Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states.

In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as "engaging in dialogue" and "using electronics". Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity's covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.

Optical Flow Estimation Improves Automated Seizure Detection in Neonatal EEG.

PURPOSE: Existing automated seizure detection algorithms report sensitivities between 43% and 77% and specificities between 56% and 90%. The algorithms suffer from false alarms when applied to neonatal EEG because of the high degree of nurse handling and rhythmic patting used to soothe neonates. Computer vision technology that quantifies movement in real time could distinguish artifactual motion and improve automated neonatal seizure detection algorithms. METHODS: The authors used video EEG recordings from 43 neonates undergoing monitoring for seizures as part of the NEOLEV2 clinical trial. The Persyst neonatal automated seizure detection algorithm ran in real time during study EEG acquisitions. Computer vision algorithms were applied to extract detailed accounts of artifactual movement of the neonate or people near the neonate though dense optical flow estimation. RESULTS: Using the methods mentioned above, 197 periods of patting activity were identified and quantified, of which 45 generated false-positive automated seizure detection events. A binary patting detection algorithm was trained with a subset of 470 event videos. This supervised detection algorithm was applied to a testing subset of 187 event videos with 8 false-positive events, which resulted in a 24% reduction in false-positive automated seizure detections and a 50% reduction in false-positive events caused by neonatal care patting, while maintaining 11 of 12 true-positive seizure detection events. CONCLUSIONS: This work presents a novel approach to improving automated seizure detection algorithms used during neonatal video EEG monitoring. This artifact detection mechanism can improve the ability of a seizure detector algorithm to distinguish between artifact and true seizure activity.

Coarse behavioral context decoding.

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: 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: 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: 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.

Neural correlates of unstructured motor behaviors.

OBJECTIVE: We studied the relationship between uninstructed, unstructured movements and neural activity in three epilepsy patients with intracranial electroencephalographic (iEEG) recordings. APPROACH: 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. MAIN RESULTS: 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. SIGNIFICANCE: 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.

Ticagrelor Use in Acute Myocardial Infarction: Balancing Evidence-Based Medicine with Affordability.

STUDY OBJECTIVE: Data from randomized controlled trials support a mortality benefit with ticagrelor versus clopidogrel among patients with acute myocardial infarction (AMI). Many healthcare providers preferentially treat patients with AMI with ticagrelor. The goal of this study was to determine the association between out-of-pocket drug costs and ticagrelor continuation compared with switching to clopidogrel among patients hospitalized for AMI, following a pharmacist-led discussion on outpatient co-payment costs for ticagrelor. DESIGN: Retrospective cohort study. SETTING: A tertiary care academic medical center. PATIENTS: Patients hospitalized with AMI between February 15, 2015 and January 23, 2017, who were loaded with ticagrelor on presentation. MAIN RESULTS: Of 143 patients with AMI loaded with ticagrelor, 70 (49%) switched to clopidogrel after cost discussion. The median monthly ticagrelor co-payment was $268.29 (interquartile range [IQR] $45-$350) for switchers, versus $18 (IQR $6-$24) for non-switchers (p<0.001). Patients with co-payments of $100/month or more were 3.4 times more likely to switch to clopidogrel (relative risk 3.41, 95% confidence interval 2.12 to 5.47), compared with patients with co-payments of less than $100/month. CONCLUSIONS: Following a discussion of outpatient costs, half of patients with AMI switched from ticagrelor to clopidogrel when given the choice.

Patient-Specific Pose Estimation in Clinical Environments.

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.

Neural correlates to automatic behavior estimations from RGB-D video in epilepsy unit.

To augment neural monitoring, a minimally intrusive multi-modal capture system was designed and implemented in the epilepsy clinic. This system provides RGB-D audio-video synchronized with patient electrocorticography (ECoG), which records neural activity across cortex. We propose an automated approach to studying the human brain in a naturalistic setting. We demonstrate coarse functional mapping of ECoG electrodes correlated to contralateral arm movements. Motor electrode mapping was generated by analyzing continuous movement data recorded over several hours from epilepsy patients in hospital rooms. From these recordings we estimate the kinematics of patient hand movement behaviors using computer vision algorithms. We compare movement behaviors to neural data collected from ECoG, specifically high-γ (70-110 Hz) spectral features. We present a functional map of electrode responses to natural arm movements, generated using a statistical test. We demonstrate that our approach has the potential to aid in the development of automated functional brain mapping using continuous video and neural recordings of patients in clinical settings.