Metastability in the brain

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In the field of computational neuroscience, the theory of metastability refers to the human brain's ability to integrate several functional parts and to produce neural oscillations in a cooperative and coordinated manner, providing the basis for conscious activity.

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Metastability, a state in which signals (such as oscillatory waves) fall outside their natural equilibrium state but persist for an extended period of time, is a principle that describes the brain's ability to make sense out of seemingly random environmental cues. In the past 25 years, interest in metastability and the underlying framework of nonlinear dynamics has been fueled by advancements in the methods by which computers model brain activity.

Overview


EEG measures the gross electrical activity of the brain that can be observed on the surface of the skull. In the metastability theory, EEG outputs produce oscillations that can be described as having identifiable patterns that correlate with each other at certain frequencies. Each neuron in a neuronal network normally outputs a dynamical oscillatory waveform, but also has the ability to output a chaotic waveform. [1] When neurons are integrated into the neural network by interfacing neurons with each other, the dynamical oscillations created by each neuron can be combined to form highly predictable EEG oscillations.

By identifying these correlations and the individual neurons that contribute to predictable EEG oscillations, scientists can determine which cortical domains are processing in parallel and which neuronal networks are intertwined. In many cases, metastability describes instances in which distal parts of the brain interact with each other to respond to environmental stimuli.

Frequency domains of metastability

It has been suggested that one integral facet of brain dynamics underlying conscious thought is the brain's ability to convert seemingly noisy or chaotic signals into predictable oscillatory patterns. [2]

In EEG oscillations of neural networks, neighboring waveform frequencies are correlated on a logarithmic scale rather than a linear scale. As a result, mean frequencies in oscillatory bands cannot link together according to linearity of their mean frequencies. Instead, phase transitions are linked according to their ability to couple with adjacent phase shifts in a constant state of transition between unstable and stable phase synchronization. [2] This phase synchronization forms the basis of metastable behavior in neural networks.

Metastable behavior occurs at the high frequency domain known as 1/f regime. This regime describes an environment in which a noisy signal (also known as pink noise) has been induced, where the amount of power the signal outputs over a certain bandwidth (its power spectral density) is inversely proportional to its frequency.

Noise at the 1/f regime can be found in many biological systems – for instance, in the output of a heartbeat in an ECG waveform—but serves a unique purpose for phase synchrony in neuronal networks. At the 1/f regime, the brain is in the critical state necessary for a conscious response to weak or chaotic environmental signals because it can shift the random signals into identifiable and predictable oscillatory waveforms. [2] While often transient, these waveforms exist in a stable form long enough to contribute to what can be thought of as conscious response to environmental stimuli.

Theories of metastability

Oscillatory activity and coordination dynamics

The dynamical system model, which represents networks composed of integrated neural systems communicating with one another between unstable and stable phases, has become an increasingly popular theory underpinning the understanding of metastability. [3] Coordination dynamics [4] forms the basis for this dynamical system model by describing mathematical formulae and paradigms governing the coupling of environmental stimuli to their effectors.

History of coordination dynamics and the Haken-Kelso-Bunz (HKB) model

The so-named HKB model is one of the earliest and well-respected theories to describe coordination dynamics in the brain. In this model, the formation of neural networks can be partly described as self-organization, where individual neurons and small neuronal systems aggregate and coordinate to either adapt or respond to local stimuli or to divide labor and specialize in function. [5]

Transition of parallel movement of index fingers to antiparallel, symmetric movement. Self-Organization in Brain Fig1.png
Transition of parallel movement of index fingers to antiparallel, symmetric movement.

In the last 20 years,[ when? ] the HKB model has become a widely accepted theory to explain the coordinated movements and behaviors of individual neurons into large, end-to-end neural networks. Originally the model described a system in which spontaneous transitions observed in finger movements could be described as a series of in-phase and out-of-phase movements. [6]

In the mid-1980s HKB model experiments, subjects were asked to wave one finger on each hand in two modes of direction: first, known as out of phase, both fingers moving in the same direction back and forth (as windshield wipers might move); and second, known as in-phase, where both fingers come together and move away to and from the midline of the body. To illustrate coordination dynamics, the subjects were asked to move their fingers out of phase with increasing speed until their fingers were moving as fast as possible. As movement approached its critical speed, the subjects’ fingers were found to move from out-of-phase (windshield-wiper-like) movement to in-phase (toward midline movement).

The HKB model, which has also been elucidated by several complex mathematical descriptors, is still a relatively simple but powerful way to describe seemingly-independent systems that come to reach synchrony just before a state of self-organized criticality. [6] [7]

Evolution of cognitive coordination dynamics

In the last 10 years,[ when? ] the HKB model has been reconciled with advanced mathematical models and supercomputer-based computation to link rudimentary coordination dynamics to higher-order processes such as learning and memory.

The traditional EEG is still useful to investigate coordination between different parts of the brain. 40 Hz gamma wave activity is a prominent example of the brain's ability to be modeled dynamically and is a common example of coordination dynamics. Continuous study of these and other oscillations has led to an important conclusion: analyzing waves as having a common signal phase but a different amplitude leads to the possibility that these different signals serve a synergistic function. [8]

Some unusual characteristics of these waves: they are virtually simultaneous and have a very short onset latency, which implies that they operate faster than synaptic conduction would allow; and that their recognizable patterns are sometimes interrupted by periods of randomness. The latter idiosyncrasy has served as the basis for assuming an interaction and transition between neural subsystems. Analysis of activation and deactivation of regions of the cortex has shown a dynamic shift between dependence and interdependence, reflecting the brain's metastable nature as a function of a coordinated dynamical system.

fMRI, large-scale electrode arrays, and MEG expand upon the patterns seen in EEG by providing visual confirmation of coordinated dynamics. The MEG, which provides an improvement over EEG in spatiotemporal characterization, allows researchers to stimulate certain parts of the brain with environmental cues and observe the response in a holistic brain model. Additionally, MEG has a response time of about one millisecond, allowing for a virtually real-time investigation of the active turning-on and -off of selected parts of the brain in response to environmental cues and conscious tasks. [9]

Social coordination dynamics and the phi complex

A developing field in coordination dynamics involves the theory of social coordination, which attempts to relate the DC to normal human development of complex social cues following certain patterns of interaction. This work is aimed at understanding how human social interaction is mediated by metastability of neural networks. fMRI and EEG are particularly useful in mapping thalamocortical response to social cues in experimental studies.

A new theory called the phi complex has been developed by J. A. Scott Kelso and fellow researchers at Florida Atlantic University to provide experimental results for the theory of social coordination dynamics. [10] In Kelso's experiments, two subjects were separated by an opaque barrier and asked to wag their fingers; then the barrier was removed and the subjects were instructed to continue to wag their fingers as if no change had occurred. After a short period, the movements of the two subjects sometimes became coordinated and synchronized (but other times continued to be asynchronous). The link between EEG and conscious social interaction is described as Phi, one of several brain rhythms operating in the 10 Hz range. Phi consists of two components: one to favor solitary behavior and another to favor interactive (interpersonal) behavior. Further analysis of Phi may reveal the social and interpersonal implications of degenerative diseases such as schizophrenia—or may provide insight into common social relationships such as the dynamics of alpha and omega-males or the popular bystander effect describing how people diffuse personal responsibility in emergency situations depending on the number of other individuals present.

Dynamic core

A second theory of metastability involves a so-called dynamic core, which is a term to loosely describe the thalamocortical region believed to be the integration center of consciousness. The dynamic core hypothesis (DCH) reflects the use and disuse of interconnected neuronal networks during stimulation of this region. A computer model of 65,000 spiking neurons [8] shows that neuronal groups existing in the cortex and thalamus interact in the form of synchronous oscillation. The interaction between distinct neuronal groups forms the dynamic core and may help explain the nature of conscious experience. A critical feature of the DCH is that instead of thinking binarily about transitions between neural integration and non-integration (i.e., that the two are either one or the other with no in-between), the metastable nature of the dynamic core can allow for a continuum of integration. [8]

Neural Darwinism


One theory used to integrate the dynamic core with conscious thought involves a developing concept known as neural Darwinism. [11] In this model, metastable interactions in the thalamocortical region cause a process of selectionism via re-entry (a phenomenon describing the overall reciprocity and interactivity between signals in distant parts of the brain through coupled signal latency). Neuronal selectivity involves mechanochemical events that take place pre- and post-natally whereby neuronal connections are influenced by environmental experiences. [12] The modification of synaptic signals as it relates to the dynamic core provides further explanation for the DCH.

Despite growing evidence for the DCH, the ability to generate mathematical constructs to model and predict dynamic core behavior has been slow to progress. [13] Continued development of stochastic processes designed to graph neuronal signals as chaotic and non-linear has provided some algorithmic basis for analyzing how chaotic environmental signals are coupled to enhance selectivity of neural outgrowth or coordination in the dynamic core.

Global workspace hypothesis

The global workspace hypothesis is another theory to elucidate metastability, and has existed in some form since 1983. [14] This hypothesis also focuses on the phenomenon of re-entry, the ability of a routine or process to be used by multiple parts of the brain simultaneously. [8] Both the DCH and global neuronal workspace (GNW) models involve re-entrance, but the GNW model elaborates on re-entrant connectivity between distant parts of the brain and long-range signal flow. Workspace neurons are similar anatomically but separated spatially from each other.

One interesting aspect of the GNW is that with sufficient intensity and length over which a signal travels, a small initiation signal can be compounded to activate an "ignition" of a critical spike-inducing state. This idea is analogous to a skier on the slope of a mountain, who, by disrupting a few blocks of ice with his skis, initiates a giant avalanche in his wake. To help prove the circuit-like amplification theory, research has shown that inducing lesions in long-distance connections corrupts performance in integrative models. [8]

A popular experiment to demonstrate the global workspace hypothesis involves showing a subject a series of backward-masked visual words (e.g., "the dog sleeps quietly" is shown as "ylteiuq speels god eht") and then asking the subject to identify the forward "translation" of these words. Not only did fMRI detect activity in the word-recognition portion of the cortex, but additionally, activity is often detected in the parietal and prefrontal cortices. [15] In almost every experiment, conscious input in word and audition tasks shows a much wider use of integrated portions of the brain than in identical unconscious input. The wide distribution and constant signal transfer between different areas of the brain in experimental results is a common method to attempt to prove the neural workspace hypothesis. More studies are being conducted to determine precisely the correlation between conscious and unconscious task deliberation in the realm of the global workspace.

The operational architectonics theory of brain–mind

Although the concept of metastability has been around in Neuroscience for some time, [16] the specific interpretation of metastability in the context of brain operations of different complexity has been developed by Andrew and Alexander Fingelkurts within their model of Operational Architectonics of brain–mind functioning. Metastability is basically a theory of how global integrative and local segregative tendencies coexist in the brain. [17] [18] The Operational Architectonics is centered on the fact that in the metastable regime of brain functioning, the individual parts of the brain exhibit tendencies to function autonomously at the same time as they exhibit tendencies for coordinated activity. [19] [20] In accordance with Operational Architectonics, [21] the synchronized operations produced by distributed neuronal assemblies constitute the metastable spatial-temporal patterns. They are metastable because intrinsic differences in the activity between neuronal assemblies are sufficiently large that they each do their own job (operation), while still retaining a tendency to be coordinated together in order to realize the complex brain operation. [22] [23]

The future of metastability

In addition to study investigating the effects of metastable interactions on traditional social function, much research will likely focus on determining the role of the coordinated dynamic system and the global workspace in the progression of debilitating diseases such as Alzheimer's disease, Parkinson's disease, stroke, and schizophrenia. [24]

An interest in the effect of a traumatic or semi-traumatic brain injury (TBI) on the coordinated dynamical system has developed in the last five years as the number of TBI cases has risen from war-related injuries.

See also

Related Research Articles

<span class="mw-page-title-main">Synchronization</span> Coordination of events to operate a system in unison

Synchronization is the coordination of events to operate a system in unison. For example, the conductor of an orchestra keeps the orchestra synchronized or in time. Systems that operate with all parts in synchrony are said to be synchronous or in sync—and those that are not are asynchronous.

Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.

The consciousness and binding problem is the problem of how objects, background and abstract or emotional features are combined into a single experience.

Electromagnetic theories of consciousness propose that consciousness can be understood as an electromagnetic phenomenon.

A gamma wave or gamma rhythm is a pattern of neural oscillation in humans with a frequency between 25 and 140 Hz, the 40 Hz point being of particular interest. Gamma rhythms are correlated with large-scale brain network activity and cognitive phenomena such as working memory, attention, and perceptual grouping, and can be increased in amplitude via meditation or neurostimulation. Altered gamma activity has been observed in many mood and cognitive disorders such as Alzheimer's disease, epilepsy, and schizophrenia.

Brainwave entrainment, also referred to as brainwave synchronization or neural entrainment, refers to the observation that brainwaves will naturally synchronize to the rhythm of periodic external stimuli, such as flickering lights, speech, music, or tactile stimuli.

<span class="mw-page-title-main">Neural oscillation</span> Brainwaves, repetitive patterns of neural activity in the central nervous system

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.

A neuronal ensemble is a population of nervous system cells involved in a particular neural computation.

<span class="mw-page-title-main">Neural binding</span>

Neural binding is the neuroscientific aspect of what is commonly known as the binding problem: the interdisciplinary difficulty of creating a comprehensive and verifiable model for the unity of consciousness. "Binding" refers to the integration of highly diverse neural information in the forming of one's cohesive experience. The neural binding hypothesis states that neural signals are paired through synchronized oscillations of neuronal activity that combine and recombine to allow for a wide variety of responses to context-dependent stimuli. These dynamic neural networks are thought to account for the flexibility and nuanced response of the brain to various situations. The coupling of these networks is transient, on the order of milliseconds, and allows for rapid activity.

J. A. Scott Kelso is an American neuroscientist, and Professor of Complex Systems and Brain Sciences, Professor of Psychology, Biological Sciences and Biomedical Science at Florida Atlantic University (FAU) in Boca Raton, Florida and The University of Ulster in Derry, N. Ireland.

Neuroinformatics is the emergent field that combines informatics and neuroscience. Neuroinformatics is related with neuroscience data and information processing by artificial neural networks. There are three main directions where neuroinformatics has to be applied:

<span class="mw-page-title-main">Neuronal noise</span> Random electric fluctuations in neurons

Neuronal noise or neural noise refers to the random intrinsic electrical fluctuations within neuronal networks. These fluctuations are not associated with encoding a response to internal or external stimuli and can be from one to two orders of magnitude. Most noise commonly occurs below a voltage-threshold that is needed for an action potential to occur, but sometimes it can be present in the form of an action potential; for example, stochastic oscillations in pacemaker neurons in suprachiasmatic nucleus are partially responsible for the organization of circadian rhythms.

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Recurrent thalamo-cortical resonance or Thalamocortical oscillation is an observed phenomenon of oscillatory neural activity between the thalamus and various cortical regions of the brain. It is proposed by Rodolfo Llinas and others as a theory for the integration of sensory information into the whole of perception in the brain. Thalamocortical oscillation is proposed to be a mechanism of synchronization between different cortical regions of the brain, a process known as temporal binding. This is possible through the existence of thalamocortical networks, groupings of thalamic and cortical cells that exhibit oscillatory properties.

Secondary consciousness is an individual's accessibility to their history and plans. The ability allows its possessors to go beyond the limits of the remembered present of primary consciousness. Primary consciousness can be defined as simple awareness that includes perception and emotion. As such, it is ascribed to most animals. By contrast, secondary consciousness depends on and includes such features as self-reflective awareness, abstract thinking, volition and metacognition. The term was coined by Gerald Edelman.

The activation-synthesis hypothesis, proposed by Harvard University psychiatrists John Allan Hobson and Robert McCarley, is a neurobiological theory of dreams first published in the American Journal of Psychiatry in December 1977. The differences in neuronal activity of the brainstem during waking and REM sleep were observed, and the hypothesis proposes that dreams result from brain activation during REM sleep. Since then, the hypothesis has undergone an evolution as technology and experimental equipment has become more precise. Currently, a three-dimensional model called AIM Model, described below, is used to determine the different states of the brain over the course of the day and night. The AIM Model introduces a new hypothesis that primary consciousness is an important building block on which secondary consciousness is constructed.

The Dehaene–Changeux model (DCM), also known as the global neuronal workspace, or global cognitive workspace model, is a part of Bernard Baars's global workspace model for consciousness.

The Haken-Kelso-Bunz (HKB) is a theoretical model of motor coordination originally formulated by Hermann Haken, J. A. Scott Kelso and H. Bunz. The model attempts to provide the framework for understanding coordinated behavior in living things. It accounts for experimental observations on human bimanual coordination that revealed fundamental features of self-organization: multistability, and phase transitions (switching). HKB is one of the most extensively tested quantitative models in the field of human movement behavior.

<span class="mw-page-title-main">Phase resetting in neurons</span> Behavior observed in neurons

Phase resetting in neurons is a behavior observed in different biological oscillators and plays a role in creating neural synchronization as well as different processes within the body. Phase resetting in neurons is when the dynamical behavior of an oscillation is shifted. This occurs when a stimulus perturbs the phase within an oscillatory cycle and a change in period occurs. The periods of these oscillations can vary depending on the biological system, with examples such as: (1) neural responses can change within a millisecond to quickly relay information; (2) In cardiac and respiratory changes that occur throughout the day, could be within seconds; (3) circadian rhythms may vary throughout a series of days; (4) rhythms such as hibernation may have periods that are measured in years. This activity pattern of neurons is a phenomenon seen in various neural circuits throughout the body and is seen in single neuron models and within clusters of neurons. Many of these models utilize phase response (resetting) curves where the oscillation of a neuron is perturbed and the effect the perturbation has on the phase cycle of a neuron is measured.

Andreas Karl Engel is a German neuroscientist. He is the director of the Department of Neurophysiology and Pathophysiology at the University Medical Center Hamburg-Eppendorf (UKE).

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