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https://hdl.handle.net/11000/34262
Characterization of Noise Signatures of Involuntary Head Motion in the Autism Brain Imaging Data Exchange Repository
Título : Characterization of Noise Signatures of Involuntary Head Motion in the Autism Brain Imaging Data Exchange Repository |
Autor : Caballero, Carla  Mistry, Sejal  Vero, Joe Torres, Elizabeth |
Editor : Frontiers |
Departamento: Departamentos de la UMH::Ciencias del Deporte |
Fecha de publicación: 2018 |
URI : https://hdl.handle.net/11000/34262 |
Resumen :
The variability inherently present in biophysical data is partly contributed by disparate
sampling resolutions across instrumentations. This poses a potential problem for
statistical inference using pooled data in open access repositories. Such repositories
combine data collected from multiple research sites using variable sampling resolutions.
One example is the Autism Brain Imaging Data Exchange repository containing
thousands of imaging and demographic records from participants in the spectrum of
autism and age-matched neurotypical controls. Further, statistical analyses of groups
from different diagnoses and demographics may be challenging, owing to the disparate
number of participants across different clinical subgroups. In this paper, we examine the
noise signatures of head motion data extracted from resting state fMRI data harnessed
under different sampling resolutions. We characterize the quality of the noise in the
variability of the raw linear and angular speeds for different clinical phenotypes in relation
to age-matched controls. Further, we use bootstrapping methods to ensure compatible
group sizes for statistical comparison and report the ranges of physical involuntary
head excursions of these groups. We conclude that different sampling rates do affect
the quality of noise in the variability of head motion data and, consequently, the type
of random process appropriate to characterize the time series data. Further, given a
qualitative range of noise, from pink to brown noise, it is possible to characterize different
clinical subtypes and distinguish themin relation to ranges of neurotypical controls. These
results may be of relevance to the pre-processing stages of the pipeline of analyses of
resting state fMRI data, whereby head motion enters the criteria to clean imaging data
from motion artifacts.
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Palabras clave/Materias: autism Asperger’s noise stochastic process head motion resting-state fMRI |
Área de conocimiento : CDU: Bellas artes: Diversiones. Espectáculos. Cine. Teatro. Danza. Juegos.Deportes |
Tipo de documento : info:eu-repo/semantics/article |
Derechos de acceso: info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
DOI : https://doi.org/10.3389/fnint.2018.00007 |
Aparece en las colecciones: Artículos Ciencias del Deporte
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La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.