Functional connectivity networks in the autistic and healthy brain assessed using Granger causality

• Published in: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Vol. 2010; pp. 1730 - 1733
• Main Authors: Pollonini, L, Patidar, U, Situ, N, Rezaie, R, Papanicolaou, A C, Zouridakis, G
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2010
• Subjects: Accuracy; Adolescent; Autism; Autistic Disorder - physiopathology; Brain - physiopathology; Brain Mapping - methods; Coherence; Computer Simulation; Electroencephalography; Female; Graph theory; Humans; Magnetoencephalography - methods; Male; Models, Neurological; Nerve Net - physiopathology; Neural Pathways - physiopathology; Neuroimaging; Variable speed drives; Young Adult
• ISBN: 1424441234; 9781424441235
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2010.5626702

In this study, we analyze brain connectivity based on Granger causality computed from magnetoencephalographic (MEG) activity obtained at the resting state in eight autistic and eight normal subjects along with measures of network connectivity derived from graph theory in an attempt to understand how communication in a human brain network is affected by autism. A connectivity matrix was computed for each subject individually and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fisher's criterion to rank the features and identify the best subset for maximum separation of the groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide 87.5% accuracy in separating the two groups. These findings suggest that analysis of functional connectivity patterns may provide a valuable method for the early detection of autism.