scholarly journals The discrete logic of the Brain - Explicit modelling of Brain State durations in EEG and MEG

2019 ◽  
Author(s):  
Nelson J. Trujillo-Barreto ◽  
David Araya ◽  
Wael El-Deredy
Keyword(s):  
Markov Models ◽  
Simulated Data ◽  
State Transitions ◽  
Brain State ◽  
Dynamical Regime ◽  
Stable Pattern ◽  
State Duration ◽  
Brain States ◽  
Generative Mechanisms ◽  
The Brain

AbstractWe consider the detection and characterisation of brain state transitions, based on ongoing Magneto and Electroencephalography (M/EEG). Here a brain state represents a specific brain dynamical regime or mode of operation, which produces a characteristic quasi-stable pattern of activity at topography, sources or network levels. These states and their transitions over time can reflect fundamental computational properties of the brain, shaping human behaviour and brain function. The Hidden Markov Model (HMM) has emerged as a useful model-based approach for uncovering the hidden dynamics of brain state transitions based on observed data. However, the Geometric distribution of state duration (dwell time) implicit in HMM places highest probability on very short durations, which makes it inappropriate for the accurate modelling of brain states in M/EEG. We propose using Hidden Semi Markov Models (HSMM), a generalisation of HMM that models the brain state duration distribution explicitly. We present a Bayesian formulation of HSMM and use the Variational Bayes framework to efficiently estimate the HSMM parameters, the number of brain states and select among alternative brain state duration distributions. We assess HSMM performance against HMM on simulated data and demonstrate that the accurate modelling of state duration is paramount for accurately and robustly modelling non-Markovian EEG brain state features. Finally, we used actual resting-state EEG data to demonstrate the approach in practice and conclude that it provides a flexible parameterised framework that permits closer interrogation of possible generative mechanisms.

scholarly journals LSD flattens the brain′s energy landscape: evidence from receptor-informed network control theory

2021 ◽  
Author(s):  
S. Parker Singleton ◽  
Andrea I Luppi ◽  
Robin L. Carhart-Harris ◽  
Josephine Cruzat ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Control Theory ◽  
Subjective Experience ◽  
Brain Activity ◽  
Energy Landscape ◽  
Network Control ◽  
State Transitions ◽  
Brain State ◽  
Control Analysis ◽  
Brain States ◽  
The Brain

Psychedelics like lysergic acid diethylamide (LSD) offer a powerful window into the function of the human brain and mind, by temporarily altering subjective experience through their neurochemical effects. The RElaxed Beliefs Under Psychedelics (REBUS) model postulates that 5-HT2a receptor agonism allows the brain to explore its dynamic landscape more readily, as suggested by more diverse (entropic) brain activity. Formally, this effect is theorized to correspond to a reduction in the energy required to transition between different brain-states, i.e. a ″flattening of the energy landscape.″ However, this hypothesis remains thus far untested. Here, we leverage network control theory to map the brain′s energy landscape, by quantifying the energy required to transition between recurrent brain states. In accordance with the REBUS model, we show that LSD reduces the energy required for brain-state transitions, and, furthermore, that this reduction in energy correlates with more frequent state transitions and increased entropy of brain-state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors, we demonstrate the specific role of this receptor in flattening the brain′s energy landscape. Also, in accordance with REBUS, we show that the occupancy of bottom-up states is increased by LSD. In addition to validating fundamental predictions of the REBUS model of psychedelic action, this work highlights the potential of receptor-informed network control theory to provide mechanistic insights into pharmacological modulation of brain dynamics.

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]

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 The hidden state-dependency of transcranial alternating current stimulation (tACS)

2020 ◽  
Author(s):  
Florian H. Kasten ◽  
Christoph S. Herrmann
Keyword(s):  
Markov Models ◽  
Alternating Current ◽  
Oscillatory Activity ◽  
Brain Disorders ◽  
State Dependency ◽  
Non Invasive ◽  
Transcranial Alternating Current Stimulation ◽  
Brain States ◽  
Invasive Techniques ◽  
The Brain

AbstractNon-invasive techniques to electrically stimulate the brain such as transcranial direct and alternating current stimulation (tDCS/tACS) are increasingly used in human neuroscience and offer potential new avenues to treat brain disorders. However, their often weak and variable effects have also raised concerns in the scientific community. A possible factor influencing the efficacy of these methods is the dependence on brain-states. Here, we utilized Hidden Markov Models (HMM) to decompose concurrent tACS-magnetoencephalography data into transient brain-states with distinct spatial, spectral and connectivity profiles. We found that out of four spontaneous brain-states only one was susceptible to tACS. No or only marginal effects were found in the remaining states. TACS did not influence the time spent in each state. Our results suggest, that tACS effects may be mediated by a hidden, spontaneous state-dependency and provide novel insights to the changes in oscillatory activity underlying aftereffects of tACS.

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 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.

scholarly journals Awakening: Predicting external stimulation to force transitions between different brain states

2019 ◽  
Vol 116 (36) ◽  
pp. 18088-18097 ◽  
Author(s):  
Gustavo Deco ◽  
Josephine Cruzat ◽  
Joana Cabral ◽  
Enzo Tagliazucchi ◽  
Helmut Laufs ◽  
...  
Keyword(s):  
Fundamental Problem ◽  
Effective Connectivity ◽  
Brain State ◽  
Whole Brain ◽  
External Stimulation ◽  
Neuropsychiatric Diseases ◽  
Brain Modeling ◽  
Brain States ◽  
Definition Of ◽  
The Brain

A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the biophysical limitations of direct electrical stimulation (DES) of microcircuits, this opens exciting possibilities for discovering stimulation targets and selecting connectivity patterns that can ensure propagation of DES-induced neural excitation, potentially making it possible to create awakenings from complex cases of brain injury.

The Temporal Dynamics of Spontaneous Emotional Brain States and Their Implications for Mental Health

2021 ◽  
pp. 1-14
Author(s):  
Philip A. Kragel ◽  
Ahmad R. Hariri ◽  
Kevin S. LaBar
Keyword(s):  
Mental Health ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Resting State Fmri ◽  
State Transitions ◽  
Brain State ◽  
Emotional States ◽  
Affective States ◽  
Emotional Responding ◽  
Brain States

Abstract Temporal processes play an important role in elaborating and regulating emotional responding during routine mind wandering. However, it is unknown whether the human brain reliably transitions among multiple emotional states at rest and how psychopathology alters these affect dynamics. Here, we combined pattern classification and stochastic process modeling to investigate the chronometry of spontaneous brain activity indicative of six emotions (anger, contentment, fear, happiness, sadness, and surprise) and a neutral state. We modeled the dynamic emergence of these brain states during resting-state fMRI and validated the results across two population cohorts—the Duke Neurogenetics Study and the Nathan Kline Institute Rockland Sample. Our findings indicate that intrinsic emotional brain dynamics are effectively characterized as a discrete-time Markov process, with affective states organized around a neutral hub. The centrality of this network hub is disrupted in individuals with psychopathology, whose brain state transitions exhibit greater inertia and less frequent resetting from emotional to neutral states. These results yield novel insights into how the brain signals spontaneous emotions and how alterations in their temporal dynamics contribute to compromised mental health.

scholarly journals Segregated brain state during hypnosis

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jarno Tuominen ◽  
Sakari Kallio ◽  
Valtteri Kaasinen ◽  
Henry Railo
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Brain State ◽  
Neural Connectivity ◽  
Single Word ◽  
Electrophysiological Response ◽  
Hypnotic Induction ◽  
Brain States ◽  
Global Brain ◽  
The Brain

Abstract Can the brain be shifted into a different state using a simple social cue, as tests on highly hypnotizable subjects would suggest? Demonstrating an altered global 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 in one ‘hypnotic virtuoso’. Such a measure produces a response arguably 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 towards a more segregated state. We interpret these findings to suggest a shift in the underlying state of the brain, likely moderating subsequent hypnotic responding.

scholarly journals Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks

2020 ◽  
Author(s):  
A. Grigis ◽  
J. Tasserie ◽  
V. Frouin ◽  
B. Jarraya ◽  
L. Uhrig
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Supervised Machine Learning ◽  
Brain State ◽  
Level Of Consciousness ◽  
Clinical Consequences ◽  
Brain States ◽  
The Brain

AbstractDecoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called “brain states” corresponding to “functional configurations” of the brain. Here, we propose to use a supervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. Because it is key to consider the topology of brain regions used to build the dynamical functional connectivity matrices describing the brain state at a given time, we applied BrainNetCNN, a graph-convolutional neural network (CNN), to predict the brain states in awake and anesthetized non-human primate rsfMRI data. BrainNetCNN achieved a high prediction accuracy that lies in [0.674, 0.765] depending on the experimental settings. We propose to derive the set of connections found to be important for predicting a brain state, reflecting the level of consciousness. The results demonstrate that deep learning methods can be used not only to predict brain states but also to provide additional insight on cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.

Sign in / Sign up

Export Citation Format

Share Document