Investigating Therapies for Freezing of Gait Targeting the Cognitive, Limbic, and Sensorimotor Domains.

BACKGROUND: Freezing of gait (FOG) is arguably the most disabling motor symptom experienced with Parkinson's disease (PD), but treatments are extremely limited due to our poor understanding of the underlying mechanisms. Three cortical domains are postulated in recent research (ie, the cognitive, limbic, and sensorimotor domains), thus, treatments targeting these mechanisms of FOG may potentially be effective. Cognitive training, cognitive behavioral therapy (CBT, a well-known anxiety intervention), and proprioceptive training may address the cognitive, limbic, and sensorimotor domains, respectively. OBJECTIVE: To investigate whether these 3 treatments could improve functional outcomes of FOG. METHODS: In a single-blind, randomized crossover design, 15 individuals with PD and FOG were randomized into different, counterbalanced orders of receiving the interventions. Each consisted of eight 1-hour sessions, twice weekly for 4 weeks. FOG severity was assessed as the primary outcome using a novel gait paradigm that was aimed at evoking FOG when the cognitive, limbic, or sensorimotor domains were independently challenged. RESULTS: FOG severity significantly improved after the cognitive intervention, with strong trends toward improvement specifically in the baseline and cognitive-challenge assessment conditions. CBT, as the anxiety intervention, resulted in significantly worse FOG severity. In contrast, proprioceptive training significantly improved FOG severity, with consistent trends across all conditions. CONCLUSIONS: The cognitive and proprioceptive treatments appeared to improve different aspects of FOG. Thus, either of these interventions could potentially be a viable treatment for FOG. However, although the results were statistically significant, they could be sensitive to the relatively small number of participants in the study. Considering the significant results together with nonsignificant trends in both FOG and gait measures, and given equal time for each intervention, proprioceptive training produced the most consistent indications of benefits in this study. (clinicaltrials.gov NCT03065127).

Surrogate population models for large-scale neural simulations.

Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to multiscale modeling of neural systems that involves constructing efficient surrogate models of populations. Given a population of neuron models with correlated activity and with specific, nonrandom connections, a surrogate model is constructed in order to approximate the aggregate outputs of the population. The surrogate model requires less computation than the neural model, but it has a clear and specific relationship with the neural model. For example, approximate spike rasters for specific neurons can be derived from a simulation of the surrogate model. This article deals specifically with neural engineering framework (NEF) circuits of leaky-integrate-and-fire point neurons. Weighted sums of spikes are modeled by interpolating over latent variables in the population activity, and linear filters operate on gaussian random variables to approximate spike-related fluctuations. It is found that the surrogate models can often closely approximate network behavior with orders-of-magnitude reduction in computational demands, although there are certain systematic differences between the spiking and surrogate models. Since individual spikes are not modeled, some simulations can be performed with much longer steps sizes (e.g., 20 ms). Possible extensions to non-NEF networks and to more complex neuron models are discussed.

Modeling the shape hierarchy for visually guided grasping.

The monkey anterior intraparietal area (AIP) encodes visual information about three-dimensional object shape that is used to shape the hand for grasping. We modeled shape tuning in visual AIP neurons and its relationship with curvature and gradient information from the caudal intraparietal area (CIP). The main goal was to gain insight into the kinds of shape parameterizations that can account for AIP tuning and that are consistent with both the inputs to AIP and the role of AIP in grasping. We first experimented with superquadric shape parameters. We considered superquadrics because they occupy a role in robotics that is similar to AIP, in that superquadric fits are derived from visual input and used for grasp planning. We also experimented with an alternative shape parameterization that was based on an Isomap dimension reduction of spatial derivatives of depth (i.e., distance from the observer to the object surface). We considered an Isomap-based model because its parameters lacked discontinuities between similar shapes. When we matched the dimension of the Isomap to the number of superquadric parameters, the superquadric model fit the AIP data somewhat more closely. However, higher-dimensional Isomaps provided excellent fits. Also, we found that the Isomap parameters could be approximated much more accurately than superquadric parameters by feedforward neural networks with CIP-like inputs. We conclude that Isomaps, or perhaps alternative dimension reductions of visual inputs to AIP, provide a promising model of AIP electrophysiology data. Further work is needed to test whether such shape parameterizations actually provide an effective basis for grasp control.

Population coding in sparsely connected networks of noisy neurons.

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Decorrelation of spiking variability and improved information transfer through feedforward divisive normalization.

Response variability is often positively correlated in pairs of similarly tuned neurons in the visual cortex. Many authors have considered correlated variability to prevent postsynaptic neurons from averaging across large groups of inputs to obtain reliable stimulus estimates. However, a simple average of variability ignores nonlinearities in cortical signal integration. This study shows that feedforward divisive normalization of a neuron's inputs effectively decorrelates their variability. Furthermore, we show that optimal linear estimates of a stimulus parameter that are based on normalized inputs are more accurate than those based on nonnormalized inputs, due partly to reduced correlations, and that these estimates improve with increasing population size up to several thousand neurons. This suggests that neurons may possess a simple mechanism for substantially decorrelating noise in their inputs. Further work is needed to reconcile this conclusion with past evidence that correlated noise impairs visual perception.

Population models of temporal differentiation.

Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. Here we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. Examples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. This comparison is based on both analytical methods and numerical simulations with spiking leaky-integrate-and-fire (LIF) neurons. Both adapting and feedforward-network circuits provide good performance for signals with frequency bands that are well matched to the time constants of postsynaptic current decay and adaptation, respectively. The synaptic depression circuit performs similarly to the adaptation circuit, although strictly speaking, precisely linear differentiation based on synaptic depression is not possible, because depression scales synaptic weights multiplicatively. Feedback circuits introduce greater errors than functionally equivalent feedforward circuits, but they have the useful property that their dynamics are determined by feedback strength. For this reason, these circuits are better suited for calculating the derivatives of signals that evolve on timescales outside the range of membrane dynamics and, possibly, for providing the wide range of timescales needed for precise fractional-order differentiation.

Online mutability of step direction during rapid stepping reactions evoked by postural perturbation.

Stepping reactions are often triggered rapidly in response to loss of balance. It has been unclear whether spatial step parameters are defined at time of step-initiation or whether they can be modulated online, during step execution, in response to sensory feedback about the evolving state of instability. This study explored the capacity to actively alter step direction subsequent to step initiation in six healthy young-adult subjects. To elicit forward-step reactions, subjects were released suddenly from a tethered forward lean. A second perturbation (medio-lateral support-surface translation) was applied at lags of 0-200 ms. Active reaction to the second perturbation was determined primarily through analysis of swing-leg hip-abductor activation. In addition, to gauge the biomechanical consequence of the changes in muscle activation, we compared the measured medio-lateral swing-foot displacement to that predicted by a simple passive mechanical model. Perturbations at 0-100 ms lag evoked active medio-lateral swing-foot deviation, allowing balance to be recovered with a single step. However, when the second perturbation occurred near foot-off (200-ms lag), there was no evidence of active alteration of step direction and subjects typically required additional steps to recover balance. The results suggest that step direction can be reparameterized during early stages of stepping reactions, but that step direction was not actively modulated in response to perturbation arising near start of swing phase.