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      Neural correlates associated with impaired global motion perception in cerebral visual impairment (CVI)

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          Highlights

          • Cerebral visual impairment (CVI) is associated with impaired global motion processing.

          • Mean motion coherence thresholds was higher in individuals with CVI.

          • fMRI responses in area hMT+ showed an aberrant response profile in CVI.

          • White matter tract reconstruction revealed cortico-cortical dysmyelination in CVI.

          Abstract

          Cerebral visual impairment (CVI) is associated with a wide range of visual perceptual deficits including global motion processing. However, the underlying neurophysiological basis for these impairments remain poorly understood. We investigated global motion processing abilities in individuals with CVI compared to neurotypical controls using a combined behavioral and multi-modal neuroimaging approach. We found that CVI participants had a significantly higher mean motion coherence threshold (determined using a random dot kinematogram pattern simulating optic flow motion) compared to controls. Using functional magnetic resonance imaging (fMRI), we investigated activation response profiles in functionally defined early (i.e. primary visual cortex; area V1) and higher order (i.e. middle temporal cortex; area hMT+) stages of motion processing. In area V1, responses to increasing motion coherence were similar in both groups. However, in the CVI group, activation in area hMT+ was significantly reduced compared to controls, and consistent with a surround facilitation (rather than suppression) response profile. White matter tract reconstruction obtained from high angular resolution diffusion imaging (HARDI) revealed evidence of increased mean, axial, and radial diffusivities within cortico-cortical (i.e. V1-hMT+), but not thalamo-hMT+ connections. Overall, our results suggest that global motion processing deficits in CVI may be associated with impaired signal integration and segregation mechanisms, as well as white matter integrity at the level of area hMT+.

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          Most cited references85

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          An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging

          In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal–Tanner gradients on a 3 T Siemens Verio, a 3 T Siemens Connectome Skyra or a 7 T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement.
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            White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI.

            Diffusion-weighted MRI (DW-MRI) has been increasingly used in imaging neuroscience over the last decade. An early form of this technique, diffusion tensor imaging (DTI) was rapidly implemented by major MRI scanner companies as a scanner selling point. Due to the ease of use of such implementations, and the plausibility of some of their results, DTI was leapt on by imaging neuroscientists who saw it as a powerful and unique new tool for exploring the structural connectivity of human brain. However, DTI is a rather approximate technique, and its results have frequently been given implausible interpretations that have escaped proper critique and have appeared misleadingly in journals of high reputation. In order to encourage the use of improved DW-MRI methods, which have a better chance of characterizing the actual fiber structure of white matter, and to warn against the misuse and misinterpretation of DTI, we review the physics of DW-MRI, indicate currently preferred methodology, and explain the limits of interpretation of its results. We conclude with a list of 'Do's and Don'ts' which define good practice in this expanding area of imaging neuroscience. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images

              Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement. The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                21 September 2021
                2021
                21 September 2021
                : 32
                : 102821
                Affiliations
                [a ]The Laboratory for Visual Neuroplasticity, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
                [b ]The Translational Vision Laboratory, Department of Psychology, Northeastern University, Boston, MA, USA
                [c ]The Neuroimaging, Perception & Attention Laboratory, Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
                Author notes
                [* ]Corresponding author. lotfi_merabet@ 123456meei.harvard.edu
                [1]

                Equal contributing first authors.

                Article
                S2213-1582(21)00265-5 102821
                10.1016/j.nicl.2021.102821
                8501506
                34628303
                e6a4a4d1-4b82-41d7-a0b6-834c36928cc6
                © 2021 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 7 April 2021
                : 7 August 2021
                : 7 September 2021
                Categories
                Regular Article

                cerebral visual impairment,global motion processing,optic flow,fmri,diffusion imaging,dorsal stream,surround suppression

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