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BioMed Research International
Volume 2014 (2014), Article ID 380531, 10 pages
Research Article

Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data

1Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Avenida dos Estados 5001, 09210-580 Santo Andre, SP, Brazil
2NIF-LIM44, Institute of Radiology, Hospital das Clinicas, University of Sao Paulo, Avenida Dr. Enéas de Carvalho Aguiar, 05403-900 Sao Paulo, SP, Brazil
3Department of Psychiatry, Federal University of Rio Grande do Sul, Rua Ramiro Barcelos 2350, 90035-903 Porto Alegre, RS, Brazil
4National Institute of Developmental Psychiatry for Children and Adolescents, Brazil

Received 16 May 2014; Revised 1 August 2014; Accepted 1 August 2014; Published 31 August 2014

Academic Editor: Yihong Yang

Copyright © 2014 Anderson dos Santos Siqueira et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The framework of graph theory provides useful tools for investigating the neural substrates of neuropsychiatric disorders. Graph description measures may be useful as predictor variables in classification procedures. Here, we consider several centrality measures as predictor features in a classification algorithm to identify nodes of resting-state networks containing predictive information that can discriminate between typical developing children and patients with attention-deficit/hyperactivity disorder (ADHD). The prediction was based on a support vector machines classifier. The analyses were performed in a multisite and publicly available resting-state fMRI dataset of healthy children and ADHD patients: the ADHD-200 database. Network centrality measures contained little predictive information for the discrimination between ADHD patients and healthy subjects. However, the classification between inattentive and combined ADHD subtypes was more promising, achieving accuracies higher than 65% (balance between sensitivity and specificity) in some sites. Finally, brain regions were ranked according to the amount of discriminant information and the most relevant were mapped. As hypothesized, we found that brain regions in motor, frontoparietal, and default mode networks contained the most predictive information. We concluded that the functional connectivity estimations are strongly dependent on the sample characteristics. Thus different acquisition protocols and clinical heterogeneity decrease the predictive values of the graph descriptors.