Principle ERP reduction and analysis: Estimating and using principle ERP waveforms underlying ERPs across tasks, subjects and electrodes

Emilie Campos; Chad Hazlett; Patricia Tan; Holly Truong; Sandra Loo; Charlotte DiStefano; Shafali Jeste; Damla Şentürk

doi:10.1016/j.neuroimage.2020.116630

Principle ERP reduction and analysis: Estimating and using principle ERP waveforms underlying ERPs across tasks, subjects and electrodes

Neuroimage. 2020 May 15:212:116630. doi: 10.1016/j.neuroimage.2020.116630. Epub 2020 Feb 20.

Authors

Emilie Campos¹, Chad Hazlett², Patricia Tan³, Holly Truong³, Sandra Loo³, Charlotte DiStefano³, Shafali Jeste³, Damla Şentürk⁴

Affiliations

¹ Department of Biostatistics, University of California, Los Angeles, USA.
² Departments of Statistics and Political Science, University of California, Los Angeles, USA.
³ Department of Psychiatry, University of California, Los Angeles, USA.
⁴ Department of Biostatistics, University of California, Los Angeles, USA. Electronic address: dsenturk@ucla.edu.

Abstract

Event-related potentials (ERP) waveforms are the summation of many overlapping signals. Changes in the peak or mean amplitude of a waveform over a given time period, therefore, cannot reliably be attributed to a particular ERP component of ex ante interest, as is the standard approach to ERP analysis. Though this problem is widely recognized, it is not well addressed in practice. Our approach begins by presuming that any observed ERP waveform - at any electrode, for any trial type, and for any participant - is approximately a weighted combination of signals from an underlying set of what we refer to as principle ERPs, or pERPs. We propose an accessible approach to analyzing complete ERP waveforms in terms of their underlying pERPs. First, we propose the principle ERP reduction (pERP-RED) algorithm for investigators to estimate a suitable set of pERPs from their data, which may span multiple tasks. Next, we provide tools and illustrations of pERP-space analysis, whereby observed ERPs are decomposed into the amplitudes of the contributing pERPs, which can be contrasted across conditions or groups to reveal which pERPs differ (substantively and/or significantly) between conditions/groups. Differences on all pERPs can be reported together rather than selectively, providing complete information on all components in the waveform, thereby avoiding selective reporting or user discretion regarding the choice of which components or windows to use. The scalp distribution of each pERP can also be plotted for any group/condition. We demonstrate this suite of tools through simulations and on real data collected from multiple experiments on participants diagnosed with Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. Software for conducting these analyses is provided in the pERPred package for R.

Keywords: Attention deficit hyperactivity disorder (ADHD); Autism spectrum disorder (ASD); EEG; ERP; ICA; PCA.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Algorithms*
Attention Deficit Disorder with Hyperactivity / physiopathology
Autism Spectrum Disorder / physiopathology
Brain / physiology*
Child
Child, Preschool
Electrodes
Electroencephalography / methods*
Evoked Potentials / physiology*
Female
Humans
Male
Signal Processing, Computer-Assisted*

Grants and funding

R01 GM111378/GM/NIGMS NIH HHS/United States