title: Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data.
abstract: 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.
authors.affiliation: Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Avenida dos Estados 5001, 09210-580 Santo Andre, SP, Brazil.
authors.forename: Anderson
authors.initials: A
authors.lastname: dos Santos Siqueira
authors.affiliation: Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Avenida dos Estados 5001, 09210-580 Santo Andre, SP, Brazil ; NIF-LIM44, Institute of Radiology, Hospital das Clinicas, University of Sao Paulo, Avenida Dr. Enéas de Carvalho Aguiar, 05403-900 Sao Paulo, SP, Brazil.
authors.forename: Claudinei Eduardo
authors.initials: CE
authors.lastname: Biazoli Junior
authors.affiliation: Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Avenida dos Estados 5001, 09210-580 Santo Andre, SP, Brazil.
authors.forename: William Edgar
authors.initials: WE
authors.lastname: Comfort
authors.affiliation: Department of Psychiatry, Federal University of Rio Grande do Sul, Rua Ramiro Barcelos 2350, 90035-903 Porto Alegre, RS, Brazil ; National Institute of Developmental Psychiatry for Children and Adolescents, Brazil.
authors.forename: Luis Augusto
authors.initials: LA
authors.lastname: Rohde
authors.affiliation: Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Avenida dos Estados 5001, 09210-580 Santo Andre, SP, Brazil ; NIF-LIM44, Institute of Radiology, Hospital das Clinicas, University of Sao Paulo, Avenida Dr. Enéas de Carvalho Aguiar, 05403-900 Sao Paulo, SP, Brazil ; National Institute of Developmental Psychiatry for Children and Adolescents, Brazil.
authors.forename: João Ricardo
authors.initials: JR
authors.lastname: Sato
mesh_terms: D000465:Algorithms; D001289:Attention Deficit Disorder with Hyperactivity / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology; D002648:Child; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D009415:Nerve Net / Q000503:physiopathology*; D046709:Pattern Recognition, Physiological; D012146:Rest; D013223:Statistics as Topic