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      Visual alertness and brain diffusion tensor imaging at term age predict neurocognitive development at preschool age in extremely preterm‐born children

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          Abstract

          Introduction

          Cognitive development is characterized by the structural and functional maturation of the brain. Diffusion‐weighted magnetic resonance imaging (dMRI) provides methods of investigating the brain structure and connectivity and their correlations with the neurocognitive outcome. Our aim was to examine the relationship between early visual abilities, brain white matter structures, and the later neurocognitive outcome.

          Methods

          This study included 20 infants who were born before 28 gestational weeks and followed until the age of 6.5 years. At term age, visual alertness was evaluated and dMRI was used to investigate the brain white matter structure using fractional anisotropy (FA) in tract‐based spatial statistics analysis. The JHU DTI white matter atlas was used to locate the findings. The neuropsychological assessment was used to assess neurocognitive performance at 6.5 years.

          Results

          Optimal visual alertness at term age was significantly associated with better visuospatial processing ( p < .05), sensorimotor functioning ( p < .05), and social perception ( p < .05) at 6.5 years of age. Optimal visual alertness related to higher FA values, and further, the FA values positively correlated with the neurocognitive outcome. The tract‐based spatial differences in FA values were detected between children with optimal and nonoptimal visual alertness according to performance at 6.5 years.

          Conclusion

          We provide neurobiological evidence for the global and tract‐based spatial differences in the white matter maturation between extremely preterm children with optimal and nonoptimal visual alertness at term age and a link between white matter maturation, visual alertness and the neurocognitive outcome at 6.5 years proposing that early visual function is a building block for the later neurocognitive development.

          Abstract

          The relationship between early visual abilities, brain white matter structures, and later neurocognitive outcome in extremely preterm born infants was examined. At term age, visual alertness was evaluated and dMRI was used to investigate the brain white matter structure and at 6.5 years neurocognitive performance was assessed. Optimal visual alertness related to widely increased mean fractional anisotropy (FA), and the mean FA values positively correlated with neurocognitive outcome proposing that early visual function is a building block for the later neurocognitive development.

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          Most cited references58

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          Advances in functional and structural MR image analysis and implementation as FSL.

          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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            Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.

            Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding.
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              Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

              There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.
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                Author and article information

                Contributors
                leena.aho@hus.fi
                Journal
                Brain Behav
                Brain Behav
                10.1002/(ISSN)2157-9032
                BRB3
                Brain and Behavior
                John Wiley and Sons Inc. (Hoboken )
                2162-3279
                10 May 2023
                July 2023
                : 13
                : 7 ( doiID: 10.1002/brb3.v13.7 )
                : e3048
                Affiliations
                [ 1 ] New Children's Hospital, Pediatric Research Center University of Helsinki and Helsinki University Hospital Helsinki Finland
                [ 2 ] BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology Children's Hospital Helsinki University Hospital and University of Helsinki Helsinki Finland
                [ 3 ] Department of Psychology and Logopedics University of Helsinki Helsinki Finland
                Author notes
                [*] [* ] Correspondence

                Leena Aho, New Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

                Email: leena.aho@ 123456hus.fi

                Author information
                https://orcid.org/0000-0001-7974-5635
                Article
                BRB33048
                10.1002/brb3.3048
                10338808
                37165734
                5d0d3ac8-2134-485f-98ba-c3b38bece5d3
                © 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 April 2023
                : 15 December 2022
                : 19 April 2023
                Page count
                Figures: 8, Tables: 3, Pages: 16, Words: 8412
                Funding
                Funded by: Suomen Lääketieteen Säätiö , doi 10.13039/100008723;
                Funded by: Orionin Tutkimussäätiö , doi 10.13039/501100007083;
                Funded by: Instrumentariumin Tiedesäätiö , doi 10.13039/501100008413;
                Funded by: Arvo and Lea Ylppö Foundation
                Funded by: Lastentautien Tutkimussäätiö , doi 10.13039/501100005744;
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                July 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.1 mode:remove_FC converted:13.07.2023

                Neurosciences
                diffusion tensor imaging,neurodevelopment,preterm birth,social cognition,visual alertness

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