Wednesday, November 9, 2022 from 1 - 3 PM

Chair: Santiago Morales (Primary Presenter), PhD, Department of Psychology, University of Southern California, USA 

Discussant: George Buzzell, PhD, Florida International University, Miami, USA

Workshop Description:

Keywords: EEG, ERP, Power, Psychophysiology, Mobile Technology 

Electroencephalography (EEG) is one of the most efficient and accessible methods to study brain function across development. The power of EEG as a neuroscience tool lies in its utility with populations across the lifespan, its temporal precision, and relatively inexpensive and portable hardware. However, most researchers continue to use a narrow number of EEG data collection and analysis approaches (i.e., in-lab assessments of ERPs and power), limiting the knowledge that could be gained from EEG. This symposium presents four innovative data collection methods or analytic advances by young investigators that (a) demonstrate how cutting-edge approaches are being used to measure and analyze EEG across development, (b) highlight how these methods better capture developmental processes, and (c) discuss the practicalities of implementing such methods. Presentation One examines and validates the use of mobile EEG systems to collect data outside of the laboratory, increasing ecological validity and sample representation of EEG studies. Presentation Two discusses innovative cortical source analysis of developmental EEG to improve EEG’s spatial resolution. The last two presentations represent important advances on the two most widely used EEG measures: power and ERPs. Presentation Three discusses the importance and utility of using a novel parameterization algorithm to characterize the EEG power spectrum. Presentation Four highlights the use of time-frequency analyses to better characterize the oscillations contained in the EEG data and reveal patterns not observed by traditional analyses (e.g., ERPs). Finally, the discussant, a pioneer in developmental EEG with five decades of experience, will suggest the necessary next steps to further develop these methods to study brain function across development and the promise they bring for better understanding development.



Sonya V. Troller-Renfree (Primary Presenter), PhD, Department of Biobehavioral Sciences, Teachers College, Columbia University, USA

Nathan A. Fox, PhD, Department of Human Development of Quantitative Methodology, University of Maryland, USA 

Kimberly G. Noble, MD, PhD, Department of Biobehavioral Sciences, Teachers College, Columbia University, USA

The last decade has seen increased availability of mobile electroencephalography (EEG). These mobile systems enable researchers to conduct data collection “in-context,” reducing participant burden and potentially increasing diversity and representation of research samples. However, such methodological innovations require scrutiny and methodological evaluation. In this talk we will provide an overview of the utility of mobile EEG systems for developmental populations. First, we will discuss considerations surrounding mobile EEG in a variety of contexts (e.g., home, lab, schools, etc.). Next, we will provide guidance for collecting high-quality, mobile EEG in infants and children. Specifically, we will offer insights and recommendations for equipment selection, data collection, and data analysis. For equipment selection, topics we will cover include the size of the recording equipment, available electrode types, reference types, and available montages. For data collection, we will highlight important recommendations surrounding non-standardized recording environments, obtaining informed consent from parents, instructions for parents during capping and recording, stimuli and task design, training field researchers, and monitoring data quality. Finally, for data analysis, we will discuss analytic difficulties facing mobile EEG systems, available analysis pipelines, as well as present data showing the robustness mobile EEG data from a recent study using this approach.  The talk will conclude with an overall assessment of the current utility of mobile EEG for developmental research as well as provide recommendations for future research.



Stefania Conte (Primary Presenter), PhD, Department of Psychology, University of South Carolina, USA 

John Richards, PhD, Department of Psychology, University of South Carolina, USA 

Cortical source analysis of electroencephalographic (EEG) signals has become an important tool in the analysis of brain activity. The aim of source analysis is to reconstruct the cortical generator(s) of the EEG signal recorded on the scalp. The construction of a realistic forward model, and the consequent inverse solution, are necessary to guarantee the accuracy of source reconstruction. An accurate forward solution is obtained when an appropriate imaging modality (i.e., structural magnetic resonance imaging – MRI) is used to describe the head geometry. Additionally, precise electrode locations should be identified with 3D maps of the sensor positions on the scalp, and realistic conductivity values determined for each tissue type of the head model. When these parameters are tailored to the individual participants, they contribute to the definition of realistic head models. In this presentation, the steps to performing distributed source analysis with pediatric EEG recordings will be detailed, along with alternative solutions that may be implemented by developmental researchers operating in different laboratory settings. Examples of source reconstruction of event-related potential (ERP) responses during face processing will be presented. The neural generators of different ERPs and changes occurring in the first year of life will be discussed, as well as the strengths and limits of source analysis to investigate developmental changes in functional brain responses. 



Brendan Ostlund (Primary Presenter), PhD, Department of Psychology, The Pennsylvania State University, USA 

Koraly E. Pérez-Edgar, PhD, Department of Psychology, The Pennsylvania State University, USA

Periodic (oscillations) and aperiodic (offset, exponent) activity co-exist in neurophysiological signals, are physiologically distinct, and are thought to be essential to dynamic neural communication. However, these spectral components may be conflated or otherwise misrepresented when estimated using traditional approaches for deriving frequency band measures. For example, a common assumption is that power in a canonical frequency band reflects a neural oscillation (e.g., theta), when it may instead be partially, or fully, conflated by aperiodic activity not appropriately accounted for by the traditional estimation approach. Failure to properly parameterize these distinct spectral components may ultimately obscure real brain-behavior associations. To address this issue, Donoghue and colleagues (2020) introduced an automated spectral parameterization (“specparam”) algorithm that fits components of power spectral densities. This toolbox efficiently disentangles periodic and aperiodic spectral features while remaining agnostic to canonical frequency bands. Application of specparam by developmental cognitive neuroscientists has allowed for novel insight into brain function across the lifespan. In this talk, we describe methodological considerations for parameterizing pediatric neural power spectra via specparam. In addition, we outline a data-driven approach for “tuning” the algorithm for improved model fitting with childhood electroencephalogram (EEG) data, which is often noisier relative to adult EEG recordings. We then discuss recent published and unpublished work from our team that underscores the utility of spectral parameterization for understanding neural activity in early life. In particular, we report on patterns of normative (e.g., longitudinal changes, attentional correlates) and aberrant (e.g., attention deficit/hyperactivity disorder) functioning in infancy, childhood, and adolescence. 



Santiago Morales (Primary Presenter), PhD, Department of Psychology, University of Southern California, USA 

EEG provides a rich measure of brain activity that can be characterized as neuronal oscillations. However, a review of the literature shows that over 90% of developmental EEG work to date has focused on analyzing EEG data as Event-Related Potentials (ERPs) or power based on the Fourier transform. While these measures have been productive, they do not leverage all the information contained within the EEG signal. Namely, ERP analyses ignore non-phase-locked signals and Fourier-based power analyses ignore temporal information. Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different frequencies, time-frequency measures provide a closer interpretation of the neurophysiological mechanisms, facilitate translation across neurophysiology disciplines, and capture processes not observed by ERP or Fourier-based analyses. Despite their unique contributions, time-frequency analyses of EEG are yet to be embraced by the developmental researchers. This presentation will first introduce time-frequency analyses. I will then present unpublished data in a large sample of children (N = 475; 249 female, 226 male; Mage=6.71; SDage=2.22; Rangeage= 4.01-11.5 years) that shows clear developmental patterns in time-frequency measures that are not observed with traditional ERP analyses. This replicates and extends previous studies from our lab and other research groups showing the increased sensitivity of time-frequency analyses to characterize developmental changes. Moreover, I will show that time-frequency measures show relations with behavioral measures that are not present with ERP measures. Finally, I will discuss the limitations and challenges of using time-frequency methods.