IntroductionThe normal transition to sleep is characterized by a reduction in higher frequency activity and an increase in lower frequency activity in frontal brain regions. In sleep onset insomnia these changes in activity are weaker and may prolong the transition to sleep.MethodsUsing a wearable device, we compared 30min of short duration repetitive transcranial electric stimulation (SDR-tES) at 0.75Hz, prior to going to bed, with an active control at 25Hz in the same individuals.ResultsTreatment with 0.75Hz significantly reduced sleep onset latency (SOL) by 53% when compared with pre-treatment baselines and was also significantly more effective than stimulation with 25Hz which reduced SOL by 30%. Reductions in SOL with 25Hz stimulation displayed order effects suggesting the possibility of placebo. No order effects were observed with 0.75Hz stimulation. The decrease in SOL with 0.75Hz treatment was proportional to an individual’s baseline wherein those suffering from the longest pre-treated SOLs realized the greatest benefits. Changes in SOL were correlated with left/right frontal EEG signal coherence around the stimulation frequency, providing a possible mechanism and target for more focused treatment. Stimulation at both frequencies also decreased perceptions of insomnia symptoms measured with the Insomnia Severity Index, and comorbid anxiety measured with the State Trait Anxiety Index.DiscussionOur study identifies a new potential treatment for sleep onset insomnia that is comparably effective to current state-of-practice options including pharmacotherapy and cognitive behavioral therapy and is safe, effective, and can be delivered in the home.

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.

OBJECTIVE Although it is known that intersurgeon variability in offering elective surgery can have major consequences for patient morbidity and healthcare spending, data addressing variability within neurosurgery are scarce. The authors performed a prospective peer review study of randomly selected neurosurgery cases in order to assess the extent of consensus regarding the decision to offer elective surgery among attending neurosurgeons across one large academic institution. METHODS All consecutive patients who had undergone standard inpatient surgical interventions of 1 of 4 types (craniotomy for tumor [CFT], nonacute redo CFT, first-time spine surgery with/without instrumentation, and nonacute redo spine surgery with/without instrumentation) during the period 2015-2017 were retrospectively enrolled (n = 9156 patient surgeries, n = 80 randomly selected individual cases, n = 20 index cases of each type randomly selected for review). The selected cases were scored by attending neurosurgeons using a need for surgery (NFS) score based on clinical data (patient demographics, preoperative notes, radiology reports, and operative notes; n = 616 independent case reviews). Attending neurosurgeon reviewers were blinded as to performing provider and surgical outcome. Aggregate NFS scores across various categories were measured. The authors employed a repeated-measures mixed ANOVA model with autoregressive variance structure to compute omnibus statistical tests across the various surgery types. Interrater reliability (IRR) was measured using Cohen's kappa based on binary NFS scores. RESULTS Overall, the authors found that most of the neurosurgical procedures studied were rated as "indicated" by blinded attending neurosurgeons (mean NFS = 88.3, all p values < 0.001) with greater agreement among neurosurgeon raters than expected by chance (IRR = 81.78%, p = 0.016). Redo surgery had lower NFS scores and IRR scores than first-time surgery, both for craniotomy and spine surgery (ANOVA, all p values < 0.01). Spine surgeries with fusion had lower NFS scores than spine surgeries without fusion procedures (p < 0.01). CONCLUSIONS There was general agreement among neurosurgeons in terms of indication for surgery; however, revision surgery of all types and spine surgery with fusion procedures had the lowest amount of decision consensus. These results should guide efforts aimed at reducing unnecessary variability in surgical practice with the goal of effective allocation of healthcare resources to advance the value paradigm in neurosurgery.

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.

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.

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.

Recent studies have shown that sensory stimulation can optimize memory consolidation in the sleeping brain for simple lab-based tasks. Based on these earlier results, we developed a closed-loop auditory stimulation (CLAS) system to more precisely target sensory stimulation and test whether this method can improve the more complex skill of spatial navigation in an urban environment. Forty participants (Mean age = 26.2 years, F = 18) were trained to navigate within a large and detailed urban environment in virtual reality. Participants first learned the environment by freely navigating to specific points of interest (24 unique landmarks) in a virtual city. As participants navigated, they encountered different auditory cues (e.g., the barking of a dog near a park) associated with areas in the environment. Following learning, all participants underwent a 90-min polysomnographically-recorded nap, with half the participants receiving CLAS. CLAS detects slow oscillations during non-rapid eye movement (NREM) sleep using a minimum negative threshold criterion coupled with online automated sleep staging, to trigger delivery of short (700 ms) auditory cues during the down-state to up-state transition (DUPT). After sleeping, participants were tested in 6 of the previously trained routes. Compared with controls, the CLAS treated group was significantly faster in post-nap navigation (p<0.01). Additionally, CLAS participants showed an increase in DUPT phase-locked spindle activity in both the slow (9–12 Hz) and fast (12–16 Hz) frequency bands. No effect of the CLAS on sleep architecture was observed. CLAS successfully improves the complex task of navigation in a virtual environment without any negative effects on sleep architecture. This work was supported by DARPA award W911NF-16-2-007. Disclaimer: The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

Transcranial electrical stimulation (TES) can take the form of direct current stimulation (TDCS), alternating current stimulation (TACS), or random noise stimulation (TRNS). Several recent reviews document the safety and efficacy of TES in healthy and in clinical populations [1,2,3]. The location of TDCS, TACS, and TRNS paradigms are largely informed by neuroimaging and the brain lesion literature. Electroencephalography (EEG) and magnetoencephalography (MEG) provide additional information that informs the frequency of TACS, while the principle of reciprocity [5] with EEG can inform the amplitude and location of TES. Advanced TES techniques, vary the amplitude of stimulation at various sites on the scalp in attempts to make TES more focal [4]. The ability to vary frequency, amplitude, and location of TES allows customized stimulation for different tasks. However, variation in brain activity includes not only frequency, amplitude, and location but also latency and duration. Combinations of these variable are unexplored in TES.

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.

Introduction: It is becoming increasingly common to use experimental paradigms that utilize synchronous eyetracking and electroencephalographic (EEG) measures to explore the neural processes underlying visual search. In these paradigms, fixation related potentials (FRPs) are used to quantify early and late components of visual processing following the onset of a fixation. However, FRPs often contain a mixture of bottom up (e.g. sensory input from the stimulus) and top down (e.g. saccade planning) processes in addition to electrooculography (EoG) artifacts and unrelated neural activity. In this study we sought to isolate the neural sources of target detection in the presence of eye movements and concurrent task demands.

Combining simultaneous recordings of electroencephalography (EEG) and eye-tracking provides a powerful method to evaluate the neural mechanisms of vision by isolating fixation-related potentials (FRPs), evoked EEG activity during periods of eye fixation. This approach provides a means to evaluate visual information processing without imposing the constraint of central eye fixation commonly required in many event-related potential (ERP) studies. The earliest and most prominent brain potential following fixation onset is commonly referred to as the lambda response and reflects the afferent flow of visual information at fixation to visual cortex. The FRP-based lambda response is affected by low level visual features similar to the P1 ERP; however, when evoked by the same stimulus, the lambda response generally peaks earlier and has a larger amplitude compared to the P1 suggesting a difference in the underlying mechanisms. While many studies have evaluated the spatial and temporal properties of the P1 ERP, few studies have investigated these properties in the lambda response. Here we utilized independent component analysis (ICA) to separate the neural sources underlying the fixation-related lambda response obtained during a guided visual search task. We found that the lambda response in the FRP consists of both early and late source components with central occipital and lateral occipital topology respectively. These results suggest that the lambda potential may be generated by independent but temporally overlapping sources in visual cortex. Meeting abstract presented at VSS 2016

Combining electroencephalography (EEG) and eye-tracking provides a means to evaluate neural mechanisms of vision without imposing central eye fixation constraints by isolating fixation-related potentials (FRPs). The earliest and most prominent potential following fixation onset is commonly referred to as the lambda response, and similar to the visual-evoked P1 ERP, it reflects the afferent flow of information to visual cortex (Kazai & Yagi, 2003; Thickbroom et al., 1991); yet when evoked by the same stimulus, the lambda response generally has an earlier peak latency and a larger amplitude compared to the P1 event-related potential (ERP) suggesting a difference in the underlying mechanisms. Studies have shown that the P1 ERP consists of both early and late neural sources (Di Russo et al., 2002); however, it is unknown if similar sources exist for the lambda response. We used independent component analysis (ICA) to separate the neural sources underlying the fixation-related lambda response. The results showed that the lambda response consists of both early and late source components similar to the P1 ERP; however, they occur earlier and are larger in magnitude suggesting influences from extraretinal mechanisms and potentially different source loci.

Recording synchronous data from EEG and eye-tracking provides a unique methodological approach for measuring the sensory and cognitive processes of overt visual search. Using this approach we obtained fixation related potentials (FRPs) during a guided visual search task specifically focusing on the lambda and P3 components. An outstanding question is whether the lambda and P3 FRP components are influenced by concurrent task demands. We addressed this question by obtaining simultaneous eye-movement and electroencephalographic (EEG) measures during a guided visual search task while parametrically modulating working memory load using an auditory N-back task. Participants performed the guided search task alone, while ignoring binaurally presented digits, or while using the auditory information in a 0, 1, or 2-back task. The results showed increased reaction time and decreased accuracy in both the visual search and N-back tasks as a function of auditory load. Moreover, high auditory task demands increased the P3 but not the lambda latency while the amplitude of both lambda and P3 was reduced during high auditory task demands. The results show that both early and late stages of visual processing indexed by FRPs are significantly affected by concurrent task demands imposed by auditory working memory.

Eye-fixations elicit a neural response commonly referred to as the lambda potential that is similar to the visually-evoked P1 event-related potential (ERP) component. An outstanding question is whether the lambda potential is influenced by concurrent auditory task demands. To address this question we obtained simultaneous eye-movement and electroencephalographic (EEG) measures during a guided visual search task while parametrically modulating working memory load using an auditory N-back task. Eye fixations were guided across a grid of letters consisting of 'L's (non-targets) at random orientations. Participants were instructed to make a button press when they fixated on the infrequent target letter 'T'. Participants performed the guided fixation task alone, while ignoring binaurally presented digits, or while using the auditory information in a 0, 1, or 2-back task. Our results show that reaction time increased and accuracy decreased in both the visual search task and auditory N-back task as a function of working memory load. Moreover, we found evidence that the amplitude of the fixation-related potentials were affected by auditory working memory demands. These results suggest that active engagement in an auditory task can influence early stages of visual processing and negatively affect visual search performance. The data provide support for a link between cross-modal processing resources. Meeting abstract presented at VSS 2015.