1
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Xu H, Newlin NR, Kim ME, Gao C, Kanakaraj P, Krishnan AR, Remedios LW, Khairi NM, Pechman K, Archer D, Hohman TJ, Jefferson AL, Isgum I, Huo Y, Moyer D, Schilling KG, Landman BA. Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites. ARXIV 2024:arXiv:2401.06798v2. [PMID: 38344221 PMCID: PMC10854272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.
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Affiliation(s)
- Hanliang Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Michael E Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Chenyu Gao
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Aravind R Krishnan
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Lucas W Remedios
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nazirah Mohd Khairi
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kimberly Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ivana Isgum
- Department of Biomedical Engineering and Physics & Radiology and Nuclear Medicine, University Medical Center Amsterdam, University of Amsterdam, Amsterdam, the Netherlands
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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Roca V, Kuchcinski G, Pruvo JP, Manouvriez D, Leclerc X, Lopes R. A three-dimensional deep learning model for inter-site harmonization of structural MR images of the brain: Extensive validation with a multicenter dataset. Heliyon 2023; 9:e22647. [PMID: 38107313 PMCID: PMC10724680 DOI: 10.1016/j.heliyon.2023.e22647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/03/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023] Open
Abstract
In multicenter MRI studies, pooling the imaging data can introduce site-related variabilities and can therefore bias the subsequent analyses. To harmonize the intensity distributions of brain MR images in a multicenter dataset, unsupervised deep learning methods can be employed. Here, we developed a model based on cycle-consistent adversarial networks for the harmonization of T1-weighted brain MR images. In contrast to previous works, it was designed to process three-dimensional whole-brain images in a stable manner while optimizing computation resources. Using six different MRI datasets for healthy adults (n=1525 in total) with different acquisition parameters, we tested the model in (i) three pairwise harmonizations with site effects of various sizes, (ii) an overall harmonization of the six datasets with different age distributions, and (iii) a traveling-subject dataset. Our results for intensity distributions, brain volumes, image quality metrics and radiomic features indicated that the MRI characteristics at the various sites had been effectively homogenized. Next, brain age prediction experiments and the observed correlation between the gray-matter volume and age showed that thanks to an appropriate training strategy and despite biological differences between the dataset populations, the model reinforced biological patterns. Furthermore, radiologic analyses of the harmonized images attested to the conservation of the radiologic information in the original images. The robustness of the harmonization model (as judged with various datasets and metrics) demonstrates its potential for application in retrospective multicenter studies.
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Affiliation(s)
- Vincent Roca
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
| | - Grégory Kuchcinski
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences & Cognition, F-59000 Lille, France
- CHU Lille, Department of Neuroradiology, F-59000 Lille, France
| | - Jean-Pierre Pruvo
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences & Cognition, F-59000 Lille, France
- CHU Lille, Department of Neuroradiology, F-59000 Lille, France
| | - Dorian Manouvriez
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
| | - Xavier Leclerc
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences & Cognition, F-59000 Lille, France
- CHU Lille, Department of Neuroradiology, F-59000 Lille, France
| | - Renaud Lopes
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences & Cognition, F-59000 Lille, France
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3
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Kushol R, Parnianpour P, Wilman AH, Kalra S, Yang YH. Effects of MRI scanner manufacturers in classification tasks with deep learning models. Sci Rep 2023; 13:16791. [PMID: 37798392 PMCID: PMC10556074 DOI: 10.1038/s41598-023-43715-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 09/27/2023] [Indexed: 10/07/2023] Open
Abstract
Deep learning has become a leading subset of machine learning and has been successfully employed in diverse areas, ranging from natural language processing to medical image analysis. In medical imaging, researchers have progressively turned towards multi-center neuroimaging studies to address complex questions in neuroscience, leveraging larger sample sizes and aiming to enhance the accuracy of deep learning models. However, variations in image pixel/voxel characteristics can arise between centers due to factors including differences in magnetic resonance imaging scanners. Such variations create challenges, particularly inconsistent performance in machine learning-based approaches, often referred to as domain shift, where the trained models fail to achieve satisfactory or improved results when confronted with dissimilar test data. This study analyzes the performance of multiple disease classification tasks using multi-center MRI data obtained from three widely used scanner manufacturers (GE, Philips, and Siemens) across several deep learning-based networks. Furthermore, we investigate the efficacy of mitigating scanner vendor effects using ComBat-based harmonization techniques when applied to multi-center datasets of 3D structural MR images. Our experimental results reveal a substantial decline in classification performance when models trained on one type of scanner manufacturer are tested with data from different manufacturers. Moreover, despite applying ComBat-based harmonization, the harmonized images do not demonstrate any noticeable performance enhancement for disease classification tasks.
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Affiliation(s)
- Rafsanjany Kushol
- Department of Computing Science, University of Alberta, Edmonton, Canada.
| | - Pedram Parnianpour
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Alan H Wilman
- Departments of Radiology and Diagnostic Imaging and Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Sanjay Kalra
- Department of Computing Science, University of Alberta, Edmonton, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Yee-Hong Yang
- Department of Computing Science, University of Alberta, Edmonton, Canada
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Zuo L, Liu Y, Xue Y, Dewey BE, Remedios SW, Hays SP, Bilgel M, Mowry EM, Newsome SD, Calabresi PA, Resnick SM, Prince JL, Carass A. HACA3: A unified approach for multi-site MR image harmonization. Comput Med Imaging Graph 2023; 109:102285. [PMID: 37657151 PMCID: PMC10592042 DOI: 10.1016/j.compmedimag.2023.102285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/11/2023] [Accepted: 08/08/2023] [Indexed: 09/03/2023]
Abstract
The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. The general idea is to disentangle anatomy and contrast information from MR images to achieve cross-site harmonization. Despite the success of existing methods, we argue that major improvements can be made from three aspects. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 can be trained and applied to any combination of MR contrasts and is robust to imaging artifacts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art harmonization performance under multiple image quality metrics. We also demonstrate the versatility and potential clinical impact of HACA3 on downstream tasks including white matter lesion segmentation for people with multiple sclerosis and longitudinal volumetric analyses for normal aging subjects. Code is available at https://github.com/lianruizuo/haca3.
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Affiliation(s)
- Lianrui Zuo
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Yihao Liu
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Yuan Xue
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Blake E Dewey
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Samuel W Remedios
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Savannah P Hays
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Ellen M Mowry
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Scott D Newsome
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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5
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Kushol R, Wilman AH, Kalra S, Yang YH. DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets. Diagnostics (Basel) 2023; 13:2947. [PMID: 37761314 PMCID: PMC10527875 DOI: 10.3390/diagnostics13182947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets.
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Affiliation(s)
- Rafsanjany Kushol
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Alan H. Wilman
- Departments of Radiology and Diagnostic Imaging and Biomedical Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Sanjay Kalra
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Yee-Hong Yang
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
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6
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de Matos K, Cury C, Chougar L, Strike LT, Rolland T, Riche M, Hemforth L, Martin A, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Papadopoulos Orfanos D, Lemaitre H, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Frouin V, Bach Cuadra M, Colliot O, Couvy-Duchesne B. Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability. Brain Struct Funct 2023; 228:1459-1478. [PMID: 37358662 DOI: 10.1007/s00429-023-02663-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023]
Abstract
The temporo-basal region of the human brain is composed of the collateral, the occipito-temporal, and the rhinal sulci. We manually rated (using a novel protocol) the connections between rhinal/collateral (RS-CS), collateral/occipito-temporal (CS-OTS) and rhinal/occipito-temporal (RS-OTS) sulci, using the MRI of nearly 3400 individuals including around 1000 twins. We reported both the associations between sulcal polymorphisms as well with a wide range of demographics (e.g. age, sex, handedness). Finally, we also estimated the heritability, and the genetic correlation between sulcal connections. We reported the frequency of the sulcal connections in the general population, which were hemisphere dependent. We found a sexual dimorphism of the connections, especially marked in the right hemisphere, with a CS-OTS connection more frequent in females (approximately 35-40% versus 20-25% in males) and an RS-CS connection more common in males (approximately 40-45% versus 25-30% in females). We confirmed associations between sulcal connections and characteristics of incomplete hippocampal inversion (IHI). We estimated the broad sense heritability to be 0.28-0.45 for RS-CS and CS-OTS connections, with hints of dominant contribution for the RS-CS connection. The connections appeared to share some of their genetic causing factors as indicated by strong genetic correlations. Heritability appeared much smaller for the (rarer) RS-OTS connection.
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Affiliation(s)
- Kevin de Matos
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
- CIBM Center for Biomedical Imaging, Vaud, Switzerland
- Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Claire Cury
- CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U-1228, University of Rennes, 35000, Rennes, France
| | - Lydia Chougar
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
- Service de neuroradiologie, AP-HP, Pitié-Salpêtrière, Paris, France
| | - Lachlan T Strike
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Thibault Rolland
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
| | - Maximilien Riche
- Department of Neurosurgery, AP-HP, La Pitié-Salpêtrière Hospital, Sorbonne University, 75013, Paris, France
| | - Lisa Hemforth
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
| | - Alexandre Martin
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
- Inria Sophia Antipolis, Morpheme Project, Paris, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Berlin Institute of Health, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Brunswick, Berlin, Germany
| | - Jean-Luc Martinot
- INSERM U 1299 "Trajectoires développementales & psychiatrie", CNRS, Institut National de la Santé et de la Recherche Médicale, Ecole Normale Supérieure Paris-Saclay, Centre Borelli, University Paris-Saclay, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- INSERM U 1299 "Trajectoires développementales & psychiatrie", CNRS, AP-HP, Institut National de la Santé et de la Recherche Médicale, Ecole Normale Supérieure Paris-Saclay, Centre Borelli, University Paris-Saclay, Gif-sur-Yvette, France
- Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
| | - Eric Artiges
- INSERM U 1299 "Trajectoires développementales & psychiatrie", CNRS, Institut National de la Santé et de la Recherche Médicale, Ecole Normale Supérieure Paris-Saclay, Centre Borelli, University Paris-Saclay, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, 68159, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
- UMR 5293, CNRS, CEA, Institut des Maladies Neurodégénératives, Université de Bordeaux, 33076, Bordeaux, France
| | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Université de Montréal and Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, ON, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Department of Psychiatry and Neuroscience, Centre for Population Neuroscience and Stratified Medicine (PONS), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Berlin Institute of Health, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Gunter Schumann
- Department of Psychiatry and Neuroscience, Centre for Population Neuroscience and Stratified Medicine (PONS), Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Vincent Frouin
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Vaud, Switzerland
- Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
| | - Baptiste Couvy-Duchesne
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France.
- Institute for Molecular Biosciences, The University of Queensland, St Lucia, QLD, 4062, Australia.
- ARAMIS Team, Pitié-Salpêtrière Hospital, Institut du Cerveau, 75013, Paris, France.
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