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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Chaofei Fan Nick Hahn Foram Kamdar Donald Avansino Guy H. Wilson Leigh Hochberg Krishna V. Shenoy Jaimie M. Henderson Francis R. Willett Department of Computer Science Stanford University Department of Neurosurgery Stanford University Howard Hughes Medical Institute at Stanford University Department of Neuroscience Stanford University School of Engineering and Carney Institute for Brain Science Brown University and VA RR & D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System and Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School Bio-X Program Stanford University and Department of Neurobiology Stanford University and Department of Bioengineering Stanford University and Wu Tsai Neurosciences Institute Stanford University and Howard Hughes Medical Institute at Stanford University and Department of Electrical Engineering Stanford University Department of Neurosurgery Stanford University and Wu Tsai Neurosciences Institute Stanford University

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

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Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication

arXiv

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arxiv 2023年

作者： Fan, Chaofei Hahn, Nick Kamdar, Foram Avansino, Donald Wilson, Guy H. Hochberg, Leigh Shenoy, Krishna V. Henderson, Jaimie M. Willett, Francis R. Bio-X Program Stanford University United States Department of Neuroscience Stanford University United States Department of Neurosurgery Stanford University United States Department of Neurobiology Stanford University United States Department of Bioengineering Stanford University United States Department of Computer Science Stanford University United States Wu Tsai Neurosciences Institute Stanford University United States Howard Hughes Medical Institute Stanford University United States Department of Electrical Engineering Stanford University United States School of Engineering Carney Institute for Brain Science Brown University United States VA RR&D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System United States Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School United States

关键词： Brain computer interface

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An integrated transcriptomic cell atlas of human neural organoids (vol 635, pg 690, 2024)

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NATURE 2025年第8045期637卷 E21-E21页

作者： He, Zhisong Dony, Leander Fleck, Jonas Simon Szalata, Artur Li, Katelyn x. Sliskovic, Irena Lin, Hsiu-Chuan Santel, Malgorzata Atamian, Alexander Quadrato, Giorgia Sun, Jieran Pasca, Sergiu P. Camp, J. Gray Theis, Fabian J. Treutlein, Barbara Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Institute of Computational Biology Computational Health Center Helmholtz Munich Neuherberg Germany Department Genes and Environment Max Planck Institute of Psychiatry Munich Germany International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden Institute of Human Biology (IHB) Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland Biozentrum University of Basel Basel Switzerland Department of Stem Cell Biology and Regenerative Medicine Keck School of Medicine University of Southern California Los Angeles CA USA Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC Keck School of Medicine University of Southern California Los Angeles CA USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA Stanford Brain Organogenesis Program Wu Tsai Neurosciences Institute and Bio-X Stanford CA USA Neural Stem Cell Institute Albany NY USA UK Dementia Research Institute at the University of Edinburgh Edinburgh Bioquarter Edinburgh UK Centre for Discovery Brain Sciences School of Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK Human Technopole Milan Italy Department of Oncology and Hemato-Oncology University of Milan Milan Italy

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An open-source realtime computational platform (short WIP paper)

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ACM SIGPLAN Notices 2018年第6期53卷 109-112页

作者： Mehrotra, Pavan Dasgupta, Sabar Robertson, Samantha Nuyujukian, Paul Stanford University Department of Electrical Engineering StanfordCA United States Stanford University Mathematical and Computational Science Program StanfordCA United States Stanford University Department of Bioengineering Department of Neurosurgery Department of Electrical Engineering Neurosciences Program Neurosciences Institute Bio-X Institute StanfordCA United States

ISBN: (纸本)9781450349116

Systems neuroscience studies involving in-vivo models often require realtime data processing. In these studies, many events must be monitored and processed quickly, including behavior of the subject (e.g., movement of a limb) or features of neural data (e.g., a neuron transmitting an action potential). Unfortunately, most realtime platforms are proprietary, require specific architectures, or are limited to low-level programming languages. Here we present a hardware-independent, open-source realtime computation platform that supports high-level programming. The resulting platform, LiCoRICE, can process on order 10e10 bits/sec of network data at 1 ms ticks with 18.2 μs jitter. It connects to various inputs and outputs (e.g., DIO, Ethernet, database logging, and analog line in/out) and minimizes reliance on custom device drivers by leveraging peripheral support via the Linux kernel. Its modular architecture supports model-based design for rapid prototyping with C and Python/Cython and can perform numerical operations via BLAS/LAPACK-optimized NumPy that is statically compiled via Numba's pycc. LiCoRICE is not only suitable for systems neuroscience research, but also for applications requiring closed-loop realtime data processing from robotics and control systems to interactive applications and quantitative financial trading. © 2018 ACM.

关键词： Python

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora x. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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Cover Feature: Near‐Infrared bioluminescence Imaging with a through‐Bond Energy Transfer Cassette (ChembioChem 15/2019)

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ChembioChem 2019年第15期20卷 1870-1870页

作者： Masahiro Abe Dr. Ryo Nishihara Yuma Ikeda Dr. Takahiro Nakajima Prof. Dr. Moritoshi Sato Dr. Naoko Iwasawa Prof. Dr. Shigeru Nishiyama Prof. Dr. Ramasamy Paulmurugan Prof. Dr. Daniel Citterio Dr. Sung Bae Kim Prof. Dr. Koji Suzuki Department of Applied Chemistry Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan These authors contributed equally to this work. Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Chemistry Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan Molecular Imaging Program at Stanford Bio-X Program Stanford University School of Medicine 3155 Porter Dr. Palo Alto CA 94304 USA National Institute of Advanced Industrial Science and Technology (AIST) Research Institute for Environmental Management Technology 16-1 Onogawa Tsukuba 305–8569 Japan

The cover feature picture shows the unique substrate we synthesized that reacts with a bioluminescence reporter (Renilla luciferase) and emits near infrared (NIR) light by binding within the active site of the luciferase. The substrate consists of coelenterazine (CTZ) and Cy5, whose linkage allows characteristic through‐bond energy transfer (TBET) between the two components. The substrate has biocompatibility in biological imaging contexts. This is the first example of achieving NIR‐TBET spectra through a dye‐conjugated CTZ. More information can be found in the communication by S. B. Kim, K. Suzuki, et al. on page 1919 in Issue 15, 2019 (DOI: 10.1002/cbic.201900149)

关键词： bioluminescence dyes/pigments energy transfer fluorescence imaging agents

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Publisher Correction: Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

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Scientific reports 2019年第1期9卷 5528页

作者： Sergey D Stavisky Jonathan C Kao Paul Nuyujukian Chethan Pandarinath Christine Blabe Stephen I Ryu Leigh R Hochberg Jaimie M Henderson Krishna V Shenoy Neurosurgery Department Stanford University Stanford CA USA. sergey.stavisky@stanford.edu. Electrical Engineering Department Stanford University Stanford CA USA. sergey.stavisky@stanford.edu. Electrical Engineering Department Stanford University Stanford CA USA. Electrical and Computer Engineering Department University of California at Los Angeles Los Angeles CA USA. Neurosurgery Department Stanford University Stanford CA USA. Bioengineering Department Stanford University Stanford CA USA. Stanford Wu Tsai Neurosciences Institute Stanford University Stanford CA USA. Bio-X Program Stanford University Stanford CA USA. Neurosurgery Department Palo Alto Medical Foundation Palo Alto CA USA. Center for Neurorestoration and Neurotechnology Rehabilitation R&D Service VA Medical Center Providence RI USA. School of Engineering and Carney Institute for Brain Science Brown University Providence RI USA. Department of Neurology Harvard Medical School Boston MA USA. Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Boston MA USA. Neurobiology Department Stanford University Stanford CA USA. Howard Hughes Medical Institute at Stanford University Stanford CA USA.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

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Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex

Auto-deleting brain machine interface: Error detection using...

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Annual International Conference of the IEEE Engineering in Medicine and biology Society (EMBC)

作者： Nir Even-Chen Sergey D. Stavisky Jonathan C. Kao Stephen I. Ryu Krishna V. Shenoy Department of Electrical Engineering Stanford University Stanford CA USA Neurosciences Graduate Program Stanford University Department of Neurosurgery at Palo Alto Medical Foundation Stanford University Palo Alto CA USA Departments of Electrical Engineering Bioengineering and Neurobiology the Neurosciences Graduate Program and the Bio-X Program and the Stanford Neurosciences Institute Stanford University

Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically “undo” wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.

关键词： Decoding Brain-computer interfaces Accuracy Kinematics Image color analysis Firing Keyboards

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Genome-wide association studies identify susceptibility loci for epithelial ovarian cancer in east Asian women

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Gynecologic oncology 2019年第2期153卷 343-355页

作者： Kate Lawrenson Fengju Song Dennis J Hazelett Siddhartha P Kar Jonathan Tyrer Catherine M Phelan Rosario I Corona Norma I Rodríguez-Malavé Ji-Hei Seo Emily Adler Simon G Coetzee Felipe Segato Marcos A S Fonseca Christopher I Amos Michael E Carney Georgia Chenevix-Trench Jiyeob Choi Jennifer A Doherty Weihua Jia Gang J Jin Byoung-Gie Kim Nhu D Le Juyeon Lee Lian Li Boon K Lim Noor A Adenan Mika Mizuno Boyoung Park Celeste L Pearce Kang Shan Yongyong Shi xiao-Ou Shu Weiva Sieh Pamela J Thompson Lynne R Wilkens Qingyi Wei Yin L Woo Li Yan Beth Y Karlan Matthew L Freedman Houtan Noushmehr Ellen L Goode Andrew Berchuck Thomas A Sellers Soo-Hwang Teo Wei Zheng Keitaro Matsuo Sue Park Kexin Chen Paul D P Pharoah Simon A Gayther Marc T Goodman Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute Cedars-Sinai Medical Center 8700 Beverly Boulevard Suite 290W Los Angeles CA USA Center for Bioinformatics and Functional Genomics Cedars Sinai Genomics Core Cedars-Sinai Medical Center Los Angeles CA USA. Electronic address: kate.lawrenson@***. National Clinical Research Center for Cancer Tianjin Medical University Cancer Institute and Hospital Tianjin China Department of Epidemiology and Biostatistics Key Laboratory of Cancer Prevention and Therapy Tianjin China. Center for Bioinformatics and Functional Genomics Cedars Sinai Genomics Core Cedars-Sinai Medical Center Los Angeles CA USA. Department of Oncology Centre for Cancer Genetic Epidemiology University of Cambridge Strangeways Research Laboratory Cambridge UK. Department of Gynecologic Oncology Moffitt Cancer Center Tampa FL USA Department of Cancer Epidemiology Moffitt Cancer Center Tampa FL USA. Center for Bioinformatics and Functional Genomics Cedars Sinai Genomics Core Cedars-Sinai Medical Center Los Angeles CA USA. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA. Department of Preventive Medicine University of Southern California Keck School of Medicine 1450 Biggy Street Los Angeles CA USA. Department of Genetics Ribeirão Preto Medical School University of São Paulo 14049-900 Brazil. Institute for Clinical and Translational Research Department of Medicine Baylor School of Medicine HoustonTX USA. Department of Obstetrics and Gynecology John A. Burns School of Medicine University of Hawaii Honolulu HI USA. Genetics and Computational Biology Department QIMR Berghofer Medical Research Institute 300 Herston Road Herston QLD 4006 Australia. Department of Preventive Medicine Seoul National University College of Medicine Seoul Republic of Korea Cancer Research Institute Seoul National University Seoul Republic of Korea Department of Biomedical Science Graduate School Seoul National Un

OBJECTIVE:Genome-wide association studies (GWASs) for epithelial ovarian cancer (EOC) have focused largely on populations of European ancestry. We aimed to identify common germline variants associated with EOC risk in Asian women. METHODS:Genotyping was performed as part of the OncoArray project. Samples with >60% Asian ancestry were included in the analysis. Genotyping was performed on 533,631 SNPs in 3238 Asian subjects diagnosed with invasive or borderline EOC and 4083 unaffected controls. After imputation, genotypes were available for 11,595,112 SNPs to identify associations. RESULTS:At chromosome 6p25.2, SNP rs7748275 was associated with risk of serous EOC (odds ratio [OR] = 1.34, P = 8.7 × 10) and high-grade serous EOC (HGSOC) (OR = 1.34, P = 4.3 × 10). SNP rs6902488 at 6p25.2 (r = 0.97 with rs7748275) lies in an active enhancer and is predicted to impact binding of STAT3, P300 and ELF1. We identified additional risk loci with low Bayesian false discovery probability (BFDP) scores, indicating they are likely to be true risk associations (BFDP <10%). At chromosome 20q11.22, rs74272064 was associated with HGSOC risk (OR = 1.27, P = 9.0 × 10). Overall EOC risk was associated with rs10260419 at chromosome 7p21.3 (OR = 1.33, P = 1.2 × 10) and rs74917072 at chromosome 2q37.3 (OR = 1.25, P = 4.7 × 10). At 2q37.3, expression quantitative trait locus analysis in 404 HGSOC tissues identified ESPNL as a putative candidate susceptibility gene (P = 1.2 × 10). CONCLUSION:While some risk loci were shared between East Asian and European populations, others were population-specific, indicating that the landscape of EOC risk in Asian women has both shared and unique features compared to women of European ancestry.

关键词： Asian ancestry Enhancer Epithelial ovarian cancer Gene regulation Genome-wide association study eQTL

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Author Correction: A consensus protocol for functional connectivity analysis in the rat brain

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Nature neuroscience 2023年 2023 Apr 18页

作者： Joanes Grandjean Gabriel Desrosiers-Gregoire Cynthia Anckaerts Diego Angeles-Valdez Fadi Ayad David A Barrière Ines Blockx Aleksandra Bortel Margaret Broadwater Beatriz M Cardoso Marina Célestine Jorge E Chavez-Negrete Sangcheon Choi Emma Christiaen Perrin Clavijo Luis Colon-Perez Samuel Cramer Tolomeo Daniele Elaine Dempsey Yujian Diao Arno Doelemeyer David Dopfel Lenka Dvořáková Claudia Falfán-Melgoza Francisca F Fernandes Caitlin F Fowler Antonio Fuentes-Ibañez Clément M Garin Eveline Gelderman Carla E M Golden Chao C G Guo Marloes J A G Henckens Lauren A Hennessy Peter Herman Nita Hofwijks Corey Horien Tudor M Ionescu Jolyon Jones Johannes Kaesser Eugene Kim Henriette Lambers Alberto Lazari Sung-Ho Lee Amanda Lillywhite Yikang Liu Yanyan Y Liu Alejandra López-Castro xavier López-Gil Zilu Ma Eilidh MacNicol Dan Madularu Francesca Mandino Sabina Marciano Matthew J McAuslan Patrick McCunn Alison McIntosh xianzong Meng Lisa Meyer-Baese Stephan Missault Federico Moro Daphne M P Naessens Laura J Nava-Gomez Hiroi Nonaka Juan J Ortiz Jaakko Paasonen Lore M Peeters Mickaël Pereira Pablo D Perez Marjory Pompilus Malcolm Prior Rustam Rakhmatullin Henning M Reimann Jonathan Reinwald Rodrigo Triana Del Rio Alejandro Rivera-Olvera Daniel Ruiz-Pérez Gabriele Russo Tobias J Rutten Rie Ryoke Markus Sack Piergiorgio Salvan Basavaraju G Sanganahalli Aileen Schroeter Bhedita J Seewoo Erwan Selingue Aline Seuwen Bowen Shi Nikoloz Sirmpilatze Joanna A B Smith Corrie Smith Filip Sobczak Petteri J Stenroos Milou Straathof Sandra Strobelt Akira Sumiyoshi Kengo Takahashi Maria E Torres-García Raul Tudela Monica van den Berg Kajo van der Marel Aran T B van Hout Roberta Vertullo Benjamin Vidal Roël M Vrooman Victora x Wang Isabel Wank David J G Watson Ting Yin Yongzhi Zhang Stefan Zurbruegg Sophie Achard Sarael Alcauter Dorothee P Auer Emmanuel L Barbier Jürgen Baudewig Christian F Beckmann Nicolau Beckmann Guillaume J P C Becq Erwin L A Blezer Radu Bolbos Susann Boretius Sandrine Bouvard Eike Budinger Joseph D Buxbaum Diana Cash Victor Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada. Integrated Program in Neuroscience McGill University Montreal QC Canada. Bio-imaging Lab University of Antwerp Antwerp Belgium. µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium. Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico. Biological and Biomedical Engineering McGill University Montreal QC Canada. McConnell Brain Imaging Centre McGill University Montreal QC Canada. Montreal Neurological Institute McGill University Montreal QC Canada. UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France. Department of Neurology and Neurosurgery McGill University Montreal QC Canada. Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA. Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal. Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France. Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México. Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cyberneti

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