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Front. Integr. Neurosci. | doi: 10.3389/fnint.2018.00007

Characterization of noise signatures of involuntary head motion in the Autism Brain Imaging Data Exchange repository

Carla Caballero1,  Joseph Vero2 and  Elizabeth B. Torres3*
  • 1Psychology, Rutgers University, The State University of New Jersey, United States
  • 2Biomedical Engineering, Rutgers University, The State University of New Jersey, United States
  • 3Psychology, Computer Science, Cognitive Science, Rutgers University, The State University of New Jersey, United States

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 contributed by different research sites using different 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 them in 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.

Keywords: autism, Asperger Syndrome, Noise, stochastic processes, Resting-state fMRI, Detrended Fluctuation Analysis.

Received: 10 Nov 2017; Accepted: 08 Feb 2018.

Edited by:

He Cui, Institute of Neuroscience, Shanghai Institutes for Biological Sciences (CAS), China

Reviewed by:

Shan Yu, Institute of Automation (CAS), China
Xiaofu He, Columbia University Medical Center, United States  

Copyright: © 2018 Caballero, Vero and Torres. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Elizabeth B. Torres, Rutgers University, The State University of New Jersey, Psychology, Computer Science, Cognitive Science, Rutgers University -Psychology Dept. Busch Campus., 152 Frelinghuysen Rd, New Brunswick, 08854, New Jersey, United States,