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Influence of Unimodal and Association Networks on Language Network Functional Connectivity and Behavior in Aphasia
Poster Session A - Sandbox Series, Thursday, October 24, 10:00 - 11:30 am, Great Hall 3 and 4
This poster is part of the Sandbox Series.
Mohammad H. Aldabbagh1, Isaac Falconer2, Maria Varkanitsa1, Swathi Kiran1,3; 1Center for Brain Recovery, Boston University, Boston, MA, USA, 2Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA, 3Department of Speech, Language, and Hearing Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA
Introduction: Recent studies have suggested that the macro-scale functional connectivity (FC) of the human cerebral cortex is hierarchically organized along a gradient that is anchored at one end by the higher-order (heteromodal) default-mode network (DMN) and at another by lower-order (unimodal) primary sensory areas (Margulies et al., 2016). The language network (LN) is adjacent to two primary areas, namely the tongue motor and auditory regions, and overlaps with the DMN, frontoparietal control (FPN), and salience networks (Braga et al., 2020). Therefore, this makes the LN situated in the middle of this cortical gradient relative to other networks. This research aims to investigate how the FC of unimodal and association networks surrounding the LN in people with aphasia (PWA) influence the FC of the LN, and how are these effects reflected behaviorally. We hypothesize that a lower FC of the LN will be associated with an overall lower FC of its surrounding networks and higher aphasia severity since FC predicts language deficits (Siegel et al., 2016). However, the FC influences of unimodal compared to heteromodal regions on the LN will not be equal since they are not traditionally known to share similar patterns of FC. Methods: 40 PWA with left hemisphere lesions and 42 controls were administered structural scans, resting-state functional magnetic resonance imaging (rs-fMRI) scans, and language localizer tasks that contrast reading intelligible sentences versus a list of nonwords. Individualized LNs are defined by FC overlapping with regions activated during the task. Additionally, PWA were administered an aphasia severity behavioral measure (WAB-R). Analysis concerning our research question will be conducted and planned as follows. All FC values will use our rs-fMRI data, preprocessed using CONN pipelines, analyzed using seed-based connectivity approaches from CONN, and projected onto the cortical parcellations from Schaefer et al. (2018). The FC of the LN will be measured using our individualized LN regions of interest (ROIs) defined by the language localizer task. To get all other networks’ FC values, seed vertices will be placed at canonical regions resembling the unimodal and association networks: auditory network (primary auditory cortex), DMN (left lateral prefrontal cortex), FPN (frontal eye fields), etc. For our statistical analysis, we will run mixed effects models. Our models will test the within-network FC of each unimodal and heteromodal network against the within-network FC of the LN. The FC of each unimodal and association network will make our independent variables. The FC of the LN will be our dependent variable, and aphasia severity (WAB-AQ) will be a covariate in our models. These models will help us understand the FC influences of unimodal and association networks on the FC of the LN in PWA. Anticipated Results: We anticipate that weak FC of tongue motor and auditory regions will be associated with FC in the LN and low WAB-AQ scores (high aphasia severity). Summary: Understanding these results will give us an understanding of how the FC of the LN in PWA is influenced by the FC of the unimodal and association networks along the macro-scale cortical organization.
Topic Areas: Disorders: Acquired, Multisensory or Sensorimotor Integration