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

ABIDE autism spectrum disorders children functional connectivity machine learning resting-state fMRI

Journal

Frontiers in psychiatry
ISSN: 1664-0640
Titre abrégé: Front Psychiatry
Pays: Switzerland
ID NLM: 101545006

Informations de publication

Date de publication:
2019
Historique:
received: 08 03 2019
accepted: 01 08 2019
entrez: 17 10 2019
pubmed: 17 10 2019
medline: 17 10 2019
Statut: epublish

Résumé

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.

Identifiants

pubmed: 31616322
doi: 10.3389/fpsyt.2019.00620
pmc: PMC6763745
doi:

Types de publication

Journal Article

Langues

eng

Pagination

620

Informations de copyright

Copyright © 2019 Spera, Retico, Bosco, Ferrari, Palumbo, Oliva, Muratori and Calderoni.

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Auteurs

Giovanna Spera (G)

National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy.

Alessandra Retico (A)

National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy.

Paolo Bosco (P)

IRCCS Stella Maris Foundation, Pisa, Italy.

Elisa Ferrari (E)

National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy.
Scuola Normale Superiore, Faculty of Sciences, Pisa, Italy.

Letizia Palumbo (L)

National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy.

Piernicola Oliva (P)

Department of Chemistry, and Pharmacy, University of Sassari, Sassari, Italy.
National Institute for Nuclear Physics (INFN), Cagliari Division, Cagliari, Italy.

Filippo Muratori (F)

IRCCS Stella Maris Foundation, Pisa, Italy.
Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.

Sara Calderoni (S)

IRCCS Stella Maris Foundation, Pisa, Italy.
Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.

Classifications MeSH