NETWORK MECHANISMS UNDERLYING CORE BEHAVIORAL FEATURES IN FRAGILE X SYNDROME

Abstract

Fragile X Syndrome (FXS) is a rare, neurodevelopmental disorder stemming from a single gene mutation leading to cognitive disruptions, anxiety, sensory hypersensitivity, and other distressing clinical symptoms. Individuals with FXS have moderate-to-severe intellectual disability (ID), representing the most common single gene cause of ID, which impacts quality of life and day-to-day activities. At present, no treatment exists to target the underlying pathophysiology of FXS, and cognitive symptoms are vastly understudied relative to their impact on the patient population. The current set of studies aimed to identify targetable mechanisms contributing to functional disruptions in frontoparietal networks supporting cognitive function and other hallmark features in FXS. The use of a performance-based cognitive task to explore network connectivity disruptions is an underutilized approach in FXS research programs and the current study is the first to report on neural dynamics underlying cognitive performance. The ability to assess functional networks during a task indexing cognitive performance is novel and informs network disruptions related to clinical features that are most impactful to individuals with FXS and their families. The concept of functional time windows that vary by context as a method for reducing heterogeneity for translational target development is timely, given the most recent clinical trial failures on metabotropic glutamate receptor 5 (mGluR5) antagonists, an area in which there are a multitude of preclinical papers showing an overall effect (Berry-Kravis, 2022; Erickson et al., 2017). The application of this concept to understanding when specific network impairments are most relevant and how they vary across individuals with FXS may help elucidate treatment-target alignment that is more similar across species than current behavioral outcomes. The current study identified robust differences on both neurodynamic and more global measures of neural network function across tasks, which may serve as useful biomarkers in both future and ongoing studies. Furthermore, findings provided critical mechanistic insight into underlying functional network disruptions in FXS, which allowed for a greater understanding of neural mechanisms contributing to cognitive disruptions in FXS. Ultimately, features from the current analyses achieved excellent to perfect diagnostic classification performance, where features and feature combinations from the current studies were more robust to heterogeneity, thus less affected by systemic sources of heterogeneity inherent to FXS. Overall, the current results provide insight into what aspects of neurophysiology are more adaptive and which are less amenable to change leading to mechanistic targets that may meaningfully translate to effective interventions for characteristic features of FXS.