Faculty Bibliography

Slow-oscillations and spindle activity during non-REM sleep have been implicated in memory consolidation. Closed-loop acoustic stimulation has previously been shown to enhance slow oscillations and spindle activity during sleep and improve verbal associative memory. We assessed the effect of closed-loop acoustic stimulation during a daytime nap on a virtual reality spatial navigation task in 12 healthy human subjects in a randomized within-subject crossover design. We show robust enhancement of slow-spindle activity during sleep. However, no effects on behavioral performance were observed when comparing real versus sham stimulation. To explore whether memory enhancement effects were task-specific and dependent on nocturnal sleep, in a second experiment with 19 healthy subjects, we aimed to replicate a previous study which used closed-loop acoustic stimulation to enhance memory for word pairs. Methods were as close as possible to the original study, except we used a double-blind protocol, in which both subject and experimenter were unaware of the test condition. Again, we successfully enhanced slow-spindle power, but again did not strengthen associative memory performance with stimulation. We conclude that enhancement of slow-spindle oscillations may be insufficient to enhance memory performance in spatial navigation or verbal association tasks, and provide possible explanations for lack of behavioral replication.SIGNIFICANCE STATEMENT Prior studies have demonstrated that a closed-loop acoustic pulse paradigm during sleep can enhance verbal memory performance. This technique has widespread scientific and clinical appeal due to its non-invasive nature and ease of application. We tested with a rigorous double-blind design whether this technique could enhance key sleep rhythms associated sleep-dependent memory performance. We discovered that we could reliably enhance slow and spindle rhythms, but did not improve memory performance in the stimulation condition compared to sham condition. Our findings suggest that enhancing slow-spindle rhythms is insufficient to enhance sleep-dependent learning.

Distorted representations of the body are observed in healthy individuals as well as in neurological and psychiatric disorders. Distortions of the body model have been attributed to the somatotopic cerebral representation. Recently, it has been demonstrated that visual biases also contribute to those distortions. To better understand the sources of such distortions, we compared the metric representations across five body parts affording different degrees of tactile sensitivity and visual accessibility. We evaluated their perceived dimensions using a Line Length Judgment task. We found that most body parts were underestimated in their dimensions. The estimation error relative to their length was predicted by their tactile acuity, supporting the influence of the cortical somatotopy on the body model. However, tactile acuity did not explain the distortions observed for the width. Visual accessibility in turn does appear to mediate body distortions, as we observed that the dimensions of the dorsal portion of the neck were the only ones accurately perceived. Coherent with the multisensory nature of body representations, we argue that the perceived dimensions of body parts are estimated by integrating visual and somatosensory information, each weighted differently, based on their availability for a given body part and a given spatial dimension.

We studied the relationship between uninstructed, unstructured movements and neural activity in three epilepsy patients with intracranial electroencephalographic (iEEG) recordings. We used a custom system to continuously record high definition video precisely time-aligned to clinical iEEG data. From these video recordings, movement periods were annotated via semi-automatic tracking based on dense optical flow. We found that neural signal features (8--32 Hz and 76--100 Hz power) previously identified from task-based experiments are also modulated before and during a variety of movement behaviors. These movement behaviors are coarsely labeled by time period and movement side (e.g. `Idle' and `Move', `Right' and `Left'); movements within a label can include a wide variety of uninstructed behaviors. A rigorous nested cross-validation framework was used to classify both movement onset and lateralization with statistical significance for all subjects. We demonstrate an evaluation framework to study neural activity related to natural movements not evoked by a task, annotated over hours of video. This work further establishes the feasibility to study neural correlates of unstructured behavior through continuous recording in the epilepsy monitoring unit. The insights gained from such studies may advance our understanding of how the brain naturally controls movement, which may inform the development of more robust and generalizable brain-computer interfaces.

OBJECTIVE:Current brain-computer interface (BCI) studies demonstrate the potential to decode neural signals obtained from structured and trial-based tasks to drive actuators with high performance within the context of these tasks. Ideally, to maximize utility, such systems will be applied to a wide range of behavioral settings or contexts. Thus, we explore the potential to augment such systems with the ability to decode abstract behavioral contextual states from neural activity. APPROACH/METHODS:To demonstrate the feasibility of such context decoding, we used electrocorticography (ECoG) and stereo-electroencephalography (sEEG) data recorded from the cortical surface and deeper brain structures, respectively, continuously across multiple days from three subjects. During this time, the subjects were engaged in a range of naturalistic behaviors in a hospital environment. Behavioral contexts were labeled manually from video and audio recordings; four states were considered: engaging in dialogue, rest, using electronics, and watching television. We decode these behaviors using a factor analysis and support vector machine (SVM) approach. MAIN RESULTS/RESULTS:We demonstrate that these general behaviors can be decoded with high accuracies of 73% for a four-class classifier for one subject and 71% and 62% for a three-class classifier for two subjects. SIGNIFICANCE/CONCLUSIONS:To our knowledge, this is the first demonstration of the potential to disambiguate abstract naturalistic behavioral contexts from neural activity recorded throughout the day from implanted electrodes. This work motivates further study of context decoding for BCI applications using continuously recorded naturalistic activity in the clinical setting.

Direct recordings from the human brain have historically involved epilepsy patients undergoing invasive electroencephalography (iEEG) for surgery. However, these measurements are temporally limited and affected by clinical variables. The RNS System (NeuroPace, Inc.) is a chronic, closed-loop electrographic seizure detection and stimulation system. When adapted by investigators for research, it facilitates cognitive testing in a controlled ambulatory setting, with measurements collected over months to years. We utilized an associative learning paradigm in 5 patients with traditional iEEG and 3 patients with chronic iEEG, and found increased hippocampal gamma (60-100 Hz) sustained at 1.3-1.5 seconds during encoding in successful versus failed trials in surgical patients, with similar results in our RNS System patients (1.4-1.6 seconds). Our findings replicate other studies demonstrating that sustained hippocampal gamma supports encoding. Importantly, we have validated the RNS System to make sensitive measurements of hippocampal dynamics during cognitive tasks in a chronic ambulatory research setting.

Noninvasive brain stimulation techniques are used in experimental and clinical fields for their potential effects on brain network dynamics and behavior. Transcranial electrical stimulation (TES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), has gained popularity because of its convenience and potential as a chronic therapy. However, a mechanistic understanding of TES has lagged behind its widespread adoption. Here, we review data and modelling on the immediate neurophysiological effects of TES in vitro as well as in vivo in both humans and other animals. While it remains unclear how typical TES protocols affect neural activity, we propose that validated models of current flow should inform study design and artifacts should be carefully excluded during signal recording and analysis. Potential indirect effects of TES (e.g., peripheral stimulation) should be investigated in more detail and further explored in experimental designs. We also consider how novel technologies may stimulate the next generation of TES experiments and devices, thus enhancing validity, specificity, and reproducibility.

Reliable posture labels in hospital environments can augment research studies on neural correlates to natural behaviors and clinical applications that monitor patient activity. However, many existing pose estimation frameworks are not calibrated for these unpredictable settings. In this paper, we propose a semi-automated approach for improving upper-body pose estimation in noisy clinical environments, whereby we adapt and build around an existing joint tracking framework to improve its robustness to environmental uncertainties. The proposed framework uses subject-specific convolutional neural network models trained on a subset of a patient's RGB video recording chosen to maximize the feature variance of each joint. Furthermore, by compensating for scene lighting changes and by refining the predicted joint trajectories through a Kalman filter with fitted noise parameters, the extended system yields more consistent and accurate posture annotations when compared with the two state-of-the-art generalized pose tracking algorithms for three hospital patients recorded in two research clinics.

Our visual environment constantly changes, yet we experience the world as a stable, unified whole. How is this stability achieved? It has been proposed that the brain preserves an implicit perceptual memory in sensory cortices [1] which stabilizes perception towards previously experienced states [2,3]. The role of higher-order areas, especially prefrontal cortex (PFC), in perceptual memory is less explored. Because PFC exhibits long neural time constants, invariance properties, and large receptive fields which may stabilize perception against time-varying inputs, it seems particularly suited to implement perceptual memory [4]. Support for this idea comes from a neuroimaging study reporting that dorsomedial PFC (dmPFC) correlates with perceptual memory [5]. But dmPFC also participates in decision making [6], so its contribution to perceptual memory could arise on a post-perceptual, decisional level [7]. To determine which role, if any, PFC plays in perceptual memory, we obtained direct intracranial recordings in six epilepsy patients while they performed sequential orientation judgements on ambiguous stimuli known to elicit perceptual memory [8]. We found that dmPFC activity in the high gamma frequency band (HGB, 70-150 Hz) correlates with perceptual memory. This effect is anatomically specific to dmPFC and functionally specific for memories of preceding percepts. Further, dmPFC appears to play a causal role, as a patient with a lesion in this area showed impaired perceptual memory. Thus, dmPFC integrates current sensory information with prior percepts, stabilizing visual experience against the perpetual variability of our surroundings.