Neuromodulation is an emerging field of research that has a proven therapeutic benefit on a number of neurological disorders, including epilepsy and stroke. It is characterized by using exogenous stimulation to modify neural activity. Prior studies have shown the positive effect of non-invasive trigeminal nerve stimulation (TNS) on motor learning. However, few studies have explored the effect of this specific neuromodulatory method on the underlying physiological processes, including heart rate variability (HRV), facial skin temperatures, skin conductance level, and respiratory rate. Here we present preliminary results of the effects of 3kHz supraorbital TNS on HRV using non-linear (Poincaré plot descriptors) and time-domain (SDNN) measures of analysis. Twenty-one (21) healthy adult subjects were randomly assigned to 2 groups: 3kHz Active stimulation (n=11) and Sham (n=10). Participants’ physiological markers were monitored continuously across three blocks: one ten-minute baseline block, one twenty-minute treatment block, and one ten-minute recovery block. TNS targeting the ophthalmic branches of the trigeminal nerve was delivered during the treatment block for twenty minutes in 30 sec. ON/OFF cycles. The active stimulation group exhibited larger values of all Poincaré descriptors and SDNN during blocks two and three, signifying increased HRV and autonomic nervous system activity.

We describe a secondary analysis of an in vitro experiment that supports the capabilities of a relatively new imaging technique known as functional Magnetic Resonance Electrical Impedance Tomography (fMREIT) to detect conductivity changes in neural tissue caused by activity. Methods: Magnetic Resonance (MR) phase data of active Aplysia ganglia tissue in artificial seawater (ASW) were collected before and after exposure to an excitotoxin using two different imaging current strengths, and these data were then used to reconstruct conductivity changes throughout the tissue. Results: We found that increases in neural activity led to significant increases in imaged conductivity when using high imaging currents, but these differences in conductivity were not seen in regions that did not contain neural tissue nor in data where there were no differences in neural activity. Conclusion: We conclude that the analysis presented here supports fMREIT as a contrast technique capable of imaging neural activity in live tissue more directly than functional imaging methods such as BOLD fMRI.

Although significant progress has been made in understanding multisensory interactions at the behavioral level, their underlying neural mechanisms remain relatively poorly understood in cortical areas, particularly during the control of action. In recent experiments where animals reached to and actively maintained their arm position at multiple spatial locations while receiving either proprioceptive or visual-proprioceptive position feedback, multisensory interactions were shown to be associated with reduced spiking (i.e. subadditivity) as well as reduced intra-trial and across-trial spiking variability in the superior parietal lobule (SPL). To further explore the nature of such interaction-induced changes in spiking variability we quantified the spike train dynamics of 231 of these neurons. Neurons were classified as Poisson, bursty, refractory, or oscillatory (in the 13–30 Hz “beta-band”) based on their spike train power spectra and autocorrelograms. No neurons were classified as Poisson-like in either the proprioceptive or visual-proprioceptive conditions. Instead, oscillatory spiking was most commonly observed with many neurons exhibiting these oscillations under only one set of feedback conditions. The results suggest that the SPL may belong to a putative beta-synchronized network for arm position maintenance and that position estimation may be subserved by different subsets of neurons within this network depending on available sensory information. In addition, the nature of the observed spiking variability suggests that models of multisensory interactions in the SPL should account for both Poisson-like and non-Poisson variability.

Visual and somatosensory signals participate together in providing an estimate of the hand's spatial location. While the ability of subjects to identify the spatial location of their hand based on visual and proprioceptive signals has previously been characterized, relatively few studies have examined in detail the spatial structure of the proprioceptive map of the arm. Here, we reconstructed and analyzed the spatial structure of the estimation errors that resulted when subjects reported the location of their unseen hand across a 2D horizontal workspace. Hand position estimation was mapped under four conditions: with and without tactile feedback, and with the right and left hands. In the task, we moved each subject's hand to one of 100 targets in the workspace while their eyes were closed. Then, we either a) applied tactile stimulation to the fingertip by allowing the index finger to touch the target or b) as a control, hovered the fingertip 2 cm above the target. After returning the hand to a neutral position, subjects opened their eyes to verbally report where their fingertip had been. We measured and analyzed both the direction and magnitude of the resulting estimation errors. Tactile feedback reduced the magnitude of these estimation errors, but did not change their overall structure. In addition, the spatial structure of these errors was idiosyncratic: each subject had a unique pattern of errors that was stable between hands and over time. Finally, we found that at the population level the magnitude of the estimation errors had a characteristic distribution over the workspace: errors were smallest closer to the body. The stability of estimation errors across conditions and time suggests the brain constructs a proprioceptive map that is reliable, even if it is not necessarily accurate. The idiosyncrasy across subjects emphasizes that each individual constructs a map that is unique to their own experiences.