Joseph Snider

Learning where to look for a hidden target

Survival depends on successfully foraging for food, for which evolution has selected diverse behaviors in different species. Humans forage not only for food, but also for information. We decide where to look over 170,000 times per day, approximately three times per wakeful second. The frequency of these saccadic eye movements belies the complexity underlying each individual choice. Experience factors into the choice of where to look and can be invoked to rapidly redirect gaze in a context and task-appropriate manner. However, remarkably little is known about how individuals learn to direct their gaze given the current context and task. We designed a task in which participants search a novel scene for a target whose location was drawn stochastically on each trial from a fixed prior distribution. The target was invisible on a blank screen, and the participants were rewarded when they fixated the hidden target location. In just a few trials, participants rapidly found the hidden targets by looking near previously rewarded locations and avoiding previously unrewarded locations. Learning trajectories were well characterized by a simple reinforcement-learning (RL) model that maintained and continually updated a reward map of locations. The RL model made further predictions concerning sensitivity to recent experience that were confirmed by the data. The asymptotic performance of both the participants and the RL model approached optimal performance characterized by an ideal-observer theory. These two complementary levels of explanation show how experience in a novel environment drives visual search in humans and may extend to other forms of search such as animal foraging.

Simultaneous Neural and Movement Recording in Large-Scale Immersive Virtual Environments

Virtual reality (VR) allows precise control and manipulation of rich, dynamic stimuli that, when coupled with on-line motion capture and neural monitoring, can provide a powerful means both of understanding brain behavioral relations in the high dimensional world and of assessing and treating a variety of neural disorders. Here we present a system that combines state-of-the-art, fully immersive, 3D, multi-modal VR with temporally aligned electroencephalographic (EEG) recordings. The VR system is dynamic and interactive across visual, auditory, and haptic interactions, providing sight, sound, touch, and force. Crucially, it does so with simultaneous EEG recordings while subjects actively move about a 20×20 ft2 space. The overall end-to-end latency between real movement and its simulated movement in the VR is approximately 40 ms. Spatial precision of the various devices is on the order of millimeters. The temporal alignment with the neural recordings is accurate to within approximately 1 ms. This powerful combination of systems opens up a new window into brain-behavioral relations and a new means of assessment and rehabilitation of individuals with motor and other disorders.

Resting-state fMRI activity predicts unsupervised learning and memory in an immersive virtual reality environment

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglias role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment.

EEG Gamma Band Oscillations Differentiate the Planning of Spatially Directed Movements of the Arm Versus Eye: Multivariate Empirical Mode Decomposition Analysis

The neural dynamics underlying the coordination of spatially-directed limb and eye movements in humans is not well understood. Part of the difficulty has been a lack of signal processing tools suitable for the analysis of nonstationary electroencephalographic (EEG) signals. Here, we use multivariate empirical mode decomposition (MEMD), a data-driven approach that does not employ predefined basis functions. High-density EEG, and arm and eye movements were synchronously recorded in 10 subjects performing time-constrained reaching and/or eye movements. Subjects were allowed to move both the hand and the eyes, only the hand, or only the eyes following a 500-700 ms delay interval where the hand and gaze remained on a central fixation cross. An additional condition involved a nonspatially-directed “lift” movement of the hand. The neural activity during a 500 ms delay interval was decomposed into intrinsic mode functions (IMFs) using MEMD. Classification analysis revealed that gamma band (30 Hz <;) IMFs produced more classifiable features differentiating the EEG according to the different upcoming movements. A benchmark test using conventional algorithms demonstrated that MEMD was the best algorithm for extracting oscillatory bands from EEG, yielding the best classification of the different movement conditions. The gamma rhythm decomposed using MEMD showed a higher correlation with the eventual movement accuracy than any other band rhythm and than any other algorithm.

Human Cortical θ during Free Exploration Encodes Space and Predicts Subsequent Memory

Spatial representations and walking speed in rodents are consistently related to the phase, frequency, and/or amplitude of θ rhythms in hippocampal local field potentials. However, neuropsychological studies in humans have emphasized the importance of parietal cortex for spatial navigation, and efforts to identify the electrophysiological signs of spatial navigation in humans have been stymied by the difficulty of recording during free exploration of complex environments. We resolved the recording problem and experimentally probed brain activity of human participants who were fully ambulant. On each of 2 d, electroencephalography was synchronized with head and body movement in 13 subjects freely navigating an extended virtual environment containing numerous unique objects. θ phase and amplitude recorded over parietal cortex were consistent when subjects walked through a particular spatial separation at widely separated times. This spatial displacement θ autocorrelation (STAcc) was quantified and found to be significant from 2 to 8 Hz within the environment. Similar autocorrelation analyses performed on an electrooculographic channel, used to measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as the source of STAcc. Strikingly, the strength of an individuals STAcc maps from day 1 significantly predicted object location recall success on day 2. θ was also significantly correlated with walking speed; however, this correlation appeared unrelated to STAcc and did not predict memory performance. This is the first demonstration of memory-related, spatial maps in humans generated during active spatial exploration.

Dopamine function and the efficiency of human movement

To sustain successful behavior in dynamic environments, active organisms must be able to learn from the consequences of their actions and predict action outcomes. One of the most important discoveries in systems neuroscience over the last 15 years has been about the key role of the neurotransmitter dopamine in mediating such active behavior. Dopamine cell firing was found to encode differences between the expected and obtained outcomes of actions. Although activity of dopamine cells does not specify movements themselves, a recent study in humans has suggested that tonic levels of dopamine in the dorsal striatum may in part enable normal movement by encoding sensitivity to the energy cost of a movement, providing an implicit “motor motivational” signal for movement. We investigated the motivational hypothesis of dopamine by studying motor performance of patients with Parkinson disease who have marked dopamine depletion in the dorsal striatum and compared their performance with that of elderly healthy adults. All participants performed rapid sequential movements to visual targets associated with different risk and different energy costs, countered or assisted by gravity. In conditions of low energy cost, patients performed surprisingly well, similar to prescriptions of an ideal planner and healthy participants. As energy costs increased, however, performance of patients with Parkinson disease dropped markedly below the prescriptions for action by an ideal planner and below performance of healthy elderly participants. The results indicate that the ability for efficient planning depends on the energy cost of action and that the effect of energy cost on action is mediated by dopamine.

Parkinson’s disease patients show impaired corrective grasp control and eye–hand coupling when reaching to grasp virtual objects

The effect of Parkinsons disease (PD) on hand-eye coordination and corrective response control during reach-to-grasp tasks remains unclear. Moderately impaired PD patients (n=9) and age-matched controls (n=12) reached to and grasped a virtual rectangular object, with haptic feedback provided to the thumb and index fingertip by two 3-degree of freedom manipulanda. The object rotated unexpectedly on a minority of trials, requiring subjects to adjust their grasp aperture. On half the trials, visual feedback of finger positions disappeared during the initial phase of the reach, when feedforward mechanisms are known to guide movement. PD patients were tested without (OFF) and with (ON) medication to investigate the effects of dopamine depletion and repletion on eye-hand coordination online corrective response control. We quantified eye-hand coordination by monitoring hand kinematics and eye position during the reach. We hypothesized that if the basal ganglia are important for eye-hand coordination and online corrections to object perturbations, then PD patients tested OFF medication would show reduced eye-hand spans and impoverished arm-hand coordination responses to the perturbation, which would be further exasperated when visual feedback of the hand was removed. Strikingly, PD patients tracked their hands with their gaze, and their movements became destabilized when having to make online corrective responses to object perturbations exhibiting pauses and changes in movement direction. These impairments largely remained even when tested in the ON state, despite significant improvement on the Unified Parkinsons Disease Rating Scale. Our findings suggest that basal ganglia-cortical loops are essential for mediating eye-hand coordination and adaptive online responses for reach-to-grasp movements, and that restoration of tonic levels of dopamine may not be adequate to remediate this coordinative nature of basal ganglia-modulated function.

Causal analysis of cortical networks involved in reaching to spatial targets.

The planning of goal-directed movement towards targets in different parts of space is an important function of the brain. Such visuo-motor planning and execution is known to involve multiple brain regions, including visual, parietal, and frontal cortices. To understand how these brain regions work together to both plan and execute goal-directed movement, it is essential to describe the dynamic causal interactions among them. Here we model causal interactions of distributed cortical source activity derived from non-invasively recorded EEG, using a combination of ICA, minimum-norm distributed source localization (cLORETA), and dynamical modeling within the Source Information Flow Toolbox (SIFT). We differentiate network causal connectivity of reach planning and execution, by comparing the causal network in a speeded reaching task with that for a control task not requiring goal-directed movement. Analysis of a pilot dataset (n=5) shows the utility of this technique and reveals increased connectivity between visual, motor and frontal brain regions during reach planning, together with decreased cross-hemisphere visual coupling during planning and execution, possibly related to task demands.

REACHING TO PROPRIOCEPTIVELY DEFINED TARGETS IN PARKINSON’S DISEASE: EFFECTS OF DEEP BRAIN STIMULATION THERAPY

Deep brain stimulation of the subthalamic nucleus (STN DBS) provides a unique window into human brain function since it can reversibly alter the functioning of specific brain circuits. Basal ganglia-cortical circuits are thought to be excessively noisy in patients with Parkinsons disease (PD), based in part on the lack of specificity of proprioceptive signals in basal ganglia-thalamic-cortical circuits in monkey models of the disease. PD patients are known to have deficits in proprioception, but the effects are often subtle, with paradigms typically restricted to one or two joint movements in a plane. Moreover, the effects of STN DBS on proprioception are virtually unexplored. We tested the following hypotheses: first, that PD patients will show substantial deficits in unconstrained, multi-joint proprioception, and, second, that STN DBS will improve multi-joint proprioception. Twelve PD patients with bilaterally implanted electrodes in the subthalamic nucleus and 12 age-matched healthy subjects were asked to position the left hand at a location that was proprioceptively defined in 3D space with the right hand. In a second condition, subjects were provided visual feedback during the task so that they were not forced to rely on proprioception. Overall, with STN DBS switched off, PD patients showed significantly larger proprioceptive localization errors, and greater variability in endpoint localizations than the control subjects. Visual feedback partially normalized PD performance, and demonstrated that the errors in proprioceptive localization were not simply due to a difficulty in executing the movements or in remembering target locations. Switching STN DBS on significantly reduced localization errors from those of control subjects when patients moved without visual feedback relative to when they moved with visual feedback (when proprioception was not required). However, this reduction in localization errors without vision came at the cost of increased localization variability.

Optimal random search for a single hidden target.

A single target is hidden at a location chosen from a predetermined probability distribution. Then, a searcher must find a second probability distribution from which random search points are sampled such that the target is found in the minimum number of trials. Here it will be shown that if the searcher must get very close to the target to find it, then the best search distribution is proportional to the square root of the target distribution regardless of dimension. For a Gaussian target distribution, the optimum search distribution is approximately a Gaussian with a standard deviation that varies inversely with how close the searcher must be to the target to find it. For a network where the searcher randomly samples nodes and looks for the fixed target along edges, the optimum is either to sample a node with probability proportional to the square root of the out-degree plus 1 or not to do so at all.