Theory of the Relation between Human Brain Activity (MEG) and Hand Movements

doi:10.1006/nimg.1999.0532

NeuroImage

Volume 11, Issue 5, May 2000, Pages 359-369

https://doi.org/10.1006/nimg.1999.0532 Get rights and content

Abstract

Earlier research established that spontaneous changes in human sensorimotor coordination are accompanied by qualitative changes in the spatiotemporal dynamics of neural activity measured by multisensor electroencephalography and magnetoencephalography. More recent research has demonstrated that a robust relation exists between brain activity and the movement profile produced. In particular, brain activity has been shown to correlate strongly with movement velocity independent of movement direction and mode of coordination. Using a recently developed field theoretical model of large-scale brain activity itself based on neuroanatomical and neurophysiological constraints we show here how these experimental findings relate to the field theory and how it is possible to reconstruct the movement profile via spatial and temporal integration of the brain signal. There is a unique relation between the quantities in the theory and the experimental data, and fit between the shape of the measured and the reconstructed time series for the movement is remarkably good given that there are no free parameters.

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Two important points considered here are worth elaborating, a theoretical one and a terminological one. Theoretically, given that we are both describing and explaining a phase transition into flow, we consider flow, as an altered state of consciousness described in our hypothetical situation, from a metastable perspective (Fuchs et al., 2000; Kelso, 1992). Crucially, this would mean that flow is a transient phenomenon—a metastable tendency.
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The low-dimensional stability in electric fields might help the brain perform computations, by allowing latent states to be reliably transferred between brain areas, in accord with modern engram theory (Ryan et al., 2015). This is also in accord with the theory of Synergetics (Basar et al., 1983, Fuchs et al., 2000; Haken, 2006; Jirsa and Kelso, 2000). The electric field can be viewed as a control variable similar to energy (Haken, 1985) and attention signals (Ditzinger and Haken, 1989) that evolves more slowly than the latent variables that represent information.
It is known that the exact neurons maintaining a given memory (the neural ensemble) change from trial to trial. This raises the question of how the brain achieves stability in the face of this representational drift. Here, we demonstrate that this stability emerges at the level of the electric fields that arise from neural activity. We show that electric fields carry information about working memory content. The electric fields, in turn, can act as “guard rails” that funnel higher dimensional variable neural activity along stable lower dimensional routes. We obtained the latent space associated with each memory. We then confirmed the stability of the electric field by mapping the latent space to different cortical patches (that comprise a neural ensemble) and reconstructing information flow between patches. Stable electric fields can allow latent states to be transferred between brain areas, in accord with modern engram theory.
Are unimanual movements bilateral?
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Neural crosstalk can influence movement characteristics, as illustrated by spontaneous switches in coordination patterns (Aramaki et al., 2005; Houweling et al., 2010). This neural crosstalk can also be identified in unimanual movements (Daffertshofer et al., 2000; Fuchs et al., 2000a, b) and therefore it is conceivable that behavioral and neural determinants of bimanual coordination may also apply to unimanual movements (Daffertshofer et al., 2005; Gross et al., 2005; Vercauteren et al., 2008). With the present review, we seek to substantiate this idea by specifying these determinants and the functional role of the often-reported, bilateral activation patterns in the cortex during unilateral hand movements in healthy humans.
Motor control is a fundamental challenge for the central nervous system. In this review, we show that unimanual movements involve bi-hemispheric activation patterns that resemble the bilateral neural activation typically observed for bimanual movements. For unimanual movements, the activation patterns in the ipsilateral hemisphere arguably entail processes that serve to suppress interhemispheric cross-talk through transcallosal tracts. Improper suppression may cause involuntary muscle co-activation and as such it comes as no surprise that these processes depend on the motor task. Identifying the detailed contributions of local and global excitatory and inhibitory cortical processes to this suppression calls for integrating findings from various behavioral paradigms and imaging modalities. Doing so systematically highlights that lateralized activity in left (pre)motor cortex modulates with task complexity, independently of the type of task and the end-effector involved. Despite this lateralization, however, our review supports the idea of bi-hemispheric cortical activation being a fundamental mode of upper extremity motor control.
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In other words, control is geared not toward the execution of a single trajectory, but rather toward instantiation of a dynamics that favors goal achievement via flow and trajectory formation, its stability and robustness, as well as redundancy and compensation. This claim is supported by a large literature on the representation of behavioral variables in brain signals (Freeman, 1988; Friston, 1997; Fuchs et al., 2000; Horwitz et al., 1999; Jerbi et al., 2007; Jirsa et al., 1998; Kelso et al., 1998; Makeig et al., 2002; Meyer-Lindenberg et al., 2002). For instance, Kelso et al. (1998) showed that MEG patterns significantly mirror the movement velocity across a broad range of initial conditions, peak velocities, and movement rates, which is consistent with single-cell studies in monkeys suggesting that speed (in addition to direction) is represented in the discharge rate of motor cortical cells (Moran and Schwartz, 1999).
In order to maintain brain function, neural activity needs to be tightly coordinated within the brain network. How this coordination is achieved and related to behavior is largely unknown. It has been previously argued that the study of the link between brain and behavior is impossible without a guiding vision. Here we propose behavioral-level concepts and mechanisms embodied as structured flows on manifold (SFM) that provide a formal description of behavior as a low-dimensional process emerging from a network’s dynamics dependent on the symmetry and invariance properties of the network connectivity. Specifically, we demonstrate that the symmetry breaking of network connectivity constitutes a timescale hierarchy resulting in the emergence of an attractive functional subspace. We show that behavior emerges when appropriate conditions imposed upon the couplings are satisfied, justifying the conductance-based nature of synaptic couplings. Our concepts propose design principles for networks predicting how behavior and task rules are represented in real neural circuits and open new avenues for the analyses of neural data.
Outline of a general theory of behavior and brain coordination
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Sensorimotor units receive proprioceptive and kinesthetic information from the respective finger movements and motor areas may be thought of as driving the fingers. The spatiotemporal details of how the motor areas drive the finger oscillation is given by the functional mapping described above (Fuchs et al., 2000; Kelso, Fuchs, & Jirsa, 1999). Mathematical analysis of these equations by Jirsa et al. (1998) and Jirsa, Fuchs, and Kelso (1999) predicts that the neuronal activity will show a spatially antisymmetric organization in the antiphase condition, but then undergo a transition to a symmetric organization simultaneously with the behavioral transition to in-phase.
Much evidence suggests that dynamic laws of neurobehavioral coordination are sui generis: they deal with collective properties that are repeatable from one system to another and emerge from microscopic dynamics but may not (even in principle) be deducible from them. Nevertheless, it is useful to try to understand the relationship between different levels while all the time respecting the autonomy of each. We report a program of research that uses the theoretical concepts of coordination dynamics and quantitative measurements of simple, well-defined experimental model systems to explicitly relate neural and behavioral levels of description in human beings. Our approach is both top-down and bottom-up and aims at ending up in the same place: top-down to derive behavioral patterns from neural fields, and bottom-up to generate neural field patterns from bidirectional coupling between astrocytes and neurons. Much progress can be made by recognizing that the two approaches—reductionism and emergentism—are complementary. A key to understanding is to couch the coordination of very different things—from molecules to thoughts—in the common language of coordination dynamics.
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Regular ArticleTheory of the Relation between Human Brain Activity (MEG) and Hand Movements

Abstract

Physica

Physica

Phys. Lett.

Math. Biosci.

Physica

Biophys. J.

Computations underlying the execution of movement: A biological perspective

Science

Functional tuning of the nervous system with control of movement or maintenance of a steady posture. III. Mechanographic analysis of execution by man of the simplest motor tasks

Biophysics

The coordination of arm movements: An experimentally confirmed mathematical model

J. Neurosci.

Phase transitions in the human brain: Spatial mode dynamics

Int. J. Bifurc. Chaos

Higher order motor control

Annu. Rev. Neurosci.

Equilibrium-point control hypothesis examined by measured arm stiffness during multijoint movement

Science

Principles of Brain Functioning

A theoretical model of phase transitions in the human brain

Biol. Cybernet.

Regular Article
Theory of the Relation between Human Brain Activity (MEG) and Hand Movements