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      Cortical Processing of Multimodal Sensory Learning in Human Neonates

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          Abstract

          Following birth, infants must immediately process and rapidly adapt to the array of unknown sensory experiences associated with their new ex-utero environment. However, although it is known that unimodal stimuli induce activity in the corresponding primary sensory cortices of the newborn brain, it is unclear how multimodal stimuli are processed and integrated across modalities. The latter is essential for learning and understanding environmental contingencies through encoding relationships between sensory experiences; and ultimately likely subserves development of life-long skills such as speech and language. Here, for the first time, we map the intracerebral processing which underlies auditory-sensorimotor classical conditioning in a group of 13 neonates (median gestational age at birth: 38 weeks + 4 days, range: 32 weeks + 2 days to 41 weeks + 6 days; median postmenstrual age at scan: 40 weeks + 5 days, range: 38 weeks + 3 days to 42 weeks + 1 days) with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (MRI) and magnetic resonance (MR) compatible robotics. We demonstrate that classical conditioning can induce crossmodal changes within putative unimodal sensory cortex even in the absence of its archetypal substrate. Our results also suggest that multimodal learning is associated with network wide activity within the conditioned neural system. These findings suggest that in early life, external multimodal sensory stimulation and integration shapes activity in the developing cortex and may influence its associated functional network architecture.

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          Most cited references 71

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          FSL.

          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

            Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Controlling the familywise error rate in functional neuroimaging: a comparative review.

              Functional neuroimaging data embodies a massive multiple testing problem, where 100,000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics: Bonferroni, random field and the permutation test. Owing to recent developments, improved Bonferroni procedures, such as Hochberg's methods, are now applicable to dependent data. Continuous random field methods use the smoothness of the image to adapt to the severity of the multiple testing problem. Also, increased computing power has made both permutation and bootstrap methods applicable to functional neuroimaging. We evaluate these approaches on t images using simulations and a collection of real datasets. We find that Bonferroni-related tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom. We also show the limitations of trying to find an equivalent number of independent tests for an image of correlated test statistics.
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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
                March 2021
                18 November 2020
                18 November 2020
                : 31
                : 3
                : 1827-1836
                Affiliations
                Department of Bioengineering , Imperial College London , London SW7 2AZ, UK
                Centre for the Developing Brain , School of Biomedical Engineering and Imaging Sciences, Kings College London , London SE1 7EH, UK
                Department of Electrical Engineering , Chalmers University of Technology , Gothenburg 412 96, Sweden
                Department of Psychiatry , Columbia University , New York 10032, NY
                Department of Psychiatry , Columbia University , New York 10032, NY
                Centre for the Developing Brain , School of Biomedical Engineering and Imaging Sciences, Kings College London , London SE1 7EH, UK
                Centre for the Developing Brain , School of Biomedical Engineering and Imaging Sciences, Kings College London , London SE1 7EH, UK
                Centre for the Developing Brain , School of Biomedical Engineering and Imaging Sciences, Kings College London , London SE1 7EH, UK
                Centre for the Developing Brain , School of Biomedical Engineering and Imaging Sciences, Kings College London , London SE1 7EH, UK
                Department of Bioengineering , Imperial College London , London SW7 2AZ, UK
                Centre for the Developing Brain , School of Biomedical Engineering and Imaging Sciences, Kings College London , London SE1 7EH, UK
                Department of Bioengineering , Imperial College London , London SW7 2AZ, UK
                Department of Bioengineering , Imperial College London , London SW7 2AZ, UK
                Centre for the Developing Brain , School of Biomedical Engineering and Imaging Sciences, Kings College London , London SE1 7EH, UK
                Paediatric Neurosciences , Evelina London Children’s Hospital, St Thomas’ Hospital, London SE1 7EH, UK
                Author notes
                Address correspondence to Prof. Etienne Burdet, Department of Bioengineering, Imperial College London, London SW7 2AZ, UK. Email: e.burdet@ 123456imperial.ac.uk or Dr Tomoki Arichi, Centre for the Developing Brain, King’s College London, London SE1 7EH, UK. Email: tomoki.arichi@ 123456kcl.ac.uk .
                Article
                bhaa340
                10.1093/cercor/bhaa340
                7869081
                33207366
                © The Author(s) 2020. Published by Oxford University Press.

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

                Page count
                Pages: 10
                Product
                Funding
                Funded by: Engineering and Physical Sciences Research Council, DOI 10.13039/501100000266;
                Award ID: EP/L016737/1
                Funded by: Chalmers University of Technology Area of Advance in Life Science Engineering;
                Funded by: Medical Research Council, DOI 10.13039/501100007155;
                Award ID: MR/P008712/1
                Funded by: European Commission, DOI 10.13039/501100000780;
                Award ID: ICT-644727
                Funded by: UK Engineering and Physical Sciences Research Council;
                Award ID: NO29003/1
                Funded by: National Institute for Health Research, DOI 10.13039/501100000272;
                Funded by: Wellcome Engineering and Physical Sciences Research Council;
                Funded by: Centre for Medical Engineering at Kings College London;
                Award ID: WT 203148/Z/16/Z
                Categories
                Original Article
                AcademicSubjects/MED00310
                AcademicSubjects/MED00385
                AcademicSubjects/SCI01870

                Neurology

                brain plasticity, classical conditioning, functional mri, multisensory integration, neonate

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