Investigating electroencephalographic predictors of default mode network anticorrelation for personalized neurofeedback

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PROJECT SUMMARY/ABSTRACT Neuropsychiatric conditions are increasingly being understood as disorders of intrinsic, functional interactions within and between widespread, distributed, brain networks. Given recent advances in functional Magnetic Resonance Imaging (fMRI) data acquisition and computational analysis, it is now possible to reliably map the functional neuroanatomy of brain networks within individuals, offering a potential avenue for identifying personalized neurotherapeutic targets. However, gold standard treatments (e.g. pharmacotherapy) in current psychiatric practice were not originally designed to target specific brain network interactions and lack protocols that leverage such individual-level data. Real-time neurofeedback— whereby patients observe and learn to regulate selected aspects of their own brain activity— is a candidate approach to personally tailor the normalization of unhealthy communication within and between brain networks. However, to target the major brain networks that function abnormally in neuropsychiatric conditions, neurofeedback relies on fMRI, which is an expensive procedure involving a complex setup and patient burden. The goal of this project is to develop an electroencephalography (EEG) “fingerprint” of fMRI network dynamics so that a neurofeedback system based on EEG (electrodes placed on the scalp) alone can be used to precisely target interactions within and between brain networks. Because EEG devices can be portable and offer relatively simple setup in flexible settings, our work could enable a scalable form of network-based neurofeedback training that patients could regularly access. Our Aim 1 is to identify an optimal, generalizable model of EEG features that are predictive of fMRI- based default mode network (DMN) “antagonism” within individuals. We focus on this DMN antagonism because it is a major feature that is relevant to cognitive dysfunction in psychiatric disease at a transdiagnostic level. We will collect high-quality, simultaneous EEG-fMRI data in 24 healthy adults (>100 mins of sampling per participant), including three conditions: (1) resting state, (2) continuous task performance, and (3) continuous fMRI-based neurofeedback from DMN antagonism states. We will apply machine learning-based methods to identify an optimal mapping between EEG signal components and fMRI-based DMN antagonism. Further, we will determine how much individual-level EEG-fMRI sampling is needed to successfully predict DMN antagonism from EEG. Our Aim 2 is to test whether EEG markers of DMN antagonism are predictive of cognitive task performance fluctuations within individuals. As such, our findings could offer validation of the behavioral relevance of an EEG neurofeedback system that would target DMN antagonism. If successful, our work can lead to development of an accessible, computational psychiatry tool that can be tested in clinical conditions in which DMN antagonism (and related cognitive function) is affected, including attention- deficit/hyperactivity disorder, depression and schizophrenia.