scholarly journals Large-scale cortical correlation structure of spontaneous oscillatory activity

2012 ◽  
Vol 15 (6) ◽  
pp. 884-890 ◽  
Author(s):  
Joerg F Hipp ◽  
David J Hawellek ◽  
Maurizio Corbetta ◽  
Markus Siegel ◽  
Andreas K Engel
Keyword(s):  
Large Scale ◽  
Correlation Structure ◽  
Oscillatory Activity

scholarly journals Distinct cortical correlation structures of fractal and oscillatory neuronal activity

2020 ◽  
Author(s):  
Andrea Ibarra Chaoul ◽  
Markus Siegel
Keyword(s):  
Neuronal Activity ◽  
Large Scale ◽  
Neuronal Population ◽  
Correlation Structure ◽  
Oscillatory Activity ◽  
Source Analysis ◽  
Population Activity ◽  
Neuronal Mechanisms ◽  
Frequency Components ◽  
Cortical Regions

AbstractElectrophysiological population signals contain oscillatory and fractal (1/frequency) components. So far research has largely focused on oscillatory activity and only recently interest in fractal population activity has gained momentum. Accordingly, while the cortical correlation structure of oscillatory population activity has been characterized, little is known about the correlation of fractal neuronal activity. To address this, we investigated fractal neuronal population activity in the human brain using resting-state magnetoencephalography (MEG). We combined source-analysis, signal orthogonalization and irregular-resampling auto-spectral analysis (IRASA) to systematically characterize the cortical distribution and correlation of fractal neuronal activity. We found that fractal population activity is robustly correlated across the cortex and that this correlation is spatially well structured. Furthermore, we found that the cortical correlation structure of fractal activity is similar but distinct from the correlation structure of oscillatory neuronal activity. Anterior cortical regions showed the strongest differences between oscillatory and fractal correlation patterns. Our results suggest that correlations of fractal population activity serve as robust markers of cortical network interactions. Furthermore, our results show that fractal and oscillatory signal components provide non-redundant information about large-scale neuronal correlations. This may reflect at least partly distinct neuronal mechanisms underlying and reflected by oscillatory and fractal neuronal population activity.

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.

Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG

NeuroImage ◽  
2016 ◽  
Vol 129 ◽  
pp. 345-355 ◽  
Author(s):  
Marcus Siems ◽  
Anna-Antonia Pape ◽  
Joerg F. Hipp ◽  
Markus Siegel
Keyword(s):  
Correlation Structure ◽  
Oscillatory Activity

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 Distributed alpha networks underlie individual differences in memory performance

2016 ◽  
Author(s):  
Roy Cox ◽  
Anna C Schapiro ◽  
Robert Stickgold
Keyword(s):  
Individual Differences ◽  
Episodic Memory ◽  
Large Scale ◽  
Memory Performance ◽  
Oscillatory Activity ◽  
Neural Basis ◽  
Individual Memory ◽  
Optimal Information ◽  
Network Approaches ◽  
Brain Behavior

AbstractEpisodic memory, our ability to remember specific events, varies considerably across individuals. However, little is known about the neural basis of this variability. To address this issue, we investigated the role of distributed networks of oscillatory activity, as measured through electroencephalography (EEG). We observed that individual differences in alpha network structure reliably predict individual memory capacity. Specifically, individuals whose network profiles during encoding were most different from their resting state networks exhibited greatest subsequent memory performance, suggesting that optimal information processing requires substantial shifts in large-scale oscillatory organization. Furthermore, these results were not observed in circumscribed topographical regions or individual connections, indicating that distributed network approaches were more sensitive to functional processes than more conventional methods. These findings uncover a physiological correlate of individual differences in episodic memory and demonstrate the utility of multivariate EEG techniques to uncover brain-behavior correlates.

scholarly journals Keeping time and rhythm by replaying a sensory-motor engram

2022 ◽  
Author(s):  
Victor de Lafuente ◽  
Mehrdad Jazayeri ◽  
Hugo Merchant ◽  
Otto Garcia-Garibay ◽  
Jaime Cadena-Valencia ◽  
...  
Keyword(s):  
Large Scale ◽  
Oscillatory Activity ◽  
Motor Processing ◽  
Sensory Motor ◽  
Trial Basis ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
The Mind ◽  
Successful Behavior ◽  
The Brain

Imagine practicing a piece of music, or a speech, solely within the mind, without any sensory input or motor output. Our ability to implement dynamic internal representations is key for successful behavior, yet how the brain achieves this is not fully understood. Here we trained primates to perceive, and internally maintain, rhythms of different tempos and performed large-scale recordings of neuronal activity across multiple areas spanning the sensory-motor processing hierarchy. Results show that perceiving and maintaining rhythms engage multiple brain areas, including visual, parietal, premotor, prefrontal, and hippocampal regions. Each area displayed oscillatory activity that reflected the temporal and spatial characteristics of an internal metronome which flexibly encoded fast, medium, and slow tempos on a trial-by-trial basis. The presence of widespread metronome-related activity across the brain, in the absence of stimuli and overt actions, is consistent with the idea that time and rhythm are maintained by a mechanism that internally replays the stimuli and actions that define well-timed behavior.

scholarly journals Low-Dimensional Dynamics of Brain Activity Associated with Manual Acupuncture in Healthy Subjects

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7432
Author(s):  
Xinmeng Guo ◽  
Jiang Wang
Keyword(s):  
Latent Variables ◽  
Large Scale ◽  
Scientific Explanation ◽  
Brain Activity ◽  
Dimensional Subspace ◽  
Oscillatory Activity ◽  
Manual Acupuncture ◽  
Brain Response ◽  
Acupuncture Stimulation ◽  
Low Dimensional

Acupuncture is one of the oldest traditional medical treatments in Asian countries. However, the scientific explanation regarding the therapeutic effect of acupuncture is still unknown. The much-discussed hypothesis it that acupuncture’s effects are mediated via autonomic neural networks; nevertheless, dynamic brain activity involved in the acupuncture response has still not been elicited. In this work, we hypothesized that there exists a lower-dimensional subspace of dynamic brain activity across subjects, underpinning the brain’s response to manual acupuncture stimulation. To this end, we employed a variational auto-encoder to probe the latent variables from multichannel EEG signals associated with acupuncture stimulation at the ST36 acupoint. The experimental results demonstrate that manual acupuncture stimuli can reduce the dimensionality of brain activity, which results from the enhancement of oscillatory activity in the delta and alpha frequency bands induced by acupuncture. Moreover, it was found that large-scale brain activity could be constrained within a low-dimensional neural subspace, which is spanned by the “acupuncture mode”. In each neural subspace, the steady dynamics of the brain in response to acupuncture stimuli converge to topologically similar elliptic-shaped attractors across different subjects. The attractor morphology is closely related to the frequency of the acupuncture stimulation. These results shed light on probing the large-scale brain response to manual acupuncture stimuli.

scholarly journals Modulation of high frequency by low-frequency Oscillations in the basal ganglia

2017 ◽  
Author(s):  
D. Nouri ◽  
R. Ebrahimpour ◽  
A. Mirzaei
Keyword(s):  
Basal Ganglia ◽  
Network Model ◽  
Large Scale ◽  
Low Frequency ◽  
Neural Mechanisms ◽  
Oscillatory Activity ◽  
Beta Band ◽  
Low Frequency Oscillations ◽  
Delta Band ◽  
Frequency Modulations

AbstractModulation of beta band fioscillatory activity (15-30 Hz) by delta band oscillatory activity (1-3 Hz) in the cortico-basal ganglia loop is important for normal basal ganglia functions. However, the neural mechanisms underlying this modulation are poorly understood. To understand the mechanisms underlying such frequency modulations in the basal ganglia, we use large scale subthalamo-pallidal network model stimulated via a delta-frequency input signal. We show that inhibition of external Globus Pallidus (GPe) and excitation of the Subthalamic nucleus (STN) using the delta-band stimulation leads to the same delta-beta interactions in the network model as the experimental results observed in healthy basal ganglia. In addition, we show that pathological beta oscillations in the network model decorrelates the delta-beta link in the network model. In general, using our simulation results, we propose that striato-pallidal inhibition and cortico-subthalamic excitation are the potential sources of the delta-beta link observed in the intact basal ganglia.

scholarly journals Coherent neural representation of hand speed in humans revealed by MEG imaging

2007 ◽  
Vol 104 (18) ◽  
pp. 7676-7681 ◽  
Author(s):  
Karim Jerbi ◽  
Jean-Philippe Lachaux ◽  
Karim N′Diaye ◽  
Dimitrios Pantazis ◽  
Richard M. Leahy ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Long Range ◽  
Large Scale ◽  
Primary Motor Cortex ◽  
Phase Synchronization ◽  
Motor Behavior ◽  
Brain Regions ◽  
Neural Representation ◽  
Oscillatory Activity ◽  
Source Imaging

The spiking activity of single neurons in the primate motor cortex is correlated with various limb movement parameters, including velocity. Recent findings obtained using local field potentials suggest that hand speed may also be encoded in the summed activity of neuronal populations. At this macroscopic level, the motor cortex has also been shown to display synchronized rhythmic activity modulated by motor behavior. Yet whether and how neural oscillations might be related to limb speed control is still poorly understood. Here, we applied magnetoencephalography (MEG) source imaging to the ongoing brain activity in subjects performing a continuous visuomotor (VM) task. We used coherence and phase synchronization to investigate the coupling between the estimated activity throughout the brain and the simultaneously recorded instantaneous hand speed. We found significant phase locking between slow (2- to 5-Hz) oscillatory activity in the contralateral primary motor cortex and time-varying hand speed. In addition, we report long-range task-related coupling between primary motor cortex and multiple brain regions in the same frequency band. The detected large-scale VM network spans several cortical and subcortical areas, including structures of the frontoparietal circuit and the cerebello–thalamo–cortical pathway. These findings suggest a role for slow coherent oscillations in mediating neural representations of hand kinematics in humans and provide further support for the putative role of long-range neural synchronization in large-scale VM integration. Our findings are discussed in the context of corticomotor communication, distributed motor encoding, and possible implications for brain–machine interfaces.

Large scale temporal coordination of cortical activity as prerequisite for conscious experience

2010 ◽  
Vol 18 (3) ◽  
pp. 570-583
Author(s):  
Wolf Singer
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Conscious Experience ◽  
Oscillatory Activity ◽  
Mammalian Brain ◽  
Large Numbers ◽  
Brain States ◽  
Phenomenal Awareness ◽  
The One ◽  
Neuronal Code

Phenomenal awareness, the ability to be aware of one’s sensations and feelings, emerges from the capacity of evolved brains to represent their own cognitive processes by iterating and self-reapplying the cortical operations that generate representations of the outer world. Search for the neuronal substrate of awareness therefore converges with the search for the neuronal code through which brains represent their environment. The hypothesis is put forward that the mammalian brain uses two complementary representational strategies. One consists of the generation of neurons responding selectively to particular constellations of features, and is based on selective recombination of inputs in hierarchically structured feed-forward architectures. The other relies on the dynamic association of large numbers of distributed neurons into functionally coherent cell assemblies which as a whole represent a content of cognition. Arguments and data are presented in favor of the second strategy as the one according to which meta-representations that support awareness are established. My hypothesis is that such distributed representations self-organize through transient synchronization of the oscillatory activity. Evidence showing that similar brain states are required both for the occurrence of these synchronization phenomena and for awareness is provided.

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