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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yansen Su Xingyi Zhang Linqiang Pan Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509016129

Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in cells. One might expect the high level of co-appearance or repression of pathways to distinguish different subtypes of NSCLC. However, it is difficult to detect coordinated regulations of pathways by existing methods. In our work, the coordinated regulations of pathways are detected using modified higher logic analysis of gene expression data. Specifically, we identify the genes whose regulation obeys a logic function by the modified higher logic analysis, which focuses on the relationships among the gene triplets that are not evident when genes are examined in a pairwise fashion. Then, the relationships among genes are mapped to pathways to predict the coordinated regulated relationships among pathways. By comparing coordinated regulations of pathways, we find that the regulation patterns of pathways which are associated with cell death are different in three subtypes of NSCLC. This method allows us to uncover co-appearance or repression of pathways in high level, and it has a potential to distinguish the subtypes for complex diseases.

关键词： Cancer Lungs

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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An improved reference point sampling method on Pareto optimal front

An improved reference point sampling method on Pareto optima...

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Congress on Evolutionary Computation

作者： Cheng He Linqiang Pan Hang Xu Ye Tian Xingyi Zhang Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Huazhong University of Science and Technology Wuhan Hubei CN Department of Computer Science Xiamen University Xiamen Fujian China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (纸本)9781509006243

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

关键词： Sampling methods Shape Convergence Evolutionary computation Optimization Euclidean distance

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Analysis of multiscale sign series entropy of the young and middle-aged electroencephalogram signals

Analysis of multiscale sign series entropy of the young and ...

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International Congress on image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Fei Du Shitong Wang Jun Wang Jiafei Dai Fengzhen Hou Jin Li Image Processing and Image Communications Key Lab Nanjing Univ. of Posts & Telecomm Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China School of Science China Pharmaceutical University Nanjing China College of Physics and Information Technology Shanxi Normal University Xian China

ISBN: (纸本)9781509037117

The physiological analysis of electroencephalogram (EEG) signals is of great significance in assessing the activity of the brain function and the physiological state. EEG is a means of clinical examination of brain diseases. Age is one of the important factors that affect the results of the EEG. EEG signal analysis is mainly to analyze the time series of the signal, multiscale entropy (MSE) analysis [1-3] is the method that used to analyze the finite length of the time series. Multiscale sign series entropy (MSSE) method is proposed for the analysis of EEG signals in the young and middle-aged. We use the proposed method to analyze the signals from several aspects of data length, word length, noise, multi scale etc. By analyzing the influence of these factors, we can still distinguish the EEG signals of different ages. Multiscale sign series entropy (MSSE) analysis algorithm can effectively separate the brain electrical signals from the young and middle aged, which is expected to have a certain reference value for the traditional pathological analysis of the EEG signals.

关键词： Electroencephalography Entropy Time series analysis Brain models Algorithm design and analysis Physiology

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Indoor localization with incomplete observation using set-membership filter

Indoor localization with incomplete observation using set-me...

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International Symposium on Micro-NanoMechatronics and Human science, MHS 2015

作者： Wang, Yuan Huang, Jian Key Lab of Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781479966783

Indoor localization is a popular topic because of the poor performance of GPS in the indoor environment. This paper has provided a new method to achieve the goal of indoor localization. Combining the usage of posture sensors with the Radio Frequency Identification (RFID) technology, we solved the problem of plentiful tags in the traditional RFID indoor localization. The relative localization subsystem is based on posture sensors and the absolute localization subsystem is based on RFID. A set-membership theory based data fusion algorithm is proposed to fuse the data of relative localization and absolute localization. Experimental results show that the proposed data fusion algorithm could significantly reduce the accumulated error of our indoor localization system. © 2015 IEEE.

关键词： Indoor positioning systems

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Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

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Molecular psychiatry 2024年第4期29卷 1216页

作者： Bo-Gyeom Kim Gakyung Kim Yoshinari Abe Pino Alonso Stephanie Ameis Alan Anticevic Paul D Arnold Srinivas Balachander Nerisa Banaj Nuria Bargalló Marcelo C Batistuzzo Francesco Benedetti Sara Bertolín Jan Carl Beucke Irene Bollettini Silvia Brem Brian P Brennan Jan K Buitelaar Rosa Calvo Miguel Castelo-Branco Yuqi Cheng Ritu Bhusal Chhatkuli Valentina Ciullo Ana Coelho Beatriz Couto Sara Dallaspezia Benjamin A Ely Sónia Ferreira Martine Fontaine Jean-Paul Fouche Rachael Grazioplene Patricia Gruner Kristen Hagen Bjarne Hansen Gregory L Hanna Yoshiyuki Hirano Marcelo Q Höxter Morgan Hough Hao Hu Chaim Huyser Toshikazu Ikuta Neda Jahanshad Anthony James Fern Jaspers-Fayer Selina Kasprzak Norbert Kathmann Christian Kaufmann Minah Kim Kathrin Koch Gerd Kvale Jun Soo Kwon Luisa Lazaro Junhee Lee Christine Lochner Jin Lu Daniela Rodriguez Manrique Ignacio Martínez-Zalacaín Yoshitada Masuda Koji Matsumoto Maria Paula Maziero Jose M Menchón Luciano Minuzzi Pedro Silva Moreira Pedro Morgado Janardhanan C Narayanaswamy Jin Narumoto Ana E Ortiz Junko Ota Jose C Pariente Chris Perriello Maria Picó-Pérez Christopher Pittenger Sara Poletti Eva Real Y C Janardhan Reddy Daan van Rooij Yuki Sakai João Ricardo Sato Cinto Segalas Roseli G Shavitt Zonglin Shen Eiji Shimizu Venkataram Shivakumar Noam Soreni Carles Soriano-Mas Nuno Sousa Mafalda Machado Sousa Gianfranco Spalletta Emily R Stern S Evelyn Stewart Philip R Szeszko Rajat Thomas Sophia I Thomopoulos Daniela Vecchio Ganesan Venkatasubramanian Chris Vriend Susanne Walitza Zhen Wang Anri Watanabe Lidewij Wolters Jian Xu Kei Yamada Je-Yeon Yun Mojtaba Zarei Qing Zhao Xi Zhu Paul M Thompson Willem B Bruin Guido A van Wingen Federica Piras Fabrizio Piras Dan J Stein Odile A van den Heuvel Helen Blair Simpson Rachel Marsh Jiook Cha Department of Psychology College of Social Sciences Seoul National University Seoul Republic of Korea. Department of Brain and Cognitive Sciences College of Natural Sciences Seoul National University Seoul Republic of Korea. Graduate School of Medical Science Kyoto Prefectural University of Medicine Department of Psychiatry Kyoto City Japan. Bellvitge Biomedical Research Insitute-IDIBELL Bellvitge University Hospital Barcelona Spain. CIBER of Mental Health (CIBERSAM) Carlos III Health Institute Madrid Spain. Department of Clinical Sciences University of Barcelona Barcelona Spain. The Margaret and Wallace McCain Centre for Child Youth & Family Mental Health and Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health Toronto ON Canada. Department of Psychiatry University of Toronto Toronto ON Canada. Program in Neurosciences and Mental Health The Hospital for Sick Children Toronto ON Canada. Department of Psychiatry Yale University School of Medicine New Haven CT 06510 USA. The Mathison Centre for Mental Health Research & Education Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary AB Canada. Departments of Psychiatry and Medical Genetics Cumming School of Medicine University of Calgary Calgary AB Canada. OCD clinic Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore India. Laboratory of Neuropsychiatry Department of Clinical and Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy. Center of Image Diagnostic Hospital Clínic de Barcelona Barcelona Spain. Magnetic Resonance Image Core Facility Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain. Departamento e Instituto de Psiquiatria do Hospital das Clinicas IPQ HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. Department of Methods and Techniques in Psychology Pontifical Catholic University São Paulo SP

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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A reduced-reference quality assessment scheme for blurred images

A reduced-reference quality assessment scheme for blurred im...

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IEEE Visual Communications and image Processing (VCIP)

作者： Zongxi Han Guangtao Zhai Yutao Liu Ke Gu Xinfeng Zhang Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Department of Computer Science Harbin Institute of Technology Harbin China School of Computer Science and Engineering Nanyang Technological University Singapore Rapid-Rich Object Search (ROSE) Lab Nanyang Technological University Singapore

ISBN: (纸本)9781509053179

In this paper, we propose a reduced-reference scheme for evaluating the quality of blurred images under the theory of free-energy principle. Specifically, the free-energy principle indicates that the brain tries to account for the input image with an internal generative model and the discrepancy between the image and its model-explained version, which can be measured by free energy, is related to the image's perceptual quality. Accordingly, we define a visual distance between the blurred image and its original image in free energy to evaluate the quality of the blurred image. Therefore, the proposed quality scheme belongs to reduced-reference methods, which needs some information from the original image for quality assessment. Experimental results on public databases, LIVE, TID2013 and C-SIQ, demonstrate the proposed method works in high consistency with subjective assessment results and outperforms representative image quality assessment approaches.

关键词： Brain modeling image quality Mathematical model Quality assessment Dictionaries Energy measurement

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image matching using mutual k-nearest neighbor graph

Image matching using mutual k-nearest neighbor graph

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Li, Ting-Ting Jiang, Bo Tu, Zheng-Zheng Luo, Bin Tang, Jin School of Computer Science and Technology Anhui University Hefei230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei230039 China

ISBN: (纸本)9783662462478

Though weighted voting matching is one of most successful image matching methods, each candidate correspondence receives voting score from all other candidates, which can not apparently distinguish correct matches and incorrect matches using voting scores. In this paper, a new image matching method based on mutual k-nearest neighbor (k-nn) graph is proposed. Firstly, the mutual k-nn graph is constructed according to similarity between candidate correspondences. Then, each candidate only receives voting score from its mutual k nearest neighbors. Finally, based on voting scores, the matching correspondences are computed by a greedy ranking technique. Experimental results demonstrate the effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2015.

关键词： image matching

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

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Molecular psychiatry 2023年第10期28卷 4320页

作者： Willem B Bruin Yoshinari Abe Pino Alonso Alan Anticevic Lea L Backhausen Srinivas Balachander Nuria Bargallo Marcelo C Batistuzzo Francesco Benedetti Sara Bertolin Triquell Silvia Brem Federico Calesella Beatriz Couto Damiaan A J P Denys Marco A N Echevarria Goi Khia Eng Sónia Ferreira Jamie D Feusner Rachael G Grazioplene Patricia Gruner Joyce Y Guo Kristen Hagen Bjarne Hansen Yoshiyuki Hirano Marcelo Q Hoexter Neda Jahanshad Fern Jaspers-Fayer Selina Kasprzak Minah Kim Kathrin Koch Yoo Bin Kwak Jun Soo Kwon Luisa Lazaro Chiang-Shan R Li Christine Lochner Rachel Marsh Ignacio Martínez-Zalacaín Jose M Menchon Pedro S Moreira Pedro Morgado Akiko Nakagawa Tomohiro Nakao Janardhanan C Narayanaswamy Erika L Nurmi Jose C Pariente Zorrilla John Piacentini Maria Picó-Pérez Fabrizio Piras Federica Piras Christopher Pittenger Janardhan Y C Reddy Daniela Rodriguez-Manrique Yuki Sakai Eiji Shimizu Venkataram Shivakumar Blair H Simpson Carles Soriano-Mas Nuno Sousa Gianfranco Spalletta Emily R Stern S Evelyn Stewart Philip R Szeszko Jinsong Tang Sophia I Thomopoulos Anders L Thorsen Tokiko Yoshida Hirofumi Tomiyama Benedetta Vai Ilya M Veer Ganesan Venkatasubramanian Nora C Vetter Chris Vriend Susanne Walitza Lea Waller Zhen Wang Anri Watanabe Nicole Wolff Je-Yeon Yun Qing Zhao Wieke A van Leeuwen Hein J F van Marle Laurens A van de Mortel Anouk van der Straten Ysbrand D van der Werf Paul M Thompson Dan J Stein Odile A van den Heuvel Guido A van Wingen Amsterdam UMC location University of Amsterdam Department of Psychiatry Meibergdreef 9 Amsterdam The Netherlands. willembenjamin@***. Amsterdam Neuroscience Amsterdam The Netherlands. willembenjamin@***. Department of Psychiatry Graduate School of Medical Science Kyoto Prefectural University of Medicine Kyoto Japan. Department of Psychiatry Bellvitge University Hospital Barcelona Spain. Department of Clinical Science Faculty of Medicine University of Barcelona Barcelona Spain. IDIBELL Bellvitge University Hospital Barcelona Spain. CIBERSAM Instituto de Salud Carlos III Madrid Spain. Department of Psychiatry Yale University New Haven CT USA. Department of Child and Adolescent Psychiatry Faculty of Medicine Technische Universität Dresden Dresden Germany. Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore India. Radiology Service Diagnosis Image Center Hospital Clinic de Barcelona Barcelona Spain. Magnetic Resonance Image Core Facility Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain. Department of Psychiatry University of Sao Paulo School of Medicine Sao Paulo Brazil. Department of Methods and Techniques in Psychology Pontifical Catholic University Sao Paulo Brazil. Vita-Salute San Raffaele University Milano Italy. Psychiatry & Clinical Psychobiology Division of Neuroscience IRCCS Scientific Institute Ospedale San Raffaele Milano Italy. Bellvitge Biomedical Research Insitute-IDIBELL Bellvitge University Hospital Barcelona Spain. Department of Child and Adolescent Psychiatry and Psychotherapy University Hospital of Psychiatry University of Zurich Zurich Switzerland. Neuroscience Center Zurich University of Zurich and ETH Zurich Zurich Switzerland. Life and Health Sciences Research Institute (ICVS) School of Medicine University of Minho Braga Portugal. ICVS/3B's PT Government Associate Laboratory Braga/Guimarães Portugal. Clinical Academic Center-Braga Braga Portuga

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