Large‐scale collaboration in ENIGMA‐EEG: A perspective on the meta‐analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity

Abstract

Abstract Background and purpose The ENIGMA‐EEG working group was established to enable large‐scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain. Methods We summarize how we have performed our currently largest genome‐wide association study of oscillatory brain activity in EEG recordings by meta‐analyzing the results across five participating cohorts, resulting in the first genome‐wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta‐analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. Results We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters. Conclusion The long list of potential challenges to our large‐scale meta‐analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.

Keywords

• brain disorders
• electroencephalography
• ENIGMA
• harmonization
• imaging genetics
• open science