Proprioceptive recalibration following implicit visuomotor adaptation is preserved in Parkinson's disease.

Individuals with Parkinson's disease (PD) and healthy adults demonstrate similar levels of visuomotor adaptation provided that the distortion is small or introduced gradually, and hence, implicit processes are engaged. Recently, implicit processes underlying visuomotor adaptation in healthy individuals have been proposed to include proprioceptive recalibration (i.e., shifts in one's proprioceptive sense of felt hand position to match the visual estimate of their hand experienced during reaches with altered visual feedback of the hand). In the current study, we asked if proprioceptive recalibration is preserved in PD patients. PD patients tested during their "off" and "on" medication states and age-matched healthy controls reached to visual targets, while visual feedback of their unseen hand was gradually rotated 30° clockwise or translated 4 cm rightwards of their actual hand trajectory. As expected, PD patients and controls produced significant reach aftereffects, indicating visuomotor adaptation after reaching with the gradually introduced visuomotor distortions. More importantly, following visuomotor adaptation, both patients and controls showed recalibration in hand position estimates, and the magnitude of this recalibration was comparable between PD patients and controls. No differences for any measures assessed were observed across medication status (i.e., PD off vs PD on). Results reveal that patients are able to adjust their sensorimotor mappings and recalibrate proprioception following adaptation to a gradually introduced visuomotor distortion, and that dopaminergic intervention does not affect this proprioceptive recalibration. These results suggest that proprioceptive recalibration does not involve striatal dopaminergic pathways and may contribute to the preserved visuomotor adaptation that arises implicitly in PD patients.

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI.

The effects of unilateral versus bilateral subthalamic nucleus deep brain stimulation on prosaccades and antisaccades in Parkinson's disease.

Unilateral deep brain stimulation (DBS) of the subthalamic nucleus (STN) in patients with Parkinson's disease improves skeletomotor function assessed clinically, and bilateral STN DBS improves motor function to a significantly greater extent. It is unknown whether unilateral STN DBS improves oculomotor function and whether bilateral STN DBS improves it to a greater extent. Further, it has also been shown that bilateral, but not unilateral, STN DBS is associated with some impaired cognitive-motor functions. The current study compared the effect of unilateral and bilateral STN DBS on sensorimotor and cognitive aspects of oculomotor control. Patients performed prosaccade and antisaccade tasks during no stimulation, unilateral stimulation, and bilateral stimulation. There were three sets of findings. First, for the prosaccade task, unilateral STN DBS had no effect on prosaccade latency and it reduced prosaccade gain; bilateral STN DBS reduced prosaccade latency and increased prosaccade gain. Second, for the antisaccade task, neither unilateral nor bilateral stimulation had an effect on antisaccade latency, unilateral STN DBS increased antisaccade gain, and bilateral STN DBS increased antisaccade gain to a greater extent. Third, bilateral STN DBS induced an increase in prosaccade errors in the antisaccade task. These findings suggest that while bilateral STN DBS benefits spatiotemporal aspects of oculomotor control, it may not be as beneficial for more complex cognitive aspects of oculomotor control. Our findings are discussed considering the strategic role the STN plays in modulating information in the basal ganglia oculomotor circuit.

A Correlation-Based Framework for Evaluating Postural Control Stochastic Dynamics.

The inability to maintain balance during varying postural control conditions can lead to falls, a significant cause of mortality and serious injury among older adults. However, our understanding of the underlying dynamical and stochastic processes in human postural control have not been fully explored. To further our understanding of the underlying dynamical processes, we examine a novel conceptual framework for studying human postural control using the center of pressure (COP) velocity autocorrelation function (COP-VAF) and compare its results to Stabilogram Diffusion Analysis (SDA). Eleven healthy young participants were studied under quiet unipedal or bipedal standing conditions with eyes either opened or closed. COP trajectories were analyzed using both the traditional posturographic measure SDA and the proposed COP-VAF. It is shown that the COP-VAF leads to repeatable, physiologically meaningful measures that distinguish postural control differences in unipedal versus bipedal stance trials with and without vision in healthy individuals. More specifically, both a unipedal stance and lack of visual feedback increased initial values of the COP-VAF, magnitude of the first minimum, and diffusion coefficient, particularly in contrast to bipedal stance trials with open eyes. Use of a stochastic postural control model, based on an Ornstein-Uhlenbeck process that accounts for natural weight-shifts, suggests an increase in spring constant and decreased damping coefficient when fitted to experimental data. This work suggests that we can further extend our understanding of the underlying mechanisms behind postural control in quiet stance under varying stance conditions using the COP-VAF and provides a tool for quantifying future neurorehabilitative interventions.

Prospective Optimization with Limited Resources.

The future is uncertain because some forthcoming events are unpredictable and also because our ability to foresee the myriad consequences of our own actions is limited. Here we studied how humans select actions under such extrinsic and intrinsic uncertainty, in view of an exponentially expanding number of prospects on a branching multivalued visual stimulus. A triangular grid of disks of different sizes scrolled down a touchscreen at a variable speed. The larger disks represented larger rewards. The task was to maximize the cumulative reward by touching one disk at a time in a rapid sequence, forming an upward path across the grid, while every step along the path constrained the part of the grid accessible in the future. This task captured some of the complexity of natural behavior in the risky and dynamic world, where ongoing decisions alter the landscape of future rewards. By comparing human behavior with behavior of ideal actors, we identified the strategies used by humans in terms of how far into the future they looked (their "depth of computation") and how often they attempted to incorporate new information about the future rewards (their "recalculation period"). We found that, for a given task difficulty, humans traded off their depth of computation for the recalculation period. The form of this tradeoff was consistent with a complete, brute-force exploration of all possible paths up to a resource-limited finite depth. A step-by-step analysis of the human behavior revealed that participants took into account very fine distinctions between the future rewards and that they abstained from some simple heuristics in assessment of the alternative paths, such as seeking only the largest disks or avoiding the smaller disks. The participants preferred to reduce their depth of computation or increase the recalculation period rather than sacrifice the precision of computation.

Delay differential analysis of electroencephalographic data.

We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.

Neurocognitive stages of spatial cognitive mapping measured during free exploration of a large-scale virtual environment.

Using a novel, fully mobile virtual reality paradigm, we investigated the EEG correlates of spatial representations formed during unsupervised exploration. On day 1, subjects implicitly learned the location of 39 objects by exploring a room and popping bubbles that hid the objects. On day 2, they again popped bubbles in the same environment. In most cases, the objects hidden underneath the bubbles were in the same place as on day 1. However, a varying third of them were misplaced in each block. Subjects indicated their certainty that the object was in the same location as the day before. Compared with bubble pops revealing correctly placed objects, bubble pops revealing misplaced objects evoked a decreased negativity starting at 145 ms, with scalp topography consistent with generation in medial parietal cortex. There was also an increased negativity starting at 515 ms to misplaced objects, with scalp topography consistent with generation in inferior temporal cortex. Additionally, misplaced objects elicited an increase in frontal midline theta power. These findings suggest that the successive neurocognitive stages of processing allocentric space may include an initial template matching, integration of the object within its spatial cognitive map, and memory recall, analogous to the processing negativity N400 and theta that support verbal cognitive maps in humans.

EEG activity during movement planning encodes upcoming peak speed and acceleration and improves the accuracy in predicting hand kinematics.

The relationship between movement kinematics and human brain activity is an important and fundamental question for the development of neural prosthesis. The peak velocity and the peak acceleration could best reflect the feedforward-type movement; thus, it is worthwhile to investigate them further. Most related studies focused on the correlation between kinematics and brain activity during the movement execution or imagery. However, human movement is the result of the motor planning phase as well as the execution phase and researchers have demonstrated that statistical correlations exist between EEG activity during the motor planning and the peak velocity and the peak acceleration using grand-average analysis. In this paper, we examined whether the correlations were concealed in trial-to-trial decoding from the low signal-to-noise ratio of EEG activity. The alpha and beta powers from the movement planning phase were combined with the alpha and beta powers from the movement execution phase to predict the peak tangential speed and acceleration. The results showed that EEG activity from the motor planning phase could also predict the peak speed and the peak acceleration with a reasonable accuracy. Furthermore, the decoding accuracy of the peak speed and the peak acceleration could both be improved by combining band powers from the motor planning phase with the band powers from the movement execution.

Motor output variability, deafferentation, and putative deficits in kinesthetic reafference in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder defined by motor impairments that include rigidity, systemic slowdown of movement (bradykinesia), postural problems, and tremor. While the progressive decline in motor output functions is well documented, less understood are impairments linked to the continuous kinesthetic sensation emerging from the flow of motions. There is growing evidence in recent years that kinesthetic problems are also part of the symptoms of PD, but objective methods to readily quantify continuously unfolding motions across different contexts have been lacking. Here we present evidence from a deafferented subject (IW) and a new statistical platform that enables new analyses of motor output variability measured as a continuous flow of kinesthetic reafferent input. Systematic increasing similarities between the patterns of motor output variability in IW and the participants with increasing degrees of PD severity suggest potential deficits in kinesthetic sensing in PD. We propose that these deficits may result from persistent, noisy, and random motor patterns as the disorder progresses. The stochastic signatures from the unfolding motions revealed levels of noise in the motor output fluctuations of these patients bound to decrease the kinesthetic signal's bandwidth. The results are interpreted in light of the concept of kinesthetic reafference ( Von Holst and Mittelstaedt, 1950). In this context, noisy motor output variability from voluntary movements in PD leads to a returning stream of noisy afference caused, in turn, by those faulty movements themselves. Faulty efferent output re-enters the CNS as corrupted sensory motor input. We find here that severity level in PD leads to the persistence of such patterns, thus bringing the statistical signatures of the subjects with PD systematically closer to those of the subject without proprioception.

Prospective Optimization.

Human performance approaches that of an ideal observer and optimal actor in some perceptual and motor tasks. These optimal abilities depend on the capacity of the cerebral cortex to store an immense amount of information and to flexibly make rapid decisions. However, behavior only approaches these limits after a long period of learning while the cerebral cortex interacts with the basal ganglia, an ancient part of the vertebrate brain that is responsible for learning sequences of actions directed toward achieving goals. Progress has been made in understanding the algorithms used by the brain during reinforcement learning, which is an online approximation of dynamic programming. Humans also make plans that depend on past experience by simulating different scenarios, which is called prospective optimization. The same brain structures in the cortex and basal ganglia that are active online during optimal behavior are also active offline during prospective optimization. The emergence of general principles and algorithms for goal-directed behavior has consequences for the development of autonomous devices in engineering applications.

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 ganglia's 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.

Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders.

Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological movement disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disorders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilitation of movement disorders.

An EEMD-IVA framework for concurrent multidimensional EEG and unidimensional kinematic data analysis.

Joint blind source separation (JBSS) is a means to extract common sources simultaneously found across multiple datasets, e.g., electroencephalogram (EEG) and kinematic data jointly recorded during reaching movements. Existing JBSS approaches are designed to handle multidimensional datasets, yet to our knowledge, there is no existing means to examine common components that may be found across a unidimensional dataset and a multidimensional one. In this paper, we propose a simple, yet effective method to achieve the goal of JBSS when concurrent multidimensional EEG and unidimensional kinematic datasets are available, by combining ensemble empirical mode decomposition (EEMD) with independent vector analysis (IVA). We demonstrate the performance of the proposed method through numerical simulations and application to data collected from reaching movements in Parkinson's disease. The proposed method is a promising JBSS tool for real-world biomedical signal processing applications.

Scaling and coordination deficits during dynamic object manipulation in Parkinson's disease.

The ability to reach for and dynamically manipulate objects in a dexterous fashion requires scaling and coordination of arm, hand, and fingertip forces during reach and grasp components of this behavior. The neural substrates underlying dynamic object manipulation are not well understood. Insight into the role of basal ganglia-thalamocortical circuits in object manipulation can come from the study of patients with Parkinson's disease (PD). We hypothesized that scaling and coordination aspects of motor control are differentially affected by this disorder. We asked 20 PD patients and 23 age-matched control subjects to reach for, grasp, and lift virtual objects along prescribed paths. The movements were subdivided into two types, intensive (scaling) and coordinative, by detecting their underlying self-similarity. PD patients off medication were significantly impaired relative to control subjects for both aspects of movement. Intensive deficits, reduced peak speed and aperture, were seen during the reach. Coordinative deficits were observed during the reach, namely, the relative position along the trajectory at which peak speed and aperture were achieved, and during the lift, when objects tilted with respect to the gravitational axis. These results suggest that basal ganglia-thalamocortical circuits may play an important role in fine motor coordination. Dopaminergic therapy significantly improved intensive but not coordinative aspects of movements. These findings are consistent with a framework in which tonic levels of dopamine in the dorsal striatum encode the energetic cost of a movement, thereby improving intensive or scaling aspects of movement. However, repletion of brain dopamine levels does not restore finely coordinated movement.

Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.

Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.

Spatiotemporal dynamics of online motor correction processing revealed by high-density electroencephalography.

The ability to control online motor corrections is key to dealing with unexpected changes arising in the environment with which we interact. How the CNS controls online motor corrections is poorly understood, but evidence has accumulated in favor of a submovement-based model in which apparently continuous movement is segmented into distinct submovements. Although most studies have focused on submovements' kinematic features, direct links with the underlying neural dynamics have not been extensively explored. This study sought to identify an electroencephalographic signature of submovements. We elicited kinematic submovements using a double-step displacement paradigm. Participants moved their wrist toward a target whose direction could shift mid-movement with a 50% probability. Movement kinematics and cortical activity were concurrently recorded with a low-friction robotic device and high-density electroencephalography. Analysis of spatiotemporal dynamics of brain activation and its correlation with movement kinematics showed that the production of each kinematic submovement was accompanied by (1) stereotyped topographic scalp maps and (2) frontoparietal ERPs time-locked to submovements. Positive ERP peaks from frontocentral areas contralateral to the moving wrist preceded kinematic submovement peaks by 220-250 msec and were followed by positive ERP peaks from contralateral parietal areas (140-250 msec latency, 0-80 msec before submovement peaks). Moreover, individual subject variability in the latency of frontoparietal ERP components following the target shift significantly predicted variability in the latency of the corrective submovement. Our results are in concordance with evidence for the intermittent nature of continuous movement and elucidate the timing and role of frontoparietal activations in the generation and control of corrective submovements.

Effect of visual feedback on the occipital-parietal-motor network in Parkinson's disease with freezing of gait.

Freezing of gait (FOG) is an elusive phenomenon that debilitates a large number of Parkinson's disease (PD) patients regardless of stage of disease, medication status, or deep brain stimulation implantation. Sensory feedback cues, especially visual feedback cues, have been shown to alleviate FOG episodes or even prevent episodes from occurring. Here, we examine cortical information flow between occipital, parietal, and motor areas during the pre-movement stage of gait in a PD-with-FOG patient that had a strong positive behavioral response to visual cues, one PD-with-FOG patient without any behavioral response to visual cues, and age-matched healthy controls, before and after training with visual feedback. Results for this case study show differences in cortical information flow between the responding PD-with-FOG patient and the other two subject types, notably, an increased information flow in the beta range. Tentatively suggesting the formation of an alternative cortical sensory-motor pathway during training with visual feedback, these results are proposed as subject for further verification employing larger cohorts of patients.

Unconstrained reaching modulates eye-hand coupling.

Eye­hand coordination is a crucial element of goal-directed movements. However, few studies have looked at the extent to which unconstrained movements of the eyes and hand made to targets influence each other. We studied human participants who moved either their eyes or both their eyes and hand to one of three static or flashed targets presented in 3D space. The eyes were directed, and hand was located at a common start position on either the right or left side of the body. We found that the velocity and scatter of memory-guided saccades (flashed targets) differed significantly when produced in combination with a reaching movement than when produced alone. Specifically, when accompanied by a reach, peak saccadic velocities were lower than when the eye moved alone. Peak saccade velocities, as well as latencies, were also highly correlated with those for reaching movements, especially for the briefly flashed targets compared to the continuous visible target. The scatter of saccade endpoints was greater when the saccades were produced with the reaching movement than when produced without, and the size of the scatter for both saccades and reaches was weakly correlated. These findings suggest that the saccades and reaches made to 3D targets are weakly to moderately coupled both temporally and spatially and that this is partly the result of the arm movement influencing the eye movement. Taken together, this study provides further evidence that the oculomotor and arm motor systems interact above and beyond any common target representations shared by the two motor systems.

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.