Camilo Libedinsky

Sleep deprivation alters valuation signals in the ventromedial prefrontal cortex.

Even a single night of total sleep deprivation (SD) can have dramatic effects on economic decision making. Here we tested the novel hypothesis that SD influences economic decisions by altering the valuation process. Using functional magnetic resonance imaging we identified value signals related to the anticipation and the experience of monetary and social rewards (attractive female faces). We then derived decision value signals that were predictive of each participant’s willingness to exchange money for brief views of attractive faces in an independent market task. Strikingly, SD altered decision value signals in ventromedial prefrontal cortex (VMPFC) in proportion to the corresponding change in economic preferences. These changes in preference were independent of the effects of SD on attention and vigilance. Our results provide novel evidence that signals in VMPFC track the current state of the individual, and thus reflect not static but constructed preferences.

Sleep deprivation alters effort discounting but not delay discounting of monetary rewards.

STUDY OBJECTIVES To determine whether sleep deprivation would affect the discounting of delayed rewards, of rewards entailing the expense of effort, or both. DESIGN We measured rates of two types of reward discounting under conditions of rested wakefulness (RW) and sleep deprivation (SD). Delay discounting was defined as the willingness to accept smaller monetary rewards sooner rather than larger monetary rewards later. Effort discounting was defined as the willingness to accept smaller rewards that require less effort to obtain (e.g., typing a small number of letter strings backward) over larger but more effortful rewards (e.g., typing more letter strings to receive the reward). The first two experiments used a crossover design in which one session was conducted after a normal night of sleep (RW), and the other after a night without sleep (SD). The first experiment evaluated only temporal discounting whereas the second evaluated temporal and effort discounting. In the second experiment, the discounting tasks were repeatedly administered prior to the state comparisons to minimize the effects of order and/or repeated testing. In a third experiment, participants were studied only once in a between-subject evaluation of discounting across states. SETTING The study took place in a research laboratory. PARTICIPANTS Seventy-seven healthy young adult participants: 20 in Experiment 1, 27 in Experiment 2, and 30 in Experiment 3. INTERVENTIONS N/A. MEASUREMENTS AND RESULTS Sleep deprivation elicited increased effort discounting but did not affect delay discounting. CONCLUSIONS The dissociable effects of sleep deprivation on two forms of discounting behavior suggest that they may have differing underlying neural mechanisms.

Separate and overlapping brain areas encode subjective value during delay and effort discounting.

Making decisions about rewards that involve delay or effort requires the integration of value and cost information. The brain areas recruited in this integration have been well characterized for delay discounting. However only a few studies have investigated how effort costs are integrated into value signals to eventually determine choice. In contrast to previous studies that have evaluated fMRI signals related to physical effort, we used a task that focused on cognitive effort. Participants discounted the value of delayed and effortful rewards. The value of cognitively effortful rewards was represented in the anterior portion of the inferior frontal gyrus and dorsolateral prefrontal cortex. Additionally, the value of the chosen option was encoded in the anterior cingulate cortex, caudate, and cerebellum. While most brain regions showed no significant dissociation between effort discounting and delay discounting, the ACC was significantly more activated in effort compared to delay discounting tasks. Finally, overlapping regions within the right orbitofrontal cortex and lateral temporal and parietal cortices encoded the value of the chosen option during both delay and effort discounting tasks. These results indicate that encoding of rewards discounted by cognitive effort and delay involves partially dissociable brain areas, but a common representation of chosen value is present in the orbitofrontal, temporal and parietal cortices.

Mixed selectivity morphs population codes in prefrontal cortex

The prefrontal cortex maintains working memory information in the presence of distracting stimuli. It has long been thought that sustained activity in individual neurons or groups of neurons was responsible for maintaining information in the form of a persistent, stable code. Here we show that, upon the presentation of a distractor, information in the lateral prefrontal cortex was reorganized into a different pattern of activity to create a morphed stable code without losing information. In contrast, the code in the frontal eye fields persisted across different delay periods but exhibited substantial instability and information loss after the presentation of a distractor. We found that neurons with mixed-selective responses were necessary and sufficient for the morphing of code and that these neurons were more abundant in the lateral prefrontal cortex than the frontal eye fields. This suggests that mixed selectivity provides populations with code-morphing capability, a property that may underlie cognitive flexibility.Neurons in the lateral prefrontal cortex (but not the frontal eye fields) appear to maintain working memory information when disrupted by a transient distractor, not by using an immutable persistent code but by morphing from one persistent code to another. This code-morphing may provide the lateral prefrontal cortex with cognitive flexibility.

Independent Mobility Achieved through a Wireless Brain-Machine Interface

Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform. We found that free-running brain control of the platform (which was not equipped with any machine intelligence) was fast and accurate, resembling the performance achieved using joystick control. The decoding algorithms can be trained in the absence of joystick movements, as would be required for use by tetraplegic individuals, demonstrating that the non-human primate model is a good pre-clinical model for developing such a cortically-controlled movement prosthetic. Interestingly, we found that the response properties of some neurons differed greatly depending on the mode of control (joystick or brain control), suggesting different roles for these neurons in encoding movement intention and movement execution. These results demonstrate that independent mobility can be achieved without first training on prescribed motor movements, opening the door for the implementation of this technology in persons with tetraplegia.

Neural representations of movement intentions during brain-controlled self-motion

Using a brain-machine interface (BMI), a non-human primate (NHP) was trained to control a mobile robotic platform in real time using spike activity from the motor cortex, enabling self-motion through brain-control. The decoding model was initially trained using neural signals recorded when the NHP controlled the platform using a joystick. Using this decoding model, we compared the performance of the BMI during brain control with and without the use of a dummy joystick, and found that the success ratio dropped by 40% and time taken increased by 45% when the dummy joystick was removed. Performance during full brain control was only restored after a recalibration of the decoding model. We aimed to understand the differences in the underlying neural representations of movement intentions with and without the use of a dummy joystick, and showed that there were significant changes in both directional tuning, as well as global firing rates. These results indicate that the strategies used by the NHP for self-motion were different depending on whether a dummy joystick was present. We propose that a recalibration of the decoding model is an important step during the implementation of a BMI system for self-motion.

Use of wavelet transform coefficients for spike detection for a Robust Intracortical Brain Machine Interface

A common problem in Brain-Machine Interface (BMI) is the variations in neural signals over time, leading to significant decrease in decoding performance if the decoder is not re-trained. However, frequent re-training is not practical in real use case. In our work, we found that a temporally more robust system may be achieved through the use of wavelet transform in feature extraction. We used wavelet transform coefficients as means to detect spikes in neural recordings, in contrast to conventional amplitude threshold methods. Using offline data as the preliminary testbed, we showed that decoding based on firing rates determined from four levels of wavelet transform decomposition resulted in a decoder with 6–12% improvement in accuracy sustained over four weeks after training. This strategy suggests that wavelet transform coefficients for spike detection may be more temporally robust as features for decoding, and offers a good starting point for further improvements to tackle nonstationarities in BMI.

Adaptive decoding using local field potentials in a brain-machine interface

Brain-machine interface (BMI) systems have the potential to restore function to people who suffer from paralysis due to a spinal cord injury. However, in order to achieve long-term use, BMI systems have to overcome two challenges - signal degeneration over time, and non-stationarity of signals. Effects of loss in spike signals over time can be mitigated by using local field potential (LFP) signals for decoding, and a solution to address the signal non-stationarity is to use adaptive methods for periodic recalibration of the decoding model. We implemented a BMI system in a nonhuman primate model that allows brain-controlled movement of a robotic platform. Using this system, we showed that LFP signals alone can be used for decoding in a closed-loop brain-controlled BMI. Further, we performed offline analysis to assess the potential implementation of an adaptive decoding method that does not presume knowledge of the target location. Our results show that with periodic signal and channel selection adaptation, decoding accuracy using LFP alone can be improved by between 5-50%. These results demonstrate the feasibility of implementing unsupervised adaptive methods during asynchronous decoding of LFP signals for long-term usage in a BMI system.

Classification of phases of hand grasp task by the extraction of miniature compound nerve action potentials (mCNAPs)

Interfacing with the nervous system to restore functional motor activity is a promising therapy to augment the classical surgical approaches to treating peripheral nerve injuries. Despite the advances in electrode microelectronics engineering, the challenge of extracting information from injured nerves to help restore motor function remains unsolved. Here we used waveform feature extraction and clustering techniques to identify a discrete set of events in intraneural recordings of the median nerve in a non-human primate (NHP) during grasping tasks. This analysis allowed the classification of the different phases of hand grasping. The waveform features were found to be significantly different for each phase of grasping. Since these waveforms can be seen as the minimal signal components that result from the activation of a group of nerve fibers, we denominated them miniature compound nerve action potentials (mCNAPs). The correlation between mCNAPs and the different stages of movement can be utilized in the near future to design high-performance neuroprosthetic therapies.