Presbycusis impacts resting-state networks
Poster No:
1203
Submission Type:
Abstract Submission
Authors:
Yingying Wang1, Lauren Secilmis2, Jordan Bollinger3
Institutions:
1Special Education and Communication Disorders, Lincoln, NE, 2University of nebraska Lincoln, Lincoln, NE, 3University of Nebraska-Lincoln, Lincoln, NE
First Author:
Co-Author(s):
Introduction:
Presbycusis occurs in one in three people in the United States between the ages of 65 and 74 years [1]. Sensory degradation and/or deprivation due to hearing loss can result in significant changes in the gray and white matter of the brain [2, 3]. Presbycusis presents a multifactorial and complex problem. The neural mechanism of presbycusis remains unclear and is controversial at many levels. The present study investigated the brain-behavioral relationship between the degree of hearing loss and functional connectivity using resting-state functional magnetic resonance imaging (fMRI) in 35 adults aged range from 40.0 to 74.3 years old.
Methods:
Participants: A group of 35 native-English-speaking adults with self-reported normal hearing (age range: 40.0-74.3, 9 male and 26 female, 34 right-handed and one left-handed) were included in this study. We divided them into three age groups, including middle age (40.0-49 years old, N=8), young old (50 – 64 years old, N=16), and old (65 years and older, N=11). The study was approved by the Institutional Review Board at the University of Nebraska–Lincoln. Written consent forms were obtained from each participant prior to the research visit. Imaging acquisition: Brain imaging data were acquired by using a 3.0 Tesla Siemens Skyra scanner (Siemens Medical Solutions, Erlangen, Germany) with a 64-channel head coil. A high-resolution T1-weighted 3D structural image and EPI functional image were acquired. Imaging data analysis: Preprocessing of the Rs-fMRI imaging was performed with the CONN toolbox including functional realignment, outlier scrubbing, structural segmentation, normalization (MNI space normalization), and spatial smoothing. A band-pass filter (0.01 – 0.09 Hz) was applied to the time series. White matter and cerebrospinal fluid time series were regressed out. Linear detrending was performed during the denoising step. After the denoising step, the distribution of voxel-to-voxel connectivity was visualized for each step. All participants showed normally distributed data after denoising and were included in further analyses. Identifying the central auditory network: The data-driven approach uses group independent component analysis (gICA) in GIFT (version 4.0b) to decompose all preprocessed data into independent components (ICs), including dimensionality reduction, IC estimation, and back-reconstruction. Specifically, a two-step principal component analysis (PCA) was applied to reduce the data. The auditory network (AN) was then manually identified [6] to include mainly the primary auditory center (Brodmann areas 41, 42, 22) and back-reconstructed into individual time courses and spatial maps using a spatial-temporal dual-regression model. At the individual level, voxels in each subject-specific AN spatial map were used to compute the intra-network functional connectivity (FC) values, which represent the degree of synchronization between regions within AN. At last, the intra-network FC were converted into Z scores [7]. Statistical Analysis: The group differences were investigated by regression analyses. The group was defined as independent variable, while the intra-network FC values defined as dependent variables with age, PTA scores, and sex included as covariates. Results were reported at the significant level of p < 0.05 (cluster-wise AlphaSim threshold).
Results:
The final cohort information was summarized in Figure 1. There were significant differences in the left PTA average of 4kHz and 8kHz (p = 0.046, Kruskal-Wallis rank sum test) and in the right PTA average of 4kHz and 8kHz (p = 0.006, Kruskal-Wallis rank sum test). The intra-network FC values of AN showed significant group effects in the left middle temporal gyrus within the AN.
·Figure 1
Conclusions:
Our findings suggest that age-related hearing loss can be reflected by resting-state connectivity in AN. The causal relationship between the two needs longitudinal investigation.
Lifespan Development:
Aging 1
Perception, Attention and Motor Behavior:
Perception: Auditory/ Vestibular 2
Keywords:
ADULTS
Aging
FUNCTIONAL MRI
Hearing
Other - resting-state functional connectivity
1|2Indicates the priority used for review
Provide references using author date format
2. Zhang et al. 2020 Front Neurosci. 14:200
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6. Rademacher et al. 2001 NeuroImage, 13 (4): 669-683
7. Wang et al. 2014 Journal of cerebral blood flow & metabolism, 34 (4), 597-605