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Left cerebral cortex complexity differences in sporadic healthy individuals with auditory verbal hallucinations: A pilot study
Psychiatry Research 285 (2020) 112834
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Left cerebral cortex complexity differences in sporadic healthy individuals with auditory verbal hallucinations: A pilot study
T
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Chuanjun Zhuoa,b,c, ,1, Gongying Lia, Ce Chenb, Feng Jia, Xiao Linb, Deguo Jiangb, Hongjun Tianc, Lina Wangc, Xiaodong Linb,1, Jing Pingb,1 a
Department of Psychiatry, School of Mental Health, Jining Medical University, Jining 272100, Shandong Province, China Department of Psychiatric-Neuroimaging-Genetics Laboratory (PNG_Lab), Wenzhou Seventh People's Hospital, Wenzhou 325000, Zhejiang Province, China c Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory (PNGC_Lab) Tianjin Mental Health Canter, Tianjin Anding Hospital, Mental Health Teaching Hospital of Tianjin Medical University, Tianjin 300222, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Gyrification index Fractal dimension Cortical complexity AVHs SBM
In the present pilot study, we aimed to characterize the brain surface differences between 42 sporadic healthy individuals with AVHs (Hi-AVHs) and 50 healthy individuals without AVHs. The Auditory Hallucinations Rating Scale (AHRS) was used to assess the severity of AVHs, while the gyrification index and fractal dimensions were used to evaluate cerebral cortex complexity. We observed a significant increase of the gyrification index was located in the left superior temporal gyrus, the left temporoparietal junction, the left superior frontal gyrus, and the left parietal lobe. The fractal dimensions had significantly increased in the left Wernicke's area, the left Broca's areas and the left parietal lobe. Our pilot findings indicated gyrification index and fractal dimensions differences were only located in the left hemisphere between the groups of Hi-with and without AVHs. However, these differences did not correlate with the AVHs symptoms, and the non-hallucinating healthy individuals did not demonstrate corresponding reverse changes; hence we cannot postulate that cerebral cortex complexity alterations are related to AVHs. Our pilot study provides a clue for further studies aimed at investigating the brain features of Hi-AVHs.
1. Introduction Over the last several decades, research has revealed that a high prevalence of auditory hallucinations in the general population. For example, according to Kråkvik et al. the prevalence of auditory verbal hallucinations is 7.5% in a Norwegian general population study (Kråkvik et al., 2015), Johns et al. reported the prevalence of AVHs in the general populations is 8–25% (Johns et al., 2001), and Sommer et al. reported that the prevalence of auditory hallucinations in populations is 8–14% according to epidemiological studies (Sommer et al., 2010). Although many factors can influence the prevalence rate in epidemiological studies, according to strict criteria proposed by Johns et al. (“Did you at any time hear voices saying quite a few words or
sentences when there was no one around that might account for it?”), only 0.7% of those in the general population experience AVHs (Johns et al., 2004). Some scholars have defined these patients as healthy individuals with AVHs (Hi-AVHs) (Daalman et al., 2016). Typically, Hi-AVHs do not exhibit clinically defined delusions, disorganization, or negative/ catatonic symptoms, nor do they meet criteria for cluster A personality disorder. Accumulating evidence has shed light on the mechanisms underlying AVHs (Larøi et al., 2012). In 2013, the International Consortium on Hallucination Research (ICHR) urged researchers to explore the mechanisms underlying AVHs in different categories of mental disorders from multiple perspectives and expand their studies to include Hi-AVHs (Waters et al., 2014; Thomas et al., 2016). Exploring
Abbreviations: AVH, auditory verbal hallucinations; Hi-AVH, healthy individuals with auditory verbal hallucinations; ICHR, International Consortium on Hallucination Research; SBM, source-based morphometry; SCID-I/P, Structured Clinical Interview for DSM-IV Axis I Disorders-Patient Edition; AHRS, Auditory Hallucinations Rating Scale; CASH, Comprehensive Assessment of Symptoms and History; GAF, Global Assessment of Functioning; SCID-II, Structured Clinical Interview for Personality Disorder; T1WI, T1-weighted images; T2WI, T2-weighted images; FLAIR, fluid-attenuated inversion recovery; TR, repetition time; TE, echo time; FWHM, full-width at half-maximum; ICA, independent component analysis; FWE, family-wise error ⁎ Corresponding author at: Department of Psychiatric-Neuroimaging-Genetics Laboratory (PNG_Lab), Wenzhou Seventh People's Hospital, Wenzhou 325000, Zhejiang Province, China. E-mail address: [email protected] (C. Zhuo). 1 These authors were the co-corresponding authors of this work. https://doi.org/10.1016/j.psychres.2020.112834 Received 19 May 2019; Received in revised form 28 January 2020; Accepted 29 January 2020 Available online 30 January 2020 0165-1781/ © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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2. Methods
pathological brain features in Hi-AVHs may help to elucidate the mechanisms underlying AVHs, allowing us to avoid several confounding factors such as therapeutic agents and other psychotic symptoms. Indeed, the ICHR has highlighted the importance of identifying the pathological brain features associated with AVH in healthy individuals (Thomas et al., 2016). Previous studies have reported that AVHs are associated with cortical abnormalities in patients with schizophrenia. For example, in 2006, Neckelmann et al. reported that the decrease in gray matter volume may be instrumental in generating spontaneous neuronal activity associated with speech perception experiences in the absence of an external acoustic stimulus that may cause hallucinations (Neckelmann et al., 2006). This study inspired many subsequent studies to investigate the gray matter alterations in the brain related to AVHs. The findings of the previous studies suggested that alterations in brain structure may reflect a pathological consequence of AVHs in this population (Kubera et al., 2014; Kubera et al., 2018). ICHR proposed to investigate the brain features of Hi-AVHs, the research hot spot of this decade. Hence, using new methods to investigate the brain structural differences in HiAVHs from new perspectives has become an urgent need this decade. Since these sporadic Hi-AVHs individuals have a lower risk of having other mental symptom, investigating these individuals in particular may provide more useful clues to understand the mechanisms of AVHs. Source-based morphometry (SBM) is commonly used to investigate subtle structural brain surface differences in patients with schizophrenia (Kubera et al., 2014; Kubera et al., 2018; Gupta et al., 2015; Huber et al., 2018; Li et al., 2019). Source-based morphometry (Xu et al., 2009) is a technique that uses independent component analysis (ICA) (Bell et al., 1995) to obtain patterns of common gray matter concentration (GMC) variation among subjects. By applying SBM in psychiatry studies, GMC deficits clustered into independent spatial regions can be identified. This approach has three main advantages. First, it performs a multivariate analysis of the whole-brain data, so it does not restrict the analysis to a single region of interest. Second, it accounts for spatial dependencies between different brain locations, which are not taken into consideration by univariate analyses such as voxel-based morphometry (VBM) (Ashburner et al. 2000). Third, it provides a better interpretation of the location of GMC variations than voxel-based approaches (Castro et al., 2014). For example, Gupta et al. reported that two neural circuits, the insula–superior temporal gyrus–inferior frontal gyrus, the superior frontal gyrus–middle frontal, and gyrus–medial frontal gyrus, are altered in patients with schizophrenia and that these alterations are correlated with the clinical symptoms of schizophrenia (Gupta et al., 2017). In previous studies, Kubera et al. adopted SMB to investigate gray matter volume in patients with schizophrenia who had persistent auditory verbal hallucinations (Kubera et al., 2014). Grecucci et al. used SBM to investigate the social deficits in the autistic brain (Grecucci et al., 2016). Wolf et al. used SBM to reveal distinct patterns of aberrant brain volume in delusional infestation (Wolf et al., 2014). These studies based on SBM provide new clues for us to understand the mechanisms of psychotic symptoms from a new perspective. However, in the last years, few studies have investigated the brain surface difference between sporadic Hi-AVHs and healthy individuals without AVHs. Inspired by the work of Kubera et al. (Kubera et al., 2014; Kubera et al., 2018), we aimed to investigate cortical surface differences between Hi-AVHs and Hi without AVHs. In the present study, we utilized SBM to investigate the cortical surface difference between sporadic Hi-AVHs and Healthy individuals without AVHs. We hypothesized that the possible differences can be observed between these two different categories of individuals and that the brain surface differences may be related to the AVHs severity.
2.1. Participants The present study was approved by the Ethics Committee of Tianjin Anding Hospital and Wenzhou Seventh People's Hospital. In this pilot study, we only recruited the Hi-AVHs patients without other mental disorders and without positive family history (in other words, sporadic individuals). Although AVHs in sporadic individuals may be a prodromal symptoms, sporadic individuals still have a lower risk develop psychosis than AVHs-individuals with other mental symptoms. We recruited the participants from our two database: the Tianjin and Wenzhou databases. The Tianjin database, established in 2011 (Yin et al., 2017; Yin et al., 2018), had the full information of 40,000 individuals. Through the Wenzhou database, established in 2015, in this database, we acquired the information of 10,000 individuals (Zhuo et al., 2020). The inclusion criteria were as follows: (1) complete accordance with AVHs criteria established by Johns et al. (“Did you at any time hear voices saying quite a few words or sentences when there was no one around that might account for it?”); (2) absence of other psychotic symptoms as determined using the Structured Clinical Interview for DSM-IV Axis I Disorders-Patient Edition (SCID-I/P) (conducted by two senior psychiatrists with more than 10 years of experience; (3) None personality disorder diagnosed by the two senior psychiatrists according to Structured Clinical Interview For Personality Disorder (SCID)-II(First et al., 1995).(4) IQ ≥ 80; (5) none positive family psychosis history assessed by Interview for Genetic Studies (FIGS), FIGS is a clinician-administered tool that comprises of three parts general screening questions, face sheet, and the symptom checklist. Symptoms checklists help establish the best estimate diagnosis in family members (Díaz de Villalvilla, 1992). (6) right-handed. The exclusion criteria were as follows: (1) IQ < 80, (2) ear diseases, (3) presence of diagnosed mental disorders, such as anxiety, depression, bipolar disorder, schizophrenic spectrum disease, substance abuse, etc.; (4) presence of organic brain diseases, (5) presence of physical and neurological diseases which can influence the mental state, (6) family history of mental illness, (7) history of unconsciousness for more than 5 min due to any cause, (8) contraindications for MRI examination; (9) claustrophobia. Furthermore, each participant was informed of the purpose and process of the experiment in detail and signed the informed consent. From July 2017 to December 2018, a total of 50 sporadic Hi-AVHs (36 male and 14 female) and 50 healthy individuals without were enrolled. 2.2. Assessment of AVHs severity and psychological, and professional function In this study, we used the Auditory Hallucinations Rating Scale (AHRS) (Hoffman et al., 2000) to assess the severity of AVHs. The lowest scores on the social, psychological, and professional functioning domains of the Global Assessment of Functioning (GAF) (American Psychiatric Association, 2003) were used to determine the highest level of functioning over the past year. 2.3. MRI data acquisition MRI was performed using a 3T GE Discovery MR750 scanner (General Electric, Milwaukee, WI, USA) and an eight-channel phasedarray head coil. Every participant was advised to maintain a resting state as much as possible, and the head was fixed with sponges to reduce movement. Routine scan sequences included axial T1WI, T2WI and fluid-attenuated inversion recovery (FLAIR), used to exclude patients with obvious intracranial lesions. The ultimate 3D high resolution T1WI structural image was acquired with a T1W-3D-TFE-ref pulse sequence in the sagittal plane. The main scan parameters were as follows: TR = 7.5 ms, TE = 3.7 ms, flip angle = 8°, voxel size = 1 mm × 1 mm × 1 mm, field of view = 240 mm × 240 mm, 2
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matrix = 232 × 227, number of layers = 150, layer spacing = 0.
Table 1 Sociodemographic characteristics of the two groups .
2.4. Processing of structural MRI data Sex (n)
All the participants completed the MRI scan, however, eight HiAVHs were excluded due to poor image quality. Totally, 92 individuals data were used to perform the analysis. In this process, gray-white matter segmentation and cortical reconstruction were performed using the CAT12 software equipped with the cortical parcellation software FreeSurfer and the Destrieux Atlas (aparc.a2009s). Pre-processing steps included head motion correction and the averaging of the volume data for multiple T1WI, removal of the non-brain structures, gray–white matter segmentation, and spatial normalization using the Gaussian kernel with full-width at half-maximum (FWHM = 8 mm). Sourcebased morphometry analysis was carried out using the GIFT toolbox (http://icatb.sourceforge.net) (Xu et al., 2009). The minimum description length (MDL) principle was used to estimate a number of independent components. MDL found seven reliable ICs. We performed ICA using a neural network algorithm (Infomax) that attempts to minimize the mutual information of the network outputs to identify naturally grouping and maximally independent sources (Bell and Sejnowski, 1995). ICA was repeated 20 times in ICASSO (http:// research.ics.aalto.fi/ica/icasso/) and the resulting components were clustered to ensure the consistency and reliability of the results. The reliability is quantified using a quality index Iq, ranging from 0 to 1 and reflecting the difference between intra-cluster and extra-cluster similarity. All the 7 components extracted from the GM images were found to be associated with an Iq > 0.97 that indicated a highly stable ICA decomposition. SBM converts each gray-matter volume into a vector. As a result, we obtained a matrix where the 92 rows represent the 92 subjects (the first 50 rows represent the controls, while the other 42 the Hi-individuals), and each column indicates a voxel. This matrix was decomposed into two matrices by ICA. The first matrix was named “mixing matrix” and it is composed by a subject per row and an IC per column. Therefore, the mixing matrix indicates how much a subject expresses a given component. For this reason, values in the mixing matrix are called “loading coefficients.” The second matrix was named “source matrix” and it specifies the relation between the Ics and the voxels. As for the gray-matter volume component visualization the source matrix was reshaped back to a three-dimensional image, scaled to unit standard deviations (Z maps) and with a threshold at Z > 2.5.
Age (year) Years of education Duration of AVHs (months) AHRS scores GAF scores
Hi-AVHs
Hi-without AVHs
X2/t
P value
Female (14) Male (28) 23.91 ± 1.45 12.40 ± 3.00 21.50 ± 3.25
Female (14) Male (36) 24.91 ± 2.22 13.55 ± 3.80 N/A
1.075
0.263
1.466 1.235 N/A
0.370 0.250 N/A
32.49 ± 4.82 72.75 ± 7.52
N/A 89.75 ± 2.78
N/A 7.520
N/A 0.012
The fractal dimensions had significantly increased in the left Wernicke's area, the leftBroca's areas, and the left parietal lobe (Figs. 1 and 2). 3.2. Correlation between AHRS scores and gyrification index/fractal dimensions We observed no significant correlations between AHRS scores and gyrification index/fractal dimensions in any of the aforementioned brain regions (P > 0.05). 4. Discussion To the best of our knowledge, the present study is the first to investigate differences in brain surface between Hi-AVHs and Hi-without AVHs. Our findings indicated that, compare to Hi-without AVHs, HiAVHs exhibited differences in the gyrification index mainly in the left superior temporal gyrus, left temporoparietal junction, superior frontal gyrus, and left parietal lobe. The fractal dimensions had significantly increased in the left Wernicke's area, Broca's area, and the parietal lobe. These findings suggest that differences in the complexity of the cerebral cortex may be the brain structural features of sporadic Hi-AVHs. Although our samples are not the patients with schizophrenia, these findings also support the interhemispheric miscommunication theory of auditory verbal hallucinations in schizophrenia (Steinmann et al., 2019). Our findings are partially consistent with and partially contrary to the findings of previous studies regarding cortical complexity differences in patients with schizophrenia experiencing AVHs (Kubera et al., 2014; Cachia et al., 2008; Bohlken et al., 2017; Zmigrod et al., 2016). In these patients, increased gyrification index values have been observed in the precuneus and superior parietal cortex, while decreased values have been observed in the right Broca's homologue and right superior middle frontal cortex (Kubera et al., 2014). In present the pilot study, the increased gyrification index and fractal dimensions were observed only in the brain regions of the left-brain hemisphere. These regions are components of the language, memory, attention, and sensory processing circuits (Kubera et al., 2014; Kubera et al., 2018, Kubera et al., 2013, Bohlken et al., 2017; Løberg et al., 2019; Steinmann et al., 2019). Increased cortical complexity in these circuits may thus represent a brain feature of AVHs in healthy individuals. Although through this pilot study we have found brain surface differences between Hi-AVHs and Hi-without AVHs, including regions involved with language, memory, attention, and sensory processing circuits, our findings are inconsistent with previous studies that focused on investigating the brain features of AVHs in patients with schizophrenia and patients with other prodromal symptoms of psychosis (Baumeister et al., 2017; Kubera et al., 2014; Kubera et al., 2018, Kubera et al., 2013, Bohlken et al., 2017; Løberg et al., 2019). We postulated that the inconsistences may be related to our sample. In this pilot study, we only recruited Hi-AVHs without other mental disorders and without positive family history. These sporadic individuals had a lower of developing psychosis than AVHs-individuals with other mental symptoms (e.g. distress) (Zmigrod et al., 2016; Zhuo et al., 2019).
2.5. Statistical analysis We used the mixing matrix to verify whether the components were differently expressed between the Hi-AVHs and the controls. A twosample t-test, without assuming equal variances (F-test revealed unequal variances), was used to test whether all the Ics are similarity expressed by either of the groups. Similarly, we used the loading coefficients in the mixing matrix to test a linear relation among AHRS scores and the level of components’ expression. All the results had the threshold at p < 0.05 corrected for Family Wise Error (FWE). 3. Results The remaining 42 Hi-AVHs and 50 Hi-without AVHs had no significant difference in age, gender, and educational level, although it deviated from the “well matched” original intention. Thus, we analyzed MRI data for the remaining 42 Hi-AVHs. (Table 1). 3.1. Comparison of gyrification index/fractal dimensions between the two groups We observed a significant increase of the gyrification index was located in the in the left superior temporal gyrus, the left temporoparietal junction, the superior frontal gyrus, and the left parietal lobe. 3
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Fig. 1. Gyrification index differences between Hi-AVHs and Hi. Note : Hi-AVHs, healthy individuals with auditory verbal hallucinations. Hi, Healthy individuals.
Fig. 2. Fractal dimension differences between Hi-AVHs and Hi. Note : Hi-AVHs, healthy individuals with auditory verbal hallucinations. Hi, Healthy individuals.
Psychiatric Association, 2003). These findings indicated that the AVHs may have not caused brain surface differences, just like patients with schizophrenia. Our postulation is tenable, but requires further studies to clarify it.
Hence, the inconsistent differences compared with previous studies may be understandable. However, a long term cohort study is required to clarify the long term brain cortex surface features of these sporadic Hi-AVHs (Sommer et al., 2010). Another notable finding in the present pilot study was that we could not observe the phenomenon in which Hi-AVHs were troubled by their "voices". This phenomenon is also inconsistent with previous studies that reported a substantial amount of non-clinical AVH individuals troubled by their “voices” (Johns et al., 2001; Larøi et al., 2012). Hence, we postulated that it may be related to the sample's special characteristics. Theoretically, since we only recruited sporadic patients, which had a lower risk of developing mental disorders than patients with a positive family history. More notably, the GAF scores of these sporadic Hi-AVHs indicated that their psychological, and professional function were only slightly worse than the healthy individuals (American
4.1. Limitations The present study possesses several limitations of note, including its cross-sectional design, which provides a lower level of evidence than long-term cohort studies. Nonetheless, our finding may provide insight for future investigations. Second, given the potential risk of developing schizophrenia or other psychiatric disorders among Hi-AVHs, future long-term cohort studies should aim to characterize the trajectory of structural brain differences in conjunction with the development of AVHs. Such studies may also help to elucidate the mechanisms 4
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underlying the development of AVHs in healthy individuals.
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4.2. Conclusion Our study observed that the gyrification index and fractal dimensions between healthy individuals with and without auditory verbal hallucinations. The differences in the gyrification index and fractal dimensions are all located on the surface of the left hemisphere. These differences did not correlate to the AVHs symptoms, and the non-hallucinating healthy individuals did not demonstrate corresponding reverse change. Therefore we cannot postulate that the cerebral cortex complexity alterations are related to AVHs. However, our current study provides clues for further studies aimed to investigate the brain features of Hi-AVHs. Funding This work was supported by grants from the National Natural Science Foundation of China (81871052 to C.Z.), the 30,000 yuan (RMB) talent project of Tianjin Anding Hospitatl; the Key Projects of the Natural Science Foundation of Tianjin, China (17JCZDJC35700 to C.Z.), the Tianjin Health Bureau Foundation (2014KR02 to C.Z.), the Zhejiang Public Welfare Fund Project (LGF18H090002 to D.J.), and the key project of Wenzhou Science and Technology Bureau (ZS2017011 to X.L.). Author statement CZ, XL, and HT planned the study. CC, GL, FJ, JP, and LW enrolled the patients. CC, DJ, Xd-L and HT analysed the data and drafted the manuscript. GL, and HT developed the statistical methods and analysed the data. All authors contributed to interpreting the results, critically evaluating the data and writing the manuscript. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psychres.2020.112834. References American Psychiatric Association, 2003. Diagnostic and Statistical Manual of Mental Disorders. Diagnostic and statistical manual of mental disorders. DSM-IV-TR 2nd éd. Paris: Masson. Ashburner, J., Friston, K.J., 2000. Voxel-based morphometry—the methods. Neuroimage 11 (6), 805–821. Baumeister, D., Sedgwick, O., Howes, O., Peters, E., 2017. Auditory verbal hallucinations and continuum models of psychosis: a systematic review of the healthy voice-hearer literature. Clin. Psychol. Rev. 51, 125–141. https://doi.org/10.1016/j.cpr.2016.10. 010. Bell, A.J., Sejnowski, T.J., 1995. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159. Bohlken, M.M., Hugdahl, K., Sommer, I.E., 2017. Auditory verbal hallucinations: neuroimaging and treatment. Psychol. Med. 47, 199–208. https://doi.org/10.1017/ S003329171600115X. Cachia, A., Paillère-Martinot, M.L., Galinowski, A., Januel, D., de Beaurepaire, R., Bellivier, F., Artiges, E., Andoh, J., Bartrés-Faz, D., Duchesnay, E., Rivière, D., Plaze, M., Mangin, J.F., Martinot, J.L., et al., 2008. Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations. Neuroimage 39, 927–935. Castro, E., Gupta, C.N., Martinez-Ramon, M., Calhoun, V.D., Arbabshirani, M.R., Turner, J., 2014. Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging. In: Eduardo (Ed.), Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014. HHS Public Access, pp. 1513–1516. Daalman, K., Diederen, K.M., Hoekema, L., van Lutterveld, R., Sommer, I.E., 2016. Five year follow-up of non-psychotic adults with frequent auditory verbal hallucinations: are they still healthy? Psychol. Med. 46, 1897–1907. https://doi.org/10.1017/ S0033291716000386. Díaz de Villalvilla, T., 1992. NIMH Genetics Initiative. Family Interview for Genetic
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Common and distinct global functional connectivity density alterations in drug-naïve patients with first-episode major depressive disorder with and without auditory verbal hallucination. Prog. Neuropsychopharmacol. Biol. Psychiatry. 96, 109738. Zmigrod, L., Garrison, J.R., Carr, J., Simons, J.S., 2016. The neural mechanisms of hallucinations: a quantitative meta-analysis of neuroimaging studies. Neurosci. Biobehav. Rev. 69, 113–123. https://doi.org/10.1016/j.neubiorev.2016.05.037.
2017. The Tianjin Mental Health Survey (TJMHS): study rationale, design and methods. Int. J. Methods Psychiatr. Res. 26. https://doi.org/10.1002/mpr.1535. Zhuo, C., Jiang, D., Liu, C., Lin, X., Li, J., Chen, G., Xie, Z., Zhou, C., Zhu, J., et al., 2019. Understanding auditory verbal hallucinations in healthy individuals and individuals with psychiatric disorders. Psychiatry Res 274, 213–219. https://doi.org/10.1016/j. psychres.2019.02.040. Zhuo, C., Zhou, C., Lin, X., Tian, H., Wang, L., Chen, C., Xu, Y., Jian, D., et al., 2020.
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Left cerebral cortex complexity differences in sporadic healthy individuals with auditory verbal hallucinations: A pilot study
T
⁎
Chuanjun Zhuoa,b,c, ,1, Gongying Lia, Ce Chenb, Feng Jia, Xiao Linb, Deguo Jiangb, Hongjun Tianc, Lina Wangc, Xiaodong Linb,1, Jing Pingb,1 a
Department of Psychiatry, School of Mental Health, Jining Medical University, Jining 272100, Shandong Province, China Department of Psychiatric-Neuroimaging-Genetics Laboratory (PNG_Lab), Wenzhou Seventh People's Hospital, Wenzhou 325000, Zhejiang Province, China c Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory (PNGC_Lab) Tianjin Mental Health Canter, Tianjin Anding Hospital, Mental Health Teaching Hospital of Tianjin Medical University, Tianjin 300222, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Gyrification index Fractal dimension Cortical complexity AVHs SBM
In the present pilot study, we aimed to characterize the brain surface differences between 42 sporadic healthy individuals with AVHs (Hi-AVHs) and 50 healthy individuals without AVHs. The Auditory Hallucinations Rating Scale (AHRS) was used to assess the severity of AVHs, while the gyrification index and fractal dimensions were used to evaluate cerebral cortex complexity. We observed a significant increase of the gyrification index was located in the left superior temporal gyrus, the left temporoparietal junction, the left superior frontal gyrus, and the left parietal lobe. The fractal dimensions had significantly increased in the left Wernicke's area, the left Broca's areas and the left parietal lobe. Our pilot findings indicated gyrification index and fractal dimensions differences were only located in the left hemisphere between the groups of Hi-with and without AVHs. However, these differences did not correlate with the AVHs symptoms, and the non-hallucinating healthy individuals did not demonstrate corresponding reverse changes; hence we cannot postulate that cerebral cortex complexity alterations are related to AVHs. Our pilot study provides a clue for further studies aimed at investigating the brain features of Hi-AVHs.
1. Introduction Over the last several decades, research has revealed that a high prevalence of auditory hallucinations in the general population. For example, according to Kråkvik et al. the prevalence of auditory verbal hallucinations is 7.5% in a Norwegian general population study (Kråkvik et al., 2015), Johns et al. reported the prevalence of AVHs in the general populations is 8–25% (Johns et al., 2001), and Sommer et al. reported that the prevalence of auditory hallucinations in populations is 8–14% according to epidemiological studies (Sommer et al., 2010). Although many factors can influence the prevalence rate in epidemiological studies, according to strict criteria proposed by Johns et al. (“Did you at any time hear voices saying quite a few words or
sentences when there was no one around that might account for it?”), only 0.7% of those in the general population experience AVHs (Johns et al., 2004). Some scholars have defined these patients as healthy individuals with AVHs (Hi-AVHs) (Daalman et al., 2016). Typically, Hi-AVHs do not exhibit clinically defined delusions, disorganization, or negative/ catatonic symptoms, nor do they meet criteria for cluster A personality disorder. Accumulating evidence has shed light on the mechanisms underlying AVHs (Larøi et al., 2012). In 2013, the International Consortium on Hallucination Research (ICHR) urged researchers to explore the mechanisms underlying AVHs in different categories of mental disorders from multiple perspectives and expand their studies to include Hi-AVHs (Waters et al., 2014; Thomas et al., 2016). Exploring
Abbreviations: AVH, auditory verbal hallucinations; Hi-AVH, healthy individuals with auditory verbal hallucinations; ICHR, International Consortium on Hallucination Research; SBM, source-based morphometry; SCID-I/P, Structured Clinical Interview for DSM-IV Axis I Disorders-Patient Edition; AHRS, Auditory Hallucinations Rating Scale; CASH, Comprehensive Assessment of Symptoms and History; GAF, Global Assessment of Functioning; SCID-II, Structured Clinical Interview for Personality Disorder; T1WI, T1-weighted images; T2WI, T2-weighted images; FLAIR, fluid-attenuated inversion recovery; TR, repetition time; TE, echo time; FWHM, full-width at half-maximum; ICA, independent component analysis; FWE, family-wise error ⁎ Corresponding author at: Department of Psychiatric-Neuroimaging-Genetics Laboratory (PNG_Lab), Wenzhou Seventh People's Hospital, Wenzhou 325000, Zhejiang Province, China. E-mail address: [email protected] (C. Zhuo). 1 These authors were the co-corresponding authors of this work. https://doi.org/10.1016/j.psychres.2020.112834 Received 19 May 2019; Received in revised form 28 January 2020; Accepted 29 January 2020 Available online 30 January 2020 0165-1781/ © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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2. Methods
pathological brain features in Hi-AVHs may help to elucidate the mechanisms underlying AVHs, allowing us to avoid several confounding factors such as therapeutic agents and other psychotic symptoms. Indeed, the ICHR has highlighted the importance of identifying the pathological brain features associated with AVH in healthy individuals (Thomas et al., 2016). Previous studies have reported that AVHs are associated with cortical abnormalities in patients with schizophrenia. For example, in 2006, Neckelmann et al. reported that the decrease in gray matter volume may be instrumental in generating spontaneous neuronal activity associated with speech perception experiences in the absence of an external acoustic stimulus that may cause hallucinations (Neckelmann et al., 2006). This study inspired many subsequent studies to investigate the gray matter alterations in the brain related to AVHs. The findings of the previous studies suggested that alterations in brain structure may reflect a pathological consequence of AVHs in this population (Kubera et al., 2014; Kubera et al., 2018). ICHR proposed to investigate the brain features of Hi-AVHs, the research hot spot of this decade. Hence, using new methods to investigate the brain structural differences in HiAVHs from new perspectives has become an urgent need this decade. Since these sporadic Hi-AVHs individuals have a lower risk of having other mental symptom, investigating these individuals in particular may provide more useful clues to understand the mechanisms of AVHs. Source-based morphometry (SBM) is commonly used to investigate subtle structural brain surface differences in patients with schizophrenia (Kubera et al., 2014; Kubera et al., 2018; Gupta et al., 2015; Huber et al., 2018; Li et al., 2019). Source-based morphometry (Xu et al., 2009) is a technique that uses independent component analysis (ICA) (Bell et al., 1995) to obtain patterns of common gray matter concentration (GMC) variation among subjects. By applying SBM in psychiatry studies, GMC deficits clustered into independent spatial regions can be identified. This approach has three main advantages. First, it performs a multivariate analysis of the whole-brain data, so it does not restrict the analysis to a single region of interest. Second, it accounts for spatial dependencies between different brain locations, which are not taken into consideration by univariate analyses such as voxel-based morphometry (VBM) (Ashburner et al. 2000). Third, it provides a better interpretation of the location of GMC variations than voxel-based approaches (Castro et al., 2014). For example, Gupta et al. reported that two neural circuits, the insula–superior temporal gyrus–inferior frontal gyrus, the superior frontal gyrus–middle frontal, and gyrus–medial frontal gyrus, are altered in patients with schizophrenia and that these alterations are correlated with the clinical symptoms of schizophrenia (Gupta et al., 2017). In previous studies, Kubera et al. adopted SMB to investigate gray matter volume in patients with schizophrenia who had persistent auditory verbal hallucinations (Kubera et al., 2014). Grecucci et al. used SBM to investigate the social deficits in the autistic brain (Grecucci et al., 2016). Wolf et al. used SBM to reveal distinct patterns of aberrant brain volume in delusional infestation (Wolf et al., 2014). These studies based on SBM provide new clues for us to understand the mechanisms of psychotic symptoms from a new perspective. However, in the last years, few studies have investigated the brain surface difference between sporadic Hi-AVHs and healthy individuals without AVHs. Inspired by the work of Kubera et al. (Kubera et al., 2014; Kubera et al., 2018), we aimed to investigate cortical surface differences between Hi-AVHs and Hi without AVHs. In the present study, we utilized SBM to investigate the cortical surface difference between sporadic Hi-AVHs and Healthy individuals without AVHs. We hypothesized that the possible differences can be observed between these two different categories of individuals and that the brain surface differences may be related to the AVHs severity.
2.1. Participants The present study was approved by the Ethics Committee of Tianjin Anding Hospital and Wenzhou Seventh People's Hospital. In this pilot study, we only recruited the Hi-AVHs patients without other mental disorders and without positive family history (in other words, sporadic individuals). Although AVHs in sporadic individuals may be a prodromal symptoms, sporadic individuals still have a lower risk develop psychosis than AVHs-individuals with other mental symptoms. We recruited the participants from our two database: the Tianjin and Wenzhou databases. The Tianjin database, established in 2011 (Yin et al., 2017; Yin et al., 2018), had the full information of 40,000 individuals. Through the Wenzhou database, established in 2015, in this database, we acquired the information of 10,000 individuals (Zhuo et al., 2020). The inclusion criteria were as follows: (1) complete accordance with AVHs criteria established by Johns et al. (“Did you at any time hear voices saying quite a few words or sentences when there was no one around that might account for it?”); (2) absence of other psychotic symptoms as determined using the Structured Clinical Interview for DSM-IV Axis I Disorders-Patient Edition (SCID-I/P) (conducted by two senior psychiatrists with more than 10 years of experience; (3) None personality disorder diagnosed by the two senior psychiatrists according to Structured Clinical Interview For Personality Disorder (SCID)-II(First et al., 1995).(4) IQ ≥ 80; (5) none positive family psychosis history assessed by Interview for Genetic Studies (FIGS), FIGS is a clinician-administered tool that comprises of three parts general screening questions, face sheet, and the symptom checklist. Symptoms checklists help establish the best estimate diagnosis in family members (Díaz de Villalvilla, 1992). (6) right-handed. The exclusion criteria were as follows: (1) IQ < 80, (2) ear diseases, (3) presence of diagnosed mental disorders, such as anxiety, depression, bipolar disorder, schizophrenic spectrum disease, substance abuse, etc.; (4) presence of organic brain diseases, (5) presence of physical and neurological diseases which can influence the mental state, (6) family history of mental illness, (7) history of unconsciousness for more than 5 min due to any cause, (8) contraindications for MRI examination; (9) claustrophobia. Furthermore, each participant was informed of the purpose and process of the experiment in detail and signed the informed consent. From July 2017 to December 2018, a total of 50 sporadic Hi-AVHs (36 male and 14 female) and 50 healthy individuals without were enrolled. 2.2. Assessment of AVHs severity and psychological, and professional function In this study, we used the Auditory Hallucinations Rating Scale (AHRS) (Hoffman et al., 2000) to assess the severity of AVHs. The lowest scores on the social, psychological, and professional functioning domains of the Global Assessment of Functioning (GAF) (American Psychiatric Association, 2003) were used to determine the highest level of functioning over the past year. 2.3. MRI data acquisition MRI was performed using a 3T GE Discovery MR750 scanner (General Electric, Milwaukee, WI, USA) and an eight-channel phasedarray head coil. Every participant was advised to maintain a resting state as much as possible, and the head was fixed with sponges to reduce movement. Routine scan sequences included axial T1WI, T2WI and fluid-attenuated inversion recovery (FLAIR), used to exclude patients with obvious intracranial lesions. The ultimate 3D high resolution T1WI structural image was acquired with a T1W-3D-TFE-ref pulse sequence in the sagittal plane. The main scan parameters were as follows: TR = 7.5 ms, TE = 3.7 ms, flip angle = 8°, voxel size = 1 mm × 1 mm × 1 mm, field of view = 240 mm × 240 mm, 2
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matrix = 232 × 227, number of layers = 150, layer spacing = 0.
Table 1 Sociodemographic characteristics of the two groups .
2.4. Processing of structural MRI data Sex (n)
All the participants completed the MRI scan, however, eight HiAVHs were excluded due to poor image quality. Totally, 92 individuals data were used to perform the analysis. In this process, gray-white matter segmentation and cortical reconstruction were performed using the CAT12 software equipped with the cortical parcellation software FreeSurfer and the Destrieux Atlas (aparc.a2009s). Pre-processing steps included head motion correction and the averaging of the volume data for multiple T1WI, removal of the non-brain structures, gray–white matter segmentation, and spatial normalization using the Gaussian kernel with full-width at half-maximum (FWHM = 8 mm). Sourcebased morphometry analysis was carried out using the GIFT toolbox (http://icatb.sourceforge.net) (Xu et al., 2009). The minimum description length (MDL) principle was used to estimate a number of independent components. MDL found seven reliable ICs. We performed ICA using a neural network algorithm (Infomax) that attempts to minimize the mutual information of the network outputs to identify naturally grouping and maximally independent sources (Bell and Sejnowski, 1995). ICA was repeated 20 times in ICASSO (http:// research.ics.aalto.fi/ica/icasso/) and the resulting components were clustered to ensure the consistency and reliability of the results. The reliability is quantified using a quality index Iq, ranging from 0 to 1 and reflecting the difference between intra-cluster and extra-cluster similarity. All the 7 components extracted from the GM images were found to be associated with an Iq > 0.97 that indicated a highly stable ICA decomposition. SBM converts each gray-matter volume into a vector. As a result, we obtained a matrix where the 92 rows represent the 92 subjects (the first 50 rows represent the controls, while the other 42 the Hi-individuals), and each column indicates a voxel. This matrix was decomposed into two matrices by ICA. The first matrix was named “mixing matrix” and it is composed by a subject per row and an IC per column. Therefore, the mixing matrix indicates how much a subject expresses a given component. For this reason, values in the mixing matrix are called “loading coefficients.” The second matrix was named “source matrix” and it specifies the relation between the Ics and the voxels. As for the gray-matter volume component visualization the source matrix was reshaped back to a three-dimensional image, scaled to unit standard deviations (Z maps) and with a threshold at Z > 2.5.
Age (year) Years of education Duration of AVHs (months) AHRS scores GAF scores
Hi-AVHs
Hi-without AVHs
X2/t
P value
Female (14) Male (28) 23.91 ± 1.45 12.40 ± 3.00 21.50 ± 3.25
Female (14) Male (36) 24.91 ± 2.22 13.55 ± 3.80 N/A
1.075
0.263
1.466 1.235 N/A
0.370 0.250 N/A
32.49 ± 4.82 72.75 ± 7.52
N/A 89.75 ± 2.78
N/A 7.520
N/A 0.012
The fractal dimensions had significantly increased in the left Wernicke's area, the leftBroca's areas, and the left parietal lobe (Figs. 1 and 2). 3.2. Correlation between AHRS scores and gyrification index/fractal dimensions We observed no significant correlations between AHRS scores and gyrification index/fractal dimensions in any of the aforementioned brain regions (P > 0.05). 4. Discussion To the best of our knowledge, the present study is the first to investigate differences in brain surface between Hi-AVHs and Hi-without AVHs. Our findings indicated that, compare to Hi-without AVHs, HiAVHs exhibited differences in the gyrification index mainly in the left superior temporal gyrus, left temporoparietal junction, superior frontal gyrus, and left parietal lobe. The fractal dimensions had significantly increased in the left Wernicke's area, Broca's area, and the parietal lobe. These findings suggest that differences in the complexity of the cerebral cortex may be the brain structural features of sporadic Hi-AVHs. Although our samples are not the patients with schizophrenia, these findings also support the interhemispheric miscommunication theory of auditory verbal hallucinations in schizophrenia (Steinmann et al., 2019). Our findings are partially consistent with and partially contrary to the findings of previous studies regarding cortical complexity differences in patients with schizophrenia experiencing AVHs (Kubera et al., 2014; Cachia et al., 2008; Bohlken et al., 2017; Zmigrod et al., 2016). In these patients, increased gyrification index values have been observed in the precuneus and superior parietal cortex, while decreased values have been observed in the right Broca's homologue and right superior middle frontal cortex (Kubera et al., 2014). In present the pilot study, the increased gyrification index and fractal dimensions were observed only in the brain regions of the left-brain hemisphere. These regions are components of the language, memory, attention, and sensory processing circuits (Kubera et al., 2014; Kubera et al., 2018, Kubera et al., 2013, Bohlken et al., 2017; Løberg et al., 2019; Steinmann et al., 2019). Increased cortical complexity in these circuits may thus represent a brain feature of AVHs in healthy individuals. Although through this pilot study we have found brain surface differences between Hi-AVHs and Hi-without AVHs, including regions involved with language, memory, attention, and sensory processing circuits, our findings are inconsistent with previous studies that focused on investigating the brain features of AVHs in patients with schizophrenia and patients with other prodromal symptoms of psychosis (Baumeister et al., 2017; Kubera et al., 2014; Kubera et al., 2018, Kubera et al., 2013, Bohlken et al., 2017; Løberg et al., 2019). We postulated that the inconsistences may be related to our sample. In this pilot study, we only recruited Hi-AVHs without other mental disorders and without positive family history. These sporadic individuals had a lower of developing psychosis than AVHs-individuals with other mental symptoms (e.g. distress) (Zmigrod et al., 2016; Zhuo et al., 2019).
2.5. Statistical analysis We used the mixing matrix to verify whether the components were differently expressed between the Hi-AVHs and the controls. A twosample t-test, without assuming equal variances (F-test revealed unequal variances), was used to test whether all the Ics are similarity expressed by either of the groups. Similarly, we used the loading coefficients in the mixing matrix to test a linear relation among AHRS scores and the level of components’ expression. All the results had the threshold at p < 0.05 corrected for Family Wise Error (FWE). 3. Results The remaining 42 Hi-AVHs and 50 Hi-without AVHs had no significant difference in age, gender, and educational level, although it deviated from the “well matched” original intention. Thus, we analyzed MRI data for the remaining 42 Hi-AVHs. (Table 1). 3.1. Comparison of gyrification index/fractal dimensions between the two groups We observed a significant increase of the gyrification index was located in the in the left superior temporal gyrus, the left temporoparietal junction, the superior frontal gyrus, and the left parietal lobe. 3
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Fig. 1. Gyrification index differences between Hi-AVHs and Hi. Note : Hi-AVHs, healthy individuals with auditory verbal hallucinations. Hi, Healthy individuals.
Fig. 2. Fractal dimension differences between Hi-AVHs and Hi. Note : Hi-AVHs, healthy individuals with auditory verbal hallucinations. Hi, Healthy individuals.
Psychiatric Association, 2003). These findings indicated that the AVHs may have not caused brain surface differences, just like patients with schizophrenia. Our postulation is tenable, but requires further studies to clarify it.
Hence, the inconsistent differences compared with previous studies may be understandable. However, a long term cohort study is required to clarify the long term brain cortex surface features of these sporadic Hi-AVHs (Sommer et al., 2010). Another notable finding in the present pilot study was that we could not observe the phenomenon in which Hi-AVHs were troubled by their "voices". This phenomenon is also inconsistent with previous studies that reported a substantial amount of non-clinical AVH individuals troubled by their “voices” (Johns et al., 2001; Larøi et al., 2012). Hence, we postulated that it may be related to the sample's special characteristics. Theoretically, since we only recruited sporadic patients, which had a lower risk of developing mental disorders than patients with a positive family history. More notably, the GAF scores of these sporadic Hi-AVHs indicated that their psychological, and professional function were only slightly worse than the healthy individuals (American
4.1. Limitations The present study possesses several limitations of note, including its cross-sectional design, which provides a lower level of evidence than long-term cohort studies. Nonetheless, our finding may provide insight for future investigations. Second, given the potential risk of developing schizophrenia or other psychiatric disorders among Hi-AVHs, future long-term cohort studies should aim to characterize the trajectory of structural brain differences in conjunction with the development of AVHs. Such studies may also help to elucidate the mechanisms 4
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underlying the development of AVHs in healthy individuals.
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4.2. Conclusion Our study observed that the gyrification index and fractal dimensions between healthy individuals with and without auditory verbal hallucinations. The differences in the gyrification index and fractal dimensions are all located on the surface of the left hemisphere. These differences did not correlate to the AVHs symptoms, and the non-hallucinating healthy individuals did not demonstrate corresponding reverse change. Therefore we cannot postulate that the cerebral cortex complexity alterations are related to AVHs. However, our current study provides clues for further studies aimed to investigate the brain features of Hi-AVHs. Funding This work was supported by grants from the National Natural Science Foundation of China (81871052 to C.Z.), the 30,000 yuan (RMB) talent project of Tianjin Anding Hospitatl; the Key Projects of the Natural Science Foundation of Tianjin, China (17JCZDJC35700 to C.Z.), the Tianjin Health Bureau Foundation (2014KR02 to C.Z.), the Zhejiang Public Welfare Fund Project (LGF18H090002 to D.J.), and the key project of Wenzhou Science and Technology Bureau (ZS2017011 to X.L.). Author statement CZ, XL, and HT planned the study. CC, GL, FJ, JP, and LW enrolled the patients. CC, DJ, Xd-L and HT analysed the data and drafted the manuscript. GL, and HT developed the statistical methods and analysed the data. All authors contributed to interpreting the results, critically evaluating the data and writing the manuscript. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psychres.2020.112834. References American Psychiatric Association, 2003. Diagnostic and Statistical Manual of Mental Disorders. Diagnostic and statistical manual of mental disorders. DSM-IV-TR 2nd éd. Paris: Masson. Ashburner, J., Friston, K.J., 2000. Voxel-based morphometry—the methods. Neuroimage 11 (6), 805–821. Baumeister, D., Sedgwick, O., Howes, O., Peters, E., 2017. Auditory verbal hallucinations and continuum models of psychosis: a systematic review of the healthy voice-hearer literature. Clin. Psychol. Rev. 51, 125–141. https://doi.org/10.1016/j.cpr.2016.10. 010. Bell, A.J., Sejnowski, T.J., 1995. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159. Bohlken, M.M., Hugdahl, K., Sommer, I.E., 2017. Auditory verbal hallucinations: neuroimaging and treatment. Psychol. Med. 47, 199–208. https://doi.org/10.1017/ S003329171600115X. Cachia, A., Paillère-Martinot, M.L., Galinowski, A., Januel, D., de Beaurepaire, R., Bellivier, F., Artiges, E., Andoh, J., Bartrés-Faz, D., Duchesnay, E., Rivière, D., Plaze, M., Mangin, J.F., Martinot, J.L., et al., 2008. Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations. Neuroimage 39, 927–935. Castro, E., Gupta, C.N., Martinez-Ramon, M., Calhoun, V.D., Arbabshirani, M.R., Turner, J., 2014. Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging. In: Eduardo (Ed.), Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014. HHS Public Access, pp. 1513–1516. Daalman, K., Diederen, K.M., Hoekema, L., van Lutterveld, R., Sommer, I.E., 2016. Five year follow-up of non-psychotic adults with frequent auditory verbal hallucinations: are they still healthy? Psychol. Med. 46, 1897–1907. https://doi.org/10.1017/ S0033291716000386. Díaz de Villalvilla, T., 1992. NIMH Genetics Initiative. Family Interview for Genetic
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