Mapping the intrinsic functional organization of auditory cortex in individual subjects using 7T MRI

Massachusetts General Hospital, Boston MA

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Abstract

In response to NOT-AG-22-025, this project combines ultra-high-resolution MRI and advanced computational analyses to identify pathological changes in the functional connectivity of auditory cortices (AC), which are associated with an increased risk for Alzheimer's disease (AD). Accumulating evidence suggest that age-related hearing loss (HL) and central auditory processing difficulties could be significant risk factors for AD. Meanwhile, neuroimaging studies in individuals with age-related peripheral HL (but no AD diagnosis) suggest anatomical and functional changes in the cerebral cortex, which extend from primary ACs to auditory association areas that support complex cognitive processes including speech and language. Here, using methods developed in R01DC017991, we test a hypothesis that functional changes in cortical networks involving AC could be early indicators of AD risk. To date, the pursuit of biomarkers in fine-grained changes in human AC has been complicated by several critical barriers: Human ACs consist of multiple adjacent small regions, which are difficult to distinguish using conventional resolutions available for human functional MRI (fMRI); These small AC areas are activated by broad combinations of features and show considerably large inter-individual variability. Examining functional predictors of auditory processing difficulties in AD thus requires a novel perspective 1) which focuses on how human ACs interact with the rest of the human brain and 2) which allows for individual-level studies of dynamic functional networks instead of conventional fMRI group analyses only. Here, we pursue an entirely novel way to characterize early indicators of pathological changes in the functional organization of human AC in individual participants who are at high risk for AD. Our proposed work is built on recent advances that allow focusing on individual subjects and dynamic functional activity patterns using ultra-high resolution 7T fMRI. The results of computational analyses using new 7T fMRI data, which is collected during the resting state and auditory stimulation of aging participants, will be compared to those obtained with larger-sample (but lower resolution) 3T fMRI data sets obtained from public repositories. (Aim 1) We will first use individualized AC functional connectivity patterns to estimate risk factors for AD, including HL, in large low-resolution fMRI data sets. To validate the results in a different data set, we will use the same AC functional connectivity patterns to classify whether a participant is cognitively normal, has mild cognitive impairment (MCI), or has AD. (Aim 2) We will collect ultra-high resolution 7T fMRI data to examine the interactive impact of age-related auditory processing difficulties and classical risk factors for AD on functional arrangement of fine-grained subregions of AC. Ultimately, the results could help find early indicators of cognitive decline and aid the interventions to target disrupted neural pathways in AD patients with auditory processing difficulties.