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Long-Range Reduced Predictive Information Transfers of Autistic Youths in EEG Sensor-Space During Face Processing

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Abstract

The majority of previous functional/effective connectivity studies conducted on the autistic patients converged to the underconnectivity theory of ASD: “long-range underconnectivity and sometimes short-rang overconnectivity”. However, to the best of our knowledge the total (linear and nonlinear) predictive information transfers (PITs) of autistic patients have not been investigated yet. Also, EEG data have rarely been used for exploring the information processing deficits in autistic subjects. This study is aimed at comparing the total (linear and nonlinear) PITs of autistic and typically developing healthy youths during human face processing by using EEG data. The ERPs of 12 autistic youths and 19 age-matched healthy control (HC) subjects were recorded while they were watching upright and inverted human face images. The PITs among EEG channels were quantified using two measures separately: transfer entropy with self-prediction optimality (TESPO), and modified transfer entropy with self-prediction optimality (MTESPO). Afterwards, the directed differential connectivity graphs (dDCGs) were constructed to characterize the significant changes in the estimated PITs of autistic subjects compared with HC ones. By using both TESPO and MTESPO, long-range reduction of PITs of ASD group during face processing was revealed (particularly from frontal channels to right temporal channels). Also, it seemed the orientation of face images (upright or upside down) did not modulate the binary pattern of PIT-based dDCGs, significantly. Moreover, compared with TESPO, the results of MTESPO were more compatible with the underconnectivity theory of ASD in the sense that MTESPO showed no long-range increase in PIT. It is also noteworthy that to the best of our knowledge it is the first time that a version of MTE is applied for patients (here ASD) and it is also its first use for EEG data analysis.

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Notes

  1. Pervasive Development Disorder, Not Otherwise Specified.

  2. Prefrontal Cortex.

  3. Anterior Cingulate Cortex.

  4. Inferior Parietal Lobules.

  5. Inferior Frontal Gyrus.

  6. Fusiform Gyrus.

  7. Superior Temporal Gyrus.

  8. http://hemera-technologies-inc.software.informer.com.

  9. TIM 1.2.0 software by Kalle Rutanen (http://www.cs.tut.fi/~timhome/tim-1.2.0).

  10. Available at (http://trentool.github.io/TRENTOOL3).

  11. Right Fusiform Gyrus.

  12. Left Fusiform Gyrus.

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Acknowledgments

The authors would like to thank Prof. Hossein Esteky, from School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran for letting us use his laboratory for EEG data acquisition.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering and Technology, Imam Khomeini International University, Qazvin, Iran

    Ali Khadem

  2. Brain Mapping Laboratory, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar Aven., POBox 14395-515, Tehran, Iran

    Gholam-Ali Hossein-Zadeh

  3. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Gholam-Ali Hossein-Zadeh

  4. Institute for Cognitive Science Studies (ICSS), Tehran, Iran

    Anahita Khorrami

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  1. Ali Khadem
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Correspondence to Gholam-Ali Hossein-Zadeh.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Khadem, A., Hossein-Zadeh, GA. & Khorrami, A. Long-Range Reduced Predictive Information Transfers of Autistic Youths in EEG Sensor-Space During Face Processing. Brain Topogr 29, 283–295 (2016). https://doi.org/10.1007/s10548-015-0452-4

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