PMv Neuronal Firing May Be Driven by a Movement Command Trajectory within Multidimensional Gaussian Fields

The premotor cortex (PM) is known to be a site of visuo-somatosensory integration for the production of movement. We sought to better understand the ventral PM (PMv) by modeling its signal encoding in greater detail. Neuronal firing data was obtained from 110 PMv neurons in two male rhesus macaques executing four reach-grasp-manipulate tasks. We found that in the large majority of neurons (∼90%) the firing patterns across the four tasks could be explained by assuming that a high-dimensional position/configuration trajectory-like signal evolving ∼250 ms before movement was encoded within a multidimensional Gaussian field (MGF). Our findings are consistent with the possibility that PMv neurons process a visually specified reference command for the intended arm/hand position trajectory with respect to a proprioceptively or visually sensed initial configuration. The estimated MGF were (hyper) disc-like, such that each neuron's firing modulated strongly only with commands that evolved along a single direction within position/configuration space. Thus, many neurons appeared to be tuned to slices of this input signal space that as a collection appeared to well cover the space. The MGF encoding models appear to be consistent with the arm-referent, bell-shaped, visual target tuning curves and target selectivity patterns observed in PMV visual-motor neurons. These findings suggest that PMv may implement a lookup table-like mechanism that helps translate intended movement trajectory into time-varying patterns of activation in motor cortex and spinal cord. MGFs provide an improved nonlinear framework for potentially decoding visually specified, intended multijoint arm/hand trajectories well in advance of movement

Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc

Guest Editorial

AFTER the tremendous clinical success of the cochlear implant over the last 20 years, neuroprosthetic systems are now being developed and applied for the blind. First results on implanted epiretinal arrays in humans are becoming available now and lead to clear suggestions of how to improve electrode design, device characteristics, and implant procedures. Besides implants in humans and animals, research on in vitro neuronal network systems is progressively expanding. Interesting combinations of multi-electrode array devices with microfluidic systems will allow pharmacological control of networks in a very precise way. Several papers in this Special Issue of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING are related to various neural neuroprosthetic systems. This Special Issue is a collective effort by active researchers who specialize in the field of neural engineering, and we hope it will provide a rich resource with regard to the state-of-the-art of neural engineering research

Brain interaction during cooperation: Evaluating local properties of multiple-brain network

Subjects’ interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects’ activities, due to high workload tendencies, were less coordinated

HemoSYS: A Toolkit for Image-based Systems Biology of Tumor Hemodynamics

Abnormal tumor hemodynamics are a critical determinant of a tumor’s microenvironment (TME), and profoundly affect drug delivery, therapeutic efficacy and the emergence of drug and radio-resistance. Since multiple hemodynamic variables can simultaneously exhibit transient and spatiotemporally heterogeneous behavior, there is an exigent need for analysis tools that employ multiple variables to characterize the anomalous hemodynamics within the TME. To address this, we developed a new toolkit called HemoSYS for quantifying the hemodynamic landscape within angiogenic microenvironments. It employs multivariable time-series data such as in vivo tumor blood flow (BF), blood volume (BV) and intravascular oxygen saturation (Hbsat) acquired concurrently using a wide-field multicontrast optical imaging system. The HemoSYS toolkit consists of propagation, clustering, coupling, perturbation and Fourier analysis modules. We demonstrate the utility of each module for characterizing the in vivo hemodynamic landscape of an orthotropic breast cancer model. With HemoSYS, we successfully described: (i) the propagation dynamics of acute hypoxia; (ii) the initiation and dissolution of distinct hemodynamic niches; (iii) tumor blood flow regulation via local vasomotion; (iv) the hemodynamic response to a systemic perturbation with carbogen gas; and (v) frequency domain analysis of hemodynamic heterogeneity in the TME. HemoSYS (freely downloadable via the internet) enables vascular phenotyping from multicontrast in vivo optical imaging data. Its modular design also enables characterization of non-tumor hemodynamics (e.g. brain), other preclinical disease models (e.g. stroke), vascular-targeted therapeutics, and hemodynamic data from other imaging modalities (e.g. MRI)