Abnormal Static and Dynamic Functional Network Connectivity in Patients With Presbycusis

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.

Download Full-text

Abnormal Functional Network Connectivity among Resting-State Networks in Children with Frontal Lobe Epilepsy

American Journal of Neuroradiology ◽

10.3174/ajnr.a3608 ◽

2013 ◽

Vol 34 (12) ◽

pp. 2386-2392 ◽

Cited By ~ 32

Author(s):

E. Widjaja ◽

M. Zamyadi ◽

C. Raybaud ◽

O.C. Snead ◽

M.L. Smith

Keyword(s):

Frontal Lobe ◽

Resting State ◽

Network Connectivity ◽

Functional Network ◽

Frontal Lobe Epilepsy ◽

Resting State Networks ◽

Functional Network Connectivity

Download Full-text

Functional network connectivity of pain-related resting state networks in somatoform pain disorder: an exploratory fMRI study

Journal of Psychiatry and Neuroscience ◽

10.1503/jpn.110187 ◽

2013 ◽

Vol 38 (1) ◽

pp. 57-65 ◽

Cited By ~ 31

Author(s):

Alexander Otti ◽

Harald Guendel ◽

Peter Henningsen ◽

Claus Zimmer ◽

Afra Wohlschlaeger ◽

...

Keyword(s):

Resting State ◽

Network Connectivity ◽

Pain Disorder ◽

Functional Network ◽

Resting State Networks ◽

Functional Network Connectivity ◽

Somatoform Pain Disorder ◽

Fmri Study

Download Full-text

Resting-state fMRI dynamic functional network connectivity and associations with psychopathy traits

NeuroImage Clinical ◽

10.1016/j.nicl.2019.101970 ◽

2019 ◽

Vol 24 ◽

pp. 101970 ◽

Cited By ~ 5

Author(s):

Flor A. Espinoza ◽

Nathaniel E. Anderson ◽

Victor M. Vergara ◽

Carla L. Harenski ◽

Jean Decety ◽

...

Keyword(s):

Resting State ◽

Network Connectivity ◽

Resting State Fmri ◽

Functional Network ◽

Functional Network Connectivity ◽

Dynamic Functional Network Connectivity

Download Full-text

Differences in Disrupted Dynamic Functional Network Connectivity Among Children, Adolescents, and Adults With Attention Deficit/Hyperactivity Disorder: A Resting-State fMRI Study

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.697696 ◽

2021 ◽

Vol 15 ◽

Author(s):

Elijah Agoalikum ◽

Benjamin Klugah-Brown ◽

Hang Yang ◽

Pan Wang ◽

Shruti Varshney ◽

...

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Attention Deficit ◽

Resting State ◽

Age Groups ◽

Network Connectivity ◽

Functional Network ◽

Age Difference ◽

Functional Network Connectivity ◽

Hyperactivity Disorder ◽

Dynamic Functional Network Connectivity

Attention deficit hyperactivity disorder (ADHD) is one of the most widespread mental disorders and often persists from childhood to adulthood, and its symptoms vary with age. In this study, we aim to determine the disrupted dynamic functional network connectivity differences in adult, adolescent, and child ADHD using resting-state functional magnetic resonance imaging (rs-fMRI) data consisting of 35 children (8.64 ± 0.81 years), 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years). We hypothesized that functional connectivity is time-varying and that there are within- and between-network connectivity differences among the three age groups. Nine functional networks were identified using group ICA, and three FC-states were recognized based on their dynamic functional network connectivity (dFNC) pattern. Fraction of time, mean dwell time, transition probability, degree-in, and degree-out were calculated to measure the state dynamics. Higher-order networks including the DMN, SN, and FPN, and lower-order networks comprising the SMN, VN, SC, and AUD were frequently distributed across all states and were found to show connectivity differences among the three age groups. Our findings imply abnormal dynamic interactions and dysconnectivity associated with different ADHD, and these abnormalities differ between the three ADHD age groups. Given the dFNC differences between the three groups in the current study, our work further provides new insights into the mechanism subserved by age difference in the pathophysiology of ADHD and may set the grounds for future case-control studies in the individual age groups, as well as serving as a guide in the development of treatment strategies to target these specific networks in each age group.

Download Full-text