LEDA-Localized-EEG Dynamics Analyzer: a MATLAB-Based Innovative Toolbox for Analysis of EEG Source Dynamics

Abstract

Establishing conclusive cortical activity dynamics from neuroimages and high dimensional neuronal data post-processing, such as scalp-EEG/ERP or its localized source data, is always challenging. For addressing this, we introduce LEDA, localized-EEG dynamics analyzer, offering our novel techniques, namely (1)the localized source activity to duration (LSAD) ratio that elegantly combines voxel activation with temporal information enabling a spatiotemporal condition comparison, (2)cortical region’s activation-duration summary generation, (3)anatomical dominance calculator, and (4)voxel activations’ GIF animation extraction feature. They effectively explore and quantify physiological spatiotemporal dynamics and provide insights more intuitive than EEG/ERP biomarkers. To demonstrate, we chose to study sensory gating deficits in schizophrenia for which the P50 wave biomarker is widely discussed. EEG response (64 channels@1 kHz) to auditory paired-click paradigm from 9 patients and 9 controls was processed to ERP. Source localization was achieved by using sLORETA, a widely-used source reconstruction algorithm. LSAD ratio of patients significantly differed from that of controls for the auditory cortical regions (p value < 0.05). It revealed that inhibitory alpha activity pattern was prominent in parietal, temporal and occipital lobe for controls and not patients. Conversely, the frontal lobe, specifically the middle frontal gyrus, was temporally the most active analogously across all subjects. LEDA also extracted that swift alpha activity exists between 100 and 300 ms time period. These derived results match with existing fMRI studies, speak beyond P50 wave suppression biomarker and emphasize that our methods successfully uncovered meaningful brain mechanisms at high spatiotemporal resolution. They are provided as an open-source MATLAB based toolbox for applicability to other neuronal pathological investigations.

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Acknowledgments

This study was supported by NSF grant ECCS-1631820, NIH grants MH112180, MH108148, MH103222, and a Brain and Behavior Research Foundation grant. We thank Dr. Jyotsna Aggarwal, Dr. Surajit Bhattacharya, Mr. Alex Miu and Amrithya Balasubramanian for their efforts towards contribution of our manuscript.

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Correspondence to Deepa Gupta.

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Supplementary Information

Excel file containing a sample subject’s dataset for the first auditory click to show how ratio and duration statistics were computed.

GIF file animation of a sample subject dataset

Toolbox is available at https://gitlab.com/deepagupta/LEDA

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Gupta, D., Summerfelt, A., Luzhansky, J. et al. LEDA-Localized-EEG Dynamics Analyzer: a MATLAB-Based Innovative Toolbox for Analysis of EEG Source Dynamics. J Sign Process Syst (2021). https://doi.org/10.1007/s11265-020-01617-z

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Keywords

  • EEG
  • Source localization
  • Schizophrenia
  • Auditory paired click paradigm
  • Sensory gating
  • Alpha wave