Effects of sound therapy on resting‐state functional brain networks in patients with tinnitus: A graph‐theoretical‐based study

2019 ◽  
Vol 50 (6) ◽  
pp. 1731-1741 ◽  
Author(s):  
Lv Han ◽  
Liu Yawen ◽  
Wang Hao ◽  
Liu Chunli ◽  
Zhao Pengfei ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Functional Brain ◽  
Sound Therapy ◽  
Functional Brain Networks

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

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.

scholarly journals High frequency functional brain networks in neonates revealed by rapid acquisition resting state fMRI

2015 ◽  
Vol 36 (7) ◽  
pp. 2483-2494 ◽  
Author(s):  
Adam P.R. Smith‐Collins ◽  
Karen Luyt ◽  
Axel Heep ◽  
Risto A. Kauppinen
Keyword(s):  
Resting State ◽  
High Frequency ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Functional Brain ◽  
Rapid Acquisition ◽  
Functional Brain Networks

scholarly journals Commentary: BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241-4

2016 ◽  
Author(s):  
Spiro P. Pantazatos ◽  
Xinyi Li
Keyword(s):  
Gene Expression ◽  
Resting State ◽  
Euclidean Distance ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Spatial Proximity ◽  
Functional Magnetic Resonance ◽  
Future Directions ◽  
Functional Brain ◽  
Functional Brain Networks

SummaryA recent report claims that functional brain networks defined with resting-state functional magnetic resonance imaging (fMRI) can be recapitulated with correlated gene expression (i.e. high within-network tissue-tissue “strength fraction”, SF) (Richiardi et al., 2015). However, the authors do not adequately control for spatial proximity. We replicated their main analysis, performed a more effective adjustment for spatial proximity, and tested whether “null networks” (i.e. clusters with center coordinates randomly placed throughout cortex) also exhibit high SF. Removing proximal tissue-tissue correlations by Euclidean distance, as opposed to removing correlations within arbitrary tissue labels as in (Richiardi et al., 2015), reduces within-network SF to no greater than null. Moreover, randomly placed clusters also have significantly high SF, indicating that high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks defined by resting-state fMRI. We discuss why additional validations in the original article are invalid and/or misleading and suggest future directions.

scholarly journals Distinct disruptions of resting-state functional brain networks in familial and sporadic schizophrenia

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Jiajia Zhu ◽  
Chuanjun Zhuo ◽  
Feng Liu ◽  
Wen Qin ◽  
Lixue Xu ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Functional Brain ◽  
Functional Brain Networks

scholarly journals Aging Reduces the Functional Brain Networks Strength—a Resting State fMRI Study of Healthy Mouse Brain

2019 ◽  
Vol 11 ◽  
Author(s):  
Ander Egimendia ◽  
Anuka Minassian ◽  
Michael Diedenhofen ◽  
Dirk Wiedermann ◽  
Pedro Ramos-Cabrer ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Resting State ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Healthy Mouse ◽  
Functional Brain ◽  
Fmri Study ◽  
Functional Brain Networks

scholarly journals Color naming and categorization depend on distinct functional brain networks

2020 ◽  
Author(s):  
Katarzyna Siuda-Krzywicka ◽  
Christoph Witzel ◽  
Paolo Bartolomeo ◽  
Laurent Cohen
Keyword(s):  
Resting State ◽  
Response Times ◽  
Brain Networks ◽  
Color Naming ◽  
Neural Mechanisms ◽  
Angular Gyrus ◽  
Functional Brain ◽  
Functional Connections ◽  
Functional Brain Networks ◽  
Color Categorization

AbstractNaming a color can be understood as an act of categorization, i.e. identifying it as a member of category of colors that are referred to by the same name. But are naming and categorization equivalent cognitive processes, and consequently rely on same neural substrates? Here, we used task and resting-state fMRI, as well as behavioral measures to identify functional brain networks that modulated naming and categorization of colors. Color naming and categorization response times were modulated by different resting state connectivity networks spanning from the color-sensitive regions in the ventro-occipital cortex. Color naming correlated with the connectivity between the left posterior color region, the left medial temporal gyrus, and the left angular gyrus; whereas color categorization involved the connectivity between the bilateral posterior color regions, the left frontal, right temporal and bilateral parietal areas. The networks supporting naming and categorization did not overlap, suggesting that the two processes rely on different neural mechanisms.SignificanceWhen we name a color, we also identify it as a member of a color category, e.g. blue or yellow. Are neural processes underlying color categorization equivalent to those of color naming? Here, we address this question by measuring how individual differences in color categorization and naming response times relate to the strength of functional connections in the brain. Color naming speed correlated with left-hemispheric connectivity between the color-sensitive visual regions and the anterior temporal lobe. Color categorization speed was modulated by a different brain network, encompassing bilateral color-sensitive visual areas, and high-level executive and semantic regions. Thus, color categorization and naming performance involved distinct, non-overlapping brain networks, suggesting that the two processes depend on different neural mechanisms.

Sign in / Sign up

Export Citation Format

Share Document