Plasticity of Sensorimotor Networks: Multiple Overlapping Mechanisms

Abstract

Redundancy is an important feature of the motor system, as abundant degrees of freedom are prominent at every level of organization across the central and peripheral nervous systems, and musculoskeletal system. This basic feature results in a system that is both flexible and robust, and which can be sustainably adapted through plasticity mechanisms in response to intrinsic organismal changes and dynamic environments. While much early work of motor system organization has focused on synaptic-based plasticity processes that are driven via experience, recent investigations of neuron-glia interactions, epigenetic mechanisms and large-scale network dynamics have revealed a plethora of plasticity mechanisms that support motor system organization across multiple, overlapping spatial and temporal scales. Furthermore, an important role of these mechanisms is the regulation of intrinsic variability. Here, we review several of these mechanisms and discuss their potential role in neurorehabilitation.

Keywords: dynamical systems; mechanisms; neurorehabilitation; plasticity; sensorimotor.

Figure 1

Dynamical system attractors and motor skills. A schematic representation of a network state-space made up of three individual neurons. A leftward and rightward goal-directed reach is defined by two distinct attractor manifolds within the network state-space. The system must adopt states within these subspaces to execute either reach behavior. In this example, the evolution of delay-period preparatory activity is represented as a trajectory for two different instructed rightward reaches (Trial 1 vs. Trial 2). Individual trial differences between the state-space trajectory during preparation, as well as differences between where the system settles within the attractor subspace can be related to behavioral features for a given instance of the action (i.e., reaction time, movement time, reach error, etc). Figure modified from Shenoy and others (2013).

Figure 2

Schematic of plasticity mechanisms in motor control along spatiotemporal scales. This figure depicts the spatiotemporal properties of various plasticity mechanisms along a continuum with small spatial scales and short temporal scales at the left/bottom and larger spatial scales and longer temporal scales on the right/top. Overlapping cellular-level (in purple, red and orange), epigenetic (blue), and systems-level (green and yellow) mechanisms are distributed throughout these scales.

Figure 3

Modular flexibility and learning. (A) Modular flexibility increases significantly during early learning of a novel motor skill (Sessions 1 to 2), and the decreases as performance gains plateau (Sessions 2 to 3). (B) The change in flexibility occurring between Sessions 1 and 2, and Sessions 2 and 3 is highly predictive of the learning exhibited in the latter session. This suggests that greater modular flexibility is indicative of high learning potential in the system. Figure modified from Bassett and others (2011).

Figure 4

(A, B) Learning curve comparisons for subgroups determined by task-relevant variability observed during baseline trials of a force-field adaptation task. There is a consistent and direct relationship between baseline task-relevant variability and initial learning rate across all subgroups. (C) This relationship holds at the single-subject level as well, where task-relevant variability observed during the baseline period is predictive of the initial learning rate observed during the first ten trials of the training period for the force-field adaptation task (error bars indicate ± standard error of the mean; **P > 0.005, *P > 0.05). Figure modified from Wu and others (2014).

Figure 5

Targeted clinical methods may induce plasticity and restore damaged neural networks. Plasticity may be induced in the injured brain via controlled and/or enriched task training environments (e.g., through virtual reality (VR), or gaming), noninvasive brain stimulation to modulate brain activity (e.g., repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), transcranial random noise stimulation (tRNS), and transcranial alternating current stimulation (tACS)), or real-time biofeedback, which allows individuals to learn to control their own neural network activity (e.g., using neuroimaging signals from electromyography (EMG), electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), or functional magnetic resonance imaging (fMRI)). Greater understanding of how each of these techniques interact with one or more plasticity mechanisms will allow for a directed, individualized approach.