Oscillatory correlates of threat imminence during virtual navigation.

The Predatory Imminence Continuum Theory proposes that defensive behaviors depend on the proximity of a threat. While the neural mechanisms underlying this proposal are well studied in animal models, it remains poorly understood in humans. To address this issue, we recorded EEG from 24 (15 female) young adults engaged in a first-person virtual reality Risk-Reward interaction task. On each trial, participants were placed in a virtual room and presented with either a threat or reward conditioned stimulus (CS) in the same room location (proximal) or different room location (distal). Behaviorally, all participants learned to avoid the threat-CS, with most using the optimal behavior to actively avoid the proximal threat-CS (88% accuracy) and passively avoid the distal threat-CS (69% accuracy). Similarly, participants learned to actively approach the distal reward-CS (82% accuracy) and to remain passive to the proximal reward-CS (72% accuracy). At an electrophysiological level, we observed a general increase in theta power (4-8 Hz) over the right posterior channel P8 across all conditions, with the proximal threat-CS evoking the largest theta response. By contrast, distal cues induced two bursts of gamma (30-60 Hz) power over midline-parietal channel Pz (200 msec post-cue) and right frontal channel Fp2 (300 msec post-cue). Interestingly, the first burst of gamma power was sensitive to the distal threat-CS and the second burst at channel Fp2 was sensitive to the distal reward-CS. Together, these findings demonstrate that oscillatory processes differentiate between the spatial proximity information during threat and reward encoding, likely optimizing the selection of the appropriate behavioral response.

Recording neural reward signals in the real-world using mobile-EEG and augmented reality.

The electrophysiological response to rewards recorded during laboratory-based tasks has been well documented over the past two decades, yet little is known about the neural response patterns in 'real-world' settings. To address this issue, we combined a mobile-EEG system with an augmented reality headset (which blends high definition "holograms" within the real-world) to record event-related brain potentials (ERP) while participants navigated an operant chamber to find rewards. 25 participants (age = 18-43, Male=6, Female=19) were asked to choose between two floating holograms marking a west or east goal-location in a large room, and once participants reached the goal location, the hologram would turn into a reward (5 cents) or no-reward (0 cents) cue. Following the feedback cue, participants were required to return to a hologram marking the start location, and once standing in it, a 3 second counter hologram would initiate the next trial. This sequence was repeated until participants completed 200 trials. Consistent with previous research, reward feedback evoked the reward positivity, an ERP component believed to index the sensitivity of the anterior cingulate cortex to reward prediction error signals. The reward positivity peaked around 235ms post-feedback with a maximal at channel FCz (M=-2.60µV, SD=1.73µV) and was significantly different than zero (p < 0.01). At a behavioral level, participants took approximately 3.38 seconds to reach the goal-location and exhibited a general lose-shift (68.3% ± 3.5) response strategy and were slightly slower to return to the start location following negative feedback (2.43 sec) compared to positive feedback (2.38 sec), evidence of post-error slowing. Overall, these findings provide the first evidence that combining mobile-EEG with augmented reality technology is a feasible solution to enhance the ecological validity of human electrophysiological studies of goal-directed behavior and a step towards a new era of human cognitive neuroscience research that blurs the line between laboratory and reality.

Scalp recorded theta activity is modulated by reward, direction, and speed during virtual navigation in freely moving humans.

Theta oscillations (~ 4-12 Hz) are dynamically modulated by speed and direction in freely moving animals. However, due to the paucity of electrophysiological recordings of freely moving humans, this mechanism remains poorly understood. Here, we combined mobile-EEG with fully immersive virtual-reality to investigate theta dynamics in 22 healthy adults (aged 18-29 years old) freely navigating a T-maze to find rewards. Our results revealed three dynamic periods of theta modulation: (1) theta power increases coincided with the participants' decision-making period; (2) theta power increased for fast and leftward trials as subjects approached the goal location; and (3) feedback onset evoked two phase-locked theta bursts over the right temporal and frontal-midline channels. These results suggest that recording scalp EEG in freely moving humans navigating a simple virtual T-maze can be utilized as a powerful translational model by which to map theta dynamics during "real-life" goal-directed behavior in both health and disease.

Modeling electrophysiological measures of decision-making and performance monitoring in neurotypical children engaging in a speeded flanker task.

This study aims to use structural equation modeling (SEM) to investigate the role of error processing in behavioral adaptation in children by testing relationships between error-related and stimulus-related event-related potentials (ERPs) obtained from two sessions of a speeded Eriksen flanker task. First, path models of averaged ERP components and mean response times (N1 â†’ P2 â†’ N2 â†’ P3 â†’ RTs) while controlling for trait effects, age, and sex, on each was examined separately for correct and incorrect trials from each session. While the model demonstrated acceptable fit statistics, the four models yielded diverse results. Next, path models for correct and incorrect trials were tested using latent variables defined by factoring together respective measures of ERP component amplitudes from each session. Comparison of correct and incorrect models revealed significant differences in the relationships between the successive measures of neural processing after controlling for trait effects. Moreover, latent variable models controlling for both trait and session-specific state variables yielded excellent model fit while models without session-specific state variables did not. In the final model, the error-related neural activity (i.e., the ERN and Pe) from incorrect trials was found to significantly relate to the stream of neural processes contributing to trials with the correct behavior. Importantly, the relationship between RT and error detection in the final model signifies a brain-and-behavior feedback loop. These findings provided empirical evidence that supports the adaptive orienting theory of error processing by demonstrating how the neural signals of error processing influence behavioral adaptations that facilitate correct behavioral performance.

Recovery of reward function in problematic substance users using a combination of robotics, electrophysiology, and TMS.

BACKGROUND: Theoretical and empirical work suggest that addictive drugs potentiate dopaminergic reinforcement learning signals and disrupt the reward function of its neural targets, including the anterior midcingulate cortex (aMCC) and the basal ganglia. Here, we aim to use prefrontal 10-Hz TMS to enhance aMCC reward activity and reward learning by the basal ganglia in problematic substance users. METHODS: 22 problematic substance users were randomized into an Active and SHAM (coil flipped) TMS group. We recorded the reward positivity-an electrophysiological signal believed to index sensitivity of the aMCC to rewards-while participants engaged in 4 blocks (100 trials per block) of a reward-based choice task. A robotic arm positioned a TMS coil over a prefrontal cortex target, and 50 pulses were delivered at 10-Hz before every 10 trials of blocks 2-4 (1500 pulses, 400 trials). Participants then completed a decision-making task that is diagnostic of striatal dopamine dysfunction. RESULTS: The present study revealed three main findings. First, both groups failed to elicit a reward positivity during the first two task blocks. Second, applying robot-assisted TMS enhanced the amplitude of the reward positivity in the Active group, but not the SHAM group, across the last two task blocks. Third, the Active group performed relatively better at reward-based learning than the SHAM group. CONCLUSION: These results demonstrate that 10-Hz TMS is successful in modulating the reward function of the aMCC and basal ganglia in problematic substance users, which may have utility in the treatment of reward-related neural dysfunction commonly associated with substance use disorders.

Beyond the Motor Cortex: Theta Burst Stimulation of the Anterior Midcingulate Cortex.

BACKGROUND: While the facilitatory and inhibitory effects of intermittent theta burst stimulation (iTBS) and continuous TBS (cTBS) protocols have been well documented on motor physiology, the action of TBS protocols on prefrontal functioning remain unclear. Here we asked whether iTBS or cTBS can differentially modulate reward-related signaling in the anterior midcingulate cortex (aMCC). METHODS: Across 2 experiments, we used a robot-assisted transcranial magnetic stimulation system, combined with electroencephalogram recordings, to investigate the aftereffects of prefrontal iTBS and cTBS on the reward positivity, an electrophysiological signal believed to index sensitivity of the aMCC to rewards. Twenty adults (age, 18-28 years) participated in experiment 1 in which we used a scalp landmark for TBS targeting, and 14 adults (age, 18-28 years) participated in experiment 2, in which we aimed to increase TBS effectiveness by utilizing cortical thickness maps to select individualized dorsal lateral prefrontal cortex targets. RESULTS: We demonstrated that prefrontal iTBS suppressed reward-related signaling in the aMCC (reduction in reward positivity) and caused a decrease in postfeedback switch choices. cTBS displayed no effect. We replicated and strengthened this effect on the reward positivity by targeting dorsal lateral prefrontal cortex regions displaying maximal cortical thickness. CONCLUSIONS: While these results are inconsistent with reported TBS effects on motor cortex, the present findings offer a novel transcranial magnetic stimulation targeting approach and normative insights into the magnitude and time course of TBS-induced changes in aMCC excitability. By modulating how the aMCC links value to goal-directed behavior, this research opens an exciting new era of investigative possibilities in the understanding of aMCC function and treatment of aMCC dysfunction.

Test-Retest Reliability of Electroencephalographic Measures of Performance Monitoring in Children and Adults.

This study examined the test-retest reliability of the error-related negativity (ERN) and error positivity (Pe) amplitudes using a Flanker task in 118 neurotypical children and 53 adults before and after latency jitter adjustments. The reliability of the ERN and Pe amplitudes was moderate for children and moderate to strong for adults. The latency variability adjustment did not improve the reliability of the ERN and Pe amplitudes for either group, suggesting that latency variability may be a trait-like measure. For comparison purposes, the reliability of the stimulus-locked ERPs was strong for correct trials, yet the reliability was weak for incorrect trials.

Sensory Processing and Attention Profiles Among Children With Sensory Processing Disorders and Autism Spectrum Disorders.

This study explores the differences in the profile of relationships between sensory processing and attention abilities among children with sensory processing disorder (SPD), autism spectrum disorder (ASD), and typically developing (TD) children. The Test of Everyday Attention for Children (TEA-Ch), a performance-based measure of attention, was administered to 69 children (TD: n = 24; SPD: n = 21; ASD: n = 24), ages 6-10 years. All participants' parents completed the Short Sensory Profile (SSP), a standardized parent-report measure of sensory-related behaviors. Discriminant analyses using the TEA-Ch and the SSP domains revealed two classification functions; the first revealed that both clinical groups significantly differed from the TD group with greater sensory processing challenges in the categories of auditory filtering, under-responsive/seeks sensation, low energy/weak, and taste/smell sensitivity subscales of the SSP. The second function discriminated between the two clinical groups, indicating that children with ASD had significantly greater control and sustained attention deficits and less sensory issues than did children with SPD. Together, the two functions correctly classified 76.8% of the participants as to their group membership. The different profiles of sensory processing and attention abilities in children with SPD and ASD may provide guidance in identifying appropriate individualized therapeutic strategies for these children.

Developmental trends of performance monitoring measures in 7- to 25-year-olds: Unraveling the complex nature of brain measures.

This study explores how trial-to-trial latency variability contributes to the developmental trends observed in ERN amplitude found in the incorrect trials of a performance monitoring task, the visual flanker task. An Adaptive Woody filter was used to measure and correct for the trial-to-trial latency variability of the ERN in 240 participants aged 7-25 years. Using three measures of latency variability, the degree of trial-to-trial latency variability was shown to decrease as the age of the participants increased from 7 to 25 years. The success of the Adaptive Woody filter technique to remove the trial-to-trial latency variability was demonstrated in a straightforward manner by the significant changes in the measures of fit and intraindividual variability obtained before and after applying the filter. After the latency variability effects were removed and adjusted averaged ERPs were obtained, a more subtle but significant nonlinear developmental trend was still found in the amplitude of the ERN component.

Towards a unified model of event-related potentials as phases of stimulus-to-response processing.

This study demonstrates the utility of combining principles of connectionist theory with a sophisticated statistical approach, structural equation modeling (SEM), to better understand brain-behavior relationships in studies using event-related potentials (ERPs). The models show how sequential phases of neural processing measured by averaged ERP waveform components can successfully predict task behavior (response time; RT) while accounting for individual differences in maturation and sex. The models assume that all ERP measures are affected by individual differences in physical and mental state that inflate measurement error. ERP data were collected from 154 neurotypical children (7-13 years, M = 10.22, SD = 1.48; 74 males) performing a cued Go/No-Go task during two separate sessions. Using SEM, we show a latent variable path model with good fit (e.g., χ2(51) = 56.20, p = .25; RMSEA = .03; CFI = .99; SRMR = .06) yielding moderate-to-large predictive coefficients from N1 through the E-wave latent variables (N1 ß = -.29 → P2 ß = -.44 → N2 ß = .28 → P3 ß =.64→ E-wave), which in turn significantly predicted RT (ß =.34, p = .02). Age significantly related to N1 and P3 latent variables as well as RT (ß =.31, -.58, & -.40 respectively), and Sex significantly related to the E-wave latent variable and RT (ß =.36 & 0.21 respectively). Additionally, the final model suggested that individual differences in emotional and physical state accounted for a significant proportion of variance in ERP measurements, and that individual states systematically varied across sessions (i.e., the variance was not just random noise). These findings suggest that modeling ERPs as a system of inter-related processes may be a more informative approach to examining brain-behavior relationships in neurotypical and clinical groups than traditional analysis techniques.