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 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 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 Temporal binding of Visual and Motor cortical oscillations revealed by BOLD response in Humans

2020 ◽  
Author(s):  
Alessandro Benedetto ◽  
Paola Binda ◽  
Mauro Costagli ◽  
Michela Tosetti ◽  
Maria Concetta Morrone
Keyword(s):  
Visual Discrimination ◽  
Oscillatory Activity ◽  
Neural Oscillations ◽  
Button Press ◽  
Temporal Binding ◽  
Motor Action ◽  
Cortical Oscillations ◽  
Motor Signal ◽  
Action And Perception ◽  
Theta Range

SummaryAction and perception need to be coordinated continuously over time, and neural oscillations may be instrumental in achieving such synchronization. Here we demonstrate that behavioral visual discrimination and the BOLD activity of V1 oscillates rhythmically in the theta range (around 5 Hz), synchronized to motor action (button press). The oscillations are present in V1 even when participants do not make a visual discrimination, suggesting an automatic modulation in synchrony with action onset. The amplitude of the oscillation in V1 is predicted by the activity in M1 before action onset, and functional connectivity between V1 and M1 change as a function of stimulus-delay. The results are well modelled by considering that V1 BOLD is modulated by preparatory motor signal and by rhythmic gain modulation in phase with action onset. They suggest that synchronous oscillatory activity between V1 and M1 mediates the strong temporal binding fundamental for active visual perception.

scholarly journals Occipital Alpha and Gamma Oscillations Support Complementary Mechanisms for Processing Stimulus Value Associations

2018 ◽  
Vol 30 (1) ◽  
pp. 119-129 ◽  
Author(s):  
Tom R. Marshall ◽  
Sebastiaan den Boer ◽  
Roshan Cools ◽  
Ole Jensen ◽  
Sean James Fallon ◽  
...  
Keyword(s):  
Selective Attention ◽  
Neural Mechanism ◽  
Gamma Oscillations ◽  
Relevant Information ◽  
Source Analysis ◽  
Neural Systems ◽  
Neural Oscillations ◽  
Alpha Power ◽  
Cortical Oscillations ◽  
Stimulus Value

Selective attention is reflected neurally in changes in the power of posterior neural oscillations in the alpha (8–12 Hz) and gamma (40–100 Hz) bands. Although a neural mechanism that allows relevant information to be selectively processed has its advantages, it may lead to lucrative or dangerous information going unnoticed. Neural systems are also in place for processing rewarding and punishing information. Here, we examine the interaction between selective attention (left vs. right) and stimulus's learned value associations (neutral, punished, or rewarded) and how they compete for control of posterior neural oscillations. We found that both attention and stimulus–value associations influenced neural oscillations. Whereas selective attention had comparable effects on alpha and gamma oscillations, value associations had dissociable effects on these neural markers of attention. Salient targets (associated with positive and negative outcomes) hijacked changes in alpha power—increasing hemispheric alpha lateralization when salient targets were attended, decreasing it when they were being ignored. In contrast, hemispheric gamma-band lateralization was specifically abolished by negative distractors. Source analysis indicated occipital generators of both attentional and value effects. Thus, posterior cortical oscillations support both the ability to selectively attend while at the same time retaining the ability to remain sensitive to valuable features in the environment. Moreover, the versatility of our attentional system to respond separately to salient from merely positively valued stimuli appears to be carried out by separate neural processes reflected in different frequency bands.

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 Two frequency bands contain the most stimulus-related information in visual cortex

2016 ◽  
Author(s):  
Christopher M. Lewis ◽  
Conrado A. Bosman ◽  
Nicolas M. Brunet ◽  
Bruss Lima ◽  
Mark J. Roberts ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Single Cells ◽  
Field Potential ◽  
Oscillatory Activity ◽  
Natural Scenes ◽  
Beta Band ◽  
Frequency Bands ◽  
Related Information ◽  
Cortical Oscillations

AbstractSensory cortices represent the world through the activity of diversely tuned cells. How the activity of single cells is coordinated within populations and across sensory hierarchies is largely unknown. Cortical oscillations may coordinate local and distributed neuronal groups. Using datasets from intracortical multi-electrode recordings and from large-scale electrocorticography (ECoG) grids, we investigated how visual features could be extracted from the local field potential (LFP) and how this compared with the information available from multi-unit activity (MUA). MUA recorded from macaque V1 contained comparable amounts of information as simultaneously recorded LFP power in two frequency bands, one in the alpha-beta band and the other in the gamma band. ECoG-LFP contained information in the same bands as microelectrode-LFP, even when identifying natural scenes. The fact that information was contained in the same bands in both intracortical and ECoG recordings suggests that oscillatory activity could play similar roles at both spatial scales.

Surrogate Population Models for Large-Scale Neural Simulations

2015 ◽  
Vol 27 (6) ◽  
pp. 1186-1222 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Surrogate Model ◽  
Latent Variables ◽  
Large Scale ◽  
Neural Model ◽  
Surrogate Models ◽  
Weighted Sums ◽  
Neural Systems ◽  
Neuron Models ◽  
Linear Filters ◽  
Population Activity

Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to multiscale modeling of neural systems that involves constructing efficient surrogate models of populations. Given a population of neuron models with correlated activity and with specific, nonrandom connections, a surrogate model is constructed in order to approximate the aggregate outputs of the population. The surrogate model requires less computation than the neural model, but it has a clear and specific relationship with the neural model. For example, approximate spike rasters for specific neurons can be derived from a simulation of the surrogate model. This article deals specifically with neural engineering framework (NEF) circuits of leaky-integrate-and-fire point neurons. Weighted sums of spikes are modeled by interpolating over latent variables in the population activity, and linear filters operate on gaussian random variables to approximate spike-related fluctuations. It is found that the surrogate models can often closely approximate network behavior with orders-of-magnitude reduction in computational demands, although there are certain systematic differences between the spiking and surrogate models. Since individual spikes are not modeled, some simulations can be performed with much longer steps sizes (e.g., 20 ms). Possible extensions to non-NEF networks and to more complex neuron models are discussed.

scholarly journals Imaging of neural oscillations with embedded inferential and group prevalence statistics

2017 ◽  
Author(s):  
Peter W. Donhauser ◽  
Esther Florin ◽  
Sylvain Baillet
Keyword(s):  
Brain Activity ◽  
Signal Strength ◽  
Brain Regions ◽  
Oscillatory Activity ◽  
Neural Oscillations ◽  
Source Reconstruction ◽  
Signal To Noise ◽  
Source Imaging ◽  
Methodological Challenges ◽  
Oscillatory Power

AbstractMagnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. Imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges1. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.Author summaryThe oscillatory activity of the brain produces a repertoire of signal dynamics that is rich and complex. Noninvasive recording techniques such as scalp magnetoencephalography and electroencephalography (MEG, EEG) are key methods to advance our comprehension of the role played by neural oscillations in brain functions and dysfunctions. Yet, there are methodological challenges in mapping these elusive components of brain activity that have remained unresolved. We introduce a new mapping technique, called imaging with embedded statistics (iES), which alleviates these difficulties. With iES, signal detection is constrained explicitly to the operational hypotheses of the study design. We show, in a variety of experimental contexts, how iES emphasizes the oscillatory components of brain activity, if any, that match the experimental hypotheses, even in deeper brain regions where signal strength is expected to be weak in MEG. Overall, the proposed method is a new imaging tool to respond to a wide range of neuroscience questions concerning the scaffolding of brain dynamics via anatomically-distributed neural oscillations.

Functional Connectivity Relationships Predict Similarities in Task Activation and Pattern Information during Associative Memory Encoding

2014 ◽  
Vol 26 (5) ◽  
pp. 1085-1099 ◽  
Author(s):  
Maureen Ritchey ◽  
Andrew P. Yonelinas ◽  
Charan Ranganath
Keyword(s):  
Functional Connectivity ◽  
Functional Properties ◽  
Associative Memory ◽  
Large Scale ◽  
Memory Task ◽  
Neural Systems ◽  
General Level ◽  
Specific Information ◽  
Memory Encoding ◽  
Cortical Regions

Neural systems may be characterized by measuring functional interactions in the healthy brain, but it is unclear whether components of systems defined in this way share functional properties. For instance, within the medial temporal lobes (MTL), different subregions show different patterns of cortical connectivity. It is unknown, however, whether these intrinsic connections predict similarities in how these regions respond during memory encoding. Here, we defined brain networks using resting state functional connectivity (RSFC) then quantified the functional similarity of regions within each network during an associative memory encoding task. Results showed that anterior MTL regions affiliated with a network of anterior temporal cortical regions, whereas posterior MTL regions affiliated with a network of posterior medial cortical regions. Importantly, these connectivity relationships also predicted similarities among regions during the associative memory task. Both in terms of task-evoked activation and trial-specific information carried in multivoxel patterns, regions within each network were more similar to one another than were regions in different networks. These findings suggest that functional heterogeneity among MTL subregions may be related to their participation in distinct large-scale cortical systems involved in memory. At a more general level, the results suggest that components of neural systems defined on the basis of RSFC share similar functional properties in terms of recruitment during cognitive tasks and information carried in voxel patterns.

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