The recovery of arousal after cardiac arrest (CA) is associated with evolution from electroencephalographic (EEG) burst-suppression to continuous activity. Orexin-A elicits arousal EEG during anesthetic burst-suppression. We hypothesized that orexin-A would improve arousal and EEG entropy after CA. Eighteen Wistar rats were subjected to 7-minute asphyxial CA and resuscitation. Rats were divided into treatment (n=9) and control (n=9) groups. Twenty minutes after resuscitation, the treatment group received 0.1 mL of 1nM orexin-A intraventricularly, while controls received saline. EEG was quantified using Information Quantity (IQ), a measure of entropy validated for detection of burst-suppression and arousal patterns. IQ values range from 0–1.0. Arousal was quantified using the neurological deficit scale (NDS). The ischemic neuronal fraction of hippocampus CA1 and cortex was histologically determined. Baseline and post-resuscitation characteristics were similar between the groups. The NDS score (mean±SD) at 4 hours was higher in the orexin-A group compared to controls (57.3±5.8 vs. 40.7±5.9, p<0.02), but scores were similar at 72 hours. Burst frequency was similar in both groups but the orexin-A group demonstrated higher IQ values compared to controls beginning within 10 minutes. IQ values remained significantly higher in the orexin-A group for the first 120 minutes (p=0.008) and subsequently converged. The ischemic neuronal fraction was similar between groups in cortex (p=0.54) and hippocampus CA1 (p=0.14). In rats resuscitated from CA, orexin-A transiently increased arousal and EEG entropy without worsening ischemic neuronal injury. The role of orexin-A in recovery of arousal after CA deserves further investigation.

Neurological complications after cardiac arrest (CA) can be fatal. Although hypothermia has been shown to be beneficial, understanding the mechanism and establishing neurological outcomes remains challenging because effects of CA and hypothermia are not well characterized. This paper aims to analyze EEG (and the α-rhythms) using multiscale entropy (MSE) to demonstrate the ability of MSE in tracking changes due to hypothermia and compare MSE during early recovery with long-term neurological examinations. Ten Wistar rats, upon post-CA resuscitation, were randomly subjected to hypothermia (32 °C-34 °C, N = 5) or normothermia (36.5 °C-37.5 °C, N = 5). EEG was recorded and analyzed using MSE during seven recovery phases for each experiment: baseline, CA, and five early recovery phases (R1-R5). Postresuscitation neurological examination was performed at 6, 24, 48, and 72 h to obtain neurological deficit scores (NDSs). Results showed MSE to be a sensitive marker of changes in α-rhythms. Significant difference (p < 0.05) was found between the MSE for two groups during recovery, suggesting that MSE can successfully reflect temperature modulation. A comparison of short-term MSE and long-term NDS suggested that MSE could be used for predicting favorability of long-term outcome. These experiments point to the role of cortical rhythms in reporting early neurological response to ischemia and therapeutic hypothermia. Index TermsCardiac arrest (CA); entropy; neurological injury; quantitative EEG

Complex reach, grasp, and object manipulation tasks require sequential, temporal coordination of movements by neurons in the brain. Detecting cognitive state transitions associated with motor tasks from sequential neural data is pivotal in rehabilitation engineering. The cognitive state detectors proposed thus far rely on task-dependent (TD) models, i.e., the detection strategy exploits a priori knowledge of the movement tasks to determine the actual cognitive states, regardless of whether these cognitive states actually depend on the movement tasks or not. This approach, however, is not viable when the tasks are not known a priori (e.g., the subject performs many different tasks) or there is paucity of neural data for each task. Moreover, some cognitive states (e.g., holding) may be invariant to the movement tasks performed. Here we propose a real-time (online) task-independent (TI) framework to detect cognitive state transitions from spike trains and kinematic measurements. We constructed this detection framework using 452 single-unit neural spike recordings collected via multielectrode arrays in the premotor dorsal and ventral (PMd and PMv) cortical regions of two nonhuman primates performing 3-D multiobject reach-to-grasp tasks. We used the detection latency and accuracy of state transitions to measure the performance. We find that, in both online and offline detection modes: 1) TI models have significantly better performance than corresponding TD models when using neuronal data alone and 2) during movements, the addition of the kinematics history to the TI models further improves detection performance. These findings suggest that TI models may accurately detect cognitive state transitions. Our framework could pave the way for a TI control of neural prosthesis from cortical neurons.

Cardiac arrest (CA) can produce complex changes in somatosensory evoked potentials (SSEPs). Somatosensory evoked potentials (SSEPs) indicate the intactness of somatosensory pathways and are commonly used for brain function monitoring during surgeries. Multiresolution biorthogonal wavelet analysis was applied to SSEPs recorded during established CA experiments and post-CA long-term recovery periods in rats. Our results showed that during the first 4 hours after CA, the amplitudes of SSEP, defined here as the difference between the amplitudes of P23 and N20, decreased greatly while the inter-peak latencies between N20 and P23 increased greatly. In the long-term recovery period (within 72 hours), both the amplitudes of SSEPs and the interpeak latencies returned to the baseline. Our results suggest that the changes of SSEPs may represent the post-CA neurological injuries and recovery in the somatosensory afferent pathways. The results here lay ground work for establishing the relationship between SSEPs and post-CA neurological injuries and functional outcomes as well as deploying SSEP in clinical settings to monitor patients resuscitated from CA in the future.