Evaluation of Altered Functional Connections in Male Children With Autism Spectrum Disorders on Multiple-Site Data Optimized With Machine Learning

Bibliographic Data

ID15519500
AuthorsGiovanna Spera (Istituto Nazionale di Fisica Nucleare, Sezione di Pisa), Alessandra Retico (0000-0001-5135-4472, Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, corresponding author), Paolo Bosco (0000-0002-4724-9423, Fondazione Stella Maris), Elisa Ferrari (0000-0001-5829-1339, Scuola Normale Superiore), Letizia Palumbo (0000-0002-4433-7401, Istituto Nazionale di Fisica Nucleare, Sezione di Pisa), P Oliva (0000-0002-9446-3967, University of Sassari), Filippo Muratori (0000-0001-9598-0096, University of Pisa), Sara Calderoni (0000-0002-6250-5739, Fondazione Stella Maris)
Year2019
Volume10
Pages620-620
Publication date2019-09-20
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2019.00620
PMID31616322
OpenAlexW2964527510
LanguageEN
References cited64

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 terms of the 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 (FC) 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 under- and over-FC occurred in a selected cohort of ASD children relative to TD controls, and that these functional alterations are spread in different brain networks

Audiology · Autism · Autism spectrum disorder · Biology · Context (archaeology · Correlation · Developmental psychology · Machine learning · Neuroimaging · Pattern recognition (psychology · Resting state fMRI · Support vector machine · Autism Spectrum Disorder Research · Computer Science · EEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies · Mathematics · Medicine · Neuroscience · Psychology · Artificial Intelligence

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