Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data
Year: 2014
Type: article
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.
Source: BioMed Research International
DOI: 10.1155/2014/380531
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,680
Funders: Ford Foundation, Autism Speaks, Leon Levy Foundation, Johns Hopkins University, Fundação de Amparo à Pesquisa do Estado de São Paulo, National Natural Science Foundation of China, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Ministry of Science and Technology of the People's Republic of China, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Stavros Niarchos Foundation, State Key Laboratory of Cognitive Neuroscience and Learning, National Institutes of Health, Oregon Clinical and Translational Research Institute, Intellectual and Developmental Disabilities Research Center, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Center for Research Resources, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Time series, wavelets and functional data analysis, G1060869203, G1071269931, G1183971480, The Study of Task-Level Control Signals Using fMRI, G1235099734, G1305410364, G1378303135, G1508703824, G1513193557, G1608546174, Neurology of Deficient Response Control in ADHD, G1699929159, G1741748395, Resource for Quantitative Functional MRI, G1779154560, G1885279740, G1929481681, G193672168, G1976269508, G2046733660, G2108532070, G2255137788, G2410197435, G2535859701, G2552598769, G257025696, G2580439521, G2703314181, G271351173, G27220811, G2727726153, G2752626688, G2756380101, G2758871867, G283115416, G285471353, G2867998824, G2953239473, G3145863556, G3155872399, G3244362721, G3256506127, G3267485165, G337668236, G3383226066, G3444917489, G346726432, G3495273489, G3581007093, G3703901849, G3732301019, G373348672, G3739250506, G3754670269, G382636585, General Clinical Research Center, G389962286, G3937059158, G4034296382, G4054715178, G4092390625, G436630487, G4413963448, The Functional Anatomy of Adolescent ADHD: Defining Markers of Recovery, G4480656814, G4480946400, G4556024451, G466280797, G469545083, G4719404209, G473988448, G5026848134, G5176642425, G5254266294, G5352236576, G5401727121, G5405822412, G5405822819, Functional and Structural Connectivity in Adult Attention Deficit Hyperactivity D, G5579402512, G5587503993, G5595499771, G5633829365, G5744933965, G57728056, G5822944968, G5996694191, G6169633257, G6302865430, G6329647372, G6462242526, G6504249233, 2/2 - Anomalous Motor System Physiology in ADHD: Biomarker Validation and Modeling Domains of Function, G6552381400, G6625186154, G6639165902, G6646713747, G6870170466, Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders, G701148663, G709773532, G7261172047, Motor Skill Learning in Autism, G7379295527, G738160294, G7433557922, G7530160993, G7539120714, G7598268833, G7617963520, G777202452, G7779033557, G7940725057, G814125525, G8249699167, G8353999919, G8569802071, G8696368980, Mental Retardation and Developmental Disabilities Research Centers 2008, G8996504914, G9000033429, G9000041002, G9000073825, G9000112492, G9000117275, G9000119412, G9000123063, G9000143263, G9000148110, G9000152600, G9000272717, G9000305379, G9000307558, G9000310903, G920178637, G939561165
OpenAlex ID: W2139708397