Abnormal regional activity and functional connectivity in resting-state brain networks associated with etiology confirmed unilateral pulsatile tinnitus in the early stage of disease

2017 ◽  
Vol 346 ◽  
pp. 55-61 ◽  
Author(s):  
Han Lv ◽  
Pengfei Zhao ◽  
Zhaohui Liu ◽  
Rui Li ◽  
Ling Zhang ◽  
...  
2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.


2020 ◽  
Vol 14 ◽  
Author(s):  
Benjamin M. Rosenberg ◽  
Eva Mennigen ◽  
Martin M. Monti ◽  
Roselinde H. Kaiser

Prior research has shown that during development, there is increased segregation between, and increased integration within, prototypical resting-state functional brain networks. Functional networks are typically defined by static functional connectivity over extended periods of rest. However, little is known about how time-varying properties of functional networks change with age. Likewise, a comparison of standard approaches to functional connectivity may provide a nuanced view of how network integration and segregation are reflected across the lifespan. Therefore, this exploratory study evaluated common approaches to static and dynamic functional network connectivity in a publicly available dataset of subjects ranging from 8 to 75 years of age. Analyses evaluated relationships between age and static resting-state functional connectivity, variability (standard deviation) of connectivity, and mean dwell time of functional network states defined by recurring patterns of whole-brain connectivity. Results showed that older age was associated with decreased static connectivity between nodes of different canonical networks, particularly between the visual system and nodes in other networks. Age was not significantly related to variability of connectivity. Mean dwell time of a network state reflecting high connectivity between visual regions decreased with age, but older age was also associated with increased mean dwell time of a network state reflecting high connectivity within and between canonical sensorimotor and visual networks. Results support a model of increased network segregation over the lifespan and also highlight potential pathways of top-down regulation among networks.


2015 ◽  
Vol 11 (3) ◽  
pp. 347-361 ◽  
Author(s):  
Yajing Pang ◽  
Qian Cui ◽  
Xujun Duan ◽  
Heng Chen ◽  
Ling Zeng ◽  
...  

2021 ◽  
Author(s):  
Hannah S. Heinrichs ◽  
Frauke Beyer ◽  
Evelyn Medawar ◽  
Kristin Prehn ◽  
Juergen Ordemann ◽  
...  

Obesity imposes serious health risks and involves alterations in resting-state functional connectivity of brain networks involved in eating behavior. Bariatric surgery is an effective treatment, but its effects on functional connectivity are still under debate. In this pre-registered study, we aimed to determine the effects of bariatric surgery on major resting-state brain networks (reward and default mode network) in a longitudinal controlled design. 33 bariatric surgery patients and 15 obese waiting-list control patients (37 females; aged 44.15 ± 11.86 SD years (range 21-68)) underwent magnetic resonance imaging at baseline, after 6 and 12 months. We conducted a pre-registered whole-brain time-by-group interaction analysis, and a time-by-group interaction analysis on within-network connectivity (https://osf.io/f8tpn/, https://osf.io/59bh7/). In exploratory analyses, we investigated the effects of weight loss and head motion. Bariatric surgery compared to waiting did not significantly affect functional connectivity (FWE-corrected p > 0.05), neither whole-brain nor within-network. In exploratory analyses, surgery-related BMI decrease (FWE-corrected p = 0.041) and higher average head motion (FWE-corrected p = 0.021) resulted in significantly stronger connectivity of the reward network with medial posterior frontal regions. This pre-registered well-controlled study did not support a strong effect of bariatric surgery, compared to waiting, on major resting-state brain networks after 6 months. Exploratory analyses indicated that head motion might have confounded the effects. Data pooling and more rigorous control of within-scanner head motion during data acquisition are needed to substantiate effects of bariatric surgery on brain organization.


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