scholarly journals Long-range functional coupling predicts performance: oscillatory EEG networks in multisensory processing

2015 ◽  
Author(s):  
Peng Wang ◽  
Florian Göschl ◽  
Uwe Friese ◽  
Peter König ◽  
Andreas K. Engel
Keyword(s):  
Long Range ◽  
Large Scale ◽  
Multisensory Processing ◽  
Data Driven ◽  
Matching Task ◽  
Functional Coupling ◽  
Phase Coupling ◽  
Brain Areas ◽  
Phase Dynamics ◽  
Brain Functions

AbstractThe integration of sensory signals from different modalities requires flexible interaction of remote brain areas. One candidate mechanism to establish communication in the brain is transient synchronization of oscillatory neural signals. Although there is abundant evidence for the involvement of cortical oscillations in brain functions based on the analysis of local power, assessment of the phase dynamics among spatially distributed neuronal populations and their relevance for behavior is still sparse. In the present study, we investigated the interaction between remote brain areas by analyzing high-density electroencephalogram (EEG) data obtained from human participants engaged in a visuotactile pattern matching task. We deployed an approach for purely data-driven clustering of neuronal phase coupling in source space, which allowed imaging of large-scale functional networks in space, time and frequency without defining a priori constraints. Based on the phase coupling results, we further explored how brain areas interacted across frequencies by computing phase-amplitude coupling. Several networks of interacting sources were identified with our approach, synchronizing their activity within and across the theta (~5 Hz), alpha (~10 Hz), and beta (~ 20 Hz) frequency bands and involving multiple brain areas that have previously been associated with attention and motor control. We demonstrate the functional relevance of these networks by showing that phase delays – in contrast to spectral power – were predictive of task performance. The data-driven analysis approach employed in the current study allowed an unbiased examination of functional brain networks based on EEG source level connectivity data. Showcased for multisensory processing, our results provide evidence that large-scale neuronal coupling is vital to long-range communication in the human brain and relevant for the behavioral outcome in a cognitive task.

scholarly journals Dynamic changes in large-scale functional connectivity predict performance in a multisensory task

2019 ◽  
Author(s):  
Karl J. Hollensteiner ◽  
Edgar Galindo-Leon ◽  
Florian Pieper ◽  
Gerhard Engler ◽  
Guido Nolte ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Long Range ◽  
Large Scale ◽  
Principal Component ◽  
Stimulus Onset ◽  
Functional Coupling ◽  
Phase Coupling ◽  
Network State ◽  
Task Period ◽  
Response Onset

AbstractComplex and variable behavior requires fast changes of functional connectivity in large-scale cortical networks. Here, we report on the cortical dynamics of functional coupling across visual, auditory and parietal areas during a lateralized detection task in the ferret. We hypothesized that fluctuations in coupling, indicative of dynamic variations in the network state, might predict the animals’ performance. While power for hit and miss trials showed significant differences only around stimulus and response onset, phase coupling already differed before stimulus onset. Principal component analysis of directed coupling at the single-trial level during this period revealed subnetworks that most strongly related to behavior. While higher global phase coupling of visual and auditory regions to parietal cortex was predictive of task performance, a second component showed that a reduction in coupling between subnetworks of sensory modalities was also necessary, probably to allow a better detection of the unimodal signals. Furthermore, we observed that long-range coupling became more predominant during the task period compared to the pre-stimulus baseline. Taken together, these results suggest that fluctuations in the network state, particular with respect to long-range connectivity, are key determinants of the animals’ behavior.

scholarly journals Long-range functional coupling predicts performance: Oscillatory EEG networks in multisensory processing

NeuroImage ◽  
2019 ◽  
Vol 196 ◽  
pp. 114-125 ◽  
Author(s):  
Peng Wang ◽  
Florian Göschl ◽  
Uwe Friese ◽  
Peter König ◽  
Andreas K. Engel
Keyword(s):  
Long Range ◽  
Multisensory Processing ◽  
Functional Coupling ◽  
Oscillatory Eeg

scholarly journals Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks

2017 ◽  
Author(s):  
Diego Vidaurre ◽  
Laurence T Hunt ◽  
Andrew J. Quinn ◽  
Benjamin A.E. Hunt ◽  
Matthew J. Brookes ◽  
...  
Keyword(s):  
Large Scale ◽  
Higher Order ◽  
Cognitive Networks ◽  
Phase Coupling ◽  
Cognitive Network ◽  
Brain Areas ◽  
Frequency Bands ◽  
Functional Brain Networks ◽  
Specific Frequency ◽  
Brain States

AbstractFrequency-specific oscillations and phase-coupling of neuronal populations have been proposed as an essential mechanism for the coordination of activity between brain areas during cognitive tasks. To provide an effective substrate for cognitive function, we reasoned that ongoing functional brain networks should also be able to reorganise and coordinate in a similar manner. To test this hypothesis, we use a novel method for identifying repeating patterns of network dynamics, and show that resting networks in magnetoencephalography are well characterised by visits to short-lived transient brain states, with spatially distinct power and phase-coupling in specific frequency bands. Brain states were identified for sensory, motor networks and higher-order cognitive networks; the latter include a posterior higher-order cognitive network in the alpha range (8-12Hz) and an anterior cognitive network in the delta/theta range (1-7Hz). Both higher-order cognitive networks exhibit especially high power and coherence, and contain brain areas corresponding to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks operate in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales.

scholarly journals Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

2018 ◽  
Author(s):  
Mangor Pedersen ◽  
Andrew Zalesky ◽  
Amir Omidvarnia ◽  
Graeme D. Jackson
Keyword(s):  
Large Scale ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Brain Areas ◽  
Multilayer Network ◽  
Time Resolved ◽  
Brain Functions ◽  
Human Connectome Project

AbstractLarge-scale brain dynamics measures repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood and whether switching between these states is important for behavior has been little studied. Our aim here is to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved functional MRI (fMRI) connectivity from one-hour long data recordings in 1003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data was used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching which we define as the rate at which each brain region transits between different fMRI networks. We found i) an inverse relationship between network switching and connectivity dynamics –defined as the difference in variance between time-resolved fMRI connectivity signals and phase randomized surrogates–; ii) brain connectivity was lower during intervals of network switching; iii) brain areas with frequent network switching had greater temporal complexity; iv) brain areas with high network switching were located in association cortices; and v) using cross-validated Elastic Net regression, network switching predicted inter-subject variation in working memory performance, planning/reasoning and amount of sleep. Our findings shed new light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

scholarly journals Synchronous brain dynamics establish brief states of communality in distant neuronal populations

2021 ◽  
Author(s):  
Martin Seeber ◽  
Christoph M. Michel
Keyword(s):  
Long Range ◽  
Large Scale ◽  
Temporal Stability ◽  
Brain Dynamics ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Scalp Eeg ◽  
Eyes Closed ◽  
Dynamic Coordination ◽  
Functional Brain Networks

Intrinsic brain dynamics co-fluctuate between distant regions in an organized manner during rest, establishing large-scale functional networks. We investigate these brain dynamics on a millisecond time scale by focusing on Electroencephalographic (EEG) source analyses. While synchrony is thought of as a neuronal mechanism grouping distant neuronal populations into assemblies, the relevance of simultaneous zero-lag synchronization between brain areas in humans remains largely unexplored. This negligence is due to the confound of volume conduction, leading inherently to temporal dependencies of source estimates derived from scalp EEG (and Magnetoencephalography, MEG), referred to as spatial leakage. Here, we focus on the analyses of simultaneous, i.e., quasi zero-lag related, synchronization that cannot be explained by spatial leakage phenomenon. In eighteen subjects during rest with eyes closed, we provide evidence that first, simultaneous synchronization is present between distant brain areas and second, that this long-range synchronization is occurring in brief epochs, i.e., 54-80 milliseconds. Simultaneous synchronization might signify the functional convergence of remote neuronal populations. Given the simultaneity of distant regions, these synchronization patterns might relate to the representation and maintenance, rather than processing of information. This long-range synchronization is briefly stable, not persistently, indicating flexible spatial reconfiguration pertaining to the establishment of particular, re-occurring states. Taken together, we suggest that the balance between temporal stability and spatial flexibility of long-range, simultaneous synchronization patterns is characteristic of the dynamic coordination of large-scale functional brain networks. As such, quasi zero-phase related EEG source fluctuations are physiologically meaningful if spatial leakage is considered appropriately.

scholarly journals Multilayer network switching rate predicts brain performance

2018 ◽  
Vol 115 (52) ◽  
pp. 13376-13381 ◽  
Author(s):  
Mangor Pedersen ◽  
Andrew Zalesky ◽  
Amir Omidvarnia ◽  
Graeme D. Jackson
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Brain Areas ◽  
Multilayer Network ◽  
Healthy Human ◽  
Time Resolved ◽  
Brain Functions ◽  
Human Connectome Project

Large-scale brain dynamics are characterized by repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood, and whether switching between these states is important for behavior has been little studied. Our aim was to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved fMRI connectivity in 1,003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data were used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching, which we define as the rate at which each brain region transits between different networks. We found (i) an inverse relationship between network switching and connectivity dynamics, where the latter was defined in terms of time-resolved fMRI connections with variance in time that significantly exceeded phase-randomized surrogate data; (ii) brain connectivity was lower during intervals of network switching; (iii) brain areas with frequent network switching had greater temporal complexity; (iv) brain areas with high network switching were located in association cortices; and (v) using cross-validated elastic net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning, and amount of sleep. Our findings shed light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

Dehydroepiandrosterone (DHEA) and its Sulphate (DHEAS) in Alzheimer’s Disease

2020 ◽  
Vol 17 (2) ◽  
pp. 141-157 ◽  
Author(s):  
Dubravka S. Strac ◽  
Marcela Konjevod ◽  
Matea N. Perkovic ◽  
Lucija Tudor ◽  
Gordana N. Erjavec ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Therapeutic Potential ◽  
Relevant Literature ◽  
Comprehensive Overview ◽  
Brain Functions ◽  
Specific Effects ◽  
And Behavior

Background: Neurosteroids Dehydroepiandrosterone (DHEA) and Dehydroepiandrosterone Sulphate (DHEAS) are involved in many important brain functions, including neuronal plasticity and survival, cognition and behavior, demonstrating preventive and therapeutic potential in different neuropsychiatric and neurodegenerative disorders, including Alzheimer’s disease. Objective: The aim of the article was to provide a comprehensive overview of the literature on the involvement of DHEA and DHEAS in Alzheimer’s disease. Method: PubMed and MEDLINE databases were searched for relevant literature. The articles were selected considering their titles and abstracts. In the selected full texts, lists of references were searched manually for additional articles. Results: We performed a systematic review of the studies investigating the role of DHEA and DHEAS in various in vitro and animal models, as well as in patients with Alzheimer’s disease, and provided a comprehensive discussion on their potential preventive and therapeutic applications. Conclusion: Despite mixed results, the findings of various preclinical studies are generally supportive of the involvement of DHEA and DHEAS in the pathophysiology of Alzheimer’s disease, showing some promise for potential benefits of these neurosteroids in the prevention and treatment. However, so far small clinical trials brought little evidence to support their therapy in AD. Therefore, large-scale human studies are needed to elucidate the specific effects of DHEA and DHEAS and their mechanisms of action, prior to their applications in clinical practice.

scholarly journals Psychiatric disorders in China: strengths and challenges of contemporary research and clinical services

2021 ◽  
pp. 1-14
Author(s):  
Xiao Chang ◽  
Qiyong Gong ◽  
Chunbo Li ◽  
Weihua Yue ◽  
Xin Yu ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Disease Burden ◽  
Large Scale ◽  
Well Being ◽  
Research Quality ◽  
Psychiatric Research ◽  
Clinical Services ◽  
Contributing Factors ◽  
Brain Functions ◽  
Neuroscience Research

Abstract China accounts for 17% of the global disease burden attributable to mental, neurological and substance use disorders. As a country undergoing profound societal change, China faces growing challenges to reduce the disease burden caused by psychiatric disorders. In this review, we aim to present an overview of progress in neuroscience research and clinical services for psychiatric disorders in China during the past three decades, analysing contributing factors and potential challenges to the field development. We first review studies in the epidemiological, genetic and neuroimaging fields as examples to illustrate a growing contribution of studies from China to the neuroscience research. Next, we introduce large-scale, open-access imaging genetic cohorts and recently initiated brain banks in China as platforms to study healthy brain functions and brain disorders. Then, we show progress in clinical services, including an integration of hospital and community-based healthcare systems and early intervention schemes. We finally discuss opportunities and existing challenges: achievements in research and clinical services are indispensable to the growing funding investment and continued engagement in international collaborations. The unique aspect of traditional Chinese medicine may provide insights to develop a novel treatment for psychiatric disorders. Yet obstacles still remain to promote research quality and to provide ubiquitous clinical services to vulnerable populations. Taken together, we expect to see a sustained advancement in psychiatric research and healthcare system in China. These achievements will contribute to the global efforts to realize good physical, mental and social well-being for all individuals.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

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