RESUMEN
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.
Asunto(s)
Trastorno Autístico/fisiopatología , Mapeo Encefálico/métodos , Encéfalo/fisiopatología , Magnetoencefalografía/métodos , Modelos Neurológicos , Red Nerviosa/fisiopatología , Vías Nerviosas/fisiopatología , Adolescente , Simulación por Computador , Femenino , Humanos , Masculino , Adulto JovenRESUMEN
Brain responses to repeated sensory stimuli are typically contaminated by extraneous activity, including background rhythms, artifacts, and interference signals. To address this issue, we have recently proposed a new iterative independent component analysis (iICA) approach that can provide reliable evoked response (ER) estimates on a single trial basis. In this paper, we present a new two-step approach that focuses on removing well-defined artifacts, such as eye movements and muscle activity, before iICA processing. Extended analyses with both simulated data and actual recordings from normal subjects demonstrate that this procedure gives better results than iICA alone. Additionally, this methodology is suitable for fast analysis of multi-electrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors, and thus, it may have a significant impact on the analysis efficiency of large datasets of single-trial ERs.