Windowed multitaper correlation analysis of multimodal brain monitoring parameters

Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury

Traumatic brain injury (TBI) causes alteration in brain functions. Generally, at intensive care units (ICU), intracranial pressure (ICP) is monitored and treated to avoid increases in ICP with associated poor clinical outcome. The aim was to develop a model which could predict future ICP levels of individual patients in the ICU, to warn treating clinicians before secondary injuries occur. A simple and explainable, probabilistic Markov model was developed for the prediction task ICP ≥ 20 mmHg. Predictions were made for 10-min intervals during 60 min, based on preceding hour of ICP. A prediction enhancement method was developed to compensate for data imbalance. The model was evaluated on 29 patients with severe TBI. With random data selection from all patients (80/20% training/testing) the specificity of the model was high (0.94–0.95) and the sensitivity good to high (0.73–0.87). Performance was similar (0.90–0.95 and 0.73–0.89 respectively) when the leave-one-out cross-validation was applied. The new model could predict increased levels of ICP in a reliable manner and the enhancement method further improved the predictions. Further advantages are the straightforward expandability of the model, enabling inclusion of other time series data and/or static parameters. Next step is evaluation on more patients and inclusion of parameters other than ICP.

Amygdalar and hippocampal beta rhythm synchrony during human fear memory retrieval

BACKGROUND

Fear, as one of the basic emotions, is crucial in helping humans to perceive hazards and adapt to social activities. Clinically, fear memory is also involved in a wide spectrum of psychiatric disorders. A better understanding of the neural mechanisms of fear thereby has both neuroscientific and clinical significance. In recent years, data from animal models have demonstrated the key role of the amygdala-hippocampal circuit in the development of fear. However, the neural processing of fear memory remains unclear in humans, which is mainly due to the limitation of indirect measure of neural activity.

METHODS

Herein, we investigated fear memory by direct intracranial recordings from 8 intractable epilepsy patients with depth electrodes in both the hippocampus and ipsilateral amygdala. All the patients were subjected to a well-established Pavlovian fear memory paradigm consisted of the familiarization task, conditioning task, and retrieval task, respectively. Simultaneous local field potentials from the hippocampus and amygdala were recorded during different stages. The oscillatory activities from the amygdala and hippocampus were analyzed during fear memory retrieval compared with neutral stages.

RESULTS

Consistent with previous rodent studies, our results showed that the amygdala was involved in fear memory retrieval rather than neutral memory retrieval, while the hippocampus was involved both in fear memory retrieval and neutral memory retrieval. In particular, we found that there was an enhanced synchronized activity between the amygdala and hippocampus at beta frequencies (14-30 Hz), which suggested that enhanced synchronized activity at beta frequencies between the amygdala and hippocampus play a pivotal role during retrieval of fear memory in human.

CONCLUSIONS

Thus, our observation that the amygdala-hippocampal system contributing to fear memory retrieval in human with frequency-depended specificity has provided new insights into the mechanism of fear and have potential clinical relevance.

A Patient-Independent Significance Test by Means of False-Positive Rates in Selected Correlation Analysis of Brain Multimodal Monitoring Data

Recently, we introduced a mathematical toolkit called selected correlation analysis (sca) that reliably detects negative and positive correlations between arterial blood pressure (ABP) and intracranial pressure (ICP) data, recorded during multimodal monitoring, in a time-resolved way. As has been shown with the aid of a mathematical model of cerebral perfusion, such correlations reflect impaired autoregulation and reduced intracranial compliance in patients with critical neurological diseases. Sca calculates a Fourier transform-based index called selected correlation (sc) that reflects the strength of correlation between the input data and simultaneously an index called mean Hilbert phase difference (mhpd) that reflects the phasing between the data. To reliably detect pathophysiological conditions during multimodal monitoring, some thresholds for the abovementioned indexes sc and mhpd have to be established that assign predefined significance levels to that thresholds. In this paper, we will present a method that determines the rate of false positives for fixed pairs of thresholds (lsc, lmhpd). We calculate these error rates as a function of the predefined thresholds for each individual out of a patient cohort of 52 patients in a retrospective way. Based on the deviation of the individual error rates, we subsequently determine a globally valid upper limit of the error rate by calculating the predictive interval. From this predictive interval, we deduce a globally valid significance level for appropriate pairs of thresholds that allows the application of sca to every future patient in a prospective, bedside fashion.

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study

Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.