The dorsomedial posterior parietal cortex (dmPPC) is part of a higher-cognition network implicated in elaborate processes underpinning memory formation, recollection, episode reconstruction, and temporal information processing. Neural coding for complex episodic processing is however under-documented. Here, we recorded extracellular neural activities from three male rhesus macaques (Macaca mulatta) and revealed a set of neural codes of “neuroethogram” in the primate parietal cortex. Analyzing neural responses in macaque dmPPC to naturalistic videos, we discovered several groups of neurons that are sensitive to different categories of ethogram items, low-level sensory features, and saccadic eye movement. We also discovered that the processing of category and feature information by these neurons is sustained by the accumulation of temporal information over a long timescale of up to 30 s, corroborating its reported long temporal receptive windows. We performed an additional behavioral experiment with additional two male rhesus macaques and found that saccade-related activities could not account for the mixed neuronal responses elicited by the video stimuli. We further observed monkeys’ scan paths and gaze consistency are modulated by video content. Taken altogether, these neural findings explain how dmPPC weaves fabrics of ongoing experiences together in real time. The high dimensionality of neural representations should motivate us to shift the focus of attention from pure selectivity neurons to mixed selectivity neurons, especially in increasingly complex naturalistic task designs.

Acute brain injury (ABI) is prevalent among patients undergoing venoarterial extracorporeal membrane oxygenation (VA-ECMO) and significantly impact recovery. Early prediction of ABI could enable timely interventions to prevent adverse outcomes, but existing predictive methods remain suboptimal. This study aimed to enhance ABI prediction using machine learning (ML) models and high-temporal-resolution granular data. We retrospectively analyzed 355 VA-ECMO patients treated at Johns Hopkins Hospital (JHH) from 2016 to 2024, collecting over 3 million data points from the JHH Research Electronic Data Capture (REDCap) database, with an average of 80,000 data points per patient. Acute brain injury was defined as ischemic stroke, intracranial hemorrhage, hypoxic-ischemic brain injury, or seizure. Four ML models were used: Random Forest, Categorical Boosting, Adaptive Boosting, and Extreme Gradient Boosting. Among 355 patients (median age 59 years, 56.9% male), 13.5% developed ABI. The models achieved an optimal area under the receiver operating characteristic curve (AUROC) of 0.79, accuracy of 87%, sensitivity of 53%, specificity of 99%, and precision-recall (PR)-AUC of 0.47. Key predictors included high minimum values of systolic blood pressure and variability in on-ECMO pulse pressure. High-resolution granular data enhanced ML performance for ABI prediction. Future efforts should focus on integrating continuous data platforms to enable real-time monitoring and personalized care, optimizing patient outcomes.

Background: Somatosensory evoked potentials (SEPs) are highly specific predictors of poor prognosis in hypoxic-ischemic coma when cortical responses (N20s) are absent. However, bilateral N20 presence is nonspecific for good outcomes. High-frequency oscillations (HFOs) in the SEP waveform predict neurologic recovery in animals, but clinical applications are poorly understood. We sought to develop a clinical measure of HFOs to potentially improve detection of good outcomes in coma. Materials and Methods: We collected SEP waveform data from all comatose inpatients (GCS<=8) who underwent neurologic prognostication from 2020 to 2022 at Johns Hopkins Hospital. We developed a novel measure - HFO evoked to spontaneous ratios (HFO-ESRs) - and applied this to those patients with bilaterally present N20s using both standard univariate classification and cubic kernal vector machine (SVM) models to predict the last documented in-hospital Glasgow Coma Scale (GCS) prior to discharge or death. Results: Of 58 total patients, 34 (58.6%) had bilaterally present N20s. Of these, 14 had final GCS>=9, and 20 had final GCS<=8. Mean age was 52 (+/- 17) years, 20.1% female. Etiologies of coma were primarily global hypoxic-ischemic brain injury (79.4%), intracranial hemorrhage (11.8%), and traumatic brain injury (2.9%). In univariate classification, the addition of averaged HFO-ESRs to bilaterally present N20s predicted final GCS>=9 with 68% specificity. The SVM model further improved specificity to 85%. Conclusions: In this pilot investigation, we developed a novel clinical measure of SEP HFOs. Incorporation of this measure may improve the specificity of the SEP to predict in-hospital GCS outcomes in coma, but requires further validation in specific neurologic injuries and with longitudinal outcomes.

Extracorporeal cardiopulmonary resuscitation (ECPR) has emerged as a promising intervention for refractory cardiac arrest, with potential benefits in survival and neurological outcomes. However, the impact of ECPR’s rapid, non-pulsatile restoration of cerebral blood flow on intracranial physiology and autoregulation remains poorly understood. Using a porcine model, this study investigated the dynamics of intracranial pressure (ICP), cerebral autoregulation, and pulsatility during key experimental phases: Baseline, Fibrillation, ECMO, and ROSC. ICP waveform features, including spectral entropy and centroid, were analyzed to assess signal complexity and dominant frequency shifts. Pressure reactivity index (PRx), the correlation between MAP and ICP, was used to assess impaired cerebral autoregulation (PRx > 0.3). Pulsatility, quantified by pulse pressure (PP) and pulsatility index (PI), was evaluated for its interaction with PRx. Significant alterations in ICP and PRx were observed across experimental phases. During Fibrillation, ICP increased, waveforms lost pulsatile structure and complexity, and elevated PRx indicated impaired autoregulation. ECMO initiation resulted in a reduction of ICP fluctuations but reduced pulsatility due to the steady, non-physiological flow. Following ROSC, ICP pulsatility partially recovered, but PRx variability remained high, suggesting inter-subject differences in autoregulatory recovery. Negative correlations between pulsatility and PRx (slope = –0.096, 95% CI: –0.136 to –0.056, p = 3.07 × 10⁻⁶) suggested that pulsatility plays a critical role in autoregulatory function. This study highlights the complex intracranial changes during ECPR, emphasizing the importance of pulsatility in maintaining cerebral autoregulation. Findings suggest the need for refined ECPR protocols to optimize cerebral protection and improve autoregulatory recovery.

Introduction: Real-time cortical and cardiac electrophysiology provides direct insight into brain and heart function, revealing early signs of neurological or circulatory instability, supporting medics with rapid triage, continuous monitoring, and intervention during transport, evacuation, or battlefield operations. However, their deployment remains limited by motion and vibration artifacts that degrade signal integrity in dynamic field settings. This study examines the feasibility of auricular (in-ear) electrode configurations for improved signal robustness compared with scalp and wrist sites under both sustained vibration and dynamic acceleration within custom-built electrodes on matched electronics. Materials and Methods: Simultaneous recordings were obtained from the auricular canal (electroencephalogram [EEG] and electrocardiogram [ECG]), temple (F7 EEG), and wrist (ECG) in n = 5 participants across 3 motion conditions: engine off (parked), engine on (parked), and city driving (∼30 mph). Signal quality was assessed via waveform morphology, frequency-domain signal-to-noise ratio (SNR), coherence, and root mean square error (RMSE) relative to inertial measurement unit (IMU) data. Results: Across 5 participants, auricular channels showed reduced 60 Hz interference (∼-8 dB) and slightly higher SNR (median increase of 2 dB during driving) compared with scalp and wrist sites. This trend may reflect mechanical and electromagnetic shielding because of the enclosure of the canal. Under motion conditions, coherence and RMSE suggest weaker coupling between IMU motion and auricular signals compared with on-body sites (∼0.03 higher RMSE and ∼0.05×10-3 lower coherence). Conclusions: Auricular recordings indicate feasible signal integrity under vehicular motion, compared to conventional scalp and wrist placements. These preliminary results indicate suitability for neurocardiac monitoring.