Viktor K. Jirsa

Emerging concepts for the dynamical organization of resting-state activity in the brain

A broad body of experimental work has demonstrated that apparently spontaneous brain activity is not random. At the level of large-scale neural systems, as measured with functional MRI (fMRI), this ongoing activity reflects the organization of a series of highly coherent functional networks. These so-called resting-state networks (RSNs) closely relate to the underlying anatomical connectivity but cannot be understood in those terms alone. Here we review three large-scale neural system models of primate neocortex that emphasize the key contributions of local dynamics, signal transmission delays and noise to the emerging RSNs. We propose that the formation and dissolution of resting-state patterns reflects the exploration of possible functional network configurations around a stable anatomical skeleton.

The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields

The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space–time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.

Noise during rest enables the exploration of the brain's dynamic repertoire.

Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1–100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space–time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics

Abstract The purpose of this paper is twofold: First, we present a semi-quantitative nonlinear field theory of the brain under realistic anatomical connectivity conditions describing the interaction between functional units within the brain. This macroscopic field theory is derived from the quasi-microscopic conversion properties of neural populations occurring at synapses and somas. The quasi-microscopic models by Wilson-Cowan (1972,1973) and Nunez (1974) can be derived from these. Functional units are treated as inhomogeneities within a nonlinear one-dimensional neural tissue. Second, for the case of the Kelso experiment the field equation is treated analytically and numerically and can be reduced to a set of ordinary differential equations which corresponds to a model by Jirsa et al. (1994, 1995). This phenomenological model reproduces the spatio-temporal phenomena experimentally observed. Here the most prominent property of the neural tissue is the parametric excitation. The macroscopic field parameters can be expressed by quasi-microscopic neural parameters.

Distinct timing mechanisms produce discrete and continuous movements

The differentiation of discrete and continuous movement is one of the pillars of motor behavior classification. Discrete movements have a definite beginning and end, whereas continuous movements do not have such discriminable end points. In the past decade there has been vigorous debate whether this classification implies different control processes. This debate up until the present has been empirically based. Here, we present an unambiguous non-empirical classification based on theorems in dynamical system theory that sets discrete and continuous movements apart. Through computational simulations of representative modes of each class and topological analysis of the flow in state space, we show that distinct control mechanisms underwrite discrete and fast rhythmic movements. In particular, we demonstrate that discrete movements require a time keeper while fast rhythmic movements do not. We validate our computational findings experimentally using a behavioral paradigm in which human participants performed finger flexion-extension movements at various movement paces and under different instructions. Our results demonstrate that the human motor system employs different timing control mechanisms (presumably via differential recruitment of neural subsystems) to accomplish varying behavioral functions such as speed constraints.

Local and global stabilization of coordination by sensory information.

Abstract. In studies of rhythmic coordination, where sensory information is often generated by an auditory stimulus, spatial and temporal variability are known to decrease at points in the movement cycle coincident with the stimulus, a phenomenon known as anchoring (Byblow et al. 1994). Here we hypothesize that the role of anchoring may be to globally stabilize coordination under conditions in which it would otherwise undergo a global coordinative change such as a phase transition. To test this hypothesis, anchoring was studied in a bimanual coordination paradigm in which either inphase or antiphase coordination was produced as auditory pacing stimuli (and hence movement frequency) were scaled over a wide range of frequencies. Two different anchoring conditions were used: a single-metronome condition, in which peak amplitude of right finger flexion coincided with the auditory stimulus; and a double-metronome condition, in which each finger reversal (flexion and extension) occurred simultaneously with the auditory stimuli. Anchored reversal points displayed lower spatial variation than unanchored reversal points, resulting in more symmetric phase plane trajectories in the double- than the single-metronome condition. The global coordination dynamics of the double-metronome condition was also more stable, with transitions from antiphase to inphase occurring less often and at higher movement frequencies than in the single-metronome condition. An extension of the Haken-Kelso-Bunz model of bimanual coordination is presented briefly which includes specific coupling of sensory information to movement through a process we call parametric stabilization. The parametric stabilization model provides a theoretical account of both local effects on the individual movement trajectories (anchoring) and global stabilization of observed coordination patterns, including the delay of phase transitions.

Coordination Dynamics: Issues and Trends

I: Philosophical Investigations of Coordination Dynamics: Perception and Action.- Impredicativity, Dynamics, and the Perception-Action Divide.- II: Cognitive Contributions to Coordination Dynamics: Attention, Intention and Learning.- A Dynamical Approach to the Interplay of Attention and Bimanual Coordination.- Intention in Bimanual Coordination Performance and Learning.- Searching for (Dynamic) Principles of Learning.- III: Coordination Dynamics of Posture: Control Mechanisms.- Using Visual Information in Functional Stabilization: Pole-Balancing Example.- Postural Coordination Dynamics in Standing Humans.- Noise Associated with the Process of Fusing Multisensory Information.- IV: Perceptual and Motoric Influences on Coordination Dynamics.- Governing Coordination. Why do Muscles Matter?.- Guiding Movements without Redundancy Problems.- A Perceptual-Cognitive Approach to Bimanual Coordination.- V: Integration and Segregation in Coordination Dynamics.- Complex Neural Dynamics.- Oscillations and Synchrony in Cognition.- Integration and Segregation of Perceptual and Motor Behavior.- Author Index.

Connecting Cortical and Behavioral Dynamics: Bimanual Coordination

For the paradigmatic case of bimanual coordination, we review levels of organization of behavioral dynamics and present a description in terms of modes of behavior. We briefly review a recently developed model of spatiotemporal brain activity that is based on short-and long-range connectivity of neural ensembles. This model is specified for the case of motor and sensorimotor units embedded in the neural sheet. Focusing on the cortical left-right symmetry, we derive a bimodal description of the brain activity that is connected to behavioral dynamics. We make predictions of global features of brain dynamics during coordination tasks and test these against experimental magnetoencephalogram (MEG) results. A key feature of our approach is that phenomenological laws at the behavioral level can be connected to a field-theoretical description of cortical dynamics.

Coordination: Neural, Behavioral and Social Dynamics

One of the most striking features of Coordination Dynamics is its interdisciplinary character. The problems we are trying to solve in this field range from behavioral phenomena of interlimb coordination and coordination between stimuli and movements (perception-action tasks) through neural activation patterns that can be observed during these tasks to clinical applications and social behavior. It is not surprising that close collaborationamong scientistsfrom different fields as psychology, kinesiology, neurology and even physics are imperative to deal with the enormous difficulties we are facing when we try to understand a system as complex as the human brain. The chapters in this volume are not simply write-ups of the lectures given by the experts at the meeting but are written in a way that they give sufficient introductory information to be comprehensible and useful for all interested scientists and students.

Connectivity and dynamics of neural information processing

In this article, we systematically review the current literature on neural connectivity and dynamics, or equivalently, structure and function. In particular, we discuss how changes in the connectivity of a neural network affect the spatiotemporal network dynamics qualitatively. The three major criteria of comparison are, first, the local dynamics at the network nodes which includes fixed point dynamics, oscillatory and chaotic dynamics; second, the presence of time delays via propagation along connecting pathways; and third, the properties of the connectivity matrix such as its statistics, symmetry, and translational invariance. Since the connection topology changes when anatomical scales are traversed, so will the corresponding network dynamics change. As a consequence different types of networks are encountered on different levels of neural organization.