, Albany Panic and Phobia Questionnaire; RPD, Treatment Responder in patients with Panic Disorder; NRPD, Treatment Non-Responder in patients with Panic Disorder; WM, White Matter; PD, Panic Disorder. 529

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and BZDs at the time of the MR scan. Clinical symptom scales, such as PDSS, APPQ, ASI-R, BAI and BDI scores as well as all their subscales at the time of the MR scans, were not statistically different; however, the NRPD group showed a trend towards higher scores. PDSS scores were significantly more decreased after 12 weeks of the study in the RPD group than in the NRPD group (mean ± SD,%, 63.51 ± 13.58 > 18.15 ± 10.39). The equivalent doses of SSRIs at 12 weeks were significantly higher in the NRPD group than in the RPD group (mean ± SD, mg, 13.52 ± 5.51 > 10.00 ± 5.00) (Mallinckrodt et al., 2007). The NRPD group also included a higher rate of remainders needing a BZD prescription (26% > 13.5%) and displayed higher equivalent doses of BZDs (lorazepam equivalence, mean ± SD, mg, 1.51 ± 1.12 > 1.09 ± 1.33) than the RPD group (Mehdi, 2012).

skeleton was thresholded by FA > 0.2 (TBSS default) to exclude minor fiber bundles. To detect regions of significant differences of FA between two groups, a voxel-wise permutation-based nonparametric inference was performed on the skeletonized FA data using FSL Randomize program (Nichols and Holmes, 2002). For accurate inference, permutationbased nonparametric inference within the framework of a general linear model was used with the significance level set to p < 0.05. To achieve this, comparisons were tested with 5000 permutations including full correction for multiple comparisons over space. Multiple comparisons were corrected with threshold-free cluster enhancement (TFCE) (Smith and Nichols, 2009), permitting the avoidance of arbitrary choices of the cluster-forming threshold while preserving the sensitivity benefits of cluster-wise correction. To further reduce the possibility of false-positive results, only clusters with more than 150 contiguous voxels were considered in the analysis. To assess the correlation analysis, the DTI data were analyzed using the TBSS General Linear Model (GLM) regression analysis with PDSS, APPQ, ASI-R, BAI and BDI as factors. Anatomical localization of significant WM regions were identified with JHU (Johns Hopkins University) DTI-based white matter atlases and MRI atlases of human WM anatomy (Mori and van Zijl, 2007).

3.2. TBSS data

2.3.2. Statistical analysis To compare the sociodemographic and clinical data between two groups, independent t–tests and Fisher's exact test were applied. Spearman correlation analyses were used to find the relationship between WM integrity and the PDSS, APPQ, ASI-R, BAI and BDI scores. Every statistical analysis was performed using the IBM SPSS Statistics 21.0 software (IBM Corporation) tools. For all tests, p < 0.05 was considered to have statistical significance.

Compared to the RPD group, the FA value was significantly higher in the NRPD group in several WM regions: the precentral gyrus, parahippocampal gyrus, posterior corona radiata, posterior thalamic radiation, posterior parts of the corpus callosum, and precuneus (Fig. 2). For each cluster, the total number of voxels, peak coordinates, Z-value, and anatomic locations are listed in Table 2 (TFCE -corrected p < 0.05). No significant intergroup differences in the mean diffusivity, axial diffusivity, or radial diffusivity values were observed between the two groups. Inclusion of age, sex, ICV, and BZD equivalent doses as covariates did not alter the results of group comparison after ANCOVA analysis. Finally, to rule out possible bias due to medication effects, we analyzed the results in the escitalopram and paroxetine groups and found the statistical conclusions remained the same.

3. Results

3.3. Correlation analysis

3.1. Sociodemographic and clinical characteristics of patients with PD

We conducted a correlational analysis between the clinical severity scales and FA values of the WM regions where the two groups showed significantly different FA values for each group separately (TFCE-corrected p < 0.05). In patients with NRPD, the ASI–R cognitive dyscontrol subscale scores were significantly positively correlated with the FA values of WM near the right middle frontal subgyral region, and ASI-R respiratory symptoms were positively correlated with FA values of the

Table 1 presents the sociodemographic and clinical characteristics of all patients with PD. There were no significant differences between the RPD (n = 37) and NRPD (n = 27) groups in terms of sex, age at scan, age of onset, presence or absence of agoraphobia, intracranial volume (ICV), years of education, or medication dose including SSRIs

Table 1 Sociodemographic and clinical characteristics of the patients with panic disorder (PD).

Sex (Male/Female) Age at scan (years) Age of onset (years) Duration of illness (years) Agoraphobia (yes,%) Education (years) Comorbid anxiety disorder Intracranial volume (ml) PDSS at baseline PDSS decrease (%) at 12 weeks APPQ ASI-R BAI BDI Kinds of SSRI at scan Paroxetine (%)/Escitalopram (%) SSRI equivalent dose at scan (mg)a SSRI equivalent dose at 12wks (mg)a

RPD (mean ± SD), n = 37

NRPD (mean ± SD), n = 27

t

13/24 36.65 ± 8.03 35.00 ± 9.51 2.25 ± 3.11 27 14.38 ± 2.59 GAD 2, SAD 1, OCD 1 1583.56 ± 310.54 12.65 ± 4.74 63.51 ± 13.58 43.69 ± 32.44 45.75 ± 22.03 26.16 ± 12.08 9.17 ± 3.21

10/17 36.11 ± 11.79 33.00 ± 12.33 3.22 ± 3.724 17 13.37 ± 2.29 GAD 2 1573.12 ± 322.35 15.56 ± 7.35 18.15 ± 10.39 67.72 ± 54.76 56.00 ± 31.39 28.56 ± 15.11 10.0 ± 3.05

1.62

0.879 0.829 0.477 0.262 0.776 0.111

0.13 −1.89 14.517 −1.97 −1.50 −0.69 −0.48

0.896 0.063 <0.001 0.057 0.139 0491 0.632

16(43.24)/21(56.75) 5.81 ± 3.23 10.00 ± 5.00

12(44.44)/15(55.56) 5.74 ± 3.59 13.52 ± 5.51

0.08 −2.66

0.935 0.010

0.22 0.72 −1.13

p

RPD, Treatment Responder in patients with PD; NRPD, Treatment Non-Responder in patients with PD; SD, Standard Deviation; GAD, Generalized Anxiety Disorder; SAD, Social Anxiety Disorder; OCD, Obsessive-Compulsive Disorder; BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory; PDSS, Panic Disorder Severity Scale; ASI-R, Anxiety Sensitivity Inventory-Revised; APPQ, Albany Panic and Phobia Questionnaire; SSRI, Selective Serotonin Reuptake Inhibitor. a The equivalent oral dose to 10 mg escitalopram are given. 530

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Fig. 2. Regions showing significant increases of FA values in NRPD compared to RPD (p < 0.05, TFCE-corrected). There are significant differences of FA between NRPD and RPD including the WM regions the precentral gyrus, parahippocampal gyrus and the part of precuneus (FA in NRPD > RPD). Images of transversal view are shown on the Montreal Neurologic Institute 1 mm template (x = 20, y=−13, z = 56) (x = 19, y=−43, z = 3) (x = 14, y=−42, z = 23) and (x = 26, y=−72, z = 29) respectively. FA, Fractional Anisotropy; NRPD, Treatment NonResponder in patients with Panic Disorder; RPD, Treatment Responder in patients with Panic Disorder; TFCE, Threshold-Free Cluster Enhancement; WM, White Matter.

anxiety and negative emotions related to interoceptive awareness provoke abnormal activities in the precentral gyrus in patients with PD (Boshuisen et al., 2002; Pollatos et al., 2007). An exaggerated phobic response to anxiety accompanying neuroticism, which may affect treatment response, has been associated with this region along with other structures in the fear circuitry (Drabant et al., 2011). In the present study, this area was also positively correlated with the ASI-R publicly observable anxiety reaction subscale scores in the NRPD group. Although it is not fully evident, our results possibly indicate that patients with PD who have poorer responses to medication might have been more susceptible to linking a perceived sense of anxiety or interoception to panic symptoms, as a form of anticipatory anxiety or phobic avoidance. Although the centered region of our frontal WM tract was the right precentral gyrus, this tract extended to near the right middle and superior frontal gyri anteriorly, mainly coursing along the superior corona radiata approaching the posterior corona radiata. Some previous studies have stated that the middle and superior frontal gyri are related to the antidepressant therapy response (Bora et al., 2012; Korgaonkar et al., 2015) and these frontal regions are associated with top-down emotion regulation including the control of anxiety or panic symptoms in PD (Bishop et al., 2004). In addition, in the present study, the FA values of the middle frontal gyrus were positively correlated with the

WM near the right temporal subgyral region. Thus, severe clinical severities of panic and related symptoms was related to higher FA values in NRPD (Fig. 3). Correlation analysis on the patients with RPD revealed no significant findings. 4. Discussion This study showed that the FA values at early stages of treatment were significantly higher in the NRPD group than in the RPD group, including the precentral gyrus, parahippocampal gyrus, posterior corona radiata, posterior thalamic radiation, posterior parts of the corpus callosum, and precuneus. In addition, parts of the fronto-temporal regions had positive correlations with clinical severity in the NRPD group. The differences between the two groups perhaps indicate a neurobiological basis underlying the response to medical treatment in patients with PD. Persisting structural deficits and changes in glucose metabolism in the precentral region after a few short weeks of antidepressant therapy were observed in patients with PD (Kang et al., 2012; Lai and Wu, 2013a). The precentral gyrus is important for multi-modal sensory integration, as it is a region clustered with poly-sensory zones for enabling defensive movement against threatening situations (Cooke and Graziano, 2004). Previous studies have reported that anticipatory

Table 2 Centered regions showing significant increases of fractional anisotropy (FA) values in NRPD compared to RPD. Cluster size (voxels)a

Peak coordinates (mm)

Z

Anatomical locations

1577 1311 658 380 300 158

34, −17, 56 26,−72,28 18,−43,1 25,−30,29 33,−72,5 16,−40,23

4.42 4.24 5.41 3.50 3.91 3.57

Frontal lobe, precentral gyrus, right Precuneus, right Limbic Lobe, parahippocampal gyrus Posterior corona radiata, right Posterior thalamic radiation, right Posterior part of corpus callosum near cingulate gyrus

Statistical significance remained same after ANCOVA analysis (PDSS, BDI, APPQ total, as covariate). Foci for significant differences are listed (TFCE-corrected p < 0.05). RPD, Treatment Responder in patients with Panic Disorder: NRPD, Treatment Non-Responder in patients with Panic Disorder. a Family-wise error corrected p value using the Threshold-Free Cluster Enhancement method. 531

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Fig. 3. Regions showing significant correlation between the ASI-R subscale scores and the FA values of the WM regions in NRPD. Voxels demonstrating significantly positive correlation (p < 0.05, TFCEcorrected) between the scores of ASI-R fear of cognitive dyscontrol (a), ASI-R fear of respiratory symptom (b), and FA values of the WM clusters showing significant group difference (between RPD and NRPD) in NRPD group in PD are shown in redyellow: In right hemisphere, the middle frontal subgyral region and the temporal subgyral region. Images of coronal view are shown on the Montreal Neurologic Institute 1 mm template (x=−1, y=−17, z = 19) and (x = 52, y=−40, z=−18) respectively. ASI-R, Anxiety Sensitivity InventoryRevised; FA, Fractional Anisotropy; WM, White Matter; NRPD, Treatment Non-Responder in patients with Panic Disorder; TFCE, Threshold-Free Cluster Enhancement: RPD; Treatment Responder in patients with Panic Disorder: PD, Panic Disorder.

high trait anxiety require higher levels of the DMN activity to achieve the same level of emotion regulation as do HCs with low trait anxiety (Campbell-Sills et al., 2011). Moreover, HCs have lower structural WM integrity in the amygdala connections of DMN, and structural WM alterations within the DMN regions were not detected (De Witte and Mueller, 2017). Taken together, the structural WM alterations of the dysfunctional DMN regions in PD could be thought of as neural biomarkers of PD as opposed to general effects of anxiety. The CC connects the neural circuits of both hemispheres; hence, it plays a crucial role in the integration of inter-hemispherical perceptual and higher cognitive functions (Doron and Gazzaniga, 2008). Previous DTI studies showed normalized FA changes in the CC after 12 weeks of SSRI treatment (Yoo et al., 2007). An increased FA value in the posterior portion of the CC connecting the occipital lobe and temporal lobe was observed in NRPD relative to RPD in the present study. This altered integrity of the CC in the present study might reflect a dysregulation between hemispheres, which might predispose these patients with PD to limitations in drug responsiveness. Thus, one of the most important findings of the present study was that drug resistant PD-affected regions include the posterior parts of the brain, such as the splenium of CC, posterior cingulate, and precuneus, in addition to the fronto-limbic regions. A recent functional connectome analysis of PD proposed a modified fear network model of the pathophysiology of PD (Lai and Wu, 2016). The central hubs were the precentral gyrus and parahippocampal gyrus, and this model emphasized the relationship of the fronto-limbic areas with the sensory-motor regions of the brain. Moreover, previous resting state functional MRI studies showed that SSRIs induce widespread effects on functional connectivity with multiple networks, including the DMN, visual networks and sensorimotor network; a commonality among these networks was the involvement of the precuneus (Klaassens et al., 2015a; van de Ven et al., 2013). In accordance with prior studies, our findings also provide some evidence that the modified fear network model might be involved in the pathophysiology of PD and regulation of DMN structures, especially the precuneus. Indeed, our results are mostly in agreement with prior functional MRI studies on the effects of SSRIs. Given that SSRI-induced alterations in functional connectivity were primarily decreases in connectivity (Schaefer et al., 2014), NRPD pathology may be largely a manifestation of abnormal WM connectivity, relatively increased FA values of these WM regions, and a resultant inadequate treatment response. It should be noted that significantly higher FA values across the two groups correlated with clinical severity scales such as ASI-R scores in this study. Anxiety sensitivity (AS) reflects an excessive fear of anxietyrelated bodily sensations, and it is characterized by fear amplification in response to stimuli, resulting in anxiety (Reiss, 1991). Previous findings

subscale scores of ASI-R cognitive dyscontrol in the NRPD group. Therefore, our results support previous reports indicating that WM alterations of the frontal lobe and related structures might represent decreased modulatory control over panic symptoms in relation to poor treatment response (Bishop et al., 2004; Kim et al., 2014; Konishi et al., 2014). We additionally found increased WM FA values in the parahippocampal region in patients with PD who showed a lower treatment response after pharmacotherapy. As a constituent part of the limbic system, parahippocampal gyrus alterations, such as decreased gray volume and asymmetry, have been observed in patients with PD (Massana et al., 2003a; Reiman et al., 1986). The limbic system, as a central component of the fear network, plays a significant role in typical PD pathophysiology (Gorman et al., 2000). Excessive activity in this region leads to autonomic and neuroendocrine activation, resulting in panic symptoms. Indeed, the limbic system, and more specifically, the parahippocampal regions, are considered important targets of PD treatment. One of the hypothesized mechanisms of action of SSRIs is increased inhibitory control of the frontal lobe over limbic areas (Dichter et al., 2015). Therefore, increased FA values in both the frontal lobe and limbic system of the NRPD group might be associated with intensified neural models of PD that underlie greater decreases in inhibitory control of the frontal lobe over the limbic system, with less amenability to restoration by medication. The precuneus, which plays a crucial role in self-processing and selfconsciousness, is the posterior core component of the DMN (Cavanna and Trimble, 2006). Disturbances of self-processing and selfconsciousness are unique components in the development of PD. Altered connectivity of the right posterior part of the DMN has been observed in PD. This might in turn convey increased sensitivity to internal visceral signals and distorted cognitive attention, which would render these individuals more vulnerable to experiencing various panic symptoms (Lai and Wu, 2014). Despite MRI studies being limited by their inability to decipher the exact functional connectivity in PD, the present study's results are consistent with those of previous studies indicating that drugs influence the posterior rather than the anterior part of the DMN (Posner et al., 2013). This may possibly reflect the relatively short duration of the onset of antidepressant effects in these posterior regions compared with longer periods of advanced antidepressant effects on the frontal lobe. Consistent with impaired functioning of the DMN during emotion regulation in PD, prior neuroimaging studies in HCs show higher levels of the DMN activity as anticipatory anxiety increases, but studies on PD report decreased activity in the DMN during emotional stimuli-related tasks (De Witte and Mueller, 2017; Holzschneider and Mulert, 2011; Sylvester et al., 2012). However, previous studies show that HCs with 532

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DMN in patients with PD. This altered WM connectivity was significantly correlated with panic symptom severities such as anxiety sensitivity.

have indicated that AS did not decrease as much as expected after medication (Simon et al., 2004) and lower AS scores were more likely observed in early PD remission groups (Park et al., 2012). Taken together, the correlation between ASI-R and increased FA in the WM tracts might reflect the role of AS as either a trait marker or a potential predictor of treatment response. Consistent with our hypothesis, structural differences were observed between the RPD and NRPD groups. Several studies on MDD have concluded that greater hyper-intense WM lesion severity was associated with poorer response to antidepressants in multiple regions (Dichter et al., 2014; Patankar et al., 2007). Although the pathophysiology may differ between patients with MDD and PD, the tendency of increased FA in the non-responsive group in the present study might reflect shared mechanisms of SSRI effects in these brain areas. Higher structural WM alterations of the NRPD group observed in the current study may suggest a more rapid or exaggerated neural transmission in a part of the network (Shimony et al., 1999). Prior functional MRI studies have demonstrated that PD is characterized by impaired functional connectivity between frontal and limbic regions (De Carvalho et al., 2010), and these changes in functional connectivity might have been secondary to the WM structural changes. Currently, this is only a hypothesis, which can best be examined in the future by combining DTI and functional neuroimaging with an antidepressant trial. The present results should be interpreted with caution owing to some limitations. First, not every patient recruited was in a drug-free state when the MRI scanning was performed, even though the scanning was performed within a few days of medication commencement. Second, there were no comparisons of HC groups for matched pairs with patients with PD. Third, the direction of causality remains unknown due to the limitations of the cross-sectional study design. A prospective and longitudinal approach is required to verify this. In addition, although baseline PDSS and APPQ scores were not significantly different between the two groups, the NRPD group tended to have slightly higher scores of the baseline symptom severities. Consequently, it may have had an impact on the results. Finally, not all patients were treated by monotherapy, and thus, we cannot entirely exclude the possibility of effects of other medications such as BZDs. However, we attempted to use minimal dosages of BZDs as adjunctive therapy followed by tapered BZDs. Considering the rapid onset and lack of a superior therapeutic effect of combined therapy (Pollack et al., 2003), it is reasonable to speculate that the therapeutic effect observed in the current study is from SSRIs rather than BZDs or combination therapy. Higher dosage of BZDs in the NRPD group at end point could be considered the secondary outcome, after poor medication response. The present preliminary study also has several strengths. Our study has the advantage of examining whole brain WM to identify any structural differences between RPD and NRPD. It is the first DTI study to investigate the correlation between WM connectivity and the various clinical symptom scales, which enabled researchers to determine the multidimensional characteristics of enrolled patients. Through this comparison, we identified comparable demographic and clinical variables between the two groups at baseline, which may affect treatment response. Consequently, we could assume that the results of the present study may not arise from disease severity at baseline. The factors that determine structural alterations of the brain in PD at early stages of treatment seems to be unclear. PD is known as a heterogeneous disorder with respect to genetics, temperament, psycho-endocrinology, and psychophysiology. Future research should therefore examine such diverse traits to determine their mutual relationships to brain function and structure.

Originality and plagiarism The authors declare that we have written entirely original works, and if we have used the words of others that this has been appropriately cited or quoted. Multiple, redundant or concurrent publication This paper has not been published elsewhere and is not under review with another journal. Authorship of the paper All those who have made significant contributions are listed as authors. The corresponding author ensures that all appropriate co-authors and no inappropriate co-authors are included on the paper, and that all co-authors have seen and approved the final version of the paper and have agreed to its submission for publication. Hazards and human or animal subjects The authors declare that all experiments on human subjects were conducted in accordance with the Declaration of Helsinki and that all procedures were carried out with the adequate understanding and written consent of the subjects. The authors certify that formal approval to conduct the experiments has been obtained from the human subjects review board of the institution and could be provided upon request. Role of funding source The funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. CRediT authorship contribution statement Se-woong Kim: Conceptualization, Data curation, Formal analysis, Writing original draft, Validation. Min-Kyoung Kim: Conceptualization, Data curation, Formal analysis, Writing - original draft, Validation. Borah Kim: Formal analysis, Writing - original draft, Validation. Tae-Kiu Choi: Supervision, Validation. Sang-Hyuk Lee: Supervision, Validation. Declaration of Competing Interest All authors declare no conflicts of interest. Acknowledgments This research was supported by a grant of the Korea Health Technology R & D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI14C2750). And this research also was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2011-0023359) and (NRF-2019M3C7A1032262) to S.H. Lee.

5. Conclusions References In conclusion, the current findings suggest that clinical improvement after medication might be associated with WM alterations of the modified fear network including the fronto-limbic regions and posterior

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