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      Impact of In Utero Exposure to Antiepileptic Drugs on Neonatal Brain Function

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          Abstract

          In utero brain development underpins brain health across the lifespan but is vulnerable to physiological and pharmacological perturbation. Here, we show that antiepileptic medication during pregnancy impacts on cortical activity during neonatal sleep, a potent indicator of newborn brain health. These effects are evident in frequency-specific functional brain networks and carry prognostic information for later neurodevelopment. Notably, such effects differ between different antiepileptic drugs that suggest neurodevelopmental adversity from exposure to antiepileptic drugs and not maternal epilepsy per se. This work provides translatable bedside metrics of brain health that are sensitive to the effects of antiepileptic drugs on postnatal neurodevelopment and carry direct prognostic value.

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          Most cited references93

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          BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

          The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).
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            Brainstorm: A User-Friendly Application for MEG/EEG Analysis

            Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
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              Network-based statistic: identifying differences in brain networks.

              Large-scale functional or structural brain connectivity can be modeled as a network, or graph. This paper presents a statistical approach to identify connections in such a graph that may be associated with a diagnostic status in case-control studies, changing psychological contexts in task-based studies, or correlations with various cognitive and behavioral measures. The new approach, called the network-based statistic (NBS), is a method to control the family-wise error rate (in the weak sense) when mass-univariate testing is performed at every connection comprising the graph. To potentially offer a substantial gain in power, the NBS exploits the extent to which the connections comprising the contrast or effect of interest are interconnected. The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. The purpose of this paper is to: (i) introduce the NBS for the first time; (ii) evaluate its power with the use of receiver operating characteristic (ROC) curves; and, (iii) demonstrate its utility with application to a real case-control study involving a group of people with schizophrenia for which resting-state functional MRI data were acquired. The NBS identified a expansive dysconnected subnetwork in the group with schizophrenia, primarily comprising fronto-temporal and occipito-temporal dysconnections, whereas a mass-univariate analysis controlled with the false discovery rate failed to identify a subnetwork. Copyright © 2010 Elsevier Inc. All rights reserved.
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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 June 2022
                29 September 2021
                29 September 2021
                : 32
                : 11
                : 2385-2397
                Affiliations
                Baby Brain Activity Center (BABA) , Department of Clinical Neurophysiology, New Children's Hospital, HUS Imaging, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
                Neuroscience Center , Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
                School of Psychology , College of Engineering, Science and the Environment, University of Newcastle, Callaghan, New South Wales, Australia
                School of Medicine and Public Health , College of Health and Medicine, University of Newcastle, Callaghan, New South Wales, Australia
                Baby Brain Activity Center (BABA) , Department of Clinical Neurophysiology, New Children's Hospital, HUS Imaging, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
                Department of Pediatric Neurology , New Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
                Baby Brain Activity Center (BABA) , Department of Clinical Neurophysiology, New Children's Hospital, HUS Imaging, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
                Neuroscience Center , Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
                Department of Complex Trait Genetics , Center for Neurogenomics and Cognitive Research, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
                Department of Complex Trait Genetics , Center for Neurogenomics and Cognitive Research, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
                Department of Child Psychiatry , Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
                Department of Genetics and Computational Biology , QIMR Berghofer Medical Research Institute, Brisbane, Australia
                Baby Brain Activity Center (BABA) , Department of Clinical Neurophysiology, New Children's Hospital, HUS Imaging, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
                Neuroscience Center , Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
                Author notes
                Address correspondence to Anton Tokariev, BABA center, Children’s Hospital, Helsinki University Hospital, P.O.Box 281, Stenbäckinkatu 11, 00029 HUS, Helsinki, Finland. Email: anton.tokariev@ 123456helsinki.fi , www.babacenter.fi; Sampsa Vanhatalo, BABA center, Children’s Hospital, Helsinki University Hospital, P.O.Box 281, Stenbäckinkatu 11, 00029 HUS, Helsinki, Finland. Email: sampsa.vanhatalo@ 123456helsinki.fi , www.babacenter.fi

                Luca Cocchi and Sampsa Vanhatalo have contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-1202-9981
                https://orcid.org/0000-0003-4943-3969
                https://orcid.org/0000-0002-8309-8247
                https://orcid.org/0000-0003-3278-3524
                https://orcid.org/0000-0002-1331-3255
                https://orcid.org/0000-0003-1570-1195
                https://orcid.org/0000-0003-3651-2676
                https://orcid.org/0000-0002-9771-7061
                Article
                bhab338
                10.1093/cercor/bhab338
                9157298
                34585721
                dc2d8f1c-a96a-4b92-b53a-fb61ee8947d9
                © The Author(s) 2021. 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
                : 21 June 2021
                : 18 August 2021
                : 22 August 2021
                Page count
                Pages: 13
                Funding
                Funded by: Finnish Brain Foundation, DOI 10.13039/501100008320;
                Funded by: Foundation for Pediatric Research, DOI 10.13039/501100005744;
                Funded by: Sigrid Jusélius Foundation, DOI 10.13039/501100006306;
                Funded by: Academy of Finland, DOI 10.13039/501100002341;
                Award ID: 310445
                Award ID: 288220
                Award ID: 313242
                Award ID: 321235
                Categories
                Original Article
                AcademicSubjects/MED00310
                AcademicSubjects/MED00385
                AcademicSubjects/SCI01870

                Neurology
                antiepileptic drug,brain network,infant,neurodevelopment,neurology
                Neurology
                antiepileptic drug, brain network, infant, neurodevelopment, neurology

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