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Biomarkers Based on Comprehensive Hierarchical EEG Coherence Analysis: Example Application to Social Competence in Autism (Preliminary Results)

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Abstract

Electroencephalography (EEG) coherence analysis, based on measurement of synchronous oscillations of neuronal clusters, has been used extensively to evaluate functional connectivity in brain networks. EEG coherence studies have used a variety of analysis variables (e.g., time and frequency resolutions corresponding to the analysis time period and frequency bandwidth), regions of the brain (e.g., connectivity within and between various cortical lobes and hemispheres) and experimental paradigms (e.g., resting state with eyes open or closed; performance of cognitive tasks). This variability in study designs has resulted in difficulties in comparing the findings from different studies and assimilating a comprehensive understanding of the underlying brain activity and regions with abnormal functional connectivity in a particular disorder. In order to address the variability in methods across studies and to facilitate the comparison of research findings between studies, this paper presents the structure and utilization of a comprehensive hierarchical electroencephalography (EEG) coherence analysis that allows for formal inclusion of analysis duration, EEG frequency band, cortical region, and experimental test condition in the computation of the EEG coherences. It further describes the method by which this EEG coherence analysis can be utilized to derive biomarkers related to brain (dys)function and abnormalities. In order to document the utility of this approach, the paper describes the results of the application of this method to EEG and behavioral data from a social synchrony paradigm in a small cohort of adolescents with and without Autism Spectral Disorder.

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Acknowledgments

Supported by internal funds from the Eunice Kennedy Shriver Center at University of Massachusetts Medical School (UMMS) and by the Collaborative Pilot Research Program (CPRP) funded through UMMS Department of Psychiatry and Assumption University and National Institutes of Health Grants R01GM105045, R01MH083320 and P41EB019936. We thank the adolescents and parents who participated in the study and Teresa Mitchell who helped with the data collection.

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Authors and Affiliations

  1. The Eunice Kennedy Shriver Center Department of Psychiatry, University of Massachusetts Medical School, 55 Lake Avenue North, Room S3-312, Worcester, MA, 01655, USA

    Mo Modarres, David Cochran, David N. Kennedy & Jean A. Frazier

  2. The Eunice Kennedy Shriver Center Department of Psychiatry, University of Massachusetts Medical School)/ UMass Memorial Health Care, Worcester, MA, USA

    David Cochran & Jean A. Frazier

  3. Psychology Department, College of the Holy Cross, Worcester, MA, USA

    Richard Schmidt

  4. Psychology Department, Assumption University, Worcester, MA, USA

    Paula Fitzpatrick

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  1. Mo Modarres
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  2. David Cochran
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  3. David N. Kennedy
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  4. Richard Schmidt
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  5. Paula Fitzpatrick
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  6. Jean A. Frazier
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Correspondence to Mo Modarres.

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Information Sharing Statement

MA MATLAB script and accompanying function for computing BCM is available under the Attribution-Non-Commercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license at: https://www.nitrc.org/doi/landing_page.php?doi=10.25790/bml0cm.83, https://www.nitrc.org/doi/landing_page.php?doi=10.25790/bml0cm.84. The datasets generated during and analyzed during the current study are available from the corresponding author upon request. TLAB script and accompanying function for computing BCM is available under the Attribution-Non-Commercial-ShareAlike 4.0 International (CC BYNC-SA 4.0) license at: https://www.nitrc.org/doi/landing_page.php?doi=10.25790/bml0cm.83, https://www.nitrc.org/doi/landing_page.php?doi=10.25790/bml0cm.84. The datasets generated during and analyzed during the current study are available from the corresponding author upon request.

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Modarres, M., Cochran, D., Kennedy, D.N. et al. Biomarkers Based on Comprehensive Hierarchical EEG Coherence Analysis: Example Application to Social Competence in Autism (Preliminary Results). Neuroinform 20, 53–62 (2022). https://doi.org/10.1007/s12021-021-09517-8

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