Functional Connectivity Networks in the Autistic and Healthy Brain Assessed using Granger Causality

Department of Engineering Technology, University of Houston, Houston, TX, USA.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2010; 2010:1730-3. DOI: 10.1109/IEMBS.2010.5626702
Source: PubMed


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.

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Available from: George Zouridakis, Jun 21, 2014
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    • "Lastly, pairwise coherence estimates are not precise in their anatomical locations as there is a presumption of a two dimensional and not a 3-dimensional space (Black et al., 2008). It has further been observed that multivariate strategies to assess coherence metrics are more accurate and effective than their pairwise counterparts (Kus et al., 2004; Barry et al., 2005; Pollonini et al., 2010). For example, Duffy and Als (2012) used principal components analysis of coherences (multivariate approach) and demonstrated the ability to distinguish between children with autism and neurotypical controls. "
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    ABSTRACT: Neuroimaging technologies and research has shown that autism is largely a disorder of neuronal connectivity. While advanced work is being done with fMRI, MRI-DTI, SPECT and other forms of structural and functional connectivity analyses, the use of EEG for these purposes is of additional great utility. Cantor et al. (1986) were the first to examine the utility of pairwise coherence measures for depicting connectivity impairments in autism. Since that time research has shown a combination of mixed over and under-connectivity that is at the heart of the primary symptoms of this multifaceted disorder. Nevertheless, there is reason to believe that these simplistic pairwise measurements under represent the true and quite complicated picture of connectivity anomalies in these persons. We have presented three different forms of multivariate connectivity analysis with increasing levels of sophistication (including one based on principle components analysis, sLORETA source coherence, and Granger causality) to present a hypothesis that more advanced statistical approaches to EEG coherence analysis may provide more detailed and accurate information than pairwise measurements. A single case study is examined with findings from MR-DTI, pairwise and coherence and these three forms of multivariate coherence analysis. In this case pairwise coherences did not resemble structural connectivity, whereas multivariate measures did. The possible advantages and disadvantages of different techniques are discussed. Future work in this area will be important to determine the validity and utility of these techniques.
    Full-text · Article · Feb 2014 · Frontiers in Human Neuroscience
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    • "Most of the previous research works in brain decoding represented functional connectivity graphs (FCGs) as vectors in a high-dimensional space (e.g. [2] [3] [4]). The main drawback of such an approach (and the related feature extraction algorithms) is that it overlooks the inherent format of FCGs. "
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    ABSTRACT: The association of functional connectivity patterns with particular cognitive tasks has long been a topic of interest in neuroscience, e.g., studies of functional connectivity have demonstrated its potential use for decoding various brain states. However, the high-dimensionality of the pairwise functional connectivity limits its usefulness in some real-time applications. In the present study, the methodology of tensor subspace analysis (TSA) is used to reduce the initial high-dimensionality of the pairwise coupling in the original functional connectivity network to a space of condensed descriptive power, which would significantly decrease the computational cost and facilitate the differentiation of brain states. We assess the feasibility of the proposed method on EEG recordings when the subject was performing mental arithmetic task which differ only in the difficulty level (easy: 1-digit addition v.s. 3-digit additions). Two different cortical connective networks were detected, and by comparing the functional connectivity networks in different work states, it was found that the task-difficulty is best reflected in the connectivity structure of sub-graphs extending over parietooccipital sites. Incorporating this data-driven information within original TSA methodology, we succeeded in predicting the difficulty level from connectivity patterns in an efficient way that can be implemented so as to work in real-time.
    Full-text · Conference Paper · Jul 2013
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    • "In addition, EEG technology has been able to demonstrate long-range, anterior to posterior and temporal hypocoherences (Coben et al. 2008 ;Murias et al. 2007). It has further been observed that multivariate strategies to assess coherence metrics are more accurate and effective than their pairwise counterparts (Barry et al. 2005 ;Kus et al. 2004 ;Pollonini et al. 2010). A clinical example is presented below in Fig. 12.3 . "

    Full-text · Chapter · Jan 2013
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