scholarly journals The Human Thalamus is an Integrative Hub for Functional Brain Networks

2016 ◽  
Author(s):  
Kai Hwang ◽  
Maxwell Bertolero ◽  
William Liu ◽  
Mark D’Esposito
Keyword(s):  
Meta Analysis ◽  
Functional Brain Imaging ◽  
Modular Organization ◽  
Functional Networks ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Human Thalamus ◽  
Functional Brain Networks ◽  
Human Participants ◽  
Cortical Regions

AbstractThe thalamus is globally connected with distributed cortical regions, yet the functional significance of this extensive thalamocortical connectivity remains largely unknown. By performing graph-theoretic analyses on thalamocortical functional connectivity data collected from human participants, we found that the human thalamus displays network properties capable of integrating multimodal information across diverse cortical functional networks. From a meta-analysis of a large dataset of functional brain imaging experiments, we further found that the thalamus is involved in multiple cognitive functions. Finally, we found that focal thalamic lesions in humans have widespread distal effects, disrupting the modular organization of cortical functional networks. This converging evidence suggests that the human thalamus is a critical hub region that could integrate heteromodal information and maintain the modular structure of cortical functional networks.

scholarly journals Topological segregation of functional networks increases in developing brains

2018 ◽  
Author(s):  
Wei He ◽  
Paul F. Sowman ◽  
Jon Brock ◽  
Andrew C. Etchell ◽  
Cornelis J. Stam ◽  
...  
Keyword(s):  
Network Topology ◽  
Minimum Spanning Tree ◽  
Brain Networks ◽  
Developmental Trajectory ◽  
Brain Maturation ◽  
Functional Networks ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Reconstructed Time Series ◽  
Cortical Regions

AbstractA growing literature conceptualises human brain development from a network perspective, but it remains unknown how functional brain networks are refined during the preschool years. The extant literature diverges in its characterisation of functional network development, with little agreement between haemodynamic- and electrophysiology-based measures. In children aged from 4 to 12 years, as well as adults, age appropriate magnetoencephalography was used to estimate unbiased network topology, using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series. During childhood, network topology becomes increasingly segregated, while cortical regions decrease in centrality. We propose a heuristic MST model, in which a clear developmental trajectory for the emergence of complex brain networks is delineated. Our results resolve topological reorganisation of functional networks across temporal and special scales in youth and fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years.

Deep Learning for the Automated Classification of Functional Brain Networks in fMRI (Preprint)

2021 ◽  
Author(s):  
Lukman Ismael ◽  
Pejman Rasti ◽  
Florian Bernard ◽  
Philippe Menei ◽  
Aram Ter Minassian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Brain Networks ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Functional Networks ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Deep Learning Model

BACKGROUND The functional MRI (fMRI) is an essential tool for the presurgical planning of brain tumor removal, allowing the identification of functional brain networks in order to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rsfMRI). However, this technique is not routinely used because of the necessity to have a expert reviewer to identify manually each functional networks. OBJECTIVE We aimed to automatize the detection of brain functional networks in rsfMRI data using deep learning and machine learning algorithms METHODS We used the rsfMRI data of 82 healthy patients to test the diagnostic performance of our proposed end-to-end deep learning model to the reference functional networks identified manually by 2 expert reviewers. RESULTS Experiment results show the best performance of 86% correct recognition rate obtained from the proposed deep learning architecture which shows its superiority over other machine learning algorithms that were equally tested for this classification task. CONCLUSIONS The proposed end-to-end deep learning model was the most performant machine learning algorithm. The use of this model to automatize the functional networks detection in rsfMRI may allow to broaden the use of the rsfMRI, allowing the presurgical identification of these networks and thus help to preserve the patient’s neurological status. CLINICALTRIAL Comité de protection des personnes Ouest II, decision reference CPP 2012-25)

Neurophysiology of Yoga Meditation

1999 ◽  
Vol 9 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Roy King ◽  
Ann Brownstone
Keyword(s):  
Brain Imaging ◽  
Sleep Onset ◽  
Brain Regions ◽  
Functional Brain Imaging ◽  
Imaging Studies ◽  
Pure Consciousness ◽  
Yoga Therapy ◽  
Functional Brain ◽  
Cortical Regions ◽  
Yoga Meditation

Recent neuroimaging studies of brain function have led to an explosion of knowledge about psychological processes and states. In this paper functional brain imaging studies of Yoga meditation are reviewed. Tantra-based meditations activate frontal and occipital cortical regions involved in focused, sustained attention and visual imagery. The overall pattern of brain activation in Tantra-based meditations is similar to that of self-hypnosis but different from that of sleep onset. Pure consciousness, the ultimate aim of Vedanta-based meditation, also activates frontal cortical areas regulating focused attention but deactivates sensory areas involved in imagery. Functional brain imaging studies thus support the distinction between meditation with conceptual support and pure-consciousness meditation without conceptual support, a distinction that appears throughout Yoga meditation texts. Brain imaging investigations also explain how Yoga therapy may be helpful to those with anxiety disorders by reducing activity in brain regions linked to the processing of negative emotions.

scholarly journals Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242985
Author(s):  
Howard Muchen Hsu ◽  
Zai-Fu Yao ◽  
Kai Hwang ◽  
Shulan Hsieh
Keyword(s):  
Individual Differences ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Motor Inhibition ◽  
Attention Network ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Dorsal Attention Network ◽  
Functional Brain Networks

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

scholarly journals Emotional states as distinct configurations of functional brain networks

2021 ◽  
Author(s):  
Rotem Dan ◽  
Marta Weinstock ◽  
Gadi Goelman
Keyword(s):  
Large Scale ◽  
Emotional Experience ◽  
Brain Networks ◽  
Emotional Valence ◽  
Individual Variability ◽  
Modular Organization ◽  
Emotional States ◽  
Functional Brain ◽  
Functional Connectome ◽  
Functional Brain Networks

The conceptualization of emotional states as patterns of interactions between large-scale brain networks has recently gained support. Yet, few studies have directly examined the brain's network structure during emotional experiences. Here, we investigated the brain's functional network organization during experiences of sadness, amusement, and neutral states elicited by movies, in addition to a resting-state. We tested the effects of the experienced emotion on individual variability in the brain's functional connectome. Next, for each state, we defined a modular organization of the brain and quantified its segregation and integration. Our results show that emotional states increase the similarity between and within individuals in the brain's functional connectome. Second, in the brain's modular organization, sadness, relative to amusement, was associated with higher integration and increased connectivity of cognitive control networks: the salience and fronto-parietal networks. Modular metrics of brain segregation and integration were further associated with the reported emotional valence. Last, in both the functional connectome and emotional report, a higher similarity was found among women. Our results suggest that the experience of emotion is linked to a reconfiguration of whole-brain distributed, not emotion-specific, functional brain networks and that the topological structure carries information about the subjective emotional experience.

scholarly journals Alternating dynamics of segregation and integration in human brain functional networks during working-memory task

2016 ◽  
Author(s):  
Antonio G. Zippo ◽  
Pasquale A. Della Rosa ◽  
Isabella Castiglioni ◽  
Gabriele E. M. Biella
Keyword(s):  
Working Memory ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Networks ◽  
Memory Task ◽  
Functional Networks ◽  
Second Phase ◽  
Functional Brain ◽  
Brain Functional Networks ◽  
Functional Brain Networks

AbstractBrain functional networks show high variability in short time windows but mechanisms governing these transient dynamics still remain unknown. In this work we studied the temporal evolution of functional brain networks involved in a working memory task while recording high-density electroencephalography in human normal subjects. We found that functional brain networks showed an initial phase characterized by an increase of the functional segregation index followed by a second phase where the functional segregation fell down and the functional integration prevailed. Notably, wrong trials were associated with different sequences of the segregation-integration profile and measures of network centrality and modularity were able to catch crucial aspects of the oscillatory network dynamics. Additionally, computational investigations further supported the experimental results. The brain functional organization may respond to the information processing demand of a working memory task following a 2-step atomic scheme wherein segregation and integration alternately dominate the functional configurations.

scholarly journals Meta-analysis of functional brain imaging in specific phobia

2013 ◽  
Vol 67 (5) ◽  
pp. 311-322 ◽  
Author(s):  
Jonathan C. Ipser ◽  
Leesha Singh ◽  
Dan J. Stein
Keyword(s):  
Brain Imaging ◽  
Meta Analysis ◽  
Specific Phobia ◽  
Functional Brain Imaging ◽  
Functional Brain

Neural correlates of dietary self-control in healthy adults: A meta-analysis of functional brain imaging studies

2018 ◽  
Vol 192 ◽  
pp. 98-108 ◽  
Author(s):  
Jung Eun Han ◽  
Nadia Boachie ◽  
Isabel Garcia-Garcia ◽  
Andréanne Michaud ◽  
Alain Dagher
Keyword(s):  
Brain Imaging ◽  
Meta Analysis ◽  
Neural Correlates ◽  
Functional Brain Imaging ◽  
Healthy Adults ◽  
Self Control ◽  
Imaging Studies ◽  
Functional Brain
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