Sensory feedback-dependent coding of arm position in local field potentials of the posterior parietal cortex.

Although multisensory integration is crucial for sensorimotor function, it is unclear how visual and proprioceptive sensory cues are combined in the brain during motor behaviors. Here we characterized the effects of multisensory interactions on local field potential (LFP) activity obtained from the superior parietal lobule (SPL) as non-human primates performed a reaching task with either unimodal (proprioceptive) or bimodal (visual-proprioceptive) sensory feedback. Based on previous analyses of spiking activity, we hypothesized that evoked LFP responses would be tuned to arm location but would be suppressed on bimodal trials, relative to unimodal trials. We also expected to see a substantial number of recording sites with enhanced beta band spectral power for only one set of feedback conditions (e.g. unimodal or bimodal), as was previously observed for spiking activity. We found that evoked activity and beta band power were tuned to arm location at many individual sites, though this tuning often differed between unimodal and bimodal trials. Across the population, both evoked and beta activity were consistent with feedback-dependent tuning to arm location, while beta band activity also showed evidence of response suppression on bimodal trials. The results suggest that multisensory interactions can alter the tuning and gain of arm position-related LFP activity in the SPL.

Multisensory Interactions Influence Neuronal Spike Train Dynamics in the Posterior Parietal Cortex.

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.

Efficiency of visual feedback integration differs between dominant and non-dominant arms during a reaching task.

Recent studies have shown that patterns of endpoint variability following double-step reach sequences reflect the influence of both planning and execution-related processes, but are strongly dominated by noise associated with the online updating of movement plans based on visual feedback. However, it is currently unclear whether these results reflect the dominant arm/hemisphere's postulated specialization for visual feedback processing, or whether these effects reflect a more general "arm/hemisphere independent" preference for visual feedback in the control of reaching. To explore this, twelve subjects performed double-step reach sequences with their dominant and non-dominant arms to targets in 3D space with and without visual feedback of the arm. Variability was quantified using the volumes, aspect ratios, and orientations of 95% confidence ellipsoids fit to the distributions of reach endpoints. In consonance with previous findings, the availability of visual feedback resulted in ellipsoids that were significantly smaller, had greater aspect ratios, and were more aligned with the depth axis than those performed without visual feedback. Moreover, the effects of vision on aspect ratio and orientation were similar in magnitude for the dominant and non-dominant arms, suggesting that noise associated with planning and execution-related processes is managed in a similar way by the sensorimotor systems of each arm. However, the degree to which vision decreased ellipsoid volume was found to be significantly greater for the dominant arm. This suggests that the feedback control system of the dominant arm uses visual information more efficiently to control reaches to visual targets.

Estimating endogenous changes in task performance from EEG.

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.

Multimodal representation of limb endpoint position in the posterior parietal cortex.

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.

Contribution of execution noise to arm movement variability in three-dimensional space.

Reaching movements are subject to noise associated with planning and execution, but precisely how these noise sources interact to determine patterns of endpoint variability in three-dimensional space is not well understood. For frontal plane movements, variability is largest along the depth axis (the axis along which visual planning noise is greatest), with execution noise contributing to this variability along the movement direction. Here we tested whether these noise sources interact in a similar way for movements directed in depth. Subjects performed sequences of two movements from a single starting position to targets that were either both contained within a frontal plane ("frontal sequences") or where the first was within the frontal plane and the second was directed in depth ("depth sequences"). For both sequence types, movements were performed with or without visual feedback of the hand. When visual feedback was available, endpoint distributions for frontal and depth sequences were generally anisotropic, with the principal axes of variability being strongly aligned with the depth axis. Without visual feedback, endpoint distributions for frontal sequences were relatively isotropic and movement direction dependent, while those for depth sequences were similar to those with visual feedback. Overall, the results suggest that in the presence of visual feedback, endpoint variability is dominated by uncertainty associated with planning and updating visually guided movements. In addition, the results suggest that without visual feedback, increased uncertainty in hand position estimation effectively unmasks the effect of execution-related noise, resulting in patterns of endpoint variability that are highly movement direction dependent.

Interacting noise sources shape patterns of arm movement variability in three-dimensional space.

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.

Comparing the performance of three generations of ActiGraph accelerometers.

ActiGraph accelerometers are a useful tool for objective assessment of physical activity in clinical and epidemiological studies. Several generations of ActiGraph are being used; however, little work has been done to verify that measurements are consistent across generations. This study employed mechanical oscillations to characterize the dynamic response and intermonitor variability of three generations of ActiGraph monitors, from the oldest 7164 (n = 13), 71256 (n = 12), to the newest GT1M (n = 12). The response due to independent radius (22.1-60.4 mm) and frequency (25-250 rpm) changes were measured, as well as intermonitor variability within each generation. The 7164 and 71256 have similar relationships between activity counts and radius (P = 0.229) but were significantly different from the GT1M (P < 0.001). The frequency responses were nonlinear in all three generations. Although the response curve shapes were similar, the differences between generations at various frequencies were significant (P < 0.017), especially in the extremes of the measurement range. Intermonitor variability was markedly reduced in the GT1M compared with the 7164 and 71256. Other measurement differences between generations include decreased peak counts and decreased sensitivity in low-frequency detection in the GT1M. The results of this study revealed an improvement of the intermonitor variability by the GT1M monitor. However, the reduced sensitivity in low-count ranges in the GT1M may not be well suited for monitoring sedentary or light-intensity movements. Furthermore, the algorithms for energy expenditure predictions developed using older 7164 monitors may need to be modified for the GT1M.