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Decoding the different states of visual attention using functional and effective connectivity features in fMRI data

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Abstract

The present paper concentrates on the impact of visual attention task on structure of the brain functional and effective connectivity networks using coherence and Granger causality methods. Since most studies used correlation method and resting-state functional connectivity, the task-based approach was selected for this experiment to boost our knowledge of spatial and feature-based attention. In the present study, the whole brain was divided into 82 sub-regions based on Brodmann areas. The coherence and Granger causality were applied to construct functional and effective connectivity matrices. These matrices were converted into graphs using a threshold, and the graph theory measures were calculated from it including degree and characteristic path length. Visual attention was found to reveal more information during the spatial-based task. The degree was higher while performing a spatial-based task, whereas characteristic path length was lower in the spatial-based task in both functional and effective connectivity. Primary and secondary visual cortex (17 and 18 Brodmann areas) were highly connected to parietal and prefrontal cortex while doing visual attention task. Whole brain connectivity was also calculated in both functional and effective connectivity. Our results reveal that Brodmann areas of 17, 18, 19, 46, 3 and 4 had a significant role proving that somatosensory, parietal and prefrontal regions along with visual cortex were highly connected to other parts of the cortex during the visual attention task. Characteristic path length results indicated an increase in functional connectivity and more functional integration in spatial-based attention compared with feature-based attention. The results of this work can provide useful information about the mechanism of visual attention at the network level.

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Acknowledgements

We would like to thank the team (especially Prof. Boyaci) from UMRAM laboratory, Bilkent University, Ankara, Turkey, for providing the infrastructure for this research.

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Authors and Affiliations

  1. Neuroscience and Neuroengineering Research Laboratory, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

    Behdad Parhizi & Mohammad Reza Daliri

  2. School of Cognitive Sciences (SCS), Institute for Research in Fundamental Science (IPM), Niavaran, Tehran, Iran

    Mehdi Behroozi

  3. Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, Bochum, Germany

    Mehdi Behroozi

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  1. Behdad Parhizi
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  2. Mohammad Reza Daliri
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  3. Mehdi Behroozi
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Correspondence to Mohammad Reza Daliri.

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Parhizi, B., Daliri, M.R. & Behroozi, M. Decoding the different states of visual attention using functional and effective connectivity features in fMRI data. Cogn Neurodyn 12, 157–170 (2018). https://doi.org/10.1007/s11571-017-9461-1

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