| Title |
A Novel Way to Measure and Predict Development: A Heuristic Approach to Facilitate the Early Detection of Neurodevelopmental Disorders
|
|---|---|
| Published in |
Current Neurology and Neuroscience Reports, April 2017
|
| DOI | 10.1007/s11910-017-0748-8 |
| Pubmed ID | |
| Authors |
Peter B . Marschik, Florian B. Pokorny, Robert Peharz, Dajie Zhang, Jonathan O’Muircheartaigh, Herbert Roeyers, Sven Bölte, Alicia J. Spittle, Berndt Urlesberger, Björn Schuller, Luise Poustka, Sally Ozonoff, Franz Pernkopf, Thomas Pock, Kristiina Tammimies, Christian Enzinger, Magdalena Krieber, Iris Tomantschger, Katrin D. Bartl-Pokorny, Jeff Sigafoos, Laura Roche, Gianluca Esposito, Markus Gugatschka, Karin Nielsen-Saines, Christa Einspieler, Walter E. Kaufmann, The BEE-PRI Study Group |
| Abstract |
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. 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. 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 50% |
| Austria | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Unknown | 174 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 36 | 21% |
| Student > Master | 31 | 18% |
| Researcher | 19 | 11% |
| Professor > Associate Professor | 9 | 5% |
| Student > Doctoral Student | 9 | 5% |
| Other | 35 | 20% |
| Unknown | 36 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 29 | 17% |
| Psychology | 24 | 14% |
| Neuroscience | 13 | 7% |
| Nursing and Health Professions | 11 | 6% |
| Engineering | 11 | 6% |
| Other | 36 | 21% |
| Unknown | 51 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 July 2017.
All research outputs
#14,059,145
of 22,965,074 outputs
Outputs from Current Neurology and Neuroscience Reports
#610
of 919 outputs
Outputs of similar age
#167,730
of 309,841 outputs
Outputs of similar age from Current Neurology and Neuroscience Reports
#26
of 33 outputs
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