| Literature DB >> 28255759 |
Jacquelin Rankine1, Erin Li1, Stacey Lurie1,2, Hillary Rieger1, Emily Fourie1, Paige M Siper1,3, A Ting Wang1,3,4,5, Joseph D Buxbaum1,3,4,6,5,7, Alexander Kolevzon8,9,10,11,12.
Abstract
Phelan-McDermid syndrome (PMS) is a single-locus cause of developmental delay, autism spectrum disorder, and minimal verbal abilities. There is an urgent need to identify objective outcome measures of expressive language for use in this and other minimally verbal populations. One potential tool is an automated language processor called Language ENvironment Analysis (LENA). LENA was used to obtain over 542 h of audio in 18 children with PMS. LENA performance was adequate in a subset of children with PMS, specifically younger children and those with fewer stereotypic vocalizations. One LENA-derived language measure, Vocalization Ratio, had improved accuracy in this sample and may represent a novel expressive language measure for use in severely affected populations.Entities:
Keywords: 22q13 deletion syndrome; Autism spectrum disorder; Automated vocal analysis; Language ENvironment Analysis; Minimally verbal; Phelan-McDermid syndrome
Mesh:
Year: 2017 PMID: 28255759 PMCID: PMC6196360 DOI: 10.1007/s10803-017-3082-8
Source DB: PubMed Journal: J Autism Dev Disord ISSN: 0162-3257