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      Modeling venous bias in resting state functional MRI metrics

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          Abstract

          Resting‐state (rs) functional magnetic resonance imaging (fMRI) is used to detect low‐frequency fluctuations in the blood oxygen‐level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD‐derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high‐resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI‐derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low‐frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel‐wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

          Abstract

          Resting state functional MRI is a highly popular technique and understanding its biases is crucial for the interpretation of results derived from it. Here, we explore the magnitude of its venous bias, and provide a biophysical model for correcting some of the most commonly used metrics. Specifically, we aimed to understand the impact of vein diameter and distance of tissue voxels to veins on common local rsfMRI metrics including the amplitudes of low‐frequency fluctuations (ALFF), fractional ALFF, Hurst exponent, regional homogeneity, and eigenvector centrality values in the whole grey matter.

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          Most cited references87

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          The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

          Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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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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              Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

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                Author and article information

                Contributors
                claudine.gauthier@concordia.ca
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                27 July 2023
                1 October 2023
                : 44
                : 14 ( doiID: 10.1002/hbm.v44.14 )
                : 4938-4955
                Affiliations
                [ 1 ] Department of Physics Concordia University Montreal Quebec Canada
                [ 2 ] PERFORM Center Montreal Quebec Canada
                [ 3 ] Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
                [ 4 ] Center for Stroke Research Berlin (CSB) Charité ‐ Universitätsmedizin Berlin Berlin Germany
                [ 5 ] Department of Biomedical Engineering University of California Davis California USA
                [ 6 ] Department of Neurology University of California Davis California USA
                [ 7 ] Faculty of Medicine and Health Sciences, Department of Biomedical Engineering McGill University Montreal Quebec Canada
                [ 8 ] McConnell Brain Imaging Centre Montreal Neurological Institute Montreal Quebec Canada
                [ 9 ] Clinic for Cognitive Neurology University of Leipzig Leipzig Germany
                [ 10 ] IFB Adiposity Diseases Leipzig University Medical Centre Leipzig Germany
                [ 11 ] Faculty of Social and Behavioural Sciences University of Amsterdam Amsterdam The Netherlands
                [ 12 ] Department of Psychology Concordia University Montreal Quebec Canada
                [ 13 ] Montreal Heart Institute Montreal Quebec Canada
                Author notes
                [*] [* ] Correspondence

                Claudine J. Gauthier, Department of Physics, Concordia University, Montreal, QC, Canada.

                Email: claudine.gauthier@ 123456concordia.ca

                Author information
                https://orcid.org/0000-0002-5701-8506
                https://orcid.org/0000-0002-3381-8453
                https://orcid.org/0000-0001-8356-6808
                https://orcid.org/0000-0003-2604-2404
                Article
                HBM26431
                10.1002/hbm.26431
                10472917
                37498014
                244190ca-0fea-45e5-aa9a-c988d07a5683
                © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 12 April 2023
                : 30 June 2022
                : 11 May 2023
                Page count
                Figures: 9, Tables: 2, Pages: 18, Words: 13507
                Funding
                Funded by: Canadian Institutes of Health Research Accelerator
                Funded by: Canadian Natural Sciences and Engineering Research Council
                Award ID: DGECR‐2020‐00146
                Award ID: RGPIN‐2015‐04665
                Award ID: RGPIN‐2020‐06812
                Funded by: Fonds de recherche du Québec ‐ Nature et Technologies
                Funded by: Heart and Stroke Foundation of Canada , doi 10.13039/100004411;
                Award ID: HNC 170723
                Funded by: Max‐Planck‐Gesellschaft
                Funded by: Michal and Renata Hornstein Chair in Cardiovascular Imaging
                Funded by: National Institute of Health
                Award ID: R00NS102884
                Funded by: Réseau en Bio‐Imagerie du Quebec
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                1 October, 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.3 mode:remove_FC converted:01.09.2023

                Neurology
                bias,rsfmri,ultra‐high field mri,vasculature
                Neurology
                bias, rsfmri, ultra‐high field mri, vasculature

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