Abnormal Static and Dynamic Functional Network Connectivity in Patients With Presbycusis.

Abstract

Aim: This study aimed to investigate abnormal static and dynamic functional network connectivity (FNC) and its association with cognitive function in patients with presbycusis. Methods: In total, 60 patients with presbycusis and 60 age-, sex-, and education-matched healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and cognitive assessments. Group independent component analysis (ICA) was carried out on the rs-fMRI data, and eight resting-state networks (RSNs) were identified. Static and dynamic FNCs (sFNC and dFNC) were then constructed to evaluate differences in RSN connectivity between the patients with presbycusis and the HCs. Furthermore, the correlations between these differences and cognitive scores were analyzed. Results: Patients with presbycusis had differences in sFNC compared with HCs, mainly reflected in decreased sFNC in the default mode network (DMN)-left frontoparietal network (LFPN) and attention network (AN)-cerebellum network (CN) pairs, but they had increased sFNC in the auditory network (AUN) between DMN domains. The decreased sFNC in the DMN-LFPN pair was negatively correlated with their TMT-B score (r = -0.441, p = 0.002). Patients with presbycusis exhibited aberrant dFNCs in State 2 and decreased dFNCs between the CN and AN and the visual network (VN). Moreover, the presbycusis group had a shorter mean dwell time (MDT) and fraction time (FT) in State 3 (p = 0.0027; p = 0.0031, respectively). Conclusion: This study highlighted differences in static and dynamic functional connectivity in patients with presbycusis and suggested that FNC may serve as an important biomarker of cognitive performance since abnormal alterations can better track cognitive impairment in presbycusis.

• Authors: Chunhua Xing, Yu-Chen Chen, Song'an Shang, Jin-Jing Xu, Huiyou Chen, Xindao Yin, Yuanqing Wu, Jin-Xia Zheng
• Journal: Frontiers in aging neuroscience (Front Aging Neurosci) Vol. 13 Pg. 774901 ( 2021) ISSN: 1663-4365 [Print] Switzerland
• PMID: 35069176 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Xing, Chen, Shang, Xu, Chen, Yin, Wu and Zheng.