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

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.

Download Full-text

The effect of external stimulation on functional networks in the aging healthy human brain

10.1101/2021.11.19.469206 ◽

2021 ◽

Author(s):

Anira Escrichs ◽

Yonatan Sanz Perl ◽

Noelia Martinez-Molina ◽

Carles Biarnes ◽

Josep Garre ◽

...

Keyword(s):

In Silico ◽

Brain Area ◽

Resting State Fmri ◽

Brain Dynamics ◽

Healthy Human ◽

Whole Brain ◽

External Stimulation ◽

Rich Club ◽

Brain States ◽

The Brain

Understanding the brain changes occurring during aging can provide new insights for developing treatments that alleviate or reverse cognitive decline. Neurostimulation techniques have emerged as potential treatments for brain disorders and to improve cognitive functions. Nevertheless, given the ethical restrictions of neurostimulation approaches, in silico perturbation protocols based on causal whole-brain models are fundamental to gaining a mechanistic understanding of brain dynamics. Furthermore, this strategy could serve as a more specific biomarker relating local activity with global brain dynamics. Here, we used a large resting-state fMRI dataset divided into middle-aged (N=310, aged < 65 years) and older adults (N=310, aged >= 65) to characterize brain states in each group as a probabilistic metastable substate (PMS) space, each with a probabilistic occurrence and frequency. Then, we fitted the PMS to a whole-brain model and applied in silico stimulations with different intensities in each node to force transitions from the brain states of the older group to the middle-age group. We found that the precuneus, a brain area belonging to the default mode network and the rich club, was the best stimulation target. These findings might have important implications for designing neurostimulation interventions to revert the effects of aging on whole-brain dynamics.

Download Full-text

A global framework for a systemic view of brain modeling

Brain Informatics ◽

10.1186/s40708-021-00126-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Frederic Alexandre

Keyword(s):

Instrumental Conditioning ◽

The Body ◽

Posterior Cortex ◽

Reciprocal Relationships ◽

Brain Modeling ◽

Brain Theory ◽

Habitual Behavior ◽

Definition Of ◽

The Brain ◽

Specific Contribution

AbstractThe brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. A systemic description of the brain is presented here, as a contribution to developing a brain theory and as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the frontal cortex. This framework highlights that responses can be prepared in different ways, from pavlovian reflexes and habitual behavior to deliberations for goal-directed planning and reasoning, and explains that these different kinds of responses coexist, collaborate and compete for the control of behavior. It also lays emphasis on the fact that cognition can be described as a dynamical system of interacting memories, some acting to provide information to others, to replace them when they are not efficient enough, or to help for their improvement. Describing the brain as an architecture of learning systems has also strong implications in Machine Learning. Our biologically informed view of pavlovian and instrumental conditioning can be very precious to revisit classical Reinforcement Learning and provide a basis to ensure really autonomous learning.

Download Full-text

Increased sensitivity to strong perturbations in a whole-brain model of LSD

10.1101/2021.01.05.425415 ◽

2021 ◽

Author(s):

Beatrice M. Jobst ◽

Selen Atasoy ◽

Adrián Ponce-Alvarez ◽

Ana Sanjuán ◽

Leor Roseman ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

External Perturbation ◽

Brain Regions ◽

Lysergic Acid ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Whole Brain ◽

Response Diversity ◽

Brain States ◽

The Brain

AbstractLysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain’s global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.HighlightsNovel offline perturbational method applied on functional magnetic resonance imaging (fMRI) data under the effect of lysergic acid diethylamide (LSD)Shift of brain’s global working point to more complex dynamics after LSD intakeConsistently longer recovery time after model perturbation under LSD influenceStrongest effects in resting state networks relevant for psychedelic experienceHigher response diversity across brain regions under LSD influence after an external in silico perturbation

Download Full-text

Segregated brain state during hypnosis

10.31234/osf.io/q9ymb ◽

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]

Download Full-text

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

PLoS Computational Biology ◽

10.1371/journal.pcbi.1004445 ◽

2015 ◽

Vol 11 (8) ◽

pp. e1004445 ◽

Cited By ~ 42

Author(s):

Adrián Ponce-Alvarez ◽

Biyu J. He ◽

Patric Hagmann ◽

Gustavo Deco

Keyword(s):

Cortical Activity ◽

Activity Space ◽

Whole Brain ◽

Brain Modeling ◽

The Brain

Download Full-text

Clinical Electroencephalography for Anesthesiologists

Anesthesiology ◽

10.1097/aln.0000000000000841 ◽

2015 ◽

Vol 123 (4) ◽

pp. 937-960 ◽

Cited By ~ 227

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.

Download Full-text

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

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021368 ◽

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>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. </sec> <sec><title>New Method</title>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. </sec> <sec><title>Results</title>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. </sec> <sec><title>Comparison with Existing Method(s)</title>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. </sec> <sec><title>Conclusions</title>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. </sec> </abstract>

Download Full-text

Macroscopic quantities of collective brain activity during wakefulness and anesthesia

10.1101/2021.02.03.429578 ◽

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.

Download Full-text

Neuromodulation of the mind-wandering brain state: the interaction between neuromodulatory tone, sharp wave-ripples and spontaneous thought

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2019.0699 ◽

2020 ◽

Vol 376 (1817) ◽

pp. 20190699 ◽

Cited By ~ 1

Author(s):

Claire O'Callaghan ◽

Ishan C. Walpola ◽

James M. Shine

Keyword(s):

Large Scale ◽

Mind Wandering ◽

Brain State ◽

Theme Issue ◽

External Stimulation ◽

Sharp Wave ◽

Brain States ◽

Spontaneous Thought ◽

The Mind

Mind-wandering has become a captivating topic for cognitive neuroscientists. By now, it is reasonably well described in terms of its phenomenology and the large-scale neural networks that support it. However, we know very little about what neurobiological mechanisms trigger a mind-wandering episode and sustain the mind-wandering brain state. Here, we focus on the role of ascending neuromodulatory systems (i.e. acetylcholine, noradrenaline, serotonin and dopamine) in shaping mind-wandering. We advance the hypothesis that the hippocampal sharp wave-ripple (SWR) is a compelling candidate for a brain state that can trigger mind-wandering episodes. This hippocampal rhythm, which occurs spontaneously in quiescent behavioural states, is capable of propagating widespread activity in the default network and is functionally associated with recollective, associative, imagination and simulation processes. The occurrence of the SWR is heavily dependent on hippocampal neuromodulatory tone. We describe how the interplay of neuromodulators may promote the hippocampal SWR and trigger mind-wandering episodes. We then identify the global neuromodulatory signatures that shape the evolution of the mind-wandering brain state. Under our proposed framework, mind-wandering emerges due to the interplay between neuromodulatory systems that influence the transitions between brain states, which either facilitate, or impede, a wandering mind. This article is part of the theme issue ‘Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

Download Full-text

Dynamics of Epileptiform Discharges Induced by Transcranial Magnetic Stimulation in Genetic Generalized Epilepsy

International Journal of Neural Systems ◽

10.1142/s012906571750037x ◽

2017 ◽

Vol 27 (07) ◽

pp. 1750037 ◽

Cited By ~ 9

Author(s):

Dimitris Kugiumtzis ◽

Christos Koutlis ◽

Alkiviadis Tsimpiris ◽

Vasilios K. Kimiskidis

Keyword(s):

Transcranial Magnetic Stimulation ◽

Magnetic Stimulation ◽

Brain Connectivity ◽

Effective Connectivity ◽

Brain Network ◽

Brain State ◽

Epileptiform Discharges ◽

Eeg Recordings ◽

Generalized Epilepsy ◽

The Brain

Objective: In patients with Genetic Generalized Epilepsy (GGE), transcranial magnetic stimulation (TMS) can induce epileptiform discharges (EDs) of varying duration. We hypothesized that (a) the ED duration is determined by the dynamic states of critical network nodes (brain areas) at the early post-TMS period, and (b) brain connectivity changes before, during and after the ED, as well as within the ED. Methods: EEG recordings from two GGE patients were analyzed. For hypothesis (a), the characteristics of the brain dynamics at the early ED stage are measured with univariate and multivariate EEG measures and the dependence of the ED duration on these measures is evaluated. For hypothesis (b), effective connectivity measures are combined with network indices so as to quantify the brain network characteristics and identify changes in brain connectivity. Results: A number of measures combined with specific channels computed on the first EEG segment post-TMS correlate with the ED duration. In addition, brain connectivity is altered from pre-ED to ED and post-ED and statistically significant changes were also detected across stages within the ED. Conclusion: ED duration is not purely stochastic, but depends on the dynamics of the post-TMS brain state. The brain network dynamics is significantly altered in the course of EDs.

Download Full-text