scholarly journals Large-scale cortical connectivity dynamics of intrinsic neural oscillations support adaptive listening behavior

2021 ◽  
Author(s):  
Mohsen Alavash ◽  
Sarah Tune ◽  
Jonas Obleser
Keyword(s):  
Neural Networks ◽  
Target Word ◽  
Large Scale ◽  
Brain Network ◽  
Oscillatory Activity ◽  
Neural Oscillations ◽  
Successful Adaptation ◽  
Frontoparietal Network ◽  
Listening Behavior ◽  
Flexible Adaptation

AbstractIn multi-talker situations individuals adapt behaviorally to the listening challenge mostly with ease, but how do brain neural networks shape this adaptation? We here establish a long-sought link between large-scale neural communications in electrophysiology and behavioral success in the control of attention in challenging listening situations. In an age-varying sample of N = 154 individuals, we find that connectivity between intrinsic neural oscillations extracted from source-reconstructed electroencephalography is top-down regulated during a challenging dual-talker listening task. These dynamics emerge as spatially organized modulations in power-envelope correlations of alpha and low-beta neural oscillations during ~2 seconds intervals most critical for listening behavior relative to resting-state baseline. First, left frontoparietal low-beta connectivity (16-24 Hz) increased during anticipation and processing of spatial-attention cue before speech presentation. Second, posterior alpha connectivity (7-11 Hz) decreased during comprehension of competing speech, particularly around target-word presentation. Connectivity dynamics of these networks were predictive of individual differences in the speed and accuracy of target-word identification, respectively, but proved unconfounded by changes in neural oscillatory activity strength. Successful adaptation to a listening challenge thus latches onto two distinct yet complementary neural systems: a beta-tuned frontoparietal network enabling the flexible adaptation to attentive listening state and an alpha-tuned posterior network supporting attention to speech.Significance StatementAttending to relevant information during listening is key to human communication. How does this adaptive behavior rely upon neural communications? We here follow up on the long-standing conjecture that, large-scale brain network dynamics constrain our successful adaptation to cognitive challenges. We provide evidence in support of two intrinsic, frequency-specific neural networks that underlie distinct behavioral aspects of successful listening: a beta-tuned frontoparietal network enabling the flexible adaptation to attentive listening state, and an alpha-tuned posterior cortical network supporting attention to speech. These findings shed light on how large-scale neural communication dynamics underlie attentive listening and open new opportunities for brain network-based intervention in hearing loss and its neurocognitive consequences.

scholarly journals Dynamic large-scale connectivity of intrinsic cortical oscillations supports adaptive listening in challenging conditions

PLoS Biology ◽  
2021 ◽  
Vol 19 (10) ◽  
pp. e3001410
Author(s):  
Mohsen Alavash ◽  
Sarah Tune ◽  
Jonas Obleser
Keyword(s):  
Target Word ◽  
Large Scale ◽  
Word Identification ◽  
Oscillatory Activity ◽  
Neural Systems ◽  
Neural Oscillations ◽  
Frontoparietal Network ◽  
Cortical Oscillations ◽  
Listening Behavior ◽  
Flexible Adaptation

In multi-talker situations, individuals adapt behaviorally to the listening challenge mostly with ease, but how do brain neural networks shape this adaptation? We here establish a long-sought link between large-scale neural communications in electrophysiology and behavioral success in the control of attention in difficult listening situations. In an age-varying sample of N = 154 individuals, we find that connectivity between intrinsic neural oscillations extracted from source-reconstructed electroencephalography is regulated according to the listener’s goal during a challenging dual-talker task. These dynamics occur as spatially organized modulations in power-envelope correlations of alpha and low-beta neural oscillations during approximately 2-s intervals most critical for listening behavior relative to resting-state baseline. First, left frontoparietal low-beta connectivity (16 to 24 Hz) increased during anticipation and processing of spatial-attention cue before speech presentation. Second, posterior alpha connectivity (7 to 11 Hz) decreased during comprehension of competing speech, particularly around target-word presentation. Connectivity dynamics of these networks were predictive of individual differences in the speed and accuracy of target-word identification, respectively, but proved unconfounded by changes in neural oscillatory activity strength. Successful adaptation to a listening challenge thus latches onto 2 distinct yet complementary neural systems: a beta-tuned frontoparietal network enabling the flexible adaptation to attentive listening state and an alpha-tuned posterior network supporting attention to speech.

scholarly journals High-frequency oscillations and the neurobiology of schizophrenia

2013 ◽  
Vol 15 (3) ◽  
pp. 301-313 ◽  
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Oscillatory Activity ◽  
Current Evidence ◽  
Neural Oscillations ◽  
Future Research ◽  
Normal Brain ◽  
Brain Functions ◽  
Critical Issues ◽  
Underlying Mechanisms

Neural oscillations at low- and high-frequency ranges are a fundamental feature of large-scale networks. Recent evidence has indicated that schizophrenia is associated with abnormal amplitude and synchrony of oscillatory activity, in particular, at high (beta/gamma) frequencies. These abnormalities are observed during task-related and spontaneous neuronal activity which may be important for understanding the pathophysiology of the syndrome. In this paper, we shall review the current evidence for impaired beta/gamma-band oscillations and their involvement in cognitive functions and certain symptoms of the disorder. In the first part, we will provide an update on neural oscillations during normal brain functions and discuss underlying mechanisms. This will be followed by a review of studies that have examined high-frequency oscillatory activity in schizophrenia and discuss evidence that relates abnormalities of oscillatory activity to disturbed excitatory/inhibitory (E/I) balance. Finally, we shall identify critical issues for future research in this area.

scholarly journals Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

2020 ◽  
Vol 4 (2) ◽  
pp. 448-466
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Resting State ◽  
Large Scale ◽  
Brain Network ◽  
Generative Models ◽  
Network Activity ◽  
Fmri Data ◽  
State Transitions ◽  
Whole Brain

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007 ). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco, 2017 ; Kashyap & Keilholz, 2019 ). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012 ; Majeed et al., 2011 ). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.

scholarly journals Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130531 ◽  
Author(s):  
Petra E. Vértes ◽  
Aaron Alexander-Bloch ◽  
Edward T. Bullmore
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Large Scale ◽  
Hebbian Learning ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Generative Models ◽  
Adaptive Behaviour ◽  
Topological Term

Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour.

scholarly journals Meditation effect in changing functional integrations across large-scale brain networks: Preliminary evidence from a meta-analysis of seed-based functional connectivity

2020 ◽  
Vol 14 ◽  
Author(s):  
Yang-Qian Shen ◽  
Hui-Xia Zhou ◽  
Xiao Chen ◽  
Francisco Xavier Castellanos ◽  
Chao-Gan Yan
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Meta Analysis ◽  
Well Being ◽  
Preliminary Evidence ◽  
Brain Network ◽  
Clinical Settings ◽  
Control Groups ◽  
Frontoparietal Network ◽  
Brain Functional Connectivity

Abstract Meditation is a type of mental training commonly applied in clinical settings and also practiced for general well-being. Brain functional connectivity (FC) patterns associated with meditation have revealed its brain mechanisms. However, the variety of FC methods applied has made it difficult to identify brain communication patterns associated with meditation. Here we carried out a coordinate-based meta-analysis to get preliminary evidence of meditation effects on changing brain network interactions. Fourteen seed-based, voxel-wise FC studies reported in 13 publications were reviewed; 10 studies with seeds in the default mode network (DMN) were meta-analyzed. Seed coordinates and the effect sizes in statistically significant regions were extracted, based on 170 subjects in meditation groups and 163 subjects in control groups. Seed-based d-mapping was used to analyze meditation versus control FC differences with DMN seeds. Meditation was associated with increased connectivity within DMN and between DMN and somatomotor network and with decreased connectivity between DMN and frontoparietal network (FPN) as well as ventral attention network (VAN). The pattern of decreased within-DMN FC and increased between-network FC (FPN and DAN with DMN) was more robust in highly experienced meditators compared to less experienced individuals. The identified neural network interactions may also promote meditation’s effectiveness in clinical interventions for treating physical and mental disorders.

scholarly journals Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Ines R Violante ◽  
Lucia M Li ◽  
David W Carmichael ◽  
Romy Lorenz ◽  
Robert Leech ◽  
...  
Keyword(s):  
Working Memory ◽  
Cognitive Processing ◽  
Large Scale ◽  
Relative Phase ◽  
Memory Performance ◽  
Network Dynamics ◽  
Oscillatory Activity ◽  
Emergent Properties ◽  
Behavioral Performance ◽  
Frontoparietal Network

Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.

scholarly journals Brain Network Constraints and Recurrent Neural Networks reproduce unique Trajectories and State Transitions seen over the span of minutes in resting state fMRI

2019 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Resting State ◽  
Large Scale ◽  
Brain Network ◽  
Generative Models ◽  
Network Activity ◽  
State Transitions ◽  
Time Step ◽  
Whole Brain

AbstractLarge scale patterns of spontaneous whole brain activity seen in resting state functional Magnetic Resonance Imaging (rsfMRI), are in part believed to arise from neural populations interacting through the structural fiber network [18]. Generative models that simulate this network activity, called Brain Network Models (BNM), are able to reproduce global averaged properties of empirical rsfMRI activity such as functional connectivity (FC) [7, 27]. However, they perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole brain data [20]. At very short timescales between measurements, it is not known how much of the variance these BNM can explain because they are not currently synchronized with the measured rsfMRI. We demonstrate that by solving for the initial conditions of BNM from an observed data point using Recurrent Neural Networks (RNN) and integrating it to predict the next time step, the trained network can explain large amounts of variance for the 5 subsequent time points of unseen future trajectory. The RNN and BNM combined system essentially models the network component of rsfMRI, and where future activity is solely based on previous neural activity propagated through the structural network. Longer instantiations of this generative model simulated over the span of minutes can reproduce average FC and the 1/f power spectrum from 0.01 to 0.3 Hz seen in fMRI. Simulated data also contain interesting resting state dynamics, such as unique repeating trajectories, called QPPs [22] that are highly correlated to the empirical trajectory which spans over 20 seconds. Moreover, it exhibits complex states and transitions as seen using k-Means analysis on windowed FC matrices [1]. This suggests that by combining BNMs with RNN to accurately predict future resting state activity at short timescales, it is learning the manifold of the network dynamics, allowing it to simulate complex resting state trajectories at longer time scales. We believe that our technique will be useful in understanding the large-scale functional organization of the brain and how different BNMs recapitulate different aspects of the system dynamics.

scholarly journals The causal role of α-oscillations in feature binding

2019 ◽  
Vol 116 (34) ◽  
pp. 17023-17028 ◽  
Author(s):  
Yanyu Zhang ◽  
Yifei Zhang ◽  
Peng Cai ◽  
Huan Luo ◽  
Fang Fang
Keyword(s):  
Large Scale ◽  
Brain Network ◽  
Feature Binding ◽  
Causal Role ◽  
Human Cognition ◽  
Neural Oscillations ◽  
Switch Rate ◽  
Transcranial Alternating Current Stimulation ◽  
Large Scale Networks

The binding problem—how to integrate features into objects—poses a fundamental challenge for the brain. Neural oscillations, especially γ-oscillations, have been proposed as a potential mechanism to solve this problem. However, since γ-oscillations usually reflect local neural activity, how to implement feature binding involving a large-scale brain network remains largely unknown. Here, combining electroencephalogram (EEG) and transcranial alternating current stimulation (tACS), we employed a bistable color-motion binding stimulus to probe the role of neural oscillations in feature binding. Subjects’ perception of the stimulus switched between its physical binding and its illusory (active) binding. The active binding has been shown to involve a large-scale network consisting of spatially distant brain areas. α-Oscillations presumably reflect the dynamics of such large-scale networks, especially due to volume conduction effects in EEG. We found that, relative to the physical binding, the α-power decreased during the active binding. Additionally, individual α-power was negatively correlated with the time proportion of the active binding. Subjects’ perceptual switch rate between the 2 bindings was positively correlated with their individual α-frequency. Furthermore, applying tACS at individual α-frequency decreased the time proportion of the active binding. Moreover, delivering tACS at different temporal frequencies in the α-band changed subjects’ perceptual switch rate through affecting the active binding process. Our findings provide converging evidence for the causal role of α-oscillations in feature binding, especially in active feature binding, thereby uncovering a function of α-oscillations in human cognition.

A Developmental Method for Evolving Large-Scale Spiking Neural Networks

2012 ◽  
Vol 35 (12) ◽  
pp. 2633 ◽  
Author(s):  
Xiang-Hong LIN ◽  
Tian-Wen ZHANG ◽  
Gui-Cang ZHANG
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spiking Neural Networks
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