Causal relationship between neuronal activity and cerebral hemodynamics in patients with ischemic stroke

Beyond life: Exploring hemodynamic patterns in postmortem mice brains

We utilize Laser Speckle Contrast Imaging (LSCI) for visualizing cerebral blood flow in mice during and post-cardiac arrest. Analyzing LSCI images, we noted temporal blood flow variations across the brain surface for hours postmortem. Fast Fourier Transform (FFT) analysis depicted blood flow and microcirculation decay post-death. Continuous Wavelet Transform (CWT) identified potential cerebral hemodynamic synchronization patterns. Additionally, non-negative matrix factorization (NMF) with four components segmented LSCI images, revealing structural subcomponent alterations over time. This integrated approach of LSCI, FFT, CWT, and NMF offers a comprehensive tool for studying cerebral blood flow dynamics, metaphorically capturing the 'end of the tunnel' experience. Results showed primary postmortem hemodynamic activity in the olfactory bulbs, followed by blood microflow relocations between somatosensory and visual cortical regions via the superior sagittal sinus. This method opens new avenues for exploring these phenomena, potentially linking neuroscientific insights with mysteries surrounding consciousness and perception at life's end.

Age-dependent neurovascular coupling characteristics in children and adults during general anesthesia

General anesthesia is an indispensable procedure in clinical practice. Anesthetic drugs induce dramatic changes in neuronal activity and cerebral metabolism. However, the age-related changes in neurophysiology and hemodynamics during general anesthesia remain unclear. Therefore, the objective of this study was to explore the neurovascular coupling between neurophysiology and hemodynamics in children and adults during general anesthesia. We analyzed frontal electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals recorded from children (6-12 years old, n = 17) and adults (18-60 years old, n = 25) during propofol-induced and sevoflurane-maintained general anesthesia. The neurovascular coupling was evaluated in wakefulness, maintenance of a surgical state of anesthesia (MOSSA), and recovery by using correlation, coherence and Granger-causality (GC) between the EEG indices [EEG power in different bands and permutation entropy (PE)], and hemodynamic responses the oxyhemoglobin (Δ[HbO]) and deoxy-hemoglobin (Δ[Hb]) from fNIRS in the frequency band in 0.01-0.1 Hz. The PE and Δ[Hb] performed well in distinguishing the anesthesia state (p > 0.001). The correlation between PE and Δ[Hb] was higher than those of other indices in the two age groups. The coherence significantly increased during MOSSA (p < 0.05) compared with wakefulness, and the coherences between theta, alpha and gamma, and hemodynamic activities of children are significantly stronger than that of adults' bands. The GC from neuronal activities to hemodynamic responses decreased during MOSSA, and can better distinguish anesthesia state in adults. Propofol-induced and sevoflurane-maintained combination exhibited age-dependent neuronal activities, hemodynamics, and neurovascular coupling, which suggests the need for separate rules for children's and adults' brain states monitoring during general anesthesia.

Multimodal and autoregulation monitoring in the neurointensive care unit

Given the complexity of cerebral pathology in patients with acute brain injury, various neuromonitoring strategies have been developed to better appreciate physiologic relationships and potentially harmful derangements. There is ample evidence that bundling several neuromonitoring devices, termed "multimodal monitoring," is more beneficial compared to monitoring individual parameters as each may capture different and complementary aspects of cerebral physiology to provide a comprehensive picture that can help guide management. Furthermore, each modality has specific strengths and limitations that depend largely on spatiotemporal characteristics and complexity of the signal acquired. In this review we focus on the common clinical neuromonitoring techniques including intracranial pressure, brain tissue oxygenation, transcranial doppler and near-infrared spectroscopy with a focus on how each modality can also provide useful information about cerebral autoregulation capacity. Finally, we discuss the current evidence in using these modalities to support clinical decision making as well as potential insights into the future of advanced cerebral homeostatic assessments including neurovascular coupling.

AOAR: an automatic ocular artifact removal approach for multi-channel electroencephalogram data based on non-negative matrix factorization and empirical mode decomposition

OBJECTIVE

Electroencephalogram (EEG) signals suffer inevitable interference from artifacts during the acquisition process. These artifacts make the analysis and interpretation of EEG data difficult. A major source of artifacts in EEGs is ocular activity. Therefore, it is important to remove ocular artifacts before further processing the EEG data.

APPROACH

In this study, an automatic ocular artifact removal (AOAR) method for EEG signals is proposed based on non-negative matrix factorization (NMF) and empirical mode decomposition (EMD). First, the amplitude of EEG data was normalized in order to ensure its non-negativity. Then, the normalized EEG data were decomposed into a set of components using NMF. The components containing ocular artifacts were extracted automatically through the fractal dimension. Subsequently, the temporal activities of these components were adaptively decomposed into some intrinsic mode functions (IMFs) by EMD. The IMFs corresponding to ocular artifacts were removed. Finally, the de-noised EEG data were reconstructed.

MAIN RESULTS

The proposed method was tested against seven other methods. In order to assess the effectiveness and reliability of the AOAR method in processing EEG data, experiments on ocular artifact removal were performed using simulated EEG data. Experimental results indicated that the proposed method was superior to the other methods in terms of root mean square error, signal-to-noise ratio (SNR) and correlation coefficient, especially in cases with a lower SNR. To further evaluate the potential applications of the proposed method in real life, the proposed method and others were applied to preprocess real EEG data recorded from children with and without attention-deficit/hyperactivity disorder (ADHD). After artifact rejection, the event-related potential feature was extracted for classification. The AOAR method was best at distinguishing the children with ADHD from the others.

SIGNIFICANCE

These results indicate that the proposed AOAR method has excellent prospects for removing ocular artifacts from EEG data.