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      The human vestibular cortex: functional anatomy of OP2, its connectivity and the effect of vestibular disease

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          Abstract

          Area OP2 in the posterior peri-sylvian cortex has been proposed to be the core human vestibular cortex. We investigated the functional anatomy of OP2 and adjacent areas (OP2 +) using spatially constrained independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data from the Human Connectome Project. Ten ICA-derived subregions were identified. OP2 + responses to vestibular and visual motion were analyzed in 17 controls and 17 right-sided vestibular neuritis patients who had previously undergone caloric and optokinetic stimulation during fMRI. In controls, a posterior part of right OP2 + showed: (i) direction-selective responses to visual motion and (ii) activation during caloric stimulation that correlated positively with perceived self-motion, and negatively with visual dependence and peak slow-phase nystagmus velocity. Patients showed abnormal OP2 + activity, with an absence of visual or caloric activation of the healthy ear and no correlations with vertigo or visual dependence—despite normal slow-phase nystagmus responses to caloric stimulation. Activity in a lateral part of right OP2 + correlated with chronic visually induced dizziness in patients. In summary, distinct functional subregions of right OP2 + show strong connectivity to other vestibular areas and a profile of caloric and visual responses, suggesting a central role for vestibular function in health and disease.

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          Fast robust automated brain extraction.

          An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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            • Record: found
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            • Article: not found

            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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              • Record: found
              • Abstract: found
              • Article: not found

              Improved optimization for the robust and accurate linear registration and motion correction of brain images.

              Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.
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                Author and article information

                Contributors
                Journal
                Cereb Cortex
                Cereb Cortex
                cercor
                Cerebral Cortex (New York, NY)
                Oxford University Press
                1047-3211
                1460-2199
                01 February 2023
                02 March 2022
                02 March 2022
                : 33
                : 3
                : 567-582
                Affiliations
                Computational , Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London , London W12 0NN, United Kingdom
                Neuro-otology Unit , Department of Brain Sciences, Imperial College London , London W6 8RP, United Kingdom
                Computational , Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London , London W12 0NN, United Kingdom
                UK Dementia Research Institute , Care Research & Technology Centre, Imperial College London , London W12 0BZ, United Kingdom
                Computational , Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London , London W12 0NN, United Kingdom
                Department of Clinical and Motor Neurosciences , Centre for Vestibular and Behavioural Neurosciences, University College London , London WC1N 3BG, United Kingdom
                Neuro-otology Unit , Department of Brain Sciences, Imperial College London , London W6 8RP, United Kingdom
                Computational , Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London , London W12 0NN, United Kingdom
                UK Dementia Research Institute , Care Research & Technology Centre, Imperial College London , London W12 0BZ, United Kingdom
                Centre for Injury Studies, Imperial College London , London SW7 2AZ, United Kingdom
                Author notes
                Author information
                https://orcid.org/0000-0001-8218-3821
                https://orcid.org/0000-0002-4050-0208
                Article
                bhac085
                10.1093/cercor/bhac085
                9890474
                35235642
                44f2e1e6-304e-470d-bc56-3d7734aa84b5
                © The Author(s) 2022. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 October 2021
                : 31 January 2022
                : 1 February 2022
                Page count
                Pages: 16
                Funding
                Funded by: UK Medical Research Council, DOI 10.13039/501100000265;
                Award ID: MR/J004685/1
                Funded by: Dunhill Medical Trust, DOI 10.13039/501100000377;
                Award ID: R481/0516
                Funded by: Imperial National Institute for Health Research;
                Categories
                Original Article
                AcademicSubjects/MED00310
                AcademicSubjects/MED00385
                AcademicSubjects/SCI01870

                Neurology
                perception,visual,vestibular neuritis,vestibular cortex
                Neurology
                perception, visual, vestibular neuritis, vestibular cortex

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