Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome

Huang, Susie Y.; Witzel, Thomas; Keil, Boris; Scholz, Alina; Davids, Mathias; Dietz, Peter; Rummert, Elmar; Ramb, Rebecca; Kirsch, John E.; Yendiki, Anastasia; Fan, Qiuyun; Tian, Qiyuan; Ramos‐Llordén, Gabriel; Lee, Hong-Hsi; Nummenmaa, Aapo; Bilgic̦, Berkin; Setsompop, Kawin; Wang, Fuyixue; Avram, Alexandru; Komlosh, Michal E.; Benjamini, Dan; Magdoom, Kulam Najmudeen; Pathak, Sudhir Kumar; Schneider, Walter; Novikov, Dmitry S.; Fieremans, Els; Tounekti, Slimane; Mekkaoui, Choukri; Augustinack, Jean C.; Berger, Daniel R.; Shapson-Coe, Alexander; Lichtman, Jeff W.; Basser, Peter J.; Wald, Lawrence L.; Rosen, Bruce R.

doi:10.1016/j.neuroimage.2021.118530

Cited by 76 publications

(59 citation statements)

References 156 publications

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“…Maximum field strength was only possible with rise times above 4000 μs; and maximum slew rate with field strength below 50 mT/m. To overcome this limitation, and to achieve even stronger and faster gradients to enable imaging of meso and ultimately micro structures of the brain in vivo, an optimized Connectome gradient, of smaller dimensions, is being developed by a multiinstitutional team 51 . Specifications of G max and SR max for this new generation Connectome gradient were more than doubled to 500 mT/m and 600 T/m/s respectively, compared to the previous system.…”

Section: Connectome Gradientsmentioning

confidence: 99%

“…The design of this gradient coil, similarly to another very fast head‐only asymmetric gradient coil recently presented, 52,53 includes a mid‐layer coil winding that allows for additional degrees of freedom in the coil design process for minimization of E‐fields and thus PNS. Considering the fact that diffusion contrast is not improved by imaging at higher B 0 field, this new gradient will be installed in a 3 T scanner as the previous Connectome gradient 51 . In addition to other engineering design constraints, in the new Connectome gradient, the optimization of the coil winding was further engineered based on the minimization of PNS using simulation methods, as it will be detailed in the next Section .…”

Section: Connectome Gradientsmentioning

confidence: 99%

“…As detailed in the previous Section , the power delivered to a gradient coil depends on the gradient amplitude, rise time and coil inductance. Advances in solid‐state technology allows for the design of megawatt high‐efficiency switch‐mode amplifiers 23 to drive gradient coils at very high amplitudes and slew rates as those specified for the Connectome scanner 51 . However, due to concerns of PNS, gradient coils in clinical systems may operate well below maximum hardware specifications.…”

Section: Pns Optimized Gradient Coilsmentioning

confidence: 99%

See 2 more Smart Citations

Advancements in Gradient System Performance for Clinical and Research MRI

Gudino

Littin

2022

Magnetic Resonance Imaging

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In magnetic resonance imaging (MRI), spatial field gradients are applied along each axis to encode the location of the nuclear spin in the frequency domain. During recent years, the development of new gradient technologies has been focused on the generation of stronger and faster gradient fields for imaging with higher spatial and temporal resolution. This benefits imaging methods, such as brain diffusion and functional MRI, and enables human imaging at ultra-high field MRI. In addition to improving gradient performance, new technologies have been presented to minimize peripheral nerve stimulation and gradient-related acoustic noise, both generated by the rapid switching of strong gradient fields. This review will provide a general background on the gradient system and update on the state-of-the-art gradient technology.

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Section: Connectome Gradientsmentioning

confidence: 99%

Section: Connectome Gradientsmentioning

confidence: 99%

Section: Pns Optimized Gradient Coilsmentioning

confidence: 99%

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Advancements in Gradient System Performance for Clinical and Research MRI

Gudino

Littin

2022

Magnetic Resonance Imaging

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“…In this regard, the possibility to investigate brain myeloarchitectonics with single fiber resolution over extended tissue volumes represents a compelling research area. The introduction of increased magnetic field strengths and the development of improved theoretical contrast models in diffusion-weighted magnetic resonance imaging (dMRI), fostered by the Human Brain Connectome project (Setsompop et al (2013)), have recently enabled the quantitative mapping of the human brain connectivity with sub-millimetric resolution, and improved angular accuracy (Tournier (2019); Trampel et al (2019); Beaujoin et al (2019); Huang et al (2021)). The ultra-high b-values available nowadays in clinical and pre-clinical scanners provide increased signal-to-noise ratios and, thus, enhanced spatial resolution; this, in turn, represents a key factor in improving the quality of dMRI-based fiber tractography (Fan et al (2014); Jones et al (2020)).…”

Section: Introductionmentioning

confidence: 99%

Fiber enhancement and 3D orientation analysis in label-free two-photon fluorescence microscopy

Sorelli

Costantini

Bocchi³

et al. 2022

Preprint

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Abstract3D fluorescence microscopy can be profitably used in combination with tissue clearing and myelin labeling techniques for investigating the human brain myeloarchitectonics with sub-micrometric resolution, and for providing detailed histological reference data for the validation of diffusion magnetic resonance imaging (dMRI) tractography. Differently from state-of-the-art polarimetry-based neuroimaging modalities such as 3D polarized light imaging and 3D polarized-sensitive optical coherence tomography, the quantitative histological analysis of cortical and white matter fiber architectures from fluorescence microscopy images requires the estimation of fiber orientations via image processing techniques, as Fourier or structure tensor analysis. However, these can be error-prone as they are not able to detect myelinated fibers in surrounding brain tissue. Here we present a novel image processing pipeline built around a 3D Frangi filter able to enhance fiber structures of varying diameters in tiled brain section reconstructions of arbitrary size, and to produce accurate 3D fiber orientation maps in both grey and white matter regions, while preserving the native resolution of the employed microscopy system. The developed software tool also features the estimation of fiber orientation distribution functions which, in view of their widespread use in the neuroimaging community, may favor a multimodal comparison with brain connectivity data obtained from the aforementioned label-free optical modalities, and the validation of modern dMRI-based tractography. The proposed image processing pipeline was applied to brain samples treated according to a novel label-free preparation technique capable of enhancing the autofluorescence of myelinated fiber axons, thus demonstrating the feasibility of single fiber orientation analyses in fluorescence microscopy without the need for exogenous staining.

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“…We propose that cellular lineage — the family tree of mitotic cell splits — plays a major role in steering connectivity patterns. Techniques like electron microscopy connectomics now yield comprehensive, synapse-resolution connectivity datasets Glasser et al (2016); Huang et al (2021); Shapson-Coe et al (2021); Witvliet et al (2021). In combination with these connectomes, we can now ask how much of the connectivity of a brain is affected by cell types, and how much by developmental lineage Kerstjens et al (2022).…”

Section: Introductionmentioning

confidence: 99%

Cell lineage predicts neural connectivity beyond cell type

Matelsky

Wester

Kording

2022

Preprint

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As an organism develops, a zygote becomes the body through repeated cell division. This process produces the mitotic family tree, and cells specialize into their ultimate phenotype through interaction with other cells but also through the history of divisions. Biologists often cluster cells in the body into "cell types," using e.g. their morphologies and molecular makeup. While morphomolecular cell type carries information about phenotype and function, there may be unknown, missing information available in this family tree. Emerging methods are making these lineage trees progressively observable. Here, using the complete mitotic family tree and connectome of the nematode C. elegans we ask about the role of cell-types and their family tree. We can thus evaluate how well we can predict synaptic connection with only cell-type information versus with the family tree lineage. We show that neglecting lineage can produce misleading insights into the mechanisms underlying neural wiring: underlying lineage can confound the effect of cell type. These results suggest that the concept of cell-type needs to be re-thought in the context of this emerging knowledge about cell lineage.

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Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome

Cited by 76 publications

References 156 publications

Advancements in Gradient System Performance for Clinical and Research MRI

Advancements in Gradient System Performance for Clinical and Research MRI

Fiber enhancement and 3D orientation analysis in label-free two-photon fluorescence microscopy

Cell lineage predicts neural connectivity beyond cell type

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