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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Psychiatry | doi: 10.3389/fpsyt.2019.00620

Evaluation of altered functional connections in male children with Autism Spectrum Disorders on multiple-site data optimized with machine learning

Giovanna Spera1,  Alessandra Retico1*,  Paolo Bosco2,  Elisa Ferrari1, 3, Letizia Palumbo1, Piernicola Oliva4, 5,  Filippo Muratori2, 6 and  Sara Calderoni2, 6
  • 1National Institute of Nuclear Physics, Section of Pisa, Italy
  • 2Fondazione Stella Maris (IRCCS), Italy
  • 3Scuola Normale Superiore di Pisa, Italy
  • 4University of Sassari, Italy
  • 5National Institute of Nuclear Physics, Section of Cagliari, Italy
  • 6University of Pisa, Italy

No univocal and reliable brain-based biomarkers have been detected to date in Autism Spectrum Disorders (ASD). Neuroimaging studies have consistently revealed alterations in brain structure and function of individuals with ASD; however, it remains difficult to ascertain the extent and localization of affected brain networks. In this context, the application of Machine Learning (ML) classification methods to neuroimaging data has the potential to contribute to a better distinction between subjects with ASD and typical development controls (TD).
This study is focused on the analysis of resting-state fMRI data of individuals with ASD and matched TD, available within the ABIDE collection. To reduce the multiple sources of heterogeneity that impact on understanding the neural underpinnings of autistic condition, we selected a subgroup of 190 subjects (102 with ASD and 88 TD) according to the following criteria: male children (age range: 6.5-13 years); rs-fMRI data acquired with open eyes; data from the University sites that provided the largest number of scans (KKI, NYU, UCLA, UM).
Connectivity values were evaluated as the linear correlation between pairs of time series of brain areas; then, a Linear kernel Support Vector Machine (L-SVM) classification, with an inter-site cross-validation scheme, was carried out. A permutation test was conducted to identify over-connectivity and under-connectivity alterations in the ASD group.
The mean L-SVM classification performance, in term of area under the ROC curve (AUC), was 0.75±0.05. The highest performance was obtained using data from KKI, NYU and UCLA sites in training and data from UM as testing set (AUC=0.83). Specifically, stronger functional connectivity in ASD with respect to TD involve (p<0.001) the angular gyrus with the precuneus in the right (R) hemisphere, and the R frontal operculum cortex with the pars opercularis of the left (L) inferior frontal gyrus. Weaker connections in ASD group with respect to TD are the intra-hemispheric R temporal fusiform cortex with the R hippocampus, and the L supramarginal gyrus with L planum polare. The results indicate that both hypo- and hyper-connectivity occurred in a selected cohort of ASD children relative to TD controls, and that these functional alterations are spread in different brain networks.

Keywords: Autism spectrum disorders (ASD), Children, Resting-state fMRI, functional connectivity, machine learning, ABIDE

Received: 08 Mar 2019; Accepted: 01 Aug 2019.

Copyright: © 2019 Spera, Retico, Bosco, Ferrari, Palumbo, Oliva, Muratori and Calderoni. 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(s) 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: Dr. Alessandra Retico, National Institute of Nuclear Physics, Section of Pisa, Pisa, Tuscany, Italy,