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.
In this study, we analyzed brain connectivity pro¯les from 10 mild traumatic brain injury (mTBI) patients and 10 age-and gender-matched normal controls. We computed Granger causality measures from magnetoencephalographic (MEG) activity obtained at the resting state, in an attempt to understand how the default network is a®ected by mTBI. 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 classi¯cation of the subjects Journal . Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/22/15. For personal use only.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 a minimum of 85% classi¯cation accuracy in separating the two groups, with a sensitivity and speci¯city of 90% and 80%, respectively. These¯ndings suggest that analysis of functional connectivity patterns may provide a valuable tool for early detection of mTBI.
Independent component analysis (ICA) has been successfully employed in the study of single-trial evoked potentials (EPs). In this paper, we present an iterative temporal ICA methodology that processes multielectrode single-trial EPs, one channel at a time, in contrast to most existing methodologies which are spatial and analyze EPs from all recording channels simultaneously. The proposed algorithm aims at enhancing individual components in an EP waveform in each single trial, and relies on a dynamic template to guide EP estimation. To quantify the performance of this method, we carried out extensive analyses with artificial EPs, using different models for EP generation, including the phase-resetting and the classical additive-signal models, and several signal-to-noise ratios and EP component latency jitters. Furthermore, to validate the technique, we employed actual recordings of the auditory N100 component obtained from normal subjects. Our results with artificial data show that the proposed procedure can provide significantly better estimates of the embedded EP signals compared to plain averaging, while with actual EP recordings, the procedure can consistently enhance individual components in single trials, in all subjects, which in turn results in enhanced average EPs. This procedure is well suited for fast analysis of very large multielectrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors. We conclude that this method can be used to study the spatiotemporal evolution of specific EP components and may have a significant impact as a clinical tool in the analysis of single-trial EPs.
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.
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