Rothney MP, Apker GA, Song Y, Chen KY. Comparing the performance of three generations of ActiGraph accelerometers.

Understanding the neural representation of limb position is important for comprehending the control of limb movements and the maintenance of body schema, as well as for the development of neuroprosthetic systems designed to replace lost limb function. Multiple subcortical and cortical areas contribute to this representation, but its multimodal basis has largely been ignored. Regarding the parietal cortex, previous results suggest that visual information about arm position is not strongly represented in area 5, although these results were obtained under conditions in which animals were not using their arms to interact with objects in their environment, which could have affected the relative weighting of relevant sensory signals. Here we examined the multimodal basis of limb position in the superior parietal lobule (SPL) as monkeys reached to and actively maintained their arm position at multiple locations in a frontal plane. On half of the trials both visual and nonvisual feedback of the endpoint of the arm were available, while on the other trials visual feedback was withheld. Many neurons were tuned to arm position, while a smaller number were modulated by the presence/absence of visual feedback. Visual modulation generally took the form of a decrease in both firing rate and variability with limb vision and was associated with more accurate decoding of position at the population level under these conditions. These findings support a multimodal representation of limb endpoint position in the SPL but suggest that visual signals are relatively weakly represented in this area, and only at the population level.

Brain wave activity is known to correlate with decrements in behavioral performance as individuals enter states of fatigue, boredom, or low alertness.Many BCI technologies are adversely affected by these changes in user state, limiting their application and constraining their use to relatively short temporal epochs where behavioral performance is likely to be stable. Incorporating a passive BCI that detects when the user is performing poorly at a primary task, and adapts accordingly may prove to increase overall user performance. Here, we explore the potential for extending an established method to generate continuous estimates of behavioral performance from ongoing neural activity; evaluating the extended method by applying it to the original task domain, simulated driving; and generalizing the method by applying it to a BCI-relevant perceptual discrimination task. Specifically, we used EEG log power spectra and sequential forward floating selection (SFFS) to estimate endogenous changes in behavior in both a simulated driving task and a perceptual discrimination task. For the driving task the average correlation coefficient between the actual and estimated lane deviation was 0.37 ± 0.22 (μ ± σ). For the perceptual discrimination task we generated estimates of accuracy, reaction time, and button press duration for each participant. The correlation coefficients between the actual and estimated behavior were similar for these three metrics (accuracy = 0.25 ± 0.37, reaction time = 0.33 ± 0.23, button press duration = 0.36 ± 0.30). These findings illustrate the potential for modeling time-on-task decrements in performance from concurrent measures of neural activity.

Reaching movements are subject to noise in both the planning and execution phases of movement production. The interaction of these noise sources during natural movements is not well understood, despite its importance for understanding movement variability in neurologically intact and impaired individuals. Here we examined the interaction of planning and execution related noise during the production of unconstrained reaching movements. Subjects performed sequences of two movements to targets arranged in three vertical planes separated in depth. The starting position for each sequence was also varied in depth with the target plane; thus required movement sequences were largely contained within the vertical plane of the targets. Each final target in a sequence was approached from two different directions, and these movements were made with or without visual feedback of the moving hand. These combined aspects of the design allowed us to probe the interaction of execution and planning related noise with respect to reach endpoint variability. In agreement with previous studies, we found that reach endpoint distributions were highly anisotropic. The principal axes of movement variability were largely aligned with the depth axis, i.e., the axis along which visual planning related noise would be expected to dominate, and were not generally well aligned with the direction of the movement vector. Our results suggest that visual planning-related noise plays a dominant role in determining anisotropic patterns of endpoint variability in three-dimensional space, with execution noise adding to this variability in a movement direction-dependent manner.

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.