Activation of primate frontal eye fields with a CMUT phased array system.

BACKGROUND: There are pushes toward non-invasive stimulation of neural tissues to prevent issues that arise from invasive brain recordings and stimulation. Transcranial Focused Ultrasound (TFUS) has been examined as a way to stimulate non-invasively, but previous studies have limitations in the application of TFUS. As a result, refinement is needed to improve stimulation results. NEW METHOD: We utilized a custom-built capacitive micromachined ultrasonic transducer (CMUT) that would send ultrasonic waves through skin and skull to targets located in the Frontal Eye Fields (FEF) region triangulated from co-registered MRI and CT scans while a non-human primate subject was performing a discrimination behavioral task. RESULTS: We observed that the stimulation immediately caused changes in the local field potential (LFP) signal that continued until stimulation ended, at which point there was higher voltage upon the cue for the animal to saccade. This co-incided with increases in activity in the alpha band during stimulation. The activity rebounded mid-way through our electrode-shank, indicating a specific point of stimulation along the shank. We observed different LFP signals for different stimulation targets, indicating the ability to"steer" the stimulation through the transducer. We also observed a bias in first saccades towards the opposite direction. CONCLUSIONS: In conclusion, we provide a new approach for non-invasive stimulation during performance of a behavioral task. With the ability to steer stimulation patterns and target using a large amount of transducers, the ability to provide non-invasive stimulation will be greatly improved for future clinical and research applications.

Uncertainty-based modulation for lifelong learning.

The creation of machine learning algorithms for intelligent agents capable of continuous, lifelong learning is a critical objective for algorithms being deployed on real-life systems in dynamic environments. Here we present an algorithm inspired by neuromodulatory mechanisms in the human brain that integrates and expands upon Stephen Grossberg's ground-breaking Adaptive Resonance Theory proposals. Specifically, it builds on the concept of uncertainty, and employs a series of "neuromodulatory" mechanisms to enable continuous learning, including self-supervised and one-shot learning. Algorithm components were evaluated in a series of benchmark experiments that demonstrate stable learning without catastrophic forgetting. We also demonstrate the critical role of developing these systems in a closed-loop manner where the environment and the agent's behaviors constrain and guide the learning process. To this end, we integrated the algorithm into an embodied simulated drone agent. The experiments show that the algorithm is capable of continuous learning of new tasks and under changed conditions with high classification accuracy (>94%) in a virtual environment, without catastrophic forgetting. The algorithm accepts high dimensional inputs from any state-of-the-art detection and feature extraction algorithms, making it a flexible addition to existing systems. We also describe future development efforts focused on imbuing the algorithm with mechanisms to seek out new knowledge as well as employ a broader range of neuromodulatory processes.

Short Duration Repetitive Transcranial Electrical Stimulation During Sleep Enhances Declarative Memory of Facts.

Transcranial electrical stimulation (tES) during sleep has been shown to successfully modulate memory consolidation. Here, we tested the effect of short duration repetitive tES (SDR-tES) during a daytime nap on the consolidation of declarative memory of facts in healthy individuals. We use a previously described approach to deliver the stimulation at regular intervals during non-rapid eye movement (NREM) sleep, specifically stage NREM2 and NREM3. Similar to previous studies using tES, we find enhanced memory performance compared to sham both after sleep and 48 h later. We also observed an increase in the proportion of time spent in NREM3 sleep and SDR-tES boosted the overall rate of slow oscillations (SOs) during NREM2/NREM3 sleep. Retrospective investigation of brain activity immediately preceding stimulation suggests that increases in the SO rate are more likely when stimulation is delivered during quiescent and asynchronous periods of activity in contrast to other closed-loop approaches which target phasic stimulation during ongoing SOs.

Closed-Loop Targeted Memory Reactivation during Sleep Improves Spatial Navigation.

Sounds associated with newly learned information that are replayed during non-rapid eye movement (NREM) sleep can improve recall in simple tasks. The mechanism for this improvement is presumed to be reactivation of the newly learned memory during sleep when consolidation takes place. We have developed an EEG-based closed-loop system to precisely deliver sensory stimulation at the time of down-state to up-state transitions during NREM sleep. Here, we demonstrate that applying this technology to participants performing a realistic navigation task in virtual reality results in a significant improvement in navigation efficiency after sleep that is accompanied by increases in the spectral power especially in the fast (12-15 Hz) sleep spindle band. Our results show promise for the application of sleep-based interventions to drive improvement in real-world tasks.

Isolating Discriminant Neural Activity in the Presence of Eye Movements and Concurrent Task Demands.

A growing number of studies use the combination of eye-tracking and electroencephalographic (EEG) measures to explore the neural processes that underlie visual perception. In these studies, fixation-related potentials (FRPs) are commonly used to quantify early and late stages of visual processing that follow the onset of each fixation. However, FRPs reflect a mixture of bottom-up (sensory-driven) and top-down (goal-directed) processes, in addition to eye movement artifacts and unrelated neural activity. At present there is little consensus on how to separate this evoked response into its constituent elements. In this study we sought to isolate the neural sources of target detection in the presence of eye movements and over a range of concurrent task demands. Here, participants were asked to identify visual targets (Ts) amongst a grid of distractor stimuli (Ls), while simultaneously performing an auditory N-back task. To identify the discriminant activity, we used independent components analysis (ICA) for the separation of EEG into neural and non-neural sources. We then further separated the neural sources, using a modified measure-projection approach, into six regions of interest (ROIs): occipital, fusiform, temporal, parietal, cingulate, and frontal cortices. Using activity from these ROIs, we identified target from non-target fixations in all participants at a level similar to other state-of-the-art classification techniques. Importantly, we isolated the time course and spectral features of this discriminant activity in each ROI. In addition, we were able to quantify the effect of cognitive load on both fixation-locked potential and classification performance across regions. Together, our results show the utility of a measure-projection approach for separating task-relevant neural activity into meaningful ROIs within more complex contexts that include eye movements.

Correlates of perceptual learning in an oculomotor decision variable.

In subjects trained extensively to indicate a perceptual decision with an action, neural commands that generate the action can represent the process of forming the decision. However, it is unknown whether this representation requires overtraining or reflects a more general link between perceptual and motor processing. We examined how perceptual processing is represented in motor commands in naive monkeys being trained on a demanding perceptual task, as they first establish the sensory-motor association and then learn to form more accurate perceptual judgments. The task required the monkeys to decide the direction of random-dot motion and respond with an eye movement to one of two visual targets. Using electrically evoked saccades, we examined oculomotor commands that developed during motion viewing. Throughout training, these commands tended to reflect both the subsequent binary choice of saccade target and the weighing of graded motion evidence used to arrive at that choice. Moreover, these decision-related oculomotor signals, along with the time needed to initiate the voluntary saccadic response, changed steadily as training progressed, approximately matching concomitant improvements in behavioral sensitivity to the motion stimulus. Thus, motor circuits may have general access to perceptual processing used to select between actions, even without extensive training. The results also suggest a novel candidate mechanism for some forms of perceptual learning, in which the brain learns rapidly to treat a perceptual decision as a problem of action selection and then over time to use sensory input more effectively to guide the selection process.

The effects of ketamine vary among inbred mouse strains and mimic schizophrenia for the P80, but not P20 or N40 auditory ERP components.

N-methyl-D-aspartate (NMDA) antagonists produce behavioral and electrophysiological effects similar to schizophrenia. The mouse P20, N40, and P80 event related potential (ERP) components were analyzed for genetic variance among inbred strains and ketamine-induced differences to model abnormalities in the P50, N100, and P200 in schizophrenia. Ketamine increased P20/N40 amplitude and decreased P80 amplitude. Therefore, the effects of ketamine in mice are inconsistent with alterations in the corresponding P50 and N100 in schizophrenia, suggesting that NMDA receptor dysfunction may not underlie abnormalities of these components in schizophrenia. However, the effects of ketamine on the mouse P80 were consistent with P200 ERP changes in schizophrenia and support the hypothesis that NMDA dysfunction may contribute to some neuronal abnormalities in schizophrenia. The current study lays the groundwork for defining the role of NMDA-mediated transmission for specific aspects of neuronal processing that vary with genetic background. Future studies could use transcription profiling to clarify such interactions between genetic background, specific neuronal circuits, and transmitter systems.

Inhibition of auditory evoked potentials and prepulse inhibition of startle in DBA/2J and DBA/2Hsd inbred mouse substrains.

Previous data have shown differences among inbred mouse strains in sensory gating of auditory evoked potentials, prepulse inhibition (PPI) of startle, and startle amplitude. These measures of sensory and sensorimotor gating have both been proposed as models for genetic determinants of sensory processing abnormalities in patients with schizophrenia and their first-degree relatives. Data from our laboratory suggest that auditory evoked potentials of DBA/2J mice differ from those previously described for DBA/2Hsd. Therefore, we compared evoked potentials and PPI in these two closely related substrains based on the hypothesis that any observed endophenotypic differences are more likely to distinguish relevant from incidental genetic heterogeneity than similar approaches using inbred strains that vary across the entire genome. We found that DBA/2Hsd substrain exhibited reduced inhibition of evoked potentials and reduced startle relative to the DBA/2J substrain without alterations in auditory sensitivity, amplitude of evoked potentials or PPI of startle. These results suggest that gating of auditory evoked potentials and PPI of startle measure different aspects of neuronal function. The differences between the substrains might reflect genetic drift. Alternatively, differences could arise from different rearing environments or other non-genetic factors. Future studies will attempt to determine the cause of these differences in sensory and sensorimotor processing between these two closely related inbred mouse strains.