Spatiotemporal Patterns Of Brain Activity Research Articles

Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states.

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

A hybrid spatiotemporal deep belief network and sparse representation-based framework reveals multilevel core functional components in decoding multitask fMRI signals.

Decoding human brain activity on various task-based functional brain imaging data is of great significance for uncovering the functioning mechanism of the human mind. Currently, most feature extraction model-based methods for brain state decoding are shallow machine learning models, which may struggle to capture complex and precise spatiotemporal patterns of brain activity from the highly noisy fMRI raw data. Moreover, although decoding models based on deep learning methods benefit from their multilayer structure that could extract spatiotemporal features at multiscale, the relatively large populations of fMRI datasets are indispensable, and the explainability of their results is elusive. To address the above problems, we proposed a computational framework based on hybrid spatiotemporal deep belief network and sparse representations to differentiate multitask fMRI (tfMRI) signals. Using a relatively small cohort of tfMRI data as a test bed, our framework can achieve an average classification accuracy of 97.86% and define the multilevel temporal and spatial patterns of multiple cognitive tasks. Intriguingly, our model can characterize the key components for differentiating the multitask fMRI signals. Overall, the proposed framework can identify the interpretable and discriminative fMRI composition patterns at multiple scales, offering an effective methodology for basic neuroscience and clinical research with relatively small cohorts.

Modelling brain dynamics by Boolean networks

Understanding the relationship between brain architecture and brain function is a central issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a human connectome with a Boolean networks model with the aim of computationally replicating certain cognitive functions as they emerge from the standardization of many fMRI studies, identified as patterns of human brain activity. Results from the analysis of simulation data, carried out for different parameters and initial conditions identified many possible paths in the space of parameters of these network models, with normal (ordered asymptotically constant patterns), chaotic (oscillating or disordered) but also highly organized configurations, with countless spatial–temporal patterns. We interpreted these results as routes to chaos, permanence of the systems in regimes of complexity, and ordered stationary behavior, associating these dynamics to cognitive processes. The most important result of this work is the study of emergent neural circuits, i.e., configurations of areas that synchronize over time, both locally and globally, determining the emergence of computational analogues of cognitive processes, which may or may not be similar to the functioning of biological brain. Furthermore, results put in evidence the creation of how the brain creates structures of remote communication. These structures have hierarchical organization, where each level allows for the emergence of brain organizations which behave at the next superior level. Taken together these results allow the interplay of dynamical and topological roots of the multifaceted brain dynamics to be understood.

Data-driven tensor independent component analysis for model-based connectivity neurofeedback

Neurofeedback based on real-time functional MRI is an emerging technique to train voluntary control over brain activity in healthy and disease states. Recent developments even allow for training of brain networks using connectivity feedback based on dynamic causal modeling (DCM). DCM is an influential hypothesis-driven approach that requires prior knowledge about the target brain network dynamics and the modulatory influences. Data-driven approaches, such as tensor independent component analysis (ICA), can reveal spatiotemporal patterns of brain activity without prior assumptions. Tensor ICA allows flexible data decomposition and extraction of components consisting of spatial maps, time-series, and session/subject-specific weights, which can be used to characterize individual neurofeedback regulation per regulation trial, run, or session. In this study, we aimed to better understand the spatiotemporal brain patterns involved and affected by model-based feedback regulation using data-driven tensor ICA. We found that task-specific spatiotemporal brain patterns obtained using tensor ICA were highly consistent with model-based feedback estimates. However, we found that the DCM approach captured specific network interdependencies that went beyond what could be detected with either general linear model (GLM) or ICA approaches. We also found that neurofeedback-guided regulation resulted in activity changes that were characteristic of the mental strategies used to control the feedback signal, and that these activity changes were not limited to periods of active self-regulation, but were also evident in distinct gradual recovery processes during subsequent rest periods. Complementary data-driven and model-based approaches could aid in interpretation of the neurofeedback data when applied post-hoc, and in the definition of the target brain area/pattern/network/model prior to the neurofeedback training study when applied to the pilot data. Systematically investigating the triad of mental effort, spatiotemporal brain network changes, and activity and recovery processes might lead to a better understanding of how learning with neurofeedback is accomplished, and how such learning can cause plastic brain changes along with specific behavioral effects.

Directed functional connectivity using dynamic graphical models

There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4–0.8 s offset between connected nodes), our method has a sensitivity of 72%–77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.

Categorization for Faces and Tools-Two Classes of Objects Shaped by Different Experience-Differs in Processing Timing, Brain Areas Involved, and Repetition Effects.

The brain mechanisms that integrate the separate features of sensory input into a meaningful percept depend upon the prior experience of interaction with the object and differ between categories of objects. Recent studies using representational similarity analysis (RSA) have characterized either the spatial patterns of brain activity for different categories of objects or described how category structure in neuronal representations emerges in time, but never simultaneously. Here we applied a novel, region-based, multivariate pattern classification approach in combination with RSA to magnetoencephalography data to extract activity associated with qualitatively distinct processing stages of visual perception. We asked participants to name what they see whilst viewing bitonal visual stimuli of two categories predominantly shaped by either value-dependent or sensorimotor experience, namely faces and tools, and meaningless images. We aimed to disambiguate the spatiotemporal patterns of brain activity between the meaningful categories and determine which differences in their processing were attributable to either perceptual categorization per se, or later-stage mentalizing-related processes. We have extracted three stages of cortical activity corresponding to low-level processing, category-specific feature binding, and supra-categorical processing. All face-specific spatiotemporal patterns were associated with bilateral activation of ventral occipito-temporal areas during the feature binding stage at 140–170 ms. The tool-specific activity was found both within the categorization stage and in a later period not thought to be associated with binding processes. The tool-specific binding-related activity was detected within a 210–220 ms window and was located to the intraparietal sulcus of the left hemisphere. Brain activity common for both meaningful categories started at 250 ms and included widely distributed assemblies within parietal, temporal, and prefrontal regions. Furthermore, we hypothesized and tested whether activity within face and tool-specific binding-related patterns would demonstrate oppositely acting effects following procedural perceptual learning. We found that activity in the ventral, face-specific network increased following the stimuli repetition. In contrast, tool processing in the dorsal network adapted by reducing its activity over the repetition period. Altogether, we have demonstrated that activity associated with visual processing of faces and tools during the categorization stage differ in processing timing, brain areas involved, and in their dynamics underlying stimuli learning.

Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep: Mechanistic Insights from Whole-Brain Computational Modelling

Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the differences in global brain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and slow-wave sleep. We applied a whole-brain model based on the normal form of a supercritical Hopf bifurcation and studied the dynamical changes when adapting the bifurcation parameter for all brain nodes to best match wakefulness and slow-wave sleep. Furthermore, we analysed differences in effective connectivity between the two states. In addition to significant changes in functional connectivity, synchrony and metastability, this analysis revealed a significant shift of the global dynamic working point of brain dynamics, from the edge of the transition between damped to sustained oscillations during wakefulness, to a stable focus during slow-wave sleep. Moreover, we identified a significant global decrease in effective interactions during slow-wave sleep. These results suggest a mechanism for the empirical functional changes observed during slow-wave sleep, namely a global shift of the brain’s dynamic working point leading to increased stability and decreased effective connectivity.

Goal-Directed and Habit-Like Modulations of Stimulus Processing during Reinforcement Learning.

Recent research has shown that perceptual processing of stimuli previously associated with high-value rewards is automatically prioritized even when rewards are no longer available. It has been hypothesized that such reward-related modulation of stimulus salience is conceptually similar to an "attentional habit." Recording event-related potentials in humans during a reinforcement learning task, we show strong evidence in favor of this hypothesis. Resistance to outcome devaluation (the defining feature of a habit) was shown by the stimulus-locked P1 component, reflecting activity in the extrastriate visual cortex. Analysis at longer latencies revealed a positive component (corresponding to the P3b, from 550-700 ms) sensitive to outcome devaluation. Therefore, distinct spatiotemporal patterns of brain activity were observed corresponding to habitual and goal-directed processes. These results demonstrate that reinforcement learning engages both attentional habits and goal-directed processes in parallel. Consequences for brain and computational models of reinforcement learning are discussed.SIGNIFICANCE STATEMENT The human attentional network adapts to detect stimuli that predict important rewards. A recent hypothesis suggests that the visual cortex automatically prioritizes reward-related stimuli, driven by cached representations of reward value; that is, stimulus-response habits. Alternatively, the neural system may track the current value of the predicted outcome. Our results demonstrate for the first time that visual cortex activity is increased for reward-related stimuli even when the rewarding event is temporarily devalued. In contrast, longer-latency brain activity was specifically sensitive to transient changes in reward value. Therefore, we show that both habit-like attention and goal-directed processes occur in the same learning episode at different latencies. This result has important consequences for computational models of reinforcement learning.

Decoding the perception of endogenous pain from resting-state MEG

Decoding the neural representations of pain is essential to obtaining an objective assessment as well as an understanding of its underlying mechanisms. The complexities involved in the subjective experience of pain make it difficult to obtain a quantitative assessment from the induced spatiotemporal patterns of brain activity of high dimensionality. Most previous studies have investigated the perception of pain by analyzing the amplitude or spatial patterns in the response of the brain to external stimulation. This study investigated the decoding of endogenous pain perceptions according to resting-state magnetoencephalographic (MEG) recordings. In our experiments, we applied a beamforming method to calculate the brain activity for every brain region and examined temporal and spectral features of brain activity for predicting the intensity of perceived pain in patients with primary dysmenorrhea undergoing menstrual pain. Our results show that the asymmetric index of sample entropy in the precuneus and the sample entropy in the left posterior cingulate gyrus were the most informative characteristics associated with the perception of menstrual pain. The correlation coefficient (ρ=0.64, p<0.001) between the predicted and self-reported pain scores demonstrated the high prediction accuracy. In addition to the estimated brain activity, we were able to predict accurate pain scores directly from MEG channel signals (ρ=0.65, p<0.001). These findings suggest the possibility of using the proposed model based on resting-state MEG to predict the perceived intensity of endogenous pain.

Intersubject consistency of cortical MEG signals during movie viewing

According to recent functional magnetic resonance imaging (fMRI) studies, spectators of a movie may share similar spatiotemporal patterns of brain activity. We aimed to extend these findings of intersubject correlation to temporally accurate single-trial magnetoencephalography (MEG). A silent 15-min black-and-white movie was shown to eight subjects twice. We adopted a spatial filtering model and estimated its parameter values by using multi-set canonical correlation analysis (M-CCA) so that the intersubject correlation was maximized. The procedure resulted in multiple (mutually uncorrelated) time-courses with statistically significant intersubject correlations at frequencies below 10Hz; the maximum correlation was 0.28±0.075 in the ≤1Hz band. Moreover, the 24-Hz frame rate elicited steady-state responses with statistically significant intersubject correlations up to 0.29±0.12. To assess the brain origin of the across-subjects correlated signals, the time-courses were correlated with minimum-norm source current estimates (MNEs) projected to the cortex. The time series implied across-subjects synchronous activity in the early visual, posterior and inferior parietal, lateral temporo-occipital, and motor cortices, and in the superior temporal sulcus (STS) bilaterally. These findings demonstrate the capability of the proposed methodology to uncover cortical MEG signatures from single-trial signals that are consistent across spectators of a movie.

Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome

The relation between large-scale brain structure and function is an outstanding open problem in neuroscience. We approach this problem by studying the dynamical regime under which realistic spatiotemporal patterns of brain activity emerge from the empirically derived network of human brain neuroanatomical connections. The results show that critical dynamics unfolding on the structural connectivity of the human brain allow the recovery of many key experimental findings obtained from functional magnetic resonance imaging, such as divergence of the correlation length, the anomalous scaling of correlation fluctuations, and the emergence of large-scale resting state networks.

A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals

There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM, and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI) paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results.

Statistical Signal Processing in Neuroscience

When the three of us decided to coedit a special issue on Statistical Signal Processing in Neuroscience, we implicitly knew that the submitted papers would span a wide field, the field measured in coordinate system with disparate axes: engineering-oriented or scientific; analysis of single neuron, populations or aggregate activity recordings; and known signal processing techniques or novel ones “invented” for the neuroscience application. We were not disappointed. The six papers chosen for this special issue represent the best of the submitted papers. The papers span a wide variety of very challenging problems. Some focus on basic estimation theory for reconstructing sensory stimuli from spike trains, while others focus on the prediction of seizure activity from EEG signals in clinical applications. Two papers are inspired by recent striking advances in Brain Machine Interface technology that is aimed at improving our basic understanding of brain function, and possibly restoring, replacing, or augmenting it when it is damaged. The paper by Lazar and Pnevmatikakis, “Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds”, employs a fresh idea—time encoding machines—inspired by neural systems that holds promise for new signal processing applications. This paper explores how to optimally decode population responses created within the time-machine encoding framework. Darmanjian and Principe’s paper, “Spatial-temporal clustering of neural data using linked-mixtures of hidden Markov models”, looks at HMM decoding of new populations using unsupervised hierarchical clustering for brainmachine interface (BMI) applications, focusing on motor control. Next, the paper by Filho et al., “Magnitude squared of coherence to detect imaginarymovement”, analyzes ERP data using cross-spectral analysis on a large scale, again for a motor BMI application. In contrast, Krusienski’s paper “A Method for visualizing independent spatio-temporal patterns of brain activity” uses a new spatio-temporal analysis of EEG data to tease out the basic independent patterns from which the measurements were constructed. Continuing along the axis of using modern algorithms, Liu, Pang and Wang explore in their paper “Epileptic seizure prediction by a system of particle filter associated with a neural network” using statistical bayesian filters to analyze EEG data. Finally, Hu et al. propose using a realistic nonlinear model rather than a linearized version to analyze multivoxel behavior in fMRI images in their paper “Nonlinear analysis of the BOLD signal.” We hope you enjoy these articles as much as we did reviewing them.

Antagonism at serotonin 5-HT2A receptors modulates functional activity of frontohippocampal circuit

Several second-generation antipsychotics are characterised by a significant antagonistic effect at serotonin 5-HT(2A) receptors (5-HT(2A)R), a feature that has been associated with lower incidence of extra-pyramidal symptoms and a putative amelioration of positive and negative symptoms experienced by schizophrenic patients. However, the neurofunctional substrate of 5-HT(2A) antagonism and its exact contribution to the complex pharmacological profile of these drugs remain to be elucidated. Here, we used pharmacological magnetic resonance imaging to map the modulatory effects of the selective 5-HT(2A)R antagonist Ml00907 on the spatiotemporal patterns of brain activity elicited by acute phencyclidine (PCP) challenge in the rat. PCP is a non-competitive NMDA receptor antagonist that induces dysregulation of corticolimbic glutamatergic neurotransmission and produces cognitive impairment and psychotic-like symptoms reminiscent of those observed in schizophrenia. Pre-administration of M100907 produced focal and region-dependent attenuation of PCP-induced response in frontoseptohippocampal areas. As early studies highlighted a permissive role of 5-HT(2A)R on frontal dopamine release, the role of post-synaptic dopamine D(1) receptors on PCP-induced response was examined by using the potent antagonist SCH23390. Interestingly, SCH23390 did not affect PCP's response in any of the regions examined. This finding rules out a significant contribution of dopamine in the functional changes mapped and, indirectly, the inhibitory effect of M100907, in favour of a glutamatergic origin. Our data expand recent evidence suggesting a key role of 5-HT(2A)R in modulating glutamate-mediated cognitive performance in the prefrontal cortex and highlight the whole frontoseptohippocampal circuit as a key functional substrate of 5-HT(2A)R antagonism in normal and disease states.

Reproducibility Distinguishes Conscious from Nonconscious Neural Representations

What qualifies a neural representation for a role in subjective experience? Previous evidence suggests that the duration and intensity of the neural response to a sensory stimulus are factors. We introduce another attribute--the reproducibility of a pattern of neural activity across different episodes--that predicts specific and measurable differences between conscious and nonconscious neural representations independently of duration and intensity. We found that conscious neural activation patterns are relatively reproducible when compared with nonconscious neural activation patterns corresponding to the same perceptual content. This is not adequately explained by a difference in signal-to-noise ratio.

Magnetic Source Imaging Studies of Dyslexia Interventions

Rapidly accumulating evidence from functional brain imaging studies indicates that developmental reading disability is associated with a functional disruption of the brain circuits that normally develop to support reading-related processes. This article briefly overviews recent advances in methods that capture the anatomical outline and temporal (dynamic) features of regional brain activation during performance of reading tasks. One of these methods, magnetoencephalography (MEG) or magnetic sources imaging (MSI) is described in more detail in the context of investigations of changes in spatiotemporal patterns of brain activity associated with improvement in reading skills in response to various types of educational interventions.

Tracking speech comprehension in space and time

A fundamental challenge for the cognitive neuroscience of language is to capture the spatio-temporal patterns of brain activity that underlie critical functional components of the language comprehension process. We combine here psycholinguistic analysis, whole-head magnetoencephalography (MEG), the Mismatch Negativity (MMN) paradigm, and state-of-the-art source localization techniques (Equivalent Current Dipole and L1 Minimum-Norm Current Estimates) to locate the process of spoken word recognition at a specific moment in space and time. The magnetic MMN to words presented as rare “deviant stimuli” in an oddball paradigm among repetitive “standard” speech stimuli, peaked 100–150 ms after the information in the acoustic input, was sufficient for word recognition. The latency with which words were recognized corresponded to that of an MMN source in the left superior temporal cortex. There was a significant correlation ( r = 0.7) of latency measures of word recognition in individual study participants with the latency of the activity peak of the superior temporal source. These results demonstrate a correspondence between the behaviorally determined recognition point for spoken words and the cortical activation in left posterior superior temporal areas. Both the MMN calculated in the classic manner, obtained by subtracting standard from deviant stimulus response recorded in the same experiment, and the identity MMN (iMMN), defined as the difference between the neuromagnetic responses to the same stimulus presented as standard and deviant stimulus, showed the same significant correlation with word recognition processes.

GROWING UP: EMERGING COMPLEXITY IN LIVING BEING

ABSTRACT Biological systems live and grow. Many aspects are inherent to the concept of living, such as the adaptation, the interaction with the environment, and the ability to deal with limited resources. Living systems present multiple levels of organization, with elements at one level interacting and aggregating to create more complex behavior at a higher level. In recent years, many new techniques used to investigate the spatio-temporal activity in living being have demonstrated the presence of features common to the behavior of self organizing dynamical systems. Thus a question arises: Is this chaos useful to model living beings? The answer is very difficult to find. Many experimental data support the dynamic chaotic modelling of living systems. Complex behaviors such as perceiving, intending, acting, learning, and remembering arise as metastable spatio-temporal patterns of brain activity that are themselves produced by the cooperative interactions among neural clusters. In this article we present and discuss that question, and we try to give indication for a possible answer, with the aim of defining the basic features of a behavioral kernel for living artefacts.

Spatio-temporal brain activation profiles associated with line bisection judgments and double simultaneous visual stimulation

We used magnetic source imaging (MSI) to investigate the spatio-temporal patterns of brain activity associated with line bisection judgments and double simultaneous visual stimulation in 14 healthy adults. Consistent with lesion and hemodynamic neuroimaging studies, we found the greatest number of activity sources in right inferior parietal cortex. These sources were most prominent, on average, between 200 and 300 ms after the onset of single (left, right, or center) target stimuli. A greater number of significant activity sources were found in right inferior parietal, occipital, and prefrontal cortices during bilateral compared with unilateral stimulus presentation. Based on these observations, we suggest that a more parsimonious physiological explanation of visual extinction than the hemispheric rivalry account may be the additional neuronal excitation required in right occipital and parietal cortices for accurate bilateral visual perception.

Metastable Coordination Dynamics of Brain and Behavior.

One of the biggest puzzles in trying to understand ourselves is how we (and our brains) make decisions or choices. What kind of thing is the brain that it can make choices? If choice is a function of the large-scale activity of the brain, what form do these large-scale spatiotemporal patterns of brain activity take and how are they related to what people do? How are localized regions of the brain coordinated so as to support large-scale network activity? Fundamentally speaking, why is one kind of pattern of behavior selected over another? What are the principles and mechanisms? Choice implies the creation (and annihilation) of information. How is information created so that it may modulate, and be modulated by, the dynamics of the brain? These are big questions, so let's treat them systematically, albeit briefly. There are good reasons to believe that the answers may lie within Coordination Dynamics, a line of scientific enquiry that aims to understand—through theory and experiment—how patterns of coordination form, persist and change in living things. Coordination is not just matter in motion. It refers to the functional ordering of parts and processes in space and time. Coordination Dynamics is unique because it contains two coexistent aspects, a self-organizing, or ‘undirected’ aspect and an informational, or ‘directed’ aspect. The former deals with collective or cooperative effects that arise spontaneously when ordinary matter takes on novel properties, as in lasers and superconductors, or when new forms of organization among water molecules arise as in the weather (Cross and Hohenberg, 19931); Haken, 19772); Nicolis and Prigogine, 19773)). The human brain and the behavior it produces has been demonstrated to exploit these self-organizing, cooperative effects, an intriguing finding in its own right (Sect. 2.0). The latter, ‘directed’ aspect of coordination dynamics reveals how information is created de novo and how information guides, directs, modulates and is modified by self-organizing dynamics. “Directed” terms like “plans,” “programs,” “intentions” and so forth, rather than reified are embraced by metastable coordination dynamics. Metastable Coordination Dynamics means that there is no such thing as perfect order or randomness in the way natural systems are coordinated. Only tendencies toward order and randomness exist in spacetime. These tendencies arise from the coexistence of intrinsic properties of individual parts and the essentially nonlinear coupling between them (Sect. 5.0).