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1. Refining antipsychotic treatment strategies in schizophrenia: discovery of genetic biomarkers for enhanced drug response prediction

Author

Chen, Luan, Huai, Cong, Song, Chuanfu, Wu, Shaochang, Xu, Yong, Yi, Zhenghui, Tang, Jinsong, Fan, Lingzi, Wu, Xuming, Ge, Zhenhua, Liu, Chuanxin, Jiang, Deguo, Weng, Saizheng, Wang, Guoqiang, Zhang, Xinfeng, Zhao, Xudong, Shen, Lu, Zhang, Na, Wu, Hao, Wang, Yongzhi, Guo, Zhenglin, Zhang, Suli, Jiang, Bixuan, Zhou, Wei, Ma, Jingsong, Li, Mo, Chu, Yunpeng, Zhou, Chenxi, Lv, Qinyu, Xu, Qingqing, Zhu, Wenli, Zhang, Yan, Lian, Weibin, Liu, Sha, Li, Xinrong, Gao, Songyin, Liu, Aihong, He, Lei, Yang, Zhenzhen, Dai, Bojian, Ye, Jiaen, Lin, Ruiqian, Lu, Yana, Yan, Qi, Hu, Yalan, Xing, Qinghe, Huang, Hailiang, and Qin, Shengying

Published
2024
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2. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Author

Zhu, Yinghan, Maikusa, Norihide, Radua, Joaquim, Sämann, Philipp, Fusar-Poli, Paolo, Agartz, Ingrid, Andreassen, Ole, Bachman, Peter, Baeza, Inmaculada, Chen, Xiaogang, Choi, Sunah, Corcoran, Cheryl, Ebdrup, Bjørn, Fortea, Adriana, Garani, Ranjini, Glenthøj, Birte, Glenthøj, Louise, Haas, Shalaila, Hamilton, Holly, Hayes, Rebecca, He, Ying, Heekeren, Karsten, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kristensen, Tina, Kwon, Jun, Lawrie, Stephen, Lebedeva, Irina, Lee, Jimmy, Loewy, Rachel, Mathalon, Daniel, McGuire, Philip, Mizrahi, Romina, Mizuno, Masafumi, Møller, Paul, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria, Raghava, Jayachandra, Røssberg, Jan, Rössler, Wulf, Salisbury, Dean, Sasabayashi, Daiki, Smigielski, Lukasz, Sugranyes, Gisela, Takahashi, Tsutomu, Tamnes, Christian, Tang, Jinsong, Theodoridou, Anastasia, Tomyshev, Alexander, Uhlhaas, Peter, Værnes, Tor, van Amelsvoort, Therese, Waltz, James, Westlye, Lars, Zhou, Juan, Thompson, Paul, Hernaus, Dennis, Jalbrzikowski, Maria, and Koike, Shinsuke

Subjects
Humans, Psychotic Disorders, Magnetic Resonance Imaging, Male, Female, Brain, Neuroimaging, Adult, Young Adult, Machine Learning, Adolescent, Prodromal Symptoms
Abstract

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Published
2024

8. Correction: The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Author

Bruin, Willem B, Abe, Yoshinari, Alonso, Pino, Anticevic, Alan, Backhausen, Lea L, Balachander, Srinivas, Bargallo, Nuria, Batistuzzo, Marcelo C, Benedetti, Francesco, Bertolin Triquell, Sara, Brem, Silvia, Calesella, Federico, Couto, Beatriz, Denys, Damiaan AJP, Echevarria, Marco AN, Eng, Goi Khia, Ferreira, Sónia, Feusner, Jamie D, Grazioplene, Rachael G, Gruner, Patricia, Guo, Joyce Y, Hagen, Kristen, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo Q, Jahanshad, Neda, Jaspers-Fayer, Fern, Kasprzak, Selina, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun Soo, Lazaro, Luisa, Li, Chiang-Shan R, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Menchon, Jose M, Moreira, Pedro S, Morgado, Pedro, Nakagawa, Akiko, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, Zorrilla, Jose C Pariente, Piacentini, John, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Janardhan YC, Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Blair H, Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily R, Evelyn Stewart, S, Szeszko, Philip R, Tang, Jinsong, Thomopoulos, Sophia I, Thorsen, Anders L, Yoshida, Tokiko, Tomiyama, Hirofumi, Vai, Benedetta, Veer, Ilya M, Venkatasubramanian, Ganesan, Vetter, Nora C, Vriend, Chris, Walitza, Susanne, Waller, Lea, Wang, Zhen, Watanabe, Anri, Wolff, Nicole, Yun, Je-Yeon, Zhao, Qing, van Leeuwen, Wieke A, van Marle, Hein JF, van de Mortel, Laurens A, van der Straten, Anouk, van der Werf, Ysbrand D, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, and van Wingen, Guido A

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Machine Learning and Artificial Intelligence, Mental Illness, Serious Mental Illness, Brain Disorders, Anxiety Disorders, Mental Health, Good Health and Well Being, ENIGMA-OCD Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Correction to: Molecular Psychiatry, published online 2 May 2023 In this article Honami Arai, Irene Bollettini, Rosa Calvo Escalona, Ana Coelho, Federica Colombo, Leila Darwich, Martine Fontaine, Toshikazu Ikuta, Jonathan C. Ipser, Asier Juaneda-Seguí, Hitomi Kitagawa, Gerd Kvale, Mafalda Machado-Sousa, Astrid Morer, Takashi Nakamae, Jin Narumoto, Joseph O’Neill, Sho Okawa, Eva Real, Veit Roessner, Joao R. Sato, Cinto Segalàs, Roseli G. Shavitt, Dick J. Veltman, Kei Yamada were missing from the author list indexed under the ENIGMA-OCD Working Group. Additionally, there was an error regarding Tokiko Yoshida’s name, where the first name and last name were written in the wrong order. The original article has been corrected.

Published
2023

9. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Author

Bruin, Willem, Abe, Yoshinari, Alonso, Pino, Anticevic, Alan, Backhausen, Lea, Balachander, Srinivas, Bargallo, Nuria, Batistuzzo, Marcelo, Benedetti, Francesco, Bertolin Triquell, Sara, Brem, Silvia, Calesella, Federico, Couto, Beatriz, Denys, Damiaan, Echevarria, Marco, Eng, Goi, Ferreira, Sónia, Feusner, Jamie, Grazioplene, Rachael, Gruner, Patricia, Guo, Joyce, Hagen, Kristen, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo, Jahanshad, Neda, Jaspers-Fayer, Fern, Kasprzak, Selina, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun, Lazaro, Luisa, Li, Chiang-Shan, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Menchon, Jose, Moreira, Pedro, Morgado, Pedro, Nakagawa, Akiko, Nakao, Tomohiro, Narayanaswamy, Janardhanan, Nurmi, Erika, Zorrilla, Jose, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Janardhan, Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Blair, Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily, Evelyn Stewart, S, Szeszko, Philip, Tang, Jinsong, Thomopoulos, Sophia, Thorsen, Anders, Yoshida, Tokiko, Tomiyama, Hirofumi, Vai, Benedetta, Veer, Ilya, Venkatasubramanian, Ganesan, Vetter, Nora, Vriend, Chris, Walitza, Susanne, Waller, Lea, Wang, Zhen, Watanabe, Anri, Wolff, Nicole, Yun, Je-Yeon, Zhao, Qing, van Leeuwen, Wieke, van Marle, Hein, van de Mortel, Laurens, van der Straten, Anouk, van der Werf, Ysbrand, Thompson, Paul, Stein, Dan, van den Heuvel, Odile, van Wingen, Guido, and Piacentini, John

Subjects
Humans, Connectome, Brain Mapping, Magnetic Resonance Imaging, Brain, Obsessive-Compulsive Disorder, Biomarkers, Neural Pathways
Abstract

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohens d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohens d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Published
2023

10. Widespread reductions in cortical thickness following ketamine abuse

Author

Tang, Jinsong, Wu, Qiuxia, Qi, Chang, Xie, An, Liu, Jianbin, Sun, Yunkai, Yuan, Tifei, Chen, Wei, Liu, Tieqiao, Hao, Wei, Shao, Xu, and Liao, Yanhui

Subjects
Brain -- Analysis, Ketamine -- Complications and side effects -- Patient outcomes, Mental illness -- Risk factors, Health, Psychology and mental health
Abstract

Background: Esketamine is a version of ketamine that has been approved for treatment-resistant depression, but our previous studies showed a link between non-medical use of ketamine and brain structural and functional alterations, including dorsal prefrontal grey matter reduction among chronic ketamine users. In this study, we sought to determine cortical thickness abnormalities following long-term, non-medical use of ketamine. Methods: We acquired structural brain images for patients with ketamine use disorder and drug-free healthy controls. We used FreeSurfer software to measure cortical thickness for 68 brain regions. We compared cortical thickness between the 2 groups using analysis of covariance with covariates of age, gender, educational level, smoking, drinking, and whole-brain mean cortical thickness. Results: We included images from 95 patients with ketamine use disorder and 169 controls. Compared with healthy controls, patients with ketamine use disorder had widespread decreased cortical thickness, with the most extensive reductions in the frontal (including the dorsolateral prefrontal cortex) and parietal (including the precuneus) lobes. Increased cortical thickness was not observed among ketamine users relative to comparison participants. Estimated total lifetime ketamine consumption was correlated with reductions in the right inferior parietal and the right rostral middle frontal cortical thickness. Limitations: We conducted a retrospective cross-sectional study, but longitudinal studies are needed to further validate decreased cortical thickness after nonmedical use of ketamine. Conclusion: This study provided evidence that, compared with healthy controls, chronic ketamine users have widespread reductions in cortical thickness. Our study underscores the importance of the long-term effects of ketamine on brain structure and serves as a reference for the antidepressant use of ketamine., Introduction Although ketamine--a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist--is valued for its clinical applications in anesthesia and pain management, and esketamine (the S-enantiomer of ketamine) has begun to be used for [...]

Published
2024
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15. Higher glutamatergic activity in the medial prefrontal cortex in chronic ketamine users

Author

Wu, Qiuxia, Tang, Jinsong, Qi, Chang, Xie, An, Liu, Jianbin, O'Neill, Joseph, Liu, Tieqiao, Hao, Wei, and Liao, Yanhui

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Depression, Neurosciences, Mental Illness, Brain Disorders, Mental Health, Aspartic Acid, Creatine, Cross-Sectional Studies, Glutamic Acid, Glutamine, Humans, Inositol, Ketamine, Prefrontal Cortex, Clinical Sciences, Cognitive Sciences, Psychiatry, Clinical sciences, Clinical and health psychology
Abstract

BackgroundThe medial prefrontal cortex (mPFC) plays an important role in depression and addiction. Previous studies have shown alterations in glutamatergic activity in the mPFC following the administration of ketamine in patients with depression and healthy controls. However, it remains unclear whether chronic, nonmedical use of ketamine affects metabolites in the mPFC.MethodsUsing proton magnetic resonance spectroscopy, we measured metabolites (glutamate and glutamine [Glx]; phosphocreatine and creatine [PCr+Cr]; myo-inositol; N-acetyl-aspartate; and glycerophosphocholine and phosphocholine [GPC+PC]) in the mPFC of chronic ketamine users (n = 20) and healthy controls (n = 43). Among ketamine users, 60% consumed ketamine once per day or more, 10% consumed it every 2 days and 30% consumed it every 3 or more days. Using analysis of covariance, we evaluated between-group differences in the ratios of Glx:PCr+Cr, myo-inositol:PCr+Cr, N-acetyl-aspartate:PCr+Cr and GPC+PC:PCr+Cr.ResultsChronic ketamine users showed significantly higher Glx:PCr+Cr ratios than healthy controls (median 1.05 v. 0.95, p = 0.008). We found no significant differences in myoinositol:PCr+Cr, N-acetyl-aspartate:PCr+Cr or GPC+PC:PCr+Cr ratios between the 2 groups. We found a positive relationship between N-acetyl-aspartate:PCr+Cr and Glx:PCr+Cr ratios in the healthy control group (R = 0.345, p = 0.023), but the ketamine use group failed to show such an association (ρ = 0.197, p = 0.40).LimitationsThe cross-sectional design of this study did not permit causal inferences related to higher Glx:PCr+Cr ratios and chronic ketamine use.ConclusionThis study provides the first evidence that chronic ketamine users have higher glutamatergic activity in the mPFC than healthy controls; this finding may provide new insights relevant to the treatment of depression with ketamine.

Published
2022

20. Metabolite differences in the medial prefrontal cortex in schizophrenia patients with and without persistent auditory verbal hallucinations: a 1H MRS study

Author

Wang, Qianjin, Ren, Honghong, Li, Chunwang, Li, Zongchang, Li, Jinguang, Li, Hong, Dai, Lulin, Dong, Min, Zhou, Jun, He, Jingqi, O’Neill, Joseph, Liao, Yanhui, He, Ying, Liu, Tieqiao, Chen, Xiaogang, and Tang, Jinsong

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Neurosciences, Mental Health, Mental Illness, Brain Disorders, Serious Mental Illness, Clinical Research, Schizophrenia, 2.1 Biological and endogenous factors, Aspartic Acid, Hallucinations, Humans, Prefrontal Cortex, Proton Magnetic Resonance Spectroscopy, Clinical Sciences, Public Health and Health Services, Clinical sciences, Biological psychology
Abstract

Studies of schizophrenia (SCZ) have associated auditory verbal hallucinations (AVH) with structural and functional abnormalities in frontal cortex, especially medial prefrontal cortex (mPFC). Although abnormal prefrontal network connectivity associated with language production has been studied extensively, the relationship between mPFC dysfunction (highly relevant to the pathophysiology of SCZ) and AVH has been rarely investigated. In this study, proton magnetic resonance spectroscopy was used to measure metabolite levels in the mPFC in 61 SCZ patients with persistent AVH (pAVH), 53 SCZ patients without AVH (non-AVH), and 59 healthy controls (HC). The pAVH group showed significantly lower levels of N-acetyl-aspartate + N-acetyl-aspartyl-glutamate (tNAA) and glutamate + glutamine (Glx), compared with the non-AVH (tNAA: p = 0.022, Glx: p = 0.012) and HC (tNAA: p = 0.001, Glx: p = 0.001) groups. No difference was found in the levels of tNAA and Glx between non-AVH and HC. The levels of tNAA and Glx in the mPFC was negatively correlated with the severity of pAVH (tNAA: r = -0.24, p = 0.014; Glx: r = -0.30, p = 0.002). In conclusion, pAVH in SCZ patients might be related to decreased levels of tNAA and Glx in the mPFC, indicating that tNAA or Glx might play a key role in the pathogenesis of pAVH.

Published
2022

21. Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis

Author

Baldwin, Helen, Radua, Joaquim, Antoniades, Mathilde, Haas, Shalaila S, Frangou, Sophia, Agartz, Ingrid, Allen, Paul, Andreassen, Ole A, Atkinson, Kimberley, Bachman, Peter, Baeza, Inmaculada, Bartholomeusz, Cali F, Chee, Michael WL, Colibazzi, Tiziano, Cooper, Rebecca E, Corcoran, Cheryl M, Cropley, Vanessa L, Ebdrup, Bjørn H, Fortea, Adriana, Glenthøj, Louise Birkedal, Hamilton, Holly K, Haut, Kristen M, Hayes, Rebecca A, He, Ying, Heekeren, Karsten, Kaess, Michael, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kindler, Jochen, Klaunig, Mallory J, Koike, Shinsuke, Koppel, Alex, Kristensen, Tina D, Bin Kwak, Yoo, Kwon, Jun Soo, Lawrie, Stephen M, Lebedeva, Irina, Lee, Jimmy, Lin, Ashleigh, Loewy, Rachel L, Mathalon, Daniel H, Michel, Chantal, Mizrahi, Romina, Møller, Paul, Nelson, Barnaby, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria A, Pantelis, Christos, Raghava, Jayachandra M, Røssberg, Jan I, Rössler, Wulf, Salisbury, Dean F, Sasabayashi, Daiki, Schall, Ulrich, Smigielski, Lukasz, Sugranyes, Gisela, Suzuki, Michio, Takahashi, Tsutomu, Tamnes, Christian K, Tang, Jinsong, Theodoridou, Anastasia, Thomopoulos, Sophia I, Tomyshev, Alexander S, Uhlhaas, Peter J, Værnes, Tor G, van Amelsvoort, Therese AMJ, Van Erp, Theo GM, Waltz, James A, Westlye, Lars T, Wood, Stephen J, Zhou, Juan H, McGuire, Philip, Thompson, Paul M, Jalbrzikowski, Maria, Hernaus, Dennis, and Fusar-Poli, Paolo

Subjects
Serious Mental Illness, Prevention, Biomedical Imaging, Mental Health, Clinical Research, Neurosciences, Brain, Humans, Magnetic Resonance Imaging, Psychotic Disorders, ENIGMA Clinical High Risk for Psychosis Working Group, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.

Published
2022

24. Gender differences in alcohol abuse/dependence among medical undergraduates during the post-COVID‑19 pandemic period (October 20, 2020-April 5, 2021) in China

Author

Wang, Xin, Peng, Pu, Liu, Yueheng, Yang, Winson Fuzun, Chen, Shubao, Wang, Yunfei, Yang, Qian, Li, Manyun, Wang, Yingying, Hao, Yuzhu, He, Li, Wang, Qianjin, Zhang, Junhong, Ma, Yuejiao, He, Haoyu, Zhou, Yanan, Long, Jiang, Qi, Chang, Tang, Yi-Yuan, Liao, Yanhui, Tang, Jinsong, Wu, Qiuxia, and Liu, Tieqiao

Published
2023
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35. Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Author

Ripke, Stephan, Neale, Benjamin M., Corvin, Aiden, Walters, James T.R., Farh, Kai-How, Holmans, Peter A., Lee, Phil, Bulik-Sullivan, Brendan, Collier, David A., Huang, Hailiang, Pers, Tune H., Agartz, Ingrid, Agerbo, Esben, Albus, Margot, Alexander, Madeline, Amin, Farooq, Bacanu, Silviu A., Begemann, Martin, Belliveau, Richard A., Jr., Bene, Judit, Bergen, Sarah E., Bevilacqua, Elizabeth, Bigdeli, Tim B., Black, Donald W., Bruggeman, Richard, Buccola, Nancy G., Buckner, Randy L., Byerley, William, Cahn, Wiepke, Cai, Guiqing, Campion, Dominique, Cantor, Rita M., Carr, Vaughan J., Carrera, Noa, Catts, Stanley V., Chambert, Kimberley D., Chan, Raymond C.K., Chan, Ronald Y.L., Chen, Eric Y.H., Cheng, Wei, Cheung, Eric FC., Chong, Siow Ann, Cloninger, C. Robert, Cohen, David, Cohen, Nadine, Cormican, Paul, Craddock, Nick, Crowley, James J., Curtis, David, Davidson, Michael, Davis, Kenneth L., Degenhardt, Franziska, Del Favero, Jurgen, Demontis, Ditte, Dikeos, Dimitris, Dinan, Timothy, Djurovic, Srdjan, Donohoe, Gary, Drapeau, Elodie, Duan, Jubao, Dudbridge, Frank, Durmishi, Naser, Eichhammer, Peter, Eriksson, Johan, Escott-Price, Valentina, Essioux, Laurent, Fanous, Ayman H., Farrell, Martilias S., Frank, Josef, Franke, Lude, Freedman, Robert, Freimer, Nelson B., Friedl, Marion, Friedman, Joseph I., Fromer, Menachem, Genovese, Giulio, Georgieva, Lyudmila, Giegling, Ina, Giusti-Rodríguez, Paola, Godard, Stephanie, Goldstein, Jacqueline I., Golimbet, Vera, Gopal, Srihari, Gratten, Jacob, de Haan, Lieuwe, Hammer, Christian, Hamshere, Marian L., Hansen, Mark, Hansen, Thomas, Haroutunian, Vahram, Hartmann, Annette M., Henskens, Frans A., Herms, Stefan, Hirschhorn, Joel N., Hoffmann, Per, Hofman, Andrea, Hollegaard, Mads V., Hougaard, David M., Ikeda, Masashi, Joa, Inge, Julià, Antonio, Kahn, René S., Kalaydjieva, Luba, Karachanak-Yankova, Sena, Karjalainen, Juha, Kavanagh, David, Keller, Matthew C., Kennedy, James L., Khrunin, Andrey, Kim, Yunjung, Klovins, Janis, Knowles, James A., Konte, Bettina, Kucinskas, Vaidutis, Kucinskiene, Zita Ausrele, Kuzelova-Ptackova, Hana, Kähler, Anna K., Laurent, Claudine, Lee, Jimmy, Lee, S. Hong, Legge, Sophie E., Lerer, Bernard, Li, Miaoxin, Li, Tao, Liang, Kung-Yee, Lieberman, Jeffrey, Limborska, Svetlana, Loughland, Carmel M., Lubinski, Jan, Lönnqvist, Jouko, Macek, Milan, Magnusson, Patrik K.E., Maher, Brion S., Maier, Wolfgang, Mallet, Jacques, Marsal, Sara, Mattheisen, Manuel, Mattingsdal, Morten, McCarley, Robert W., McDonald, Colm, McIntosh, Andrew M., Meier, Sandra, Meijer, Carin J., Melegh, Bela, Melle, Ingrid, Mesholam-Gately, Raquelle I., Metspalu, Andres, Michie, Patricia T., Milani, Lili, Milanova, Vihra, Mokrab, Younes, Morris, Derek W., Mors, Ole, Murphy, Kieran C., Murray, Robin M., Myin-Germeys, Inez, Müller-Myhsok, Bertram, Nelis, Mari, Nenadic, Igor, Nertney, Deborah A., Nestadt, Gerald, Nicodemus, Kristin K., Nikitina-Zake, Liene, Nisenbaum, Laura, Nordin, Annelie, O'Callaghan, Eadbhard, O'Dushlaine, Colm, O'Neill, F. Anthony, Oh, Sang-Yun, Olincy, Ann, Olsen, Line, Van Os, Jim, Pantelis, Christos, Papadimitriou, George N., Papiol, Sergi, Parkhomenko, Elena, Pato, Michele T., Paunio, Tiina, Pejovic-Milovancevic, Milica, Perkins, Diana O., Pietiläinen, Olli, Pimm, Jonathan, Pocklington, Andrew J., Powell, John, Price, Alkes, Pulver, Ann E., Purcell, Shaun M., Quested, Digby, Rasmussen, Henrik B., Reichenberg, Abraham, Reimers, Mark A., Richards, Alexander L., Roffman, Joshua L., Roussos, Panos, Ruderfer, Douglas M., Salomaa, Veikko, Sanders, Alan R., Schall, Ulrich, Schubert, Christian R., Schulze, Thomas G., Schwab, Sibylle G., Scolnick, Edward M., Scott, Rodney J., Seidman, Larry J., Shi, Jianxin, Sigurdsson, Engilbert, Silagadze, Teimuraz, Silverman, Jeremy M., Sim, Kang, Slominsky, Petr, Smoller, Jordan W., So, Hon-Cheong, Spencer, Chris C.A., Stahl, Eli A., Stefansson, Hreinn, Steinberg, Stacy, Stogmann, Elisabeth, Straub, Richard E., Strengman, Eric, Strohmaier, Jana, Stroup, T Scott, Subramaniam, Mythily, Suvisaari, Jaana, Svrakic, Dragan M., Szatkiewicz, Jin P., Söderman, Erik, Thirumalai, Srinivas, Toncheva, Draga, Tosato, Sarah, Veijola, Juha, Waddington, John, Walsh, Dermot, Wang, Dai, Wang, Qiang, Webb, Bradley T., Weiser, Mark, Wildenauer, Dieter B., Williams, Nigel M., Williams, Stephanie, Witt, Stephanie H., Wolen, Aaron R., Wong, Emily H.M., Wormley, Brandon K., Xi, Hualin Simon, Zai, Clement C., Zheng, Xuebin, Zimprich, Fritz, Wray, Naomi R., Stefansson, Kari, Visscher, Peter M., Adolfsson, Rolf, Andreassen, Ole A., Blackwood, Douglas H.R., Bramon, Elvira, Buxbaum, Joseph D., Børglum, Anders D., Cichon, Sven, Darvasi, Ariel, Domenici, Enrico, Ehrenreich, Hannelore, Esko, Tõnu, Gejman, Pablo V., Gill, Michael, Gurling, Hugh, Hultman, Christina M., Iwata, Nakao, Jablensky, Assen V., Jönsson, Erik G., Kendler, Kenneth S., Kirov, George, Knight, Jo, Lencz, Todd, Levinson, Douglas F., Li, Qingqin S., Liu, Jianjun, Malhotra, Anil K., McCarroll, Steven A., McQuillin, Andrew, Moran, Jennifer L., Mortensen, Preben B., Mowry, Bryan J., Nöthen, Markus M., Ophoff, Roel A., Owen, Michael J., Palotie, Aarno, Pato, Carlos N., Petryshen, Tracey L., Posthuma, Danielle, Rietschel, Marcella, Riley, Brien P., Rujescu, Dan, Sham, Pak C., Sklar, Pamela, St Clair, David, Weinberger, Daniel R., Wendland, Jens R., Werge, Thomas, Daly, Mark J., Sullivan, Patrick F., O'Donovan, Michael C., Qin, Shengying, Sawa, Akira, Kahn, Rene, Hong, Kyung Sue, Shi, Wenzhao, Tsuang, Ming, Itokawa, Masanari, Feng, Gang, Glatt, Stephen J., Ma, Xiancang, Tang, Jinsong, Ruan, Yunfeng, Liu, Ruize, Zhu, Feng, Horiuchi, Yasue, Lee, Byung Dae, Joo, Eun-Jeong, Myung, Woojae, Ha, Kyooseob, Won, Hong-Hee, Baek, Ji Hyung, Chung, Young Chul, Kim, Sung-Wan, Kusumawardhani, Agung, Chen, Wei J., Hwu, Hai-Gwo, Hishimoto, Akitoyo, Otsuka, Ikuo, Sora, Ichiro, Toyota, Tomoko, Yoshikawa, Takeo, Kunugi, Hiroshi, Hattori, Kotaro, Ishiwata, Sayuri, Numata, Shusuke, Ohmori, Tetsuro, Arai, Makoto, Ozeki, Yuji, Fujii, Kumiko, Kim, Se Joo, Lee, Heon-Jeong, Ahn, Yong Min, Kim, Se Hyun, Akiyama, Kazufumi, Shimoda, Kazutaka, Kinoshita, Makoto, Hsu, Yu-Han H., Pintacuda, Greta, Nacu, Eugeniu, Kim, April, Tsafou, Kalliopi, Petrossian, Natalie, Crotty, William, Suh, Jung Min, Riseman, Jackson, Martin, Jacqueline M., Biagini, Julia C., Mena, Daya, Ching, Joshua K.T., Malolepsza, Edyta, Li, Taibo, Singh, Tarjinder, Ge, Tian, Egri, Shawn B., Tanenbaum, Benjamin, Stanclift, Caroline R., Apffel, Annie M., Carr, Steven A., Schenone, Monica, Jaffe, Jake, Fornelos, Nadine, Eggan, Kevin C., and Lage, Kasper

Published
2023
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36. Right Prefrontal Cortical Thickness Is Associated With Response to Cognitive-Behavioral Therapy in Children With Obsessive-Compulsive Disorder

Author

Real, Eva, Segalas, Cinto, Morer, Astrid, Brem, Silvia, Ferreira, Sonia, Moreira, Pedro Silva, Hagen, Kristen, Hamatani, Sayo, Takahashi, Jumpei, Yoshida, Tokiko, de Mathis, Maria Alice, Miguel, Euripedes C., Pariente, Jose C., Tang, Jinsong, Bertolín, Sara, Alonso, Pino, Martínez-Zalacaín, Ignacio, Menchón, Jose M., Jimenez-Murcia, Susana, Baker, Justin T., Bargalló, Nuria, Batistuzzo, Marcelo Camargo, Boedhoe, Premika S.W., Brennan, Brian P., Feusner, Jamie D., Fitzgerald, Kate D., Fontaine, Martine, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo Q., Huyser, Chaim, Jahanshad, Neda, Jaspers-Fayer, Fern, Kuno, Masaru, Kvale, Gerd, Lazaro, Luisa, Machado-Sousa, Mafalda, Marsh, Rachel, Morgado, Pedro, Nakagawa, Akiko, Norman, Luke, Nurmi, Erika L., O’Neill, Joseph, Ortiz, Ana E., Perriello, Chris, Piacentini, John, Picó-Pérez, Maria, Shavitt, Roseli G., Shimizu, Eiji, Simpson, Helen Blair, Stewart, S. Evelyn, Thomopoulos, Sophia I., Thorsen, Anders Lillevik, Walitza, Susanne, Wolters, Lidewij H., Thompson, Paul M., van den Heuvel, Odile A., Stein, Dan J., and Soriano-Mas, Carles

Published
2023
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40. The prevalence and clinical correlates of anxiety in Chinese patients with first-episode and drug-naïve major depressive disorder at different ages of onset

Author

Wang, Xin, Wang, Qianjin, Ren, Honghong, Wang, Xuyi, Tang, Jinsong, Liao, Yanhui, Wu, Qiuxia, Liu, Yueheng, Chen, Shubao, Zhou, Yanan, Hao, Yuzhu, Ma, Yuejiao, He, Li, Wang, Yunfei, Li, Manyun, Zhang, Junhong, Yang, Qian, Peng, Pu, Xu, Huixue, He, Haoyu, Wang, Yingying, Long, Jiang, Liu, Tieqiao, and Zhang, Xiang-Yang

Published
2023
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41. Mental health conditions and academic burnout among medical and non-medical undergraduates during the mitigation of COVID-19 pandemic in China

Author

Yang, Qian, Liu, Yueheng, Yang, Winson Fuzun, Peng, Pu, Chen, Shubao, Wang, Yunfei, Wang, Xin, Li, Manyun, Wang, Yingying, Hao, Yuzhu, He, Li, Wang, Qianjin, Zhang, Junhong, Ma, Yuejiao, He, Haoyu, Zhou, Yanan, Long, Jiang, Qi, Chang, Tang, Yi-Yuan, Liao, Yanhui, Tang, Jinsong, Wu, Qiuxia, and Liu, Tieqiao

Published
2022
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43. N-Acetyl and Glutamatergic Neurometabolites in Perisylvian Brain Regions of Methamphetamine Users

Author

Tang, Jinsong, O’Neill, Joseph, Alger, Jeffry R, Shen, Zhiwei, Johnson, Maritza C, and London, Edythe D

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Substance Misuse, Brain Disorders, Depression, Clinical Research, Methamphetamine, Biomedical Imaging, Neurosciences, Mental Health, Mental health, Adolescent, Adult, Amphetamine-Related Disorders, Anxiety, Aspartic Acid, Central Nervous System Stimulants, Cerebral Cortex, Cross-Sectional Studies, Female, Glutamic Acid, Glutamine, Humans, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male, Middle Aged, Young Adult, methamphetamine, N-acetyl-aspartate, glutamate, abstinence, depression, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundMethamphetamine induces neuronal N-acetyl-aspartate synthesis in preclinical studies. In a preliminary human proton magnetic resonance spectroscopic imaging investigation, we also observed that N-acetyl-aspartate+N-acetyl-aspartyl-glutamate in right inferior frontal cortex correlated with years of heavy methamphetamine abuse. In the same brain region, glutamate+glutamine is lower in methamphetamine users than in controls and is negatively correlated with depression. N-acetyl and glutamatergic neurochemistries therefore merit further investigation in methamphetamine abuse and the associated mood symptoms.MethodsMagnetic resonance spectroscopic imaging was used to measure N-acetyl-aspartate+N-acetyl-aspartyl-glutamate and glutamate+glutamine in bilateral inferior frontal cortex and insula, a neighboring perisylvian region affected by methamphetamine, of 45 abstinent methamphetamine-dependent and 45 healthy control participants. Regional neurometabolite levels were tested for group differences and associations with duration of heavy methamphetamine use, depressive symptoms, and state anxiety.ResultsIn right inferior frontal cortex, N-acetyl-aspartate+N-acetyl-aspartyl-glutamate correlated with years of heavy methamphetamine use (r = +0.45); glutamate+glutamine was lower in methamphetamine users than in controls (9.3%) and correlated negatively with depressive symptoms (r = -0.44). In left insula, N-acetyl-aspartate+N-acetyl-aspartyl-glutamate was 9.1% higher in methamphetamine users than controls. In right insula, glutamate+glutamine was 12.3% lower in methamphetamine users than controls and correlated negatively with depressive symptoms (r = -0.51) and state anxiety (r = -0.47).ConclusionsThe inferior frontal cortex and insula show methamphetamine-related abnormalities, consistent with prior observations of increased cortical N-acetyl-aspartate in methamphetamine-exposed animal models and associations between cortical glutamate and mood in human methamphetamine users.

Published
2019

47. Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa.

Author

Wu, Yuezhong, Xie, Huan, Gu, Lin, Chen, Rongrong, Chen, Shanshan, Wang, Fanglan, Liu, Yiwen, Chen, Lingjiao, and Tang, Jinsong

Subjects
MENTAL health services, NATURAL language processing, FINITE state machines, ARTIFICIAL intelligence, RESOURCE allocation
Abstract

As mental health issues become increasingly prominent, we are now facing challenges such as the severe unequal distribution of medical resources and low diagnostic efficiency. This paper integrates finite state machines, retrieval algorithms, semantic-matching models, and medical-knowledge graphs to design an innovative intelligent auxiliary evaluation tool and a personalized medical-advice generation application, aiming to improve the efficiency of mental health assessments and the provision of personalized medical advice. The main contributions include the folowing: (1) Developing an auxiliary diagnostic tool that combines the Mini-International Neuropsychiatric Interview (M.I.N.I.) with finite state machines to systematically collect patient information for preliminary assessments; (2) Enhancing data processing by optimizing retrieval algorithms for efficient filtering and employing a fine-tuned RoBERTa model for deep semantic matching and analysis, ensuring accurate and personalized medical-advice generation; (3) Generating intelligent suggestions using NLP techniques; when semantic matching falls below a specific threshold, integrating medical-knowledge graphs to produce general medical advice. Experimental results show that this application achieves a semantic-matching degree of 0.9 and an accuracy of 0.87, significantly improving assessment accuracy and the ability to generate personalized medical advice. This optimizes the allocation of medical resources, enhances diagnostic efficiency, and provides a reference for advancing mental health care through artificial-intelligence technology. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Grouting Reinforcement Measures for a Shield Tunnel Undercrossing Foundation of a Pile Group.

Author

Tang, Jinsong, Bi, Yingqian, and Zhang, Bao

Subjects
*BUILDING foundations, *GROUTING, *COMPUTER simulation, *EXCAVATION, *ENGINEERING
Abstract

Aiming at the condition of shield tunnel undercrossing the foundation of a pile group directly, using the settlement and inclination of the pile caps as the evaluation index, the reinforcing effect of the pregrouting of the soil around the pile foundation, the deep hole grouting behind the tunnel segment, and the combination of the two reinforcement methods are analyzed through three-dimensional numerical simulation. A reasonable reinforcement scheme is found and applied to practical engineering. The results show that for the pregrouting reinforcement of the soil around the pile, the optimal reinforcement scheme is to extend 0.5 m around the pile cap in the plane and vertically from the surroundings of the pile cap to 1/3 of the pile length below the bottom of the cap. When deep hole grouting reinforcement is adopted, larger reinforcement range and a certain range of radial reinforcement radius of tunnel section will result in a better effect of reinforcement. The combination of the two schemes is better than the single use of one. The practical application results show that the combined reinforcement scheme can effectively control the influence of shield tunnel excavation on the pile foundation of an elevated waiting hall. [ABSTRACT FROM AUTHOR]

Published
2024
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50. The thalamus and its subnuclei—a gateway to obsessive-compulsive disorder

Author

Weeland, Cees J., Kasprzak, Selina, de Joode, Niels T., Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H., Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargallo, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Beucke, Jan C., Bollettini, Irene, Brecke, Vilde, Brem, Silvia, Cappi, Carolina, Cheng, Yuqi, Cho, Kang Ik K., Costa, Daniel L. C., Dallaspezia, Sara, Denys, Damiaan, Eng, Goi Khia, Ferreira, Sónia, Feusner, Jamie D., Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael G., Gruner, Patricia, He, Mengxin, Hirano, Yoshiyuki, Hoexter, Marcelo Q., Huyser, Chaim, Hu, Hao, Jaspers-Fayer, Fern, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun Soo, Lazaro, Luisa, Li, Chiang-shan R., Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, Jose M., Minnuzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Narayanaswamy, Janardhanan C., Nurmi, Erika L., Ortiz, Ana E., Pariente, Jose C., Piacentini, John, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Y. C. Janardhan, Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Helen Blair, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily R., Stevens, Michael C., Stewart, S. Evelyn, Szeszko, Philip R., Takahashi, Jumpei, Tanamatis, Tais, Tang, Jinsong, Thorsen, Anders Lillevik, Tolin, David, van der Werf, Ysbrand D., van Marle, Hein, van Wingen, Guido A., Vecchio, Daniela, Venkatasubramanian, G., Walitza, Susanne, Wang, Jicai, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij H., Xu, Xiufeng, Yun, Je-Yeon, Zhao, Qing, White, Tonya, Thompson, Paul M., Stein, Dan J., van den Heuvel, Odile A., and Vriend, Chris

Published
2022
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