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      A large normative connectome for exploring the tractographic correlates of focal brain interventions

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          Abstract

          Diffusion-weighted MRI (dMRI) is a widely used neuroimaging modality that permits the in vivo exploration of white matter connections in the human brain. Normative structural connectomics – the application of large-scale, group-derived dMRI datasets to out-of-sample cohorts – have increasingly been leveraged to study the network correlates of focal brain interventions, insults, and other regions-of-interest (ROIs). Here, we provide a normative, whole-brain connectome in MNI space that enables researchers to interrogate fiber streamlines that are likely perturbed by given ROIs, even in the absence of subject-specific dMRI data. Assembled from multi-shell dMRI data of 985 healthy Human Connectome Project subjects using generalized Q-sampling imaging and multispectral normalization techniques, this connectome comprises ~12 million unique streamlines, the largest to date. It has already been utilized in at least 18 peer-reviewed publications, most frequently in the context of neuromodulatory interventions like deep brain stimulation and focused ultrasound. Now publicly available, this connectome will constitute a useful tool for understanding the wider impact of focal brain perturbations on white matter architecture going forward.

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          The minimal preprocessing pipelines for the Human Connectome Project.

          The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. Copyright © 2013 Elsevier Inc. All rights reserved.
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            Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

            An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
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              A reproducible evaluation of ANTs similarity metric performance in brain image registration.

              The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling. Copyright © 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                lozano@uhnresearch.ca
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                8 April 2024
                8 April 2024
                2024
                : 11
                : 353
                Affiliations
                [1 ]Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, ( https://ror.org/03dbr7087) Toronto, Canada
                [2 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Krembil Research Institute, , University of Toronto, ; Toronto, Canada
                [3 ]Center for Advancing Neurotechnological Innovation to Application (CRANIA), University Health Network, ( https://ror.org/042xt5161) Toronto, Canada
                [4 ]GRID grid.464811.e, GE Global Research, ; Bangalore, India
                [5 ]Department of Neurology, Charité-Universitätsmedizin Berlin, ( https://ror.org/001w7jn25) Berlin, Germany
                [6 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Einstein Center for Neurosciences Berlin, , Charité-Universitätsmedizin Berlin, ; Berlin, Germany
                [7 ]GRID grid.38142.3c, ISNI 000000041936754X, Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women’s Hospital, , Harvard Medical School, ; Boston, USA
                [8 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Neurosurgery, Massachusetts General Hospital, , Harvard Medical School, ; Boston, USA
                [9 ]Joint Department of Medical Imaging, University of Toronto, ( https://ror.org/03dbr7087) Toronto, Canada
                Author information
                http://orcid.org/0000-0002-7495-550X
                http://orcid.org/0000-0003-0995-8226
                http://orcid.org/0000-0003-3315-3591
                http://orcid.org/0000-0002-0695-6025
                http://orcid.org/0000-0001-6942-5195
                Article
                3197
                10.1038/s41597-024-03197-0
                11002007
                38589407
                905dcfe8-4f3c-4b27-963c-c96858aab970
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 September 2023
                : 28 March 2024
                Funding
                Funded by: RR Tasker Chair in Functional Neurosurgery at University Health Network; Tier 1 Canada Research Chair in Neuroscience
                Funded by: FundRef https://doi.org/10.13039/501100000024, Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada);
                Award ID: 164235
                Award ID: 471913
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: 424778381 – TRR 295
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002946, Deutsches Zentrum für Luft- und Raumfahrt (German Centre for Air and Space Travel);
                Award ID: DynaSti grant within the EU Joint Programme Neurodegenerative Disease Research, JPND
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000009, Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.);
                Award ID: R01 13478451
                Award ID: 1R01NS127892-01
                Award ID: 2R01 MH113929
                Award Recipient :
                Funded by: New Venture Fund (FFOR Seed Grant)
                Categories
                Data Descriptor
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                © Springer Nature Limited 2024

                brain injuries,neural circuits,brain,biomarkers
                brain injuries, neural circuits, brain, biomarkers

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