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      A longitudinal neuroimaging dataset on arithmetic processing in school children

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          Abstract

          We describe functional and structural data acquired using a 3T scanner in a sample of 132 typically developing children, who were scanned when they were approximately 11 years old (i.e. Time 1). Sixty-three of them were scanned again approximately 2 years later (i.e. Time 2). Children performed four tasks inside the scanner: two arithmetic tasks and two localizer tasks. The arithmetic tasks were a single-digit multiplication and a single-digit subtraction task. The localizer tasks, a written rhyming judgment task and a numerosity judgment task, were used to independently identify verbal and quantity brain areas, respectively. Additionally, we provide data on behavioral performance on the tasks inside the scanner, participants’ scores on standardized tests, including reading and math skill, and a developmental history questionnaire completed by parents. This dataset could be useful to answer questions regarding the neural bases of the development of math in children and its relation to individual differences in skill. The data, entitled “ Brain Correlates of Math Development”, are freely available from OpenNeuro ( https://openneuro.org).

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          Most cited references20

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          Modeling the Effects of Perceptual Load: Saliency, Competitive Interactions, and Top-Down Biases

          A computational model of visual selective attention has been implemented to account for experimental findings on the Perceptual Load Theory (PLT) of attention. The model was designed based on existing neurophysiological findings on attentional processes with the objective to offer an explicit and biologically plausible formulation of PLT. Simulation results verified that the proposed model is capable of capturing the basic pattern of results that support the PLT as well as findings that are considered contradictory to the theory. Importantly, the model is able to reproduce the behavioral results from a dilution experiment, providing thus a way to reconcile PLT with the competing Dilution account. Overall, the model presents a novel account for explaining PLT effects on the basis of the low-level competitive interactions among neurons that represent visual input and the top-down signals that modulate neural activity. The implications of the model concerning the debate on the locus of selective attention as well as the origins of distractor interference in visual displays of varying load are discussed.
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            A technique for the deidentification of structural brain MR images.

            Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. [2002]: Neuron 33:341-355; Fischl et al. [2004]: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects. (c) 2007 Wiley-Liss, Inc.
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              Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion.

              We present the use of an entropy focus criterion to enable automatic focusing of motion corrupted magnetic resonance images. We demonstrate the principle using illustrative examples from cooperative volunteers. Our technique can determine unknown patient motion or use knowledge of motion from other measures as a starting estimate. The motion estimate is used to compensate the acquired data and is iteratively refined using the image entropy. Entropy focuses the whole image principally by favoring the removal of motion induced ghosts and blurring from otherwise dark regions of the image. Using only the image data, and no special hardware or pulse sequences, we demonstrate correction for arbitrary rigid-body translational motion in the imaging plane and for a single rotation. Extension to three-dimensional (3-D) and more general motion should be possible. The algorithm is able to determine volunteer motion well. The mean absolute deviation between algorithm and navigator-echo-determined motion is comparable to the displacement step size used in the algorithm. Local deviations from the recorded motion or navigator-determined motion are explained and we indicate how enhanced focus criteria may be derived. In all cases we were able to compensate images for patient motion, reducing blurring and ghosting.
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                Author and article information

                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                2052-4463
                05 March 2019
                2019
                : 6
                : 190040
                Affiliations
                [1 ]Department of Psychology and Human Development, Vanderbilt University , Nashville, TN, USA
                [2 ]Neurology Department, Neuroscape, University of California San Francisco , San Francisco, CA, USA
                Author notes
                []

                M.S.P.: Contribution to data sharing plan, data preparation and analysis. Manuscript writing. Critical review and final approval of the version submitted for publication. M.L.: Data preparation, validation, quality control, curation and upload. Critical review and final approval of the version submitted for publication. J.W.Y.: Contribution to data sharing plan and data preparation. Critical review and final approval of the version submitted for publication. J.R.B.: Conception and design of the study and data collection supervision. Design of data sharing plan. Critical review and final approval of the version submitted for publication.

                Author information
                http://orcid.org/0000-0002-9056-9561
                Article
                sdata201940
                10.1038/sdata.2019.40
                6400102
                30835258
                069f13b0-422f-4141-8482-92e35e5a0f27
                Copyright © 2019, The Author(s)

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.

                History
                : 19 September 2018
                : 13 December 2018
                Categories
                Data Descriptor

                cognitive neuroscience,functional magnetic resonance imaging,human behaviour,brain imaging

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