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Title: A Novel Way to Measure and Predict Development: A Heuristic Approach to Facilitate the Early Detection of Neurodevelopmental Disorders
Authors: Marschik, Peter B .
Pokorny, Florian B.
Peharz, Robert
Zhang, Dajie
O’Muircheartaigh, Jonathan
Roeyers, Herbert
Bölte, Sven
Spittle, Alicia J.
Urlesberger, Berndt
Schuller, Björn
Poustka, Luise
Ozonoff, Sally
Pernkopf, Franz
Pock, Thomas
Tammimies, Kristiina
Enzinger, Christian
Krieber, Magdalena
Tomantschger, Iris
Bartl-Pokorny, Katrin D.
Sigafoos, Jeff
Roche, Laura
Esposito, Gianluca
Gugatschka, Markus
Nielsen-Saines, Karin
Einspieler, Christa
Kaufmann, Walter E.
Keywords: Computer vision
Issue Date: 2017
Source: Marschik, P. B., Pokorny, F. B., Peharz, R., Zhang, D., O’Muircheartaigh, J., Roeyers, H., et al. (2017). A Novel Way to Measure and Predict Development: A Heuristic Approach to Facilitate the Early Detection of Neurodevelopmental Disorders. Current Neurology and Neuroscience Reports, 17, 43-.
Series/Report no.: Current Neurology and Neuroscience Reports
Abstract: Purpose of Review: Substantial research exists focusing on the various aspects and domains of early human development. However, there is a clear blind spot in early postnatal development when dealing with neurodevelopmental disorders, especially those that manifest themselves clinically only in late infancy or even in childhood. Recent Findings: This early developmental period may represent an important timeframe to study these disorders but has historically received far less research attention. We believe that only a comprehensive interdisciplinary approach will enable us to detect and delineate specific parameters for specific neurodevelopmental disorders at a very early age to improve early detection/diagnosis, enable prospective studies and eventually facilitate randomised trials of early intervention. Summary: In this article, we propose a dynamic framework for characterising neurofunctional biomarkers associated with specific disorders in the development of infants and children. We have named this automated detection ‘Fingerprint Model’, suggesting one possible approach to accurately and early identify neurodevelopmental disorders.
ISSN: 1528-4042
DOI: 10.1007/s11910-017-0748-8
Schools: School of Humanities and Social Sciences 
Rights: © 2017 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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