Identifying optimal electrophysiological markers for predicting developmental outcomes in infants

Children'S Hosp Of Philadelphia, Philadelphia PA

Investigators

Abstract

Project Summary Understanding neural processes from the earliest time possible is of interest as neural activity is a readout of current brain processes as well as a driver of brain development. Specifically, the modification of neural circuits as a function of experience results in more complex neural circuits that then allow more complex behaviors. A K08 Award would support training in advanced brain imaging and advanced statistics. K08 research focuses on examining the similarity between resting-state (RS) and auditory electroencephalography (EEG) and magnetoencephalography (MEG) measures as well as their associations with age and brain structure. Forty typically developing infants (50% female) will undergo simultaneous EEG+MEG exams at 1 month + 1 week, 6 months + 1 month, and 12 months + 1 month, as well as a magnetic resonance imaging (MRI) exam and developmental milestone testing at the final 12-month time point. The proposed grant builds upon the candidate’s prior training in pediatric EEG and MEG research and speech-language pathology, extending this knowledge to whole brain source level analyses and advanced statistical methods. Filling gaps in the PI’s training as well as gaps in the literature, Aim 1 will determine optimal recording and analysis strategies for assessing RS and auditory neural function in infants. Hypothesis 1 predicts that EEG-MEG reliability will be higher at 12 months than 1 and 6 months due to less accurate EEG estimates of neural activity at the earlier ages given ongoing changes in head tissue conductivity and electrical field distortion due to open fontanels. Aim 2 will determine optimal recording and analysis strategies for assessing RS and auditory neural function in infants. Hypothesis 2 predicts that given the higher dimensionality of MEG data and the fewer assumptions required for MEG source localization, EEG and MEG comparisons will show that (1) regional differences in the RS aperiodic activity are best assessed using MEG, and (2) regional differences in the association between age and aperiodic activity are best identified using MEG. Finally, Aim 3 compares the ability of EEG and MEG to detect neural function and brain structure associations (white, gray matter) at 12 months. Hypothesis 3 predicts that local measures of neural activity will be more strongly associated with local brain structure for MEG than EEG. The research plan is feasible given the candidate’s background and institutional resources (scientists and technology). Study findings will inform Dr. Green’s future research program, provide the candidate with pilot data for a future R01 examining associations between behavioral development and brain function and structure, and provide the field with a better understanding of infant EEG and MEG measures. The award will also provide the candidate with the opportunity to obtain training in cutting-edge imaging techniques and advanced statistical analyses. The research and training provided by this award are critical to the candidate’s long-term goal of conducting longitudinal research assessing brain function and structure in typically developing infants and children and infants at-risk for developmental disabilities.