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Poster E50, Friday, November 10, 10:00 – 11:15 am, Harborview and Loch Raven Ballrooms

A multi-modal approach to quantify the reading network using the neurochemical-neurovascular relationship to predict decoding and fluency

Lisa Krishnamurthy1,2,3, Venkatagiri Krishnamurthy2,3,4, Dina Schwam5, Daphne Greenberg5, Robin Morris3,6;1Dept. of Physics & Astronomy, Georgia State University, Atlanta, GA, United States, 2Center for Visual and Neurocognitive Rehabilitation, Atlanta VAMC, Decatur, GA, United States, 3Center for Advanced Brain Imaging, GSU/GT, Atlanta, GA, United States, 4Dept. of Neurology, Emory University, Atlanta, GA, United States, 5Dept. of Educational Psychology, Special Education, and Communication Disorders, Georgia State University, Atlanta, GA, United States, 6Dept. Of Psychology, Georgia State University, Atlanta, GA, United States

Reading disability (RD) has been characterized by atypical connections of a specialized network of brain regions. Understanding the ‘resting’ connectivity and related neurochemical components of this ‘reading network’ in RD could help provide critical evidence of which connections are most available for learning, and which would be the best target of specific interventions. Because of this network’s complexity, a multi-modal approach may provide improved understanding of RD and treatment choices. The goal of this study was to model how the network’s neurochemical attributes interplay with connectivity to form the foundations of the decoding and fluency components of the reading circuit. Eighteen adults (age 20-59) with varying levels of reading proficiency were recruited. rsFC and MEGA-PRESS MRS used to identify the gamma-amino butyric acid (GABA+) and glutamate+glutamine (GLX) metabolites were acquired on each subject. The 3x3x3cm^3 voxel was placed over the posterior Inferior Frontal Gyrus (IFG) and anterior Superior Temporal Gyrus (STG). An unsuppressed water spectrum from the same area was also acquired for eddy current compensation and H2O normalization. The rsFC images were corrected for slice timing, global head motion, EPI distortions, physiological noise, spatially normalized to MNI, low-pass filtered, and smoothed. Seed-based CC analysis was applied in a whole-brain manner by seeding in the center of mass (CoM) of the MRS voxel overlap from all 18 subjects. The MRS data underwent spectral registration, aligning on Creatine (Cr), subtraction (edit-control=difference), and appodization. The difference and control spectra were separately fitted to a simulated basis set using LCModel, and reported as a ratio with Cr. We evaluated whether removing age effects and tissue content from GABA+/Cr and GLX/Cr data improved the relationships with reading measures. Finally, we related reading behavior with rsFC and GABA+/Cr (or GLX/Cr), to determine the neurochemical-neurovascular-cognitive relationship within the reading network. As previously shown, GABA+/Cr and GLX/Cr concentration decline with age, and statistically adjusting for the sample’s age effects and normalizing the data by tissue content improved GABA+/Cr and GLX/Cr relationships with reading decoding and fluency measures (assessed via R2). The rsFC measures were also of high quality, such that they were sensitized predominantly to grey matter. Seeding the voxel CoM, we found significant connections with multiple expected reading network areas. The rsFC strength was significantly related to the GABA+/Cr concentration and reading decoding and fluency. These results were integrated within a neurochemical-neurovascular-cognitive model, in which the left hemisphere (LH) connections, in tandem with the neurochemical attributes, best predicted reading levels. It has been suggested that some struggling readers, and those with reading disabilities, utilize a right-lateralized circuit for reading, which is considered much less efficient and effective than the typical left-lateralized reading network. Our model is in support of these observations, but goes beyond traditional single-modality analyses by evaluating the underlying network that supports reading, and combines that information with the neurochemistry attributes that best predict the trait. These preliminary results from combining MRS and rsFC are promising, and may help to further identify the underlying dysfunction(s) in struggling adult readers’ brain circuitry.

Topic Area: Methods

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