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      The visual white matter: The application of diffusion MRI and fiber tractography to vision science

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          Abstract

          Visual neuroscience has traditionally focused much of its attention on understanding the response properties of single neurons or neuronal ensembles. The visual white matter and the long-range neuronal connections it supports are fundamental in establishing such neuronal response properties and visual function. This review article provides an introduction to measurements and methods to study the human visual white matter using diffusion MRI. These methods allow us to measure the microstructural and macrostructural properties of the white matter in living human individuals; they allow us to trace long-range connections between neurons in different parts of the visual system and to measure the biophysical properties of these connections. We also review a range of findings from recent studies on connections between different visual field maps, the effects of visual impairment on the white matter, and the properties underlying networks that process visual information supporting visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open datasets that are becoming available to study brain connectivity and white matter properties, and open source software for the analysis of these data.

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          Most cited references140

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          Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.

          Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.
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            A cortical representation of the local visual environment.

            Medial temporal brain regions such as the hippocampal formation and parahippocampal cortex have been generally implicated in navigation and visual memory. However, the specific function of each of these regions is not yet clear. Here we present evidence that a particular area within human parahippocampal cortex is involved in a critical component of navigation: perceiving the local visual environment. This region, which we name the 'parahippocampal place area' (PPA), responds selectively and automatically in functional magnetic resonance imaging (fMRI) to passively viewed scenes, but only weakly to single objects and not at all to faces. The critical factor for this activation appears to be the presence in the stimulus of information about the layout of local space. The response in the PPA to scenes with spatial layout but no discrete objects (empty rooms) is as strong as the response to complex meaningful scenes containing multiple objects (the same rooms furnished) and over twice as strong as the response to arrays of multiple objects without three-dimensional spatial context (the furniture from these rooms on a blank background). This response is reduced if the surfaces in the scene are rearranged so that they no longer define a coherent space. We propose that the PPA represents places by encoding the geometry of the local environment.
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              Demyelination increases radial diffusivity in corpus callosum of mouse brain.

              Myelin damage, as seen in multiple sclerosis (MS) and other demyelinating diseases, impairs axonal conduction and can also be associated with axonal degeneration. Accurate assessments of these conditions may be highly beneficial in evaluating and selecting therapeutic strategies for patient management. Recently, an analytical approach examining diffusion tensor imaging (DTI) derived parameters has been proposed to assess the extent of axonal damage, demyelination, or both. The current study uses the well-characterized cuprizone model of experimental demyelination and remyelination of corpus callosum in mouse brain to evaluate the ability of DTI parameters to detect the progression of myelin degeneration and regeneration. Our results demonstrate that the extent of increased radial diffusivity reflects the severity of demyelination in corpus callosum of mouse brain affected by cuprizone treatment. Subsequently, radial diffusivity decreases with the progression of remyelination. Furthermore, radial diffusivity changes were specific to the time course of changes in myelin integrity as distinct from axonal injury, which was detected by betaAPP immunostaining and shown to be most extensive prior to demyelination. Radial diffusivity offers a specific assessment of demyelination and remyelination, as distinct from acute axonal damage.
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                Author and article information

                Contributors
                Journal
                J Vis
                J Vis
                jovi
                jovi
                JOVI
                Journal of Vision
                The Association for Research in Vision and Ophthalmology
                1534-7362
                14 February 2017
                2017
                : 17
                : 2
                : 4
                Affiliations
                arokem@ 123456uw.edu

                http://arokem.org
                htakemur@ 123456nict.go.jp
                bock.andrew@ 123456gmail.com
                suzyscherf@ 123456psu.edu
                behrmann@ 123456cmu.edu
                wandell@ 123456stanford.edu
                ionefine@ 123456uw.edu
                holly.bridge@ 123456ndcn.ox.ac.uk
                franpest@ 123456indiana.edu

                http://francopestilli.com
                [1]The University of Washington eScience Institute, Seattle, WA, USA
                [2]Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita-shi, Japan
                [3]Graduate School of Frontier Biosciences, Osaka University, Suita-shi, Japan
                [4]University of Pennsylvania, Philadelphia, PA, USA
                [5]Penn State University, State College, PA, USA
                [6]Carnegie Mellon University, Pittsburgh, PA, USA
                [7]Stanford University, Stanford, CA, USA
                [8]University of Washington, Seattle, WA, USA
                [9]Oxford University, Oxford, UK
                [10]Indiana University, Bloomington, IN, USA
                Article
                jovi-17-01-14 JOV-05449-2016
                10.1167/17.2.4
                5317208
                28196374
                c452060c-ef4a-42e6-ac73-22ef40c8edc4
                Copyright 2017 The Authors

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 21 August 2016
                : 12 December 2016
                Categories
                Review

                mri,diffusion mri,brain,white matter,brain connectivity,visual cortex,visual disability,visual development,categorical perception,face perception,software,computational modeling

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