Examining the electroencephalographic fingerprint of default mode network hyperconnectivity for scalable and personalized neurofeedback in schizophrenia

点击翻译按钮获取中文摘要

PROJECT ABSTRACT Auditory hallucinations (AHs) are one of the core symptoms of schizophrenia (SZ) and constitute a significant source of suffering and disability. One third of SZ patients experience pharmacology-resistant AHs, such that it is imperative to develop alternative/complementary treatment strategies. Researchers are beginning to appreciate how mental illnesses are associated with specific changes in the complex patterns of communication between different brain regions thanks to new advances in Magnetic Resonance Imaging (MRI). In particular, innovations in functional Magnetic Resonance Imaging (fMRI) data acquisition and computational analysis, make it now possible to reliably map the functional neuroanatomy of brain networks in a personalized way, offering a potential avenue for identifying unique and individualized neurotherapeutic targets. Moreover, it is now possible to tailor a personal and noninvasive intervention to help patients normalize communication within and between complex brain networks using real-time neurofeedback— whereby patients observe and learn to regulate selected aspects of their own brain activity—. AHs are characterized by elevated intrinsic functional connectivity within the default mode network (DMN) and between DMN and other large-scale networks like the frontoparietal control network (FPCN) and auditory cortices (i.e., superior temporal gyrus (STG)). We recently developed an innovative real-time fMRI circuit neurofeedback (rt-fMRI-NF) paradigm whereby people observe a visual display of ongoing DMN activation levels and use mindfulness as a strategy to volitionally regulate this difference. Our research has shown that rt-fMRI-NF reduces DMN hyperconnectivity and increases DMN-FPCN anticorrelations, with a correlated reduction of AHs among adults diagnosed with SZ. Unfortunately, to target the major brain networks that function abnormally in neuropsychiatric conditions, neurofeedback currently relies on fMRI technology, which is an expensive procedure involving a complex setup and patient burden. Since frequency- specific components of electroencephalography (EEG) signals recorded on the scalp can serve as correlates of fMRI activity patterns, including DMN activity and connectivity. Here we propose to validate the EEG correlates of DMN interactions implicated in AHs using concurrent EEG-fMRI and to develop an EEG “fingerprint” of these fMRI network dynamics. Hence, we will expand our successful rt-fMRI-NF strategy with the innovative addition of concurrent EEG measurements. We will apply the latest advances in personalized fMRI functional network mapping to define the features of EEG signal to predict and optimize the EEG fingerprint of fMRI activity using advances in machine learning for bio-signals that may lead to future personalized, network- based EEG neurofeedback circuit therapy for AHs in SZ. This study will offer key technical innovations that could lead to novel and scalable clinical applications. We will richly (>30 minutes) sample 40 patients with SZ and AHs with simultaneous EEG-fMRI to develop a pioneering and personalized EEG fingerprint of DMN dynamics and so enable a scalable form of accurate network-based neurofeedback training to patients.