Steering Toward Normative Wide-Dynamic-Range Neuron Activity in Nerve-Injured Rats With Closed-Loop Peripheral Nerve Stimulation.

Objectives: Chronic pain is primarily treated with pharmaceuticals, but the effects remain unsatisfactory. A promising alternative therapy is peripheral nerve stimulation (PNS), but it has been associated with suboptimal efficacy because its modulation mechanisms are not clear and the current therapies are primarily open loop (ie, manually adjusting the stimulation parameters). In this study, we developed a proof-of-concept computational modeling as the first step toward implementing closed-loop PNS in future biological studies.

Predicting Wide-Dynamic Range Neuron Activity from Peripheral Nerve Stimulation using Linear Parameter Varying Models.

Neuromodulation treatments for chronic pain are programmed with limited knowledge of how electrical stimulation of nerve fibers affects the dynamic response of pain-processing neurons in the spinal cord and the brain. By modeling these effects with tractable representations, we may be able to improve efficacy of stimulation therapy. However, pain transmitting neurons in the dorsal horn of the spinal cord, the first pain relay station in the nervous system, have complex responses to peripheral nerve stimulation (PNS) with nonlinearities and history effects.

Towards Robust Control of PNS for Chronic Pain: Modeling Spinal Cord Wide-Dynamic Range Neurons with Structured Uncertainty.

Pain is a protective physiological system essential for survival. However, it can malfunction and create a debilitating disease known as chronic pain (CP), which is primarily treated with drugs that can produce negative side effects (e.g.

Modeling Responses to Peripheral Nerve Stimulation in the Dorsal Horn.

Pain is a protective physiological system essential for survival. However, it can malfunction and create a debilitating disease known as chronic pain (CP). CP is primarily treated with drugs that can have negative side effects (e.

Comparing brain connectivity metrics: a didactic tutorial with a toy model and experimental data.

Objective: The objective of this paper is to didactically compare resting state connectivity networks computed using two different methods called phase locking value (PLV) and convergent cross-mapping (CCM). PLV is a ubiquitous measure of connectivity in electrophysiological research but is less often applied to fMRI BOLD timeseries since this model-based metric assumes that oscillatory coupling is a sufficient condition for connectivity. Alternatively, CCM is a model-free method, which detects potentially nonlinear causal influences based on the ability to estimate one timeseries with another and does not assume an oscillatory structure.

The effect of binaural beats on verbal working memory and cortical connectivity.

Objective: Synchronization in activated regions of cortical networks affect the brain's frequency response, which has been associated with a wide range of states and abilities, including memory. A non-invasive method for manipulating cortical synchronization is binaural beats. Binaural beats take advantage of the brain's response to two pure tones, delivered independently to each ear, when those tones have a small frequency mismatch.

The Effect of Binaural Beats on Visuospatial Working Memory and Cortical Connectivity.

Binaural beats utilize a phenomenon that occurs within the cortex when two different frequencies are presented separately to each ear. This procedure produces a third phantom binaural beat, whose frequency is equal to the difference of the two presented tones and which can be manipulated for non-invasive brain stimulation. The effects of binaural beats on working memory, the system in control of temporary retention and online organization of thoughts for successful goal directed behavior, have not been well studied.