- Email: [email protected]
Altered functional connectivity between primary and secondary somatosensory areas in panic disorder
Psychiatry Research 285 (2020) 112808
Contents lists available at ScienceDirect
Psychiatry Research journal homepage: www.elsevier.com/locate/psychres
Altered functional connectivity between primary and secondary somatosensory areas in panic disorder
T
Chia-Hsiung Chenga,b,c,d, , Chia-Yih Liud,e, Shih-Chieh Hsud,e, ⁎
⁎⁎
a
Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan, Taiwan Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan c Laboratory of Brain Imaging and Neural Dynamics (BIND Lab), Chang Gung University, Taoyuan, Taiwan d Department of Psychiatry, Chang Gung Memorial Hospital, Linkou, Taiwan e College of Medicine, Chang Gung University, Taoyuan, Taiwan b
ARTICLE INFO
ABSTRACT
Keywords: Panic disorder Somatosensory evoked field (SEF) Primary somatosensory cortex (SI) Secondary somatosensory cortex (SII) Functional connectivity Magnetoencephalography (MEG)
Disturbance in the interpretation of bodily sensation has been widely reported in patients with panic disorder (PD). However, it remains substantially unknown whether patients with PD exhibit any defect in cortical somatosensory processing of non-threatening stimuli. Thus, the present study aimed to examine the functional integrity of the cortical somatosensory system in patients with PD using neurophysiological recordings. A total of 20 patients with PD and 20 healthy controls (HC) were recruited to investigate the cortical responses to median nerve stimulation through whole-head magnetoencephalographic (MEG) imaging. To comprehensively investigate all somatosensory functioning, we studied the regional activation of the primary somatosensory cortex (SI), contralateral (SIIc), and ipsilateral (SIIi) secondary somatosensory cortices, as well as functional connectivity among the SI, SIIc, and SIIi in alpha, beta, and gamma frequency bands. We found that patients with PD demonstrated a reduction in SI activity compared with those in the HC group. Furthermore, a significantly weaker gamma-band functional connectivity between SI and SIIc was found in the PD group relative to the HC group. Our data suggest that patients with PD exhibit abnormal responses to non-threatening (i.e., pain-free) stimuli in the cortical somatosensory system.
1. Introduction In addition to recurrent unexpected anxiety attacks, the misinterpretation of bodily sensations has been extensively reported in patients with panic disorder (PD) (Hoehn-Saric et al., 2004; De Berardis et al., 2007; Cheng et al., 2019). This somatic abnormality can be evaluated through self-reported assessment, such as the Body Sensation Questionnaire (BSQ) (Chambless et al., 1984). However, a more objective measure of somatosensory processing is necessary to further elucidate the pathophysiological changes of somatic sensations in patients with PD. Somatosensory evoked activity is an objective method used for studying the neurophysiological function of the cortical somatosensory system. Electrical stimulation of the median nerve activates several somatosensory areas, including the contralateral primary somatosensory cortex (SI), contralateral secondary somatosensory cortex (SIIc), and ipsilateral secondary somatosensory cortex (SIIi) (Cheng and
Lin, 2013; Cheng et al., 2015; Cheng, 2018). The role of the SI is related to the registration and identification of the somatosensory information. The SII, which is conceptualized as a higher-order center in the somatosensory system, is implicated in many aspects of cognitive and affective processing of sensory inputs, such as pain perception, sensorimotor integration, action observation, and integration of sensory information from both limbs (Lin and Forss, 2002; Yoshino et al., 2012). Despite the breadth of study conducted on the somatosensory system, to our knowledge, no study has systematically investigated whether patients with PD exhibit abnormalities in the registration and interpretation of somatosensory information using neurophysiological recordings to directly and noninvasively probe neural activities. In this regard, the first aim of the present study was to examine electrophysiological characteristics of the cortical somatosensory system in patients with PD, using time-domain analyses. It is thought that the SI and SII regions do not function individually. Instead, these regions communicate with each other to process cortical
Corresponding author at: Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, No. 259, Wenhua 1st Rd., Taoyuan City 333, Taoyuan, Taiwan. ⁎⁎ Co-corresponding author: Department of Psychiatry, Chang Gung Memorial Hospital, No. 5, Fu-Hsing St., Taoyuan City 333, Taiwan, Linkou, Taiwan. E-mail addresses: [email protected] (C.-H. Cheng), [email protected] (S.-C. Hsu). ⁎
https://doi.org/10.1016/j.psychres.2020.112808 Received 25 November 2019; Received in revised form 20 January 2020; Accepted 21 January 2020 Available online 23 January 2020 0165-1781/ © 2020 Elsevier B.V. All rights reserved.
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
somatosensory information (Simoes et al., 2003; Kropf et al., 2019). Along with time-domain measurements, a time-frequency approach can be used to solve the problems where signal properties are non-phase locked (Bertrand and Tallon-Baudry, 2000). Through this method, it is possible to assess functional connectivity among cortical somatosensory areas. Previous studies have demonstrated a strong functional connectivity between the SI and SII at the alpha, beta, and gamma oscillations in healthy adults (Simoes et al., 2003; Hagiwara et al., 2010; Gao et al., 2015; Cheng, 2018). Notably, connectivity strength was reduced in patients with multiple sclerosis (Hagiwara et al., 2010) and writer's cramp (Cheng et al., 2016b). However, it remains unknown whether the interplay of SI and SII is affected in patients with PD. Thus, the second aim of the present study was to examine the functional connectivity among the SI, SIIc, and SIIi in alpha, beta, and gamma frequency bands. Magnetoencephalography (MEG) can be used to directly measure neural activities with higher spatial resolution than electroencephalography, and higher temporal resolution than functional magnetic resonance imaging (fMRI) (Hari et al., 2010; Baillet, 2017). Use of MEG is suitable for studying cortical somatosensory function, as neural generators of SI and SII belong to tangential dipoles, whose magnetic signals can be effectively captured by MEG sensors. Specifically, the present study had three objectives. Firstly, we tested whether cortical activation of the SI, SIIc, and SIIi in patients with PD would significantly differ from that observed in healthy controls (HC). Secondly, we assessed the oscillatory connectivity between the SI and SIIc (SI–SIIc), SI and SIIi (SI–SIIi), and SIIc and SIIi (SIIc–SIIi) in alpha, beta, and gamma frequency bands. Finally, based on significant between-group differences, where recorded, we explored the relationships between somatosensory activities and clinical assessments.
2.3. Data acquisition and processing The somatosensory evoked fields (SEFs) were measured continuously at 1000 Hz with a 306-channel MEG instrument (Vectorview; Elekta Neuromag, Helsinki, Finland). The precise head position in relation to the sensor arrays was monitored prior to recordings using four indicators, and the three fiducial points based on the Cartesian coordinate system were determined using a three-dimensional digitizer. In this study, the data from 204-channel gradiometers were analyzed, as this type of sensor can reduce geomagnetic and other environmental artifacts and detect the largest signals directly above the activated cerebral areas. The online bandpass filter was 0.1–200 Hz, and at least 100 trials were obtained from all the participants. For each trial of raw data, a spatiotemporal signal space (tsss) method was applied using MaxFilter to remove internal and external artifacts (Taulu and Simola, 2006). The signals contaminated by eye blinks and heart beats were also removed using the signal space projection (SSP) method. Subsequently, the epochs of 500 ms (including 100 ms before the onset of the stimulus) were averaged across trials for further analysis. The averaged SEFs were filtered with a bandpass in the 0.1–120 Hz range, with a 100-ms baseline correction. An overlapping-sphere model was applied to resolve the forward problem (Huang et al., 1999). The individual source space, consisting of approximately 15,000 dipoles over the whole cortex, was rescaled to the ICBM152 brain template using Brainstorm registration methods with default settings. Modeling of cortical spatiotemporal dynamics was performed using a cortically constrained, depth-weighted minimum norm estimate (MNE) (Hamalainen and Ilmoniemi, 1994), implemented in the Brainstorm software (Tadel et al., 2011). Regions of interest (ROIs) in the cortical somatosensory system were identified in the SI (including M20 and M35 components), SIIc, and SIIi. A cluster of 40 vertices, corresponding to 6 − 7 cm2, was manually selected according to the individual's maximal response in each ROI. The time-resolved magnitude of each dipole was normalized over the baseline to yield a set of z scores. The z-score values were rectified to obtain the absolute magnitude. The functional connectivity among the identified ROIs was analyzed using the coherence method implemented in the Brainstorm software. Each raw trial with a time window from −100 ms to +400 ms was applied to perform the source-based coherence analysis using a magnitude-square measure, with a maximum frequency resolution of 1 Hz and highest frequency interest of 50 Hz. The evoked response from each trial was removed in the calculation of functional connectivity. The cortical coherence of the SI and SIIc (SI–SIIc), SI and SIIi (SI–SIIi), as well as SIIc and SIIi (SIIc–SIIi) in each subject was determined in the alpha (8–12 Hz), beta (13–30 Hz), and gamma (31–50 Hz) frequency bands.
2. Methods 2.1. Subjects Twenty healthy right-handed volunteers (11 females; mean age 43.05 ± 1.81 years) and 20 right-handed outpatients with PD (12 females; mean age 43.55 ± 2.00 years) were selected according to the DSM-5 criteria. In PD patients, the mean duration of disease was 5.80 ± 1.01 years, and the mean Hamilton Scale for Anxiety (HAM-A) was 15.0 ± 1.19. Disease severity was evaluated using the Panic Disorder Severity Scale (PDSS) with a mean value of 9.65 ± 1.19. All patients reported mild to severe somatic abnormalities as evaluated using the BSQ (41.0 ± 3.27). Antidepressant treatment was administered to 19 patients, while 17 patients received combined treatment with antidepressants and benzodiazepines. This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital (Linkou, Taiwan), and was performed in accordance with relevant guidelines and regulations. Written informed consent was provided by all participants following detailed description of the experimental procedures.
2.4. Statistical analysis Kolmogorov–Smirnov one-sample tests showed that not all data exhibited normal distribution. Therefore, statistical comparisons were performed by means of non-parametric analysis. Specifically, the cortical activation of the SI, SIIc, and SIIi as well as the connectivity strength of alpha, beta, and gamma frequency bands between the HC and PD groups were evaluated with Mann-Whitney U tests. Associations between MEG signals (e.g., cortical activation of the SI, SIIc, and SIIi, as well as the strength of functional connectivity in the studied frequency bands) and clinical data (e.g., mean duration of disease, and scores of HAM-A, PDSS, and BSQ) were assessed through Spearman's correlation coefficients. A p-value < 0.05 denoted statistical significance.
2.2. Stimuli The right median nerve was stimulated at the wrist with constant current pulses of 0.2-ms duration using an electrical stimulator (Konstant-Strom Stimulator, Germany). The intensity was set at 20% above the motor threshold to elicit a visible twitch of the thumb, which has previously been reported to result in significant activation of the SI and SII (Huang et al., 2010; Sugawara et al., 2015; Cheng et al., 2016a; Cheng et al., 2017). The interstimulus interval was varied from 1.6 to 2.0 s to avoid any expectation effects. During the entire MEG recordings, subjects were instructed to watch a silent video and ignore the electrical stimulation.
3. Results The upper panel of Fig. 1(A) shows the grand-averaged sensor 2
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
Fig. 1. (A) Upper panel: Grand-averaged somatosensory evoked fields recorded from the MEG sensor arrays in 20 healthy controls (HC) and 20 patients with panic disorder (PD). Lower panel: The grand-averaged minimum norm estimate is mapped onto the ICBM152 template. M20 and M35 activities of the primary somatosensory cortex (SI) are located in the anterior parietal lobe. The activation of contralateral and ipsilateral secondary somatosensory cortices (SIIc, SIIi, respectively) occurs in the parietal operculum. (B) Mann-Whitney U tests revealed significant reduced M35 cortical activation in patients with PD relative to HC.
waveforms of SEFs elicited by electrical stimulation of the right median nerve in the HC and PD groups. The lower panel of this figure shows the MNE source activation mapped onto the ICBM152 template. In the SI
region, the M20 component was the first deflection of somatosensory evoked responses, followed by the M35 component. Longer-latency responses, peaking at approximately 70–150 ms after the onset of the 3
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
Fig. 2. (A) Functional connectivity as measured by cortical coherence of alpha, beta, and gamma bands among SI, SIIc, and SIIi in HC and PD groups. (B) MannWhitney U tests revealed significantly reduced SI–SIIc gamma coherence in patients with PD relative to HC.
stimulus, were generated bilaterally in the inferior parietal operculum (i.e., SIIc and SIIi). Compared with the HC, patients with PD demonstrated a significant reduction in M35 cortical strength (Z = 2.556, p = 0.009, two-tailed) (Fig. 1B). The amplitudes of M20, SIIc, and SIIi
did not differ significantly between the HC and PD groups. Fig. 2(A) depicts the grand-averaged functional connectivity maps among three identified ROIs in the HC (upper panel) and PD (lower panel) groups. Inspection from the coherence maps, the connectivity 4
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
strength of the alpha, beta, and gamma oscillations was weaker in the PD group than in the HC group. The results of the statistical analysis confirmed significant between-group differences in the gamma-band SI–SIIc coherence (Z = 2.137, p = 0.033, two-tailed). These significant findings regarding M35 cortical strength and SI–SIIc functional connectivity, led us to also examine the relationships between MEG signals and clinical data in patients with PD. However, this analysis did not detect a significant association.
target for pharmacotherapy. This finding provided neurophysiological support of treatment response of PD patients to this medication. Our findings also raised an interesting question: is the transmission of somatosensory information from the thalamus to the SI and SII sequential or parallel? It has been conceptualized that information processing in the somatosensory system occurs in a serial manner, i.e., from the ventral posterior nucleus of the thalamus to the SI, terminating at the SIIc and SIIi. This contention is supported by findings demonstrating that the SI has reciprocal connections with the SII (Kropf et al., 2019). In addition to serial processing, information in the somatosensory system can be transmitted in a parallel manner, i.e., both the SI and SII receive information directly from the thalamus. Our results illustrated in Fig. 1 showed that only the SI cortical strength was reduced in patients with PD, suggesting that one of the somatosensory pathways is conveyed directly from the thalamus to the SII. Our hypothesis is supported by anatomical evidence. The ventral posterior nucleus can be divided into medial and lateral nuclei, and it has been shown that the medial nucleus is connected with the SII, since it receives neurons from the trigeminal tract (Nieuwenhuys et al., 1988; Catani and Thiebaut de Schotten, 2012). Results from a lesion study involving patients with stroke further confirmed our hypothesis, demonstrating that, despite deficient SEFs in the SI and SIIc, activities of the SIIi were relatively preserved (Forss et al., 1999). Recent fMRI studies using dynamic causal modeling also provided solid support to the parallel hypothesis, suggesting that the SII cortex receives information from the SI and thalamic inputs (Kalberlah et al., 2013; Klingner et al., 2015). Some limitations should be considered when interpreting the present results. Firstly, the relatively small sample size may reduce the statistical power of this study, potentially leading to a nonsignificant correlation between MEG signals and clinical data. Secondly, most of the patients had comorbidities, such as depression, generalized anxiety disorder, etc. In the clinical setting, it was difficult to recruit patients with pure PD. Thus, the current sample may be representative of the general population with PD. Finally, most of our patients received treatment with antidepressants, which may have influenced the results. However, a human study has shown that serotonin did not affect information processing in the SI (Kahkonen et al., 2003). Thus, it was improbable that the reduced SEFs in patients with PD were influenced by antidepressants. In conclusion, our study is the first neuroimaging study to comprehensively assess the cortical somatosensory function in patients with PD. Our current findings demonstrate a diminished SI activity in patients with PD relative to that observed in HC. Patients with PD also show reduced functional connectivity between the SI and SIIc in the gamma oscillatory band. Collectively, our data suggest that patients with PD exhibit an abnormal response to non-threatening (i.e., painfree) stimuli in the cortical somatosensory system.
4. Discussion To the best of our knowledge, this was the first study to comprehensively investigate the functional integrity of the cortical somatosensory system in patients with PD. Our MEG findings indicated that patients with PD showed a lack of cortical responsiveness to nonthreatening stimuli in the SI region compared with HC. Moreover, the SI–SIIc functional connectivity of gamma oscillations was significantly reduced in the PD patients. The somatosensory deficits noted in this study were consistent with broad impairments in sensory processing (e.g., auditory) previously reported in PD (Ludewig et al., 2005). SI activity (particularly the M35 component) was significantly reduced in response to non-threatening somatosensory stimulation in patients with PD, but not in HC. According to the information processing model of anxiety (Beck and Clark, 1997; Karl et al., 2006), cognitive resources may be increased in patients with PD in response to threatening stimuli, thus reducing the processing resources in response to neutral stimuli. Several lines of evidence have also shown that patients engage substantially in self-directed attention, leading to a concomitant reduced response to external stimuli (Hayward et al., 2000). Alternatively, the lack of SI cortical responsiveness early in the timecourse may reflect an information processing characteristic in patients with PD. A previous MEG study showed that veterans with post-traumatic stress disorder demonstrated reduced alpha oscillatory activity in the SI in response to non-threatening tactile stimulation, compared to those without post-traumatic stress disorder (Badura-Brack et al., 2015). Based on these findings, we hypothesized that diminished SI activity may reflect a categorization of the repetitive, neutral stimulation as non-important in our patients with PD. A recent neuroimaging study demonstrated an increased restingstate functional connectivity between the SI and thalamus in patients with PD (Cui et al., 2016). The thalamus plays a critical role in gating somatosensory information to the neocortex. The augmented functional connectivity between these two regions may be related to the abnormally high somatosensory information processing during the resting state, which in turn leads to a maladaptive response to external sensory inputs. Another interpretation of the present finding may be the defect of integrity of SI per se in patients with PD. This claim was supported by the results of a resting-state fMRI study, demonstrating that medicationnaïve patients with PD displayed lower brain activity in the SI, which was reversed through treatment with antidepressants (Lai and Wu, 2016). Collectively, the aforementioned findings suggest a deficit of somatosensory information processing in patients with PD. The functional connectivity of SI–SIIc, particularly the gamma oscillation, was significantly reduced in patients with PD. Oscillations in the gamma frequency have been reported to be modulated by the neurotransmitter γ-Aminobutyric acid (GABA), which plays a role in cortico-cortical circuits (Cardin et al., 2009). Using magnetic resonance spectroscopy imaging, there is ample evidence showing that a strong relationship exists between gamma oscillations and the concentration of GABA in the sensory cortex (Edden et al., 2009; Muthukumaraswamy et al., 2009). Based on this evidence, it is reasonable to postulate that the reduced gamma-band functional connectivity of SI–SIIc may be an aberration of the balance between inhibitory and excitatory neurotransmission. Benzodiazepines, which are used to treat most patients with PD, exert their function by binding to GABA receptors. Hence, the GABAergic modulation is considered a
CRediT authorship contribution statement Chia-Hsiung Cheng: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing original draft, Writing - review & editing. Chia-Yih Liu: Data curation, Investigation, Resources, Writing - review & editing. Shih-Chieh Hsu: Conceptualization, Data curation, Investigation, Resources, Writing review & editing. Declaration of Competing Interest The authors declare that they have no conflict of interest. Acknowledgments This work was supported by Chang Gung Memorial Hospital (CMRPD1E0291, CMRPD1E0292, CMRPD1E0293, CMRPD1E0294), 5
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
Chang Gung University (BMRPE25), Healthy Aging Research Center, Chang Gung University from the Featured Areas Research Center Program within the Framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan (EMRPD1K0431), and Ministry of Science and Technology (MOST-104-2314-B-182-001-MY2, MOST-105-2628-B-182-004-MY3, MOST-108-2628-B-182-002), Taiwan.
somatosensory network. Sci. Rep. 5, 10399. Hagiwara, K., Okamoto, T., Shigeto, H., Ogata, K., Somehara, Y., Matsushita, T., Kira, J., Tobimatsu, S., 2010. Oscillatory gamma synchronization binds the primary and secondary somatosensory areas in humans. Neuroimage 51 (1), 412–420. Hamalainen, M.S., Ilmoniemi, R.J., 1994. Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 32 (1), 35–42. Hari, R., Parkkonen, L., Nangini, C., 2010. The brain in time: insights from neuromagnetic recordings. Ann. N. Y. Acad. Sci. 1191, 89–109. Hayward, P., Ahmad, T., Wardle, J., 2000. Attention to bodily sensations: a test of the cognitive-attentional model of panic. Depress. Anxiety 12 (4), 203–208. Hoehn-Saric, R., McLeod, D.R., Funderburk, F., Kowalski, P., 2004. Somatic symptoms and physiologic responses in generalized anxiety disorder and panic disorder: an ambulatory monitor study. Arch. Gen. Psychiatry 61 (9), 913–921. Huang, M.X., Lee, R.R., Gaa, K.M., Song, T., Harrington, D.L., Loh, C., Theilmann, R.J., Edgar, J.C., Miller, G.A., Canive, J.M., Granholm, E., 2010. Somatosensory system deficits in schizophrenia revealed by MEG during a median-nerve oddball task. Brain Topogr. 23 (1), 82–104. Huang, M.X., Mosher, J.C., Leahy, R.M., 1999. A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG. Phys. Med. Biol. 44 (2), 423–440. Kahkonen, S., Ahveninen, J., Jaaskelainen, I.P., Pennanen, S., Liesivuori, J., Nikulin, V.V., 2003. Acute tryptophan depletion does not change somatosensory evoked magnetic fields. Psychopharmacol. (Berl.) 170 (3), 332–333. Kalberlah, C., Villringer, A., Pleger, B., 2013. Dynamic causal modeling suggests serial processing of tactile vibratory stimuli in the human somatosensory cortex–an fMRI study. Neuroimage 74, 164–171. Karl, A., Malta, L.S., Maercker, A., 2006. Meta-analytic review of event-related potential studies in post-traumatic stress disorder. Biol. Psychol. 71 (2), 123–147. Klingner, C.M., Brodoehl, S., Huonker, R., Gotz, T., Baumann, L., Witte, O.W., 2015. Parallel processing of somatosensory information: evidence from dynamic causal modeling of MEG data. Neuroimage 118, 193–198. Kropf, E., Syan, S.K., Minuzzi, L., Frey, B.N., 2019. From anatomy to function: the role of the somatosensory cortex in emotional regulation. Braz. J. Psychiatry 41 (3), 261–269. Lai, C.H., Wu, Y.T., 2016. The changes in the low-frequency fluctuations of cingulate cortex and postcentral gyrus in the treatment of panic disorder: the MRI study. World J. Biol. Psychiatry 17 (1), 58–65. Lin, Y.Y., Forss, N., 2002. Functional characterization of human second somatosensory cortex by magnetoencephalography. Behav. Brain Res. 135 (1–2), 141–145. Ludewig, S., Geyer, M.A., Ramseier, M., Vollenweider, F.X., Rechsteiner, E., CattapanLudewig, K., 2005. Information-processing deficits and cognitive dysfunction in panic disorder. J. Psychiatry Neurosci. 30 (1), 37–43. Muthukumaraswamy, S.D., Edden, R.A., Jones, D.K., Swettenham, J.B., Singh, K.D., 2009. Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans. Proc. Natl. Acad. Sci. U. S. A. 106 (20), 8356–8361. Nieuwenhuys, R., Voogd, J., 1988. The Human Central Nervous System: a Synopsis and Atlas. Springer-Verlag, Berlin. Simoes, C., Jensen, O., Parkkonen, L., Hari, R., 2003. Phase locking between human primary and secondary somatosensory cortices. Proc. Natl. Acad. Sci. U. S. A. 100 (5), 2691–2694. Sugawara, K., Onishi, H., Yamashiro, K., Kojima, S., Miyaguchi, S., Kirimoto, H., Tsubaki, A., Tamaki, H., Shirozu, H., Kameyama, S., 2015. The effect of anodal transcranial direct current stimulation over the primary motor or somatosensory cortices on somatosensory evoked magnetic fields. Clin. Neurophysiol. 126 (1), 60–67. Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M., 2011. Brainstorm: a userfriendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 879716. Taulu, S., Simola, J., 2006. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys. Med. Biol. 51 (7), 1759–1768. Yoshino, A., Okamoto, Y., Onoda, K., Shishida, K., Yoshimura, S., Kunisato, Y., Demoto, Y., Okada, G., Toki, S., Yamashita, H., Yamawaki, S., 2012. Sadness enhances the experience of pain and affects pain-evoked cortical activities: an MEG study. J. Pain 13 (7), 628–635.
References Badura-Brack, A.S., Becker, K.M., McDermott, T.J., Ryan, T.J., Becker, M.M., Hearley, A.R., Heinrichs-Graham, E., Wilson, T.W., 2015. Decreased somatosensory activity to non-threatening touch in combat veterans with posttraumatic stress disorder. Psychiatry Res. 233 (2), 194–200. Baillet, S., 2017. Magnetoencephalography for brain electrophysiology and imaging. Nat. Neurosci. 20 (3), 327–339. Beck, A.T., Clark, D.A., 1997. An information processing model of anxiety: automatic and strategic processes. Behav. Res. Ther. 35 (1), 49–58. Bertrand, O., Tallon-Baudry, C., 2000. Oscillatory gamma activity in humans: a possible role for object representation. Int. J. Psychophysiol. 38 (3), 211–223. Cardin, J.A., Carlen, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., Tsai, L.H., Moore, C.I., 2009. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459 (7247), 663–667. Catani, M., Thiebaut de Schotten, M., 2012. Atlas of Human Brain Connections. Oxford University, New York. Chambless, D.L., Caputo, G.C., Bright, P., Gallagher, R., 1984. Assessment of fear of fear in agoraphobics: the body sensations questionnaire and the agoraphobic cognitions questionnaire. J. Consult. Clin. Psychol. 52 (6), 1090–1097. Cheng, C.H., 2018. Effects of observing normal and abnormal goal-directed hand movements on somatosensory cortical activation. Eur. J. Neurosci. 47 (1), 48–57. Cheng, C.H., Chan, P.S., Hsu, S.C., Liu, C.Y., 2019. Abnormal frontal generator during auditory sensory gating in panic disorder: an MEG study. Psychiatry Res. Neuroimaging 288, 60–66. Cheng, C.H., Chan, P.Y., Baillet, S., Lin, Y.Y., 2015. Age-Related reduced somatosensory gating is associated with altered alpha frequency desynchronization. Neural Plast. 2015, 302878. Cheng, C.H., Chan, P.Y., Niddam, D.M., Tsai, S.Y., Hsu, S.C., Liu, C.Y., 2016a. Sensory gating, inhibition control and gamma oscillations in the human somatosensory cortex. Sci. Rep. 6, 20437. Cheng, C.H., Lin, Y.Y., 2013. Aging-related decline in somatosensory inhibition of the human cerebral cortex. Exp. Brain Res. 226 (1), 145–152. Cheng, C.H., Tsai, S.Y., Liu, C.Y., Niddam, D.M., 2017. Automatic inhibitory function in the human somatosensory and motor cortices: an MEG-MRS study. Sci. Rep. 7 (1), 4234. Cheng, C.H., Tseng, Y.J., Chen, R.S., Lin, Y.Y., 2016b. Reduced functional connectivity of somatosensory network in writer's cramp patients. Brain Behav. 6 (3), e00433. 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. De Berardis, D., Campanella, D., Gambi, F., La Rovere, R., Sepede, G., Core, L., Canfora, G., Santilli, E., Valchera, A., Mancini, E., Salerno, R.M., Moschetta, F.S., Ferro, F.M., 2007. Alexithymia, fear of bodily sensations, and somatosensory amplification in young outpatients with panic disorder. Psychosomatics 48 (3), 239–246. Edden, R.A., Muthukumaraswamy, S.D., Freeman, T.C., Singh, K.D., 2009. Orientation discrimination performance is predicted by GABA concentration and gamma oscillation frequency in human primary visual cortex. J. Neurosci. 29 (50), 15721–15726. Forss, N., Hietanen, M., Salonen, O., Hari, R., 1999. Modified activation of somatosensory cortical network in patients with right-hemisphere stroke. Brain 122 (Pt 10), 1889–1899. Gao, L., Sommerlade, L., Coffman, B., Zhang, T., Stephen, J.M., Li, D., Wang, J., Grebogi, C., Schelter, B., 2015. Granger causal time-dependent source connectivity in the
6
Contents lists available at ScienceDirect
Psychiatry Research journal homepage: www.elsevier.com/locate/psychres
Altered functional connectivity between primary and secondary somatosensory areas in panic disorder
T
Chia-Hsiung Chenga,b,c,d, , Chia-Yih Liud,e, Shih-Chieh Hsud,e, ⁎
⁎⁎
a
Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan, Taiwan Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan c Laboratory of Brain Imaging and Neural Dynamics (BIND Lab), Chang Gung University, Taoyuan, Taiwan d Department of Psychiatry, Chang Gung Memorial Hospital, Linkou, Taiwan e College of Medicine, Chang Gung University, Taoyuan, Taiwan b
ARTICLE INFO
ABSTRACT
Keywords: Panic disorder Somatosensory evoked field (SEF) Primary somatosensory cortex (SI) Secondary somatosensory cortex (SII) Functional connectivity Magnetoencephalography (MEG)
Disturbance in the interpretation of bodily sensation has been widely reported in patients with panic disorder (PD). However, it remains substantially unknown whether patients with PD exhibit any defect in cortical somatosensory processing of non-threatening stimuli. Thus, the present study aimed to examine the functional integrity of the cortical somatosensory system in patients with PD using neurophysiological recordings. A total of 20 patients with PD and 20 healthy controls (HC) were recruited to investigate the cortical responses to median nerve stimulation through whole-head magnetoencephalographic (MEG) imaging. To comprehensively investigate all somatosensory functioning, we studied the regional activation of the primary somatosensory cortex (SI), contralateral (SIIc), and ipsilateral (SIIi) secondary somatosensory cortices, as well as functional connectivity among the SI, SIIc, and SIIi in alpha, beta, and gamma frequency bands. We found that patients with PD demonstrated a reduction in SI activity compared with those in the HC group. Furthermore, a significantly weaker gamma-band functional connectivity between SI and SIIc was found in the PD group relative to the HC group. Our data suggest that patients with PD exhibit abnormal responses to non-threatening (i.e., pain-free) stimuli in the cortical somatosensory system.
1. Introduction In addition to recurrent unexpected anxiety attacks, the misinterpretation of bodily sensations has been extensively reported in patients with panic disorder (PD) (Hoehn-Saric et al., 2004; De Berardis et al., 2007; Cheng et al., 2019). This somatic abnormality can be evaluated through self-reported assessment, such as the Body Sensation Questionnaire (BSQ) (Chambless et al., 1984). However, a more objective measure of somatosensory processing is necessary to further elucidate the pathophysiological changes of somatic sensations in patients with PD. Somatosensory evoked activity is an objective method used for studying the neurophysiological function of the cortical somatosensory system. Electrical stimulation of the median nerve activates several somatosensory areas, including the contralateral primary somatosensory cortex (SI), contralateral secondary somatosensory cortex (SIIc), and ipsilateral secondary somatosensory cortex (SIIi) (Cheng and
Lin, 2013; Cheng et al., 2015; Cheng, 2018). The role of the SI is related to the registration and identification of the somatosensory information. The SII, which is conceptualized as a higher-order center in the somatosensory system, is implicated in many aspects of cognitive and affective processing of sensory inputs, such as pain perception, sensorimotor integration, action observation, and integration of sensory information from both limbs (Lin and Forss, 2002; Yoshino et al., 2012). Despite the breadth of study conducted on the somatosensory system, to our knowledge, no study has systematically investigated whether patients with PD exhibit abnormalities in the registration and interpretation of somatosensory information using neurophysiological recordings to directly and noninvasively probe neural activities. In this regard, the first aim of the present study was to examine electrophysiological characteristics of the cortical somatosensory system in patients with PD, using time-domain analyses. It is thought that the SI and SII regions do not function individually. Instead, these regions communicate with each other to process cortical
Corresponding author at: Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, No. 259, Wenhua 1st Rd., Taoyuan City 333, Taoyuan, Taiwan. ⁎⁎ Co-corresponding author: Department of Psychiatry, Chang Gung Memorial Hospital, No. 5, Fu-Hsing St., Taoyuan City 333, Taiwan, Linkou, Taiwan. E-mail addresses: [email protected] (C.-H. Cheng), [email protected] (S.-C. Hsu). ⁎
https://doi.org/10.1016/j.psychres.2020.112808 Received 25 November 2019; Received in revised form 20 January 2020; Accepted 21 January 2020 Available online 23 January 2020 0165-1781/ © 2020 Elsevier B.V. All rights reserved.
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
somatosensory information (Simoes et al., 2003; Kropf et al., 2019). Along with time-domain measurements, a time-frequency approach can be used to solve the problems where signal properties are non-phase locked (Bertrand and Tallon-Baudry, 2000). Through this method, it is possible to assess functional connectivity among cortical somatosensory areas. Previous studies have demonstrated a strong functional connectivity between the SI and SII at the alpha, beta, and gamma oscillations in healthy adults (Simoes et al., 2003; Hagiwara et al., 2010; Gao et al., 2015; Cheng, 2018). Notably, connectivity strength was reduced in patients with multiple sclerosis (Hagiwara et al., 2010) and writer's cramp (Cheng et al., 2016b). However, it remains unknown whether the interplay of SI and SII is affected in patients with PD. Thus, the second aim of the present study was to examine the functional connectivity among the SI, SIIc, and SIIi in alpha, beta, and gamma frequency bands. Magnetoencephalography (MEG) can be used to directly measure neural activities with higher spatial resolution than electroencephalography, and higher temporal resolution than functional magnetic resonance imaging (fMRI) (Hari et al., 2010; Baillet, 2017). Use of MEG is suitable for studying cortical somatosensory function, as neural generators of SI and SII belong to tangential dipoles, whose magnetic signals can be effectively captured by MEG sensors. Specifically, the present study had three objectives. Firstly, we tested whether cortical activation of the SI, SIIc, and SIIi in patients with PD would significantly differ from that observed in healthy controls (HC). Secondly, we assessed the oscillatory connectivity between the SI and SIIc (SI–SIIc), SI and SIIi (SI–SIIi), and SIIc and SIIi (SIIc–SIIi) in alpha, beta, and gamma frequency bands. Finally, based on significant between-group differences, where recorded, we explored the relationships between somatosensory activities and clinical assessments.
2.3. Data acquisition and processing The somatosensory evoked fields (SEFs) were measured continuously at 1000 Hz with a 306-channel MEG instrument (Vectorview; Elekta Neuromag, Helsinki, Finland). The precise head position in relation to the sensor arrays was monitored prior to recordings using four indicators, and the three fiducial points based on the Cartesian coordinate system were determined using a three-dimensional digitizer. In this study, the data from 204-channel gradiometers were analyzed, as this type of sensor can reduce geomagnetic and other environmental artifacts and detect the largest signals directly above the activated cerebral areas. The online bandpass filter was 0.1–200 Hz, and at least 100 trials were obtained from all the participants. For each trial of raw data, a spatiotemporal signal space (tsss) method was applied using MaxFilter to remove internal and external artifacts (Taulu and Simola, 2006). The signals contaminated by eye blinks and heart beats were also removed using the signal space projection (SSP) method. Subsequently, the epochs of 500 ms (including 100 ms before the onset of the stimulus) were averaged across trials for further analysis. The averaged SEFs were filtered with a bandpass in the 0.1–120 Hz range, with a 100-ms baseline correction. An overlapping-sphere model was applied to resolve the forward problem (Huang et al., 1999). The individual source space, consisting of approximately 15,000 dipoles over the whole cortex, was rescaled to the ICBM152 brain template using Brainstorm registration methods with default settings. Modeling of cortical spatiotemporal dynamics was performed using a cortically constrained, depth-weighted minimum norm estimate (MNE) (Hamalainen and Ilmoniemi, 1994), implemented in the Brainstorm software (Tadel et al., 2011). Regions of interest (ROIs) in the cortical somatosensory system were identified in the SI (including M20 and M35 components), SIIc, and SIIi. A cluster of 40 vertices, corresponding to 6 − 7 cm2, was manually selected according to the individual's maximal response in each ROI. The time-resolved magnitude of each dipole was normalized over the baseline to yield a set of z scores. The z-score values were rectified to obtain the absolute magnitude. The functional connectivity among the identified ROIs was analyzed using the coherence method implemented in the Brainstorm software. Each raw trial with a time window from −100 ms to +400 ms was applied to perform the source-based coherence analysis using a magnitude-square measure, with a maximum frequency resolution of 1 Hz and highest frequency interest of 50 Hz. The evoked response from each trial was removed in the calculation of functional connectivity. The cortical coherence of the SI and SIIc (SI–SIIc), SI and SIIi (SI–SIIi), as well as SIIc and SIIi (SIIc–SIIi) in each subject was determined in the alpha (8–12 Hz), beta (13–30 Hz), and gamma (31–50 Hz) frequency bands.
2. Methods 2.1. Subjects Twenty healthy right-handed volunteers (11 females; mean age 43.05 ± 1.81 years) and 20 right-handed outpatients with PD (12 females; mean age 43.55 ± 2.00 years) were selected according to the DSM-5 criteria. In PD patients, the mean duration of disease was 5.80 ± 1.01 years, and the mean Hamilton Scale for Anxiety (HAM-A) was 15.0 ± 1.19. Disease severity was evaluated using the Panic Disorder Severity Scale (PDSS) with a mean value of 9.65 ± 1.19. All patients reported mild to severe somatic abnormalities as evaluated using the BSQ (41.0 ± 3.27). Antidepressant treatment was administered to 19 patients, while 17 patients received combined treatment with antidepressants and benzodiazepines. This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital (Linkou, Taiwan), and was performed in accordance with relevant guidelines and regulations. Written informed consent was provided by all participants following detailed description of the experimental procedures.
2.4. Statistical analysis Kolmogorov–Smirnov one-sample tests showed that not all data exhibited normal distribution. Therefore, statistical comparisons were performed by means of non-parametric analysis. Specifically, the cortical activation of the SI, SIIc, and SIIi as well as the connectivity strength of alpha, beta, and gamma frequency bands between the HC and PD groups were evaluated with Mann-Whitney U tests. Associations between MEG signals (e.g., cortical activation of the SI, SIIc, and SIIi, as well as the strength of functional connectivity in the studied frequency bands) and clinical data (e.g., mean duration of disease, and scores of HAM-A, PDSS, and BSQ) were assessed through Spearman's correlation coefficients. A p-value < 0.05 denoted statistical significance.
2.2. Stimuli The right median nerve was stimulated at the wrist with constant current pulses of 0.2-ms duration using an electrical stimulator (Konstant-Strom Stimulator, Germany). The intensity was set at 20% above the motor threshold to elicit a visible twitch of the thumb, which has previously been reported to result in significant activation of the SI and SII (Huang et al., 2010; Sugawara et al., 2015; Cheng et al., 2016a; Cheng et al., 2017). The interstimulus interval was varied from 1.6 to 2.0 s to avoid any expectation effects. During the entire MEG recordings, subjects were instructed to watch a silent video and ignore the electrical stimulation.
3. Results The upper panel of Fig. 1(A) shows the grand-averaged sensor 2
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
Fig. 1. (A) Upper panel: Grand-averaged somatosensory evoked fields recorded from the MEG sensor arrays in 20 healthy controls (HC) and 20 patients with panic disorder (PD). Lower panel: The grand-averaged minimum norm estimate is mapped onto the ICBM152 template. M20 and M35 activities of the primary somatosensory cortex (SI) are located in the anterior parietal lobe. The activation of contralateral and ipsilateral secondary somatosensory cortices (SIIc, SIIi, respectively) occurs in the parietal operculum. (B) Mann-Whitney U tests revealed significant reduced M35 cortical activation in patients with PD relative to HC.
waveforms of SEFs elicited by electrical stimulation of the right median nerve in the HC and PD groups. The lower panel of this figure shows the MNE source activation mapped onto the ICBM152 template. In the SI
region, the M20 component was the first deflection of somatosensory evoked responses, followed by the M35 component. Longer-latency responses, peaking at approximately 70–150 ms after the onset of the 3
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
Fig. 2. (A) Functional connectivity as measured by cortical coherence of alpha, beta, and gamma bands among SI, SIIc, and SIIi in HC and PD groups. (B) MannWhitney U tests revealed significantly reduced SI–SIIc gamma coherence in patients with PD relative to HC.
stimulus, were generated bilaterally in the inferior parietal operculum (i.e., SIIc and SIIi). Compared with the HC, patients with PD demonstrated a significant reduction in M35 cortical strength (Z = 2.556, p = 0.009, two-tailed) (Fig. 1B). The amplitudes of M20, SIIc, and SIIi
did not differ significantly between the HC and PD groups. Fig. 2(A) depicts the grand-averaged functional connectivity maps among three identified ROIs in the HC (upper panel) and PD (lower panel) groups. Inspection from the coherence maps, the connectivity 4
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
strength of the alpha, beta, and gamma oscillations was weaker in the PD group than in the HC group. The results of the statistical analysis confirmed significant between-group differences in the gamma-band SI–SIIc coherence (Z = 2.137, p = 0.033, two-tailed). These significant findings regarding M35 cortical strength and SI–SIIc functional connectivity, led us to also examine the relationships between MEG signals and clinical data in patients with PD. However, this analysis did not detect a significant association.
target for pharmacotherapy. This finding provided neurophysiological support of treatment response of PD patients to this medication. Our findings also raised an interesting question: is the transmission of somatosensory information from the thalamus to the SI and SII sequential or parallel? It has been conceptualized that information processing in the somatosensory system occurs in a serial manner, i.e., from the ventral posterior nucleus of the thalamus to the SI, terminating at the SIIc and SIIi. This contention is supported by findings demonstrating that the SI has reciprocal connections with the SII (Kropf et al., 2019). In addition to serial processing, information in the somatosensory system can be transmitted in a parallel manner, i.e., both the SI and SII receive information directly from the thalamus. Our results illustrated in Fig. 1 showed that only the SI cortical strength was reduced in patients with PD, suggesting that one of the somatosensory pathways is conveyed directly from the thalamus to the SII. Our hypothesis is supported by anatomical evidence. The ventral posterior nucleus can be divided into medial and lateral nuclei, and it has been shown that the medial nucleus is connected with the SII, since it receives neurons from the trigeminal tract (Nieuwenhuys et al., 1988; Catani and Thiebaut de Schotten, 2012). Results from a lesion study involving patients with stroke further confirmed our hypothesis, demonstrating that, despite deficient SEFs in the SI and SIIc, activities of the SIIi were relatively preserved (Forss et al., 1999). Recent fMRI studies using dynamic causal modeling also provided solid support to the parallel hypothesis, suggesting that the SII cortex receives information from the SI and thalamic inputs (Kalberlah et al., 2013; Klingner et al., 2015). Some limitations should be considered when interpreting the present results. Firstly, the relatively small sample size may reduce the statistical power of this study, potentially leading to a nonsignificant correlation between MEG signals and clinical data. Secondly, most of the patients had comorbidities, such as depression, generalized anxiety disorder, etc. In the clinical setting, it was difficult to recruit patients with pure PD. Thus, the current sample may be representative of the general population with PD. Finally, most of our patients received treatment with antidepressants, which may have influenced the results. However, a human study has shown that serotonin did not affect information processing in the SI (Kahkonen et al., 2003). Thus, it was improbable that the reduced SEFs in patients with PD were influenced by antidepressants. In conclusion, our study is the first neuroimaging study to comprehensively assess the cortical somatosensory function in patients with PD. Our current findings demonstrate a diminished SI activity in patients with PD relative to that observed in HC. Patients with PD also show reduced functional connectivity between the SI and SIIc in the gamma oscillatory band. Collectively, our data suggest that patients with PD exhibit an abnormal response to non-threatening (i.e., painfree) stimuli in the cortical somatosensory system.
4. Discussion To the best of our knowledge, this was the first study to comprehensively investigate the functional integrity of the cortical somatosensory system in patients with PD. Our MEG findings indicated that patients with PD showed a lack of cortical responsiveness to nonthreatening stimuli in the SI region compared with HC. Moreover, the SI–SIIc functional connectivity of gamma oscillations was significantly reduced in the PD patients. The somatosensory deficits noted in this study were consistent with broad impairments in sensory processing (e.g., auditory) previously reported in PD (Ludewig et al., 2005). SI activity (particularly the M35 component) was significantly reduced in response to non-threatening somatosensory stimulation in patients with PD, but not in HC. According to the information processing model of anxiety (Beck and Clark, 1997; Karl et al., 2006), cognitive resources may be increased in patients with PD in response to threatening stimuli, thus reducing the processing resources in response to neutral stimuli. Several lines of evidence have also shown that patients engage substantially in self-directed attention, leading to a concomitant reduced response to external stimuli (Hayward et al., 2000). Alternatively, the lack of SI cortical responsiveness early in the timecourse may reflect an information processing characteristic in patients with PD. A previous MEG study showed that veterans with post-traumatic stress disorder demonstrated reduced alpha oscillatory activity in the SI in response to non-threatening tactile stimulation, compared to those without post-traumatic stress disorder (Badura-Brack et al., 2015). Based on these findings, we hypothesized that diminished SI activity may reflect a categorization of the repetitive, neutral stimulation as non-important in our patients with PD. A recent neuroimaging study demonstrated an increased restingstate functional connectivity between the SI and thalamus in patients with PD (Cui et al., 2016). The thalamus plays a critical role in gating somatosensory information to the neocortex. The augmented functional connectivity between these two regions may be related to the abnormally high somatosensory information processing during the resting state, which in turn leads to a maladaptive response to external sensory inputs. Another interpretation of the present finding may be the defect of integrity of SI per se in patients with PD. This claim was supported by the results of a resting-state fMRI study, demonstrating that medicationnaïve patients with PD displayed lower brain activity in the SI, which was reversed through treatment with antidepressants (Lai and Wu, 2016). Collectively, the aforementioned findings suggest a deficit of somatosensory information processing in patients with PD. The functional connectivity of SI–SIIc, particularly the gamma oscillation, was significantly reduced in patients with PD. Oscillations in the gamma frequency have been reported to be modulated by the neurotransmitter γ-Aminobutyric acid (GABA), which plays a role in cortico-cortical circuits (Cardin et al., 2009). Using magnetic resonance spectroscopy imaging, there is ample evidence showing that a strong relationship exists between gamma oscillations and the concentration of GABA in the sensory cortex (Edden et al., 2009; Muthukumaraswamy et al., 2009). Based on this evidence, it is reasonable to postulate that the reduced gamma-band functional connectivity of SI–SIIc may be an aberration of the balance between inhibitory and excitatory neurotransmission. Benzodiazepines, which are used to treat most patients with PD, exert their function by binding to GABA receptors. Hence, the GABAergic modulation is considered a
CRediT authorship contribution statement Chia-Hsiung Cheng: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing original draft, Writing - review & editing. Chia-Yih Liu: Data curation, Investigation, Resources, Writing - review & editing. Shih-Chieh Hsu: Conceptualization, Data curation, Investigation, Resources, Writing review & editing. Declaration of Competing Interest The authors declare that they have no conflict of interest. Acknowledgments This work was supported by Chang Gung Memorial Hospital (CMRPD1E0291, CMRPD1E0292, CMRPD1E0293, CMRPD1E0294), 5
Psychiatry Research 285 (2020) 112808
C.-H. Cheng, et al.
Chang Gung University (BMRPE25), Healthy Aging Research Center, Chang Gung University from the Featured Areas Research Center Program within the Framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan (EMRPD1K0431), and Ministry of Science and Technology (MOST-104-2314-B-182-001-MY2, MOST-105-2628-B-182-004-MY3, MOST-108-2628-B-182-002), Taiwan.
somatosensory network. Sci. Rep. 5, 10399. Hagiwara, K., Okamoto, T., Shigeto, H., Ogata, K., Somehara, Y., Matsushita, T., Kira, J., Tobimatsu, S., 2010. Oscillatory gamma synchronization binds the primary and secondary somatosensory areas in humans. Neuroimage 51 (1), 412–420. Hamalainen, M.S., Ilmoniemi, R.J., 1994. Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 32 (1), 35–42. Hari, R., Parkkonen, L., Nangini, C., 2010. The brain in time: insights from neuromagnetic recordings. Ann. N. Y. Acad. Sci. 1191, 89–109. Hayward, P., Ahmad, T., Wardle, J., 2000. Attention to bodily sensations: a test of the cognitive-attentional model of panic. Depress. Anxiety 12 (4), 203–208. Hoehn-Saric, R., McLeod, D.R., Funderburk, F., Kowalski, P., 2004. Somatic symptoms and physiologic responses in generalized anxiety disorder and panic disorder: an ambulatory monitor study. Arch. Gen. Psychiatry 61 (9), 913–921. Huang, M.X., Lee, R.R., Gaa, K.M., Song, T., Harrington, D.L., Loh, C., Theilmann, R.J., Edgar, J.C., Miller, G.A., Canive, J.M., Granholm, E., 2010. Somatosensory system deficits in schizophrenia revealed by MEG during a median-nerve oddball task. Brain Topogr. 23 (1), 82–104. Huang, M.X., Mosher, J.C., Leahy, R.M., 1999. A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG. Phys. Med. Biol. 44 (2), 423–440. Kahkonen, S., Ahveninen, J., Jaaskelainen, I.P., Pennanen, S., Liesivuori, J., Nikulin, V.V., 2003. Acute tryptophan depletion does not change somatosensory evoked magnetic fields. Psychopharmacol. (Berl.) 170 (3), 332–333. Kalberlah, C., Villringer, A., Pleger, B., 2013. Dynamic causal modeling suggests serial processing of tactile vibratory stimuli in the human somatosensory cortex–an fMRI study. Neuroimage 74, 164–171. Karl, A., Malta, L.S., Maercker, A., 2006. Meta-analytic review of event-related potential studies in post-traumatic stress disorder. Biol. Psychol. 71 (2), 123–147. Klingner, C.M., Brodoehl, S., Huonker, R., Gotz, T., Baumann, L., Witte, O.W., 2015. Parallel processing of somatosensory information: evidence from dynamic causal modeling of MEG data. Neuroimage 118, 193–198. Kropf, E., Syan, S.K., Minuzzi, L., Frey, B.N., 2019. From anatomy to function: the role of the somatosensory cortex in emotional regulation. Braz. J. Psychiatry 41 (3), 261–269. Lai, C.H., Wu, Y.T., 2016. The changes in the low-frequency fluctuations of cingulate cortex and postcentral gyrus in the treatment of panic disorder: the MRI study. World J. Biol. Psychiatry 17 (1), 58–65. Lin, Y.Y., Forss, N., 2002. Functional characterization of human second somatosensory cortex by magnetoencephalography. Behav. Brain Res. 135 (1–2), 141–145. Ludewig, S., Geyer, M.A., Ramseier, M., Vollenweider, F.X., Rechsteiner, E., CattapanLudewig, K., 2005. Information-processing deficits and cognitive dysfunction in panic disorder. J. Psychiatry Neurosci. 30 (1), 37–43. Muthukumaraswamy, S.D., Edden, R.A., Jones, D.K., Swettenham, J.B., Singh, K.D., 2009. Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans. Proc. Natl. Acad. Sci. U. S. A. 106 (20), 8356–8361. Nieuwenhuys, R., Voogd, J., 1988. The Human Central Nervous System: a Synopsis and Atlas. Springer-Verlag, Berlin. Simoes, C., Jensen, O., Parkkonen, L., Hari, R., 2003. Phase locking between human primary and secondary somatosensory cortices. Proc. Natl. Acad. Sci. U. S. A. 100 (5), 2691–2694. Sugawara, K., Onishi, H., Yamashiro, K., Kojima, S., Miyaguchi, S., Kirimoto, H., Tsubaki, A., Tamaki, H., Shirozu, H., Kameyama, S., 2015. The effect of anodal transcranial direct current stimulation over the primary motor or somatosensory cortices on somatosensory evoked magnetic fields. Clin. Neurophysiol. 126 (1), 60–67. Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M., 2011. Brainstorm: a userfriendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 879716. Taulu, S., Simola, J., 2006. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys. Med. Biol. 51 (7), 1759–1768. Yoshino, A., Okamoto, Y., Onoda, K., Shishida, K., Yoshimura, S., Kunisato, Y., Demoto, Y., Okada, G., Toki, S., Yamashita, H., Yamawaki, S., 2012. Sadness enhances the experience of pain and affects pain-evoked cortical activities: an MEG study. J. Pain 13 (7), 628–635.
References Badura-Brack, A.S., Becker, K.M., McDermott, T.J., Ryan, T.J., Becker, M.M., Hearley, A.R., Heinrichs-Graham, E., Wilson, T.W., 2015. Decreased somatosensory activity to non-threatening touch in combat veterans with posttraumatic stress disorder. Psychiatry Res. 233 (2), 194–200. Baillet, S., 2017. Magnetoencephalography for brain electrophysiology and imaging. Nat. Neurosci. 20 (3), 327–339. Beck, A.T., Clark, D.A., 1997. An information processing model of anxiety: automatic and strategic processes. Behav. Res. Ther. 35 (1), 49–58. Bertrand, O., Tallon-Baudry, C., 2000. Oscillatory gamma activity in humans: a possible role for object representation. Int. J. Psychophysiol. 38 (3), 211–223. Cardin, J.A., Carlen, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., Tsai, L.H., Moore, C.I., 2009. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459 (7247), 663–667. Catani, M., Thiebaut de Schotten, M., 2012. Atlas of Human Brain Connections. Oxford University, New York. Chambless, D.L., Caputo, G.C., Bright, P., Gallagher, R., 1984. Assessment of fear of fear in agoraphobics: the body sensations questionnaire and the agoraphobic cognitions questionnaire. J. Consult. Clin. Psychol. 52 (6), 1090–1097. Cheng, C.H., 2018. Effects of observing normal and abnormal goal-directed hand movements on somatosensory cortical activation. Eur. J. Neurosci. 47 (1), 48–57. Cheng, C.H., Chan, P.S., Hsu, S.C., Liu, C.Y., 2019. Abnormal frontal generator during auditory sensory gating in panic disorder: an MEG study. Psychiatry Res. Neuroimaging 288, 60–66. Cheng, C.H., Chan, P.Y., Baillet, S., Lin, Y.Y., 2015. Age-Related reduced somatosensory gating is associated with altered alpha frequency desynchronization. Neural Plast. 2015, 302878. Cheng, C.H., Chan, P.Y., Niddam, D.M., Tsai, S.Y., Hsu, S.C., Liu, C.Y., 2016a. Sensory gating, inhibition control and gamma oscillations in the human somatosensory cortex. Sci. Rep. 6, 20437. Cheng, C.H., Lin, Y.Y., 2013. Aging-related decline in somatosensory inhibition of the human cerebral cortex. Exp. Brain Res. 226 (1), 145–152. Cheng, C.H., Tsai, S.Y., Liu, C.Y., Niddam, D.M., 2017. Automatic inhibitory function in the human somatosensory and motor cortices: an MEG-MRS study. Sci. Rep. 7 (1), 4234. Cheng, C.H., Tseng, Y.J., Chen, R.S., Lin, Y.Y., 2016b. Reduced functional connectivity of somatosensory network in writer's cramp patients. Brain Behav. 6 (3), e00433. 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. De Berardis, D., Campanella, D., Gambi, F., La Rovere, R., Sepede, G., Core, L., Canfora, G., Santilli, E., Valchera, A., Mancini, E., Salerno, R.M., Moschetta, F.S., Ferro, F.M., 2007. Alexithymia, fear of bodily sensations, and somatosensory amplification in young outpatients with panic disorder. Psychosomatics 48 (3), 239–246. Edden, R.A., Muthukumaraswamy, S.D., Freeman, T.C., Singh, K.D., 2009. Orientation discrimination performance is predicted by GABA concentration and gamma oscillation frequency in human primary visual cortex. J. Neurosci. 29 (50), 15721–15726. Forss, N., Hietanen, M., Salonen, O., Hari, R., 1999. Modified activation of somatosensory cortical network in patients with right-hemisphere stroke. Brain 122 (Pt 10), 1889–1899. Gao, L., Sommerlade, L., Coffman, B., Zhang, T., Stephen, J.M., Li, D., Wang, J., Grebogi, C., Schelter, B., 2015. Granger causal time-dependent source connectivity in the
6