scholarly journals Causal roles of prefrontal cortex during spontaneous perceptual switching are determined by brain state dynamics

2021 ◽  
Author(s):  
Takamitsu Watanabe
Keyword(s):  
Prefrontal Cortex ◽  
Visual Perception ◽  
Large Scale ◽  
Brain State ◽  
Energy Landscapes ◽  
Cognitive Dynamics ◽  
State Dependent ◽  
Brain States ◽  
Stimulation System ◽  
The Brain

AbstractThe prefrontal cortex (PFC) is thought to orchestrate cognitive dynamics. However, in tests of bistable visual perception, no direct evidence supporting such presumable causal roles of the PFC has been reported. Here, using a novel brain-state-dependent neural stimulation system, we found that three PFC regions—right frontal eye fields and anterior/posterior dorsolateral PFCs (a/pDLPFCs)—have causal effects on perceptual dynamics but the behavioural effects are detectable only when we modulated the PFC activity in brain-state-/state-history-dependent manners. Also, we revealed that the brain-dynamics-dependent behavioural causality is underpinned by transient changes in the brain state dynamics, and such neural changes are determined by structural transformations of hypothetical energy landscapes. Moreover, we identified different functions of the three PFC areas: in particular, we found that aDLPFC enhances the integration of the two PFC-active brain states, whereas pDLPFC promotes the diversity between them. This work resolves the controversy over the PFC roles in spontaneous perceptual switching and underlines brain state dynamics in fine investigations of brain-behaviour causality.Impact statementPrefrontal causal roles are changing during bistable visual perception, which was determined by large-scale brain state dynamics and attributable to hypothetical energy landscapes that underpin the brain state dynamics.

scholarly journals Oscillations, criticality and responsiveness in complex brain networks

2019 ◽  
Author(s):  
MinKyung Kim ◽  
UnCheol Lee
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Conscious State ◽  
Brain Network ◽  
State Dependent ◽  
Model Study ◽  
Brain States ◽  
Specific Stimulation ◽  
The Brain ◽  
Oscillation Properties

AbstractBrain networks during unconscious states resulting from sleep, anesthesia, or traumatic injuries are associated with a limited capacity for complex responses to stimulation. Even during the conscious resting state, responsiveness to stimulus is highly dependent on spontaneous brain activities. Many empirical findings have been suggested that the brain responsiveness is determined mainly by the ongoing brain activity when a stimulus is given. However, there has been no systematic study exploring how such various brain activities with high or low synchronization, amplitude, and phase response to stimuli. In this model study, we simulated large-scale brain network dynamics in three brain states (below, near, and above the critical state) and investigated a relationship between ongoing oscillation properties and a stimulus decomposing the brain activity into fundamental oscillation properties (instantaneous global synchronization, amplitude, and phase). We identified specific stimulation conditions that produce varying levels of brain responsiveness. When a single pulsatile stimulus was applied to globally desynchronized low amplitude of oscillation, the network generated a large response. By contrast, when a stimulus was applied to specific phases of oscillation that were globally synchronized with high amplitude activity, the response was inhibited. This study proposes the oscillatory conditions to induce specific stimulation outcomes in the brain that can be systematically derived from networked oscillator properties, and reveals the presence of state-dependent temporal windows for optimal brain stimulation. The identified relationship will help advance understanding of the small/large responsiveness of the brain in different states of consciousness and suggest state-dependent methods to modulate responsiveness.Author SummaryA responsiveness of the brain network to external stimulus is different across brain states such as wakefulness, sleep, anesthesia, and traumatic injuries. It has been shown that responsiveness of the brain during conscious state also varies due to the diverse transient states of the brain characterized by different global and local oscillation properties. In this computational model study using large-scale brain network, we hypothesized that the brain responsiveness is determined by the interactions of networked oscillators when a stimulus is applied to the brain. We examined relationships between responsiveness of the brain network, global synchronization levels, and instantaneous oscillation properties such as amplitude and phase in different brain states. We found specific stimulation conditions of the brain that produce large or small levels of responsiveness. The identified relationship suggests the existence of temporal windows that periodically inhibit sensory information processing during conscious state and develops state-dependent methods to modulate brain responsiveness considering dynamically changed functional brain network.

scholarly journals Arousal impacts distributed hubs modulating the integration of brain functional connectivity

2021 ◽  
Author(s):  
Kangjoo Lee ◽  
Corey Horien ◽  
David O'Connor ◽  
Bronwen Garand-Sheridan ◽  
Fuyuze Tokoglu ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Network Integration ◽  
State Dependent ◽  
Show Evidence ◽  
Brain States ◽  
High Level ◽  
The Impact

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications.

scholarly journals Segregated brain state during hypnosis

2019 ◽  
Author(s):  
Jarno Tuominen ◽  
Sakari Kallio ◽  
Valtteri Kaasinen ◽  
Henry Railo
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Brain Activation ◽  
Brain State ◽  
Neural Connectivity ◽  
Single Word ◽  
Electrophysiological Response ◽  
Hypnotic Induction ◽  
Brain States ◽  
The Brain

Can the brain be shifted into a different state using a simple social cue, as tests on highly hypnotisable subjects would suggest? Demonstrating an altered brain state is difficult. Brain activation varies greatly during wakefulness and can be voluntarily influenced. We measured the complexity of electrophysiological response to transcranial magnetic stimulation (TMS) in one “hypnotic virtuoso”. Such a measure produces a response outside the subject’s voluntary control and has been proven adequate for discriminating conscious from unconscious brain states. We show that a single-word hypnotic induction robustly shifted global neural connectivity into a state where activity remained sustained but failed to ignite strong, coherent activity in frontoparietal cortices. Changes in perturbational complexity indicate a similar move toward a more segregated state. We interpret these findings to suggest a shift in the underlying state of the brain, likely moderating subsequent hypnotic responding. [preprint updated 20/02/2020]

Control of Transcranial Direct Current Stimulation Duration by Assessing Functional Connectivity of Near-Infrared Spectroscopy Signals

2021 ◽  
Author(s):  
M. Atif Yaqub ◽  
Keum-Shik Hong ◽  
Amad Zafar ◽  
Chang-Seok Kim
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Direct Current ◽  
Network Theory ◽  
Short Range ◽  
Near Infrared ◽  
Brain State ◽  
Stimulation Period ◽  
The Brain ◽  
Connection Density

Transcranial direct current stimulation (tDCS) has been shown to create neuroplasticity in healthy and diseased populations. The control of stimulation duration by providing real-time brain state feedback using neuroimaging is a topic of great interest. This study presents the feasibility of a closed-loop modulation for the targeted functional network in the prefrontal cortex. We hypothesize that we cannot improve the brain state further after reaching a specific state during a stimulation therapy session. A high-definition tDCS of 1[Formula: see text]mA arranged in a ring configuration was applied at the targeted right prefrontal cortex of 15 healthy male subjects for 10[Formula: see text]min. Functional near-infrared spectroscopy was used to monitor hemoglobin chromophores during the stimulation period continuously. The correlation matrices obtained from filtered oxyhemoglobin were binarized to form subnetworks of short- and long-range connections. The connectivity in all subnetworks was analyzed individually using a new quantification measure of connectivity percentage based on the correlation matrix. The short-range network in the stimulated hemisphere showed increased connectivity in the initial stimulation phase. However, the increase in connection density reduced significantly after 6[Formula: see text]min of stimulation. The short-range network of the left hemisphere and the long-range network gradually increased throughout the stimulation period. The connectivity percentage measure showed a similar response with network theory parameters. The connectivity percentage and network theory metrics represent the brain state during the stimulation therapy. The results from the network theory metrics, including degree centrality, efficiency, and connection density, support our hypothesis and provide a guideline for feedback on the brain state. The proposed neuro-feedback scheme is feasible to control the stimulation duration to avoid overdosage.

scholarly journals Clinical Electroencephalography for Anesthesiologists

2015 ◽  
Vol 123 (4) ◽  
pp. 937-960 ◽  
Author(s):  
Patrick L. Purdon ◽  
Aaron Sampson ◽  
Kara J. Pavone ◽  
Emery N. Brown
Keyword(s):  
Neural Circuits ◽  
Brain State ◽  
Anesthesia Care ◽  
Depth Of Anesthesia ◽  
State Monitoring ◽  
Inhaled Anesthetics ◽  
Anesthesia Monitoring ◽  
Brain States ◽  
Index Value ◽  
The Brain

Abstract The widely used electroencephalogram-based indices for depth-of-anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care.

scholarly journals State-dependent linearisation of mixture representations by the olfactory bulb

2021 ◽  
Author(s):  
Aliya Mari Adefuin ◽  
Janine K Reinert ◽  
Sannder Lindeman ◽  
Izumi Fukunaga
Keyword(s):  
Olfactory Bulb ◽  
Sensory Processing ◽  
Piriform Cortex ◽  
Brain State ◽  
Complex Signals ◽  
Two Photon ◽  
State Dependent ◽  
The Difference ◽  
Apical Dendrites ◽  
The Brain

Sensory systems are often tasked to analyse complex signals from the environment, to separate relevant from irrelevant parts. This process of decomposing signals is challenging when component signals interfere with each other. For example, when a mixture of signals does not equal the sum of its parts, this leads to an unpredictable corruption of signal patterns, making the target recognition harder. In olfaction, nonlinear summation is prevalent at various stages of sensory processing, from stimulus transduction in the nasal epithelium to higher areas, including the olfactory bulb (OB) and the piriform cortex. Here, we investigate how the olfactory system deals with binary mixtures of odours, using two-photon imaging with several behavioural paradigms. Unlike previous studies using anaesthetised animals, we found the mixture summation to be substantially more linear when using awake, head-fixed mice performing an odour detection task. This linearisation was also observed in awake, untrained mice, in both engaged and disengaged states, revealing that the bulk of the difference in mixture summation is explained by the brain state. However, in the apical dendrites of M/T cells, mixture representation is dominated by sublinear summation. Altogether, our results demonstrate that the property of mixture representation in the primary olfactory area likely reflects state-dependent differences in sensory processing.

scholarly journals A novel dynamic brain network in arousal for brain states and emotion analysis

2021 ◽  
Vol 18 (6) ◽  
pp. 7440-7463
Author(s):  
Yunyuan Gao ◽  
◽  
Zhen Cao ◽  
Jia Liu ◽  
Jianhai Zhang ◽  
...  
Keyword(s):  
Parietal Lobe ◽  
Brain Networks ◽  
Brain Network ◽  
Brain State ◽  
High Arousal ◽  
Emotion Analysis ◽  
Significant Difference ◽  
Brain States ◽  
The Brain ◽  
Channel Norm

<abstract> <sec><title>Background</title><p>Brain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals.</p> </sec> <sec><title>New Method</title><p>Time factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain.</p> </sec> <sec><title>Results</title><p>The frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal.</p> </sec> <sec><title>Comparison with Existing Method(s)</title><p>Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis.</p> </sec> <sec><title>Conclusions</title><p>The proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.</p> </sec> </abstract>

scholarly journals Brain state-dependent modulation of thalamic visual processing by cortico-thalamic feedback

2021 ◽  
Author(s):  
Kimberly Reinhold ◽  
Arbora Resulaj ◽  
Massimo Scanziani
Keyword(s):  
Visual Stimulus ◽  
Visual Processing ◽  
Visual Information ◽  
Early Stage ◽  
Visual Stimuli ◽  
Brain State ◽  
Spatial Features ◽  
State Dependent ◽  
Temporal And Spatial ◽  
The Brain

The behavioral state of a mammal impacts how the brain responds to visual stimuli as early as in the dorsolateral geniculate nucleus of the thalamus (dLGN), the primary relay of visual information to the cortex. A clear example of this is the markedly stronger response of dLGN neurons to higher temporal frequencies of the visual stimulus in alert as compared to quiescent animals. The dLGN receives strong feedback from the visual cortex, yet whether this feedback contributes to these state-dependent responses to visual stimuli is poorly understood. Here we show that in mice, silencing cortico-thalamic feedback abolishes state-dependent differences in the response of dLGN neurons to visual stimuli. This holds true for dLGN responses to both temporal and spatial features of the visual stimulus. These results reveal that the state-dependent shift of the response to visual stimuli in an early stage of visual processing depends on cortico-thalamic feedback.

scholarly journals Macroscopic quantities of collective brain activity during wakefulness and anesthesia

2021 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Lynn Uhrig ◽  
Nikolas Deco ◽  
Camilo M. Signorelli ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Loss Of Consciousness ◽  
Information Theoretic ◽  
Maximum Entropy Models ◽  
Information Theoretic Measures ◽  
Brain States ◽  
Parietal Cortices ◽  
The Brain

AbstractThe study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

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