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Sleep Quality and Hippocampal Resting State Functional Connectivity to the Language Network in the Aging Population
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Poster E116 in Poster Session E, Thursday, October 26, 10:15 am - 12:00 pm CEST, Espace Vieux-Port
This poster is part of the Sandbox Series.
Emily Goldberg1,2,3, Monique Tardif1,2, Michael Dickey1,2,3, Matthew Schnur, Jason Bohland1,2,4; 1Department of Communication Science and Disorders, University of Pittsburgh, PA, USA, 2Center for the Neural Basis of Cognition, University of Pittsburgh & Carnegie Mellon University, PA, USA, 3Veterans Administration Pittsburgh Healthcare System, Department of Speech Language Pathology and Audiology, PA, USA, 4Department of Bioengineering, University of Pittsburgh, PA USA
The hippocampus is not widely studied within the neurobiology of language. However, growing evidence implicates hippocampus as a critical neural substrate of language processing, including in lexical-phonological processes[1], relational binding[2], and word learning[3]. Blank et al.[4] identified language-specialized subregions of healthy adult hippocampi that activated exclusively for language-based cognitive tasks, and which were functionally connected at rest to putative cortical language areas. Yet the functional connectivity between hippocampus and the language network remains underexplored. Increased age is known to influence resting state functional connectivity (RSFC) between hippocampus and neocortical networks, including the Default Mode Network (DMN)[5]. Despite abundant evidence that hippocampus is part of the language network, age-related changes in hippocampal-language network RSFC have not been systematically studied (Aim 1). In addition to age, sleep quality may moderate hippocampal-neocortical RSFC. Sleep quality decreases with age, which may negatively impact consolidation, a neurobiological process that relies on hippocampal-neocortical connectivity and supports hippocampus-dependent language functions [3]. It is unknown if sleep quality influences hippocampal-neocortical connectivity (Aim 2). We used minimally preprocessed archival Lifespan Human Connectome Project Aging T1-weighted structural and resting state functional data. A subset (n=176) of a larger planned sample (N=631) were examined. Participants were 35-100 years old, right-hand dominant, and had no history of stroke. Participants provided self-ratings of sleep quality using the Pittsburgh Sleep Quality Index (PSQI). We used the CONN toolbox for preprocessing and analysis. First level analyses applied structural segmentation, functional artifact detection, functional smoothing (4mm kernel), CompCor-based denoising, and band-pass filtering (0.01-1.0 Hz). Seed Regions of Interest (ROIs) were defined using the Tian et al.[6] subcortical atlas, which delineates 4 hippocampal subregions per hemisphere. Second level multiple regression models examined the influence of age, PSQI, and their interaction on hippocampal-language network connectivity. We further examined hippocampal-DMN connectivity as a control. Preliminary findings suggest that increasing age significantly decreases RSFC between hippocampal subregions and multiple regions comprising the DMN, including bilateral medial prefrontal cortex and post-cingulate cortex. Age also reduces RSFC between hippocampus and several language network areas, though less robustly compared to DMN, including aspects of bilateral inferior frontal and superior temporal gyri, and left angular gyrus. PSQI score has less of an effect on hippocampal RSFC, with modest findings involving bilateral angular gyri, right superior temporal gyrus, and right middle and inferior frontal gyri. The interaction between PSQI and age had no moderating effects. By the time of this Sandbox presentation, results for the full sample will be available. We will examine resting state functional connectivity between individual hippocampal ROIS and the language network compared to the DMN, both networks which will be defined using demarcations from established atlases. The moderating effects of age, PSQI, and their interaction will be explored. 1. Hula et al., Brain 143(8), 2532-2544 (2020). 2. Covington & Duff, Trends Cognitive Science 20(12), 869-870 (2016). 3. Schimke et al. Psychonomic Bulletin Review 28(6), 1811-1838 (2021). 4. Blank et al. BioRxiv 091900, (2016). 5. Salami et al., PNAS 111(49), 17654-17659 (2014). 6. Tian et al., Nature Neuroscience 23(11), 1421-1432 (2020).
Topic Areas: History of the Neurobiology of Language,