Gabriel Ramos-Llordén, Hong-Hsi Lee, Mathias Davids, Peter Dietz, Andreas Krug, John E. Kirsch, Mirsad Mahmutovic, Alina Müller, Yixin Ma, Hansol Lee, Chiara Maffei, Anastasia Yendiki, Berkin Bilgic, Daniel J. Park, Qiyuan Tian, Bryan Clifford, Wei-Ching Lo, Stefan Stocker, Jasmine Fischer, Gudrun Ruyters, Manuela Roesler, Andreas Potthast, Thomas Benner, Elmar Rummert, Rebecca Schuster, Peter J. Basser, Thomas Witzel, Lawrence L. Wald, Bruce R. Rosen, Boris Keil, Susie Y. Huang
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Here we report the design of the Connectome 2.0 human magnetic resonance imaging (MRI) scanner to perform connectomics at the mesoscopic and microscopic scales with strong gradients for in vivo human imaging. We construct a 3-layer head-only gradient coil optimized to minimize peripheral nerve stimulation while achieving a gradient strength of 500 mT m<sup>−1</sup> and a slew rate of 600 T m<sup>−1</sup> s<sup>−1</sup>, corresponding to a 5-fold greater gradient performance than state-of-the-art research gradient systems, including the original Connectome (Connectome 1.0) scanner. We find that gains in sensitivity of up to two times were achieved by integrating a 72-channel in vivo head coil and a 64-channel ex vivo whole-brain radiofrequency coil with built-in field monitoring for data fidelity. We demonstrate mapping of fine white matter pathways and inferences of cellular and axonal size and morphology approaching the single-micron level, with at least a 30% sensitivity improvement compared with Connectome 1.0.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"27 1","pages":""},"PeriodicalIF":26.8000,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ultra-high gradient connectomics and microstructure MRI scanner for imaging of human brain circuits across scales\",\"authors\":\"Gabriel Ramos-Llordén, Hong-Hsi Lee, Mathias Davids, Peter Dietz, Andreas Krug, John E. Kirsch, Mirsad Mahmutovic, Alina Müller, Yixin Ma, Hansol Lee, Chiara Maffei, Anastasia Yendiki, Berkin Bilgic, Daniel J. 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We construct a 3-layer head-only gradient coil optimized to minimize peripheral nerve stimulation while achieving a gradient strength of 500 mT m<sup>−1</sup> and a slew rate of 600 T m<sup>−1</sup> s<sup>−1</sup>, corresponding to a 5-fold greater gradient performance than state-of-the-art research gradient systems, including the original Connectome (Connectome 1.0) scanner. We find that gains in sensitivity of up to two times were achieved by integrating a 72-channel in vivo head coil and a 64-channel ex vivo whole-brain radiofrequency coil with built-in field monitoring for data fidelity. 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Ultra-high gradient connectomics and microstructure MRI scanner for imaging of human brain circuits across scales
Defining the connectome, the complete matrix of structural connections between the nervous system nodes, is a challenge for human systems neuroscience due to the range of scales that must be bridged. Here we report the design of the Connectome 2.0 human magnetic resonance imaging (MRI) scanner to perform connectomics at the mesoscopic and microscopic scales with strong gradients for in vivo human imaging. We construct a 3-layer head-only gradient coil optimized to minimize peripheral nerve stimulation while achieving a gradient strength of 500 mT m−1 and a slew rate of 600 T m−1 s−1, corresponding to a 5-fold greater gradient performance than state-of-the-art research gradient systems, including the original Connectome (Connectome 1.0) scanner. We find that gains in sensitivity of up to two times were achieved by integrating a 72-channel in vivo head coil and a 64-channel ex vivo whole-brain radiofrequency coil with built-in field monitoring for data fidelity. We demonstrate mapping of fine white matter pathways and inferences of cellular and axonal size and morphology approaching the single-micron level, with at least a 30% sensitivity improvement compared with Connectome 1.0.
期刊介绍:
Nature Biomedical Engineering is an online-only monthly journal that was launched in January 2017. It aims to publish original research, reviews, and commentary focusing on applied biomedicine and health technology. The journal targets a diverse audience, including life scientists who are involved in developing experimental or computational systems and methods to enhance our understanding of human physiology. It also covers biomedical researchers and engineers who are engaged in designing or optimizing therapies, assays, devices, or procedures for diagnosing or treating diseases. Additionally, clinicians, who make use of research outputs to evaluate patient health or administer therapy in various clinical settings and healthcare contexts, are also part of the target audience.