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Surgical microscope is an important medical equipment in surgeries that require delicate operations. There have been demanding clinical needs for multi-modality functional information in the intraoperative setting, such as real-time blood flow monitoring and blood vessel visualization. Aiming at the shortcomings of ICG-based angiography technology that requires contrast agents, we propose a design scheme for surgical microscope based on laser speckle contrast imaging (LSCI) technology for an intraoperative full-field label-free blood flow monitoring, using augmented reality (AR) technology to superimpose the blood flow imaging results in the field of surgeon's eyepiece. With the high-speed processing capability of GPU, the system is capable of real-time blood flow monitoring, which would provide great assistance in neurosurgery.

The coupling between neuronal activity and cerebral blood flow (CBF), known as neurovascular coupling, has been reported to be impaired after stroke. This study aims to investigate the neurovascular coupling impairment at the acute stage after ischemic stroke. Laser speckle contrast imaging (LSCI) was applied to measure the hemodynamic response to optogenetic excitation of sensorimotor neurons in healthy and ischemic brain. The results showed that the hemodynamic response to optogenetic stimulation decreased and the regional CBF response was correlated with the distance from the ischemic core at the acute stage, regardless of the change in resting CBF. Our results also demonstrated that excitatory neuronal stimulation of intact area could promote the recovery of neurovascular coupling, whereas peri-infarct neuronal excitation failed to restore neurovascular function 24 hrs after ischemia. These results suggested the intact periphery of penumbra as the target for excitatory stimulation in aspect of restoring the perfusion after ischemic stroke.

Meditation is a progressive process in which meditators may experience different stages to finally reach a deep meditation state. However, the temporal-dynamic process of meditation remains unclear. In this study, we recruited 38 experienced Tibetan Buddhism meditators to explore the dynamic process from resting to deep meditation from the aspects of EEG oscillations and networks. We found that the delta power was reduced and the transitivity of the delta network was increased immediately after the start of meditation. Then, the beta and gamma power increased, and the clustering coefficient and global efficiency of the gamma network increased at the deep meditation substage. Our results suggested that the participants entered into a wakeful and non-judgmental state immediately after the start of meditation, and then achieved a more efficient information processing and transferring only at a deep meditation state. Our study showed that meditation was a time-varying process, deep meditation was a different substage dominated by high frequency EEG oscillations and more small-world like brain networks.

It has been reported that meditation is a progressive procedure to achieve a thoughtless and transcendental deep meditation state. However, it remains difficult to know whether and when the practitioner has achieved deep meditation state for lack of a reliable and objective measure. The aim of this study is to explore an electrophysiological biomarker for the deep meditation stage by studying the autonomic nervous system (ANS) activities during meditation using heart rate variability (HRV). We recruited 70 experienced Tibetan Buddhist monks, and recorded electrocardiogram signal for ~10 min of rest followed by ~30 min of meditation. We found two different stages of meditation, i.e., light meditation (0-10min) and deep meditation (after 10min) stages, which can be distinguished by the increased very low frequency (VLF, 0.003-0.04Hz) and low frequency (LF, 0.04-0.15Hz) power. The light meditation stage was comparable with the rest in the ANS activities, while the deep meditation stage was significantly different from the rest and light meditation stage. We speculate that the deep meditation stage could be marked by significant increases of VLF and LF. Meanwhile, ratio of low and high frequency power, standard deviation of RR intervals and fractal scaling exponents of detrended fluctuation analysis also increased in the deep meditation stage. Our results indicated that meditation is a dynamic process and the deep meditation was dominated by activated sympathetic nervous system.

Voluntarily shifting attention to a location of the visual field improves the perception of stimulus at the attended location and suppresses information processing at unattended locations, something referred to as visual spatial attention. Previous studies have shown that alpha band phase synchronization between frontal and posterior regions was modulated by visual spatial attention. However, the causality of such long-range connectivity is still unknown, which makes it difficult to assess its relation to the underlying neural mechanisms of visual spatial attention. In this study, 64-channel scalp electroencephalography (EEG) was collected from 26 healthy adults when performing a visual spatial-cueing attention task, and a recently proposed algorithm, phase transfer entropy (PTE), was used to study the directed alpha phase interactions between frontal and posterior regions during attention orienting. We found that: (i) The PTE from frontal regions to posterior regions significantly increased after cue onset; (ii) The PTE from frontal regions (especially the right hemisphere) to posterior regions showed significant lateralization during late cue-target interval, with the greater PTE of contralateral frontal-to-posterior pair than that of ipsilateral pair relative to cue direction. These results suggest that alpha phase interactions might reveal top-down control signals from frontal to posterior regions during visual spatial attention, and support the right frontal regions as the causal role in top-down attentional control.

Brain plasticity after amputation is related to the short-term unmasking of latent synapses as well as the long-term reorganization due to the sprouting new synaptic connections. The cortical functional reorganization has been reported along the intact somatosensory pathway after unilateral deafferentation. Cerebral blood flow (CBF) change serves as an important biomarker of the functional reorganization of brain. Using laser speckle contrast imaging (LSCI) technology, we performed a longitudinal study to unveil the cortical functional reorganization after forelimb amputation in rodent model, particularly along the intact somatosensory pathway. Our results showed that the CBF response to electrical stimulation of the intact forepaw increased significantly at 9 hours after amputation in acute stage. While in chronic stage (>14 days), the CBF response showed a pattern similar to the control group. The results showed the dynamic brain functional response along the intact somatosensory pathway at different stages after amputation and indicated that cortical functional reorganization occurred within the acute stage. Our work provided additional insights in understanding the inter-hemispheric functional changes from acute to chronic stages of amputation.

Major depressive disorder (MDD) is associated with excessive self-focus and a tendency to engage in self-reflection. Women are more inclined to form negative thoughts when facing problems such as social rejection. The neuroimaging studies addressing MDD females are still rare. The medial prefrontal cortex, a central node of anterior default mode network (DMN), plays an important role in the pathophysiology of MDD. In this study, we explored the inter-regional correlation feature of mPFC with whole brain through functional connectivity (FC) analysis of resting state functional magnetic resonance imaging (rs-fMRI) data. Increased FC with left anterior cingulate cortex (ACC_L) was found in female MDD patients compared with healthy controls. Furthermore, the FC strength of ACC_L was positively correlated with clinical scores, which implied the important role of mPFC-ACC pathway in MDD females. Moreover, decreased FC with right middle temporal gyrus (mTG_R) and left middle temporal gyrus (mTG_L) was found in MDD females, which may result from the low self-evaluation in MDD. This work provides additional insights in understanding the pathophysiological mechanisms of MDD women.

To study the metabolic changes following amputation, we investigated the metabolite concentrations in the secondary somatosensory cortex and thalamus from twenty-three upper limb amputees and sixteen age-matched healthy controls using proton magnetic resonance spectroscopy (MRS). The ratios of major metabolites to the creatine (Cr) peak were quantified to indicate metabolite levels. Compared with the healthy controls, the amputees showed significantly reduced NAA/Cr and Cho/Cr ratios in the secondary somatosensory cortex and thalamus contralateral to the amputation side, indicating neuronal loss and dysfunctions in these brain regions due to the loss of sensory input. Meanwhile, the stump sensation was found to be positively correlated with NAA/Cr ratio and negatively correlated with GSH/Cr ratio in the thalamus. The results suggested that both the thalamus and somatosensory cortex may undergo metabolic changes after amputation.

Monitoring hemodynamic responses in the rodent stroke model is important in the study of ischemic stroke. However, it is hard for a single imaging modality to comprehensively explore these changes. In this study, we implemented laser speckle imaging (LSI) and visible-light optical coherence tomography (Vis-OCT) to detect cerebral blood flow (CBF), angiography and oxygen saturation (sO 2 ) in the mouse model of distal middle cerebral artery occlusion (dMCAO). LSI provides full-field, real-time cerebral blood flow (CBF) map to guide the Vis-OCT imaging. Vis-OCT offers angiography and sO 2 measurements. The results showed the detailed CBF and vasculature changes before and after dMCAO. After dMCAO, sO 2 changed little in arteries and arterioles, while sO 2 decreased in veins and venules. We also showed that larger veins had a higher decrease in sO 2 value in consecutive vein branches after dMCAO. It is suggested that the combination of LSI/Vis-OCT provides a promising tool for the rodent stroke model study.

Electroencephalography (EEG) and brain-computer interfaces (BCI) are receiving increasing attention and expanding application in stroke study. To identify stroke patients and normal controls during mental rotation task, common spatial pattern (CSP) algorithm is employed to extract features from binary-class EEG which will be further to form the dictionary for sparse representation. In the classification process, sparse representation-based classification (SRC) method is used; specifically, each test trial is sparsely represented over the formed dictionary and the sparse coefficient is obtained by solving a l1-norm regularized least squares minimization objective. A series of experiments demonstrated the effectiveness of features extracted by CSP of both classes and the SRC could obtain excellent results in classification. These result suggest that stroke patients have distinct EEG feature from normal controls. These EEG features may be potential biomarkers to monitor the rehabilitation process of stroke patients.

Schizophrenia is a severe psychiatric disorder that affects individuals across the course of their lifetime. Neurocognitive deficits are a typical characteristic of this disorder and are prominent in many cognitive domains including attention and fluency. Despite the recognized importance, patient cognition is rarely monitored as a regular part of treatment and even then, only with behavioral metrics. Functional Near Infrared Spectroscopy (fNIRS) is a non-invasive technique which measures cortical hemodynamic responses using near-infrared light with safe, portable, and cost-effective sensors. The measurement of fNIRS during cognitive processing tasks could offer objective insights into the dysfunction and cognitive inefficiency inherent in Schizophrenia. In this preliminary study, we examined cognition in patients with Schizophrenia and Controls under three different cognitive tasks (Continuous Performance Test, Verbal Fluency Test, Psychomotor Vigilance Test) to evaluate differences in behavioral and candidate biomarkers in the disorder. Our results support findings of left-hemispheric lateralization during language tasks, right-hemispheric lateralization during attention tasks, and imply task-related abnormal activation patterns in schizophrenia patients.

The coupling of cerebral blood flow (CBF) to neuronal activation, referred to as activation flow coupling (AFC), has been a fundamental brain physiology property. The stimulus-evoked CBF response was usually considered as a surrogate marker for neuronal activity in AFC studies. The selection of appropriate stimulation parameters, e.g., current amplitude and pulse width, is of great importance yet the effect of pulse width changes remained contradictory in previous studies. In this work, we use laser speckle contrast imaging (LSCI) to study the spatiotemporal CBF response to hindpaw somatosensory stimulation of different pulse widths (0.3 ms vs 1 ms) and current amplitudes (3 mA vs 6 mA) in a rodent experiment. The results showed that the change of pulse width significantly affected the CBF peak value at a lower current level (p;0.05). In addition, the duration for observing significantly different average CBF response, denoted as td, at various pulse widths, was dependent on stimulus current amplitude. At a lower amplitude (3 mA), td was 6.5 s; While at a higher amplitude (6 mA), td was 2.5 s. It was indicated that the changes of pulse width had longer influence on the average CBF response at a lower current amplitude. Our findings may help to understand and explain the inconsistent AFC with different stimulation parameters in fundamental brain physiology.

Stroke recovery involves a battery of plastic changes in the brain. Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) provides brain activation information with exquisite spatial resolution as a powerful tool for investigating changes in brain plasticity. In this paper, we performed a longitudinal study examining plasticity of functional activation by BOLD-fMRI following stroke. Data were collected from 11 patients with corticospinal tract (CST) damage at three stages of recovery, i.e., acute stage ( 3mons) post stroke. The evolution of cortical activations for both affected and unaffected hand motion tasks were studied. Quantitative activation measurements including the effective size and sum of t values were calculated and the correlations with patient Fugl-Meyer index were analyzed across all stages. Stroke patients showed a shift from bilateral activation in acute and early stage to the ipsilesional activation in chronic stage when performing a movement task with the affected hand, which suggests a compensation effect from the contralesional hemisphere during the recovery process. The correlation analysis showed a significantly negative correlation with cingulate cortex activity at early stage from both quantitative activation measurements, implying the special role of cingulate cortex in stroke recovery. Further investigations are in need to improve the understanding of brain plasticity in stroke patients.

Our previous study demonstrated that incongruent audiovisual change could result in the illusory perception of the change in sound intensity. In this study, we investigated the causal cortical networks for audiovisual interactions during this visual-affected auditory discrimination using partial directed coherence. In the early stage (49–198 ms), the audiovisual trials activated more left-to-right connections than unimodal ones, especially those inter-hemispheric frontal-to-occipital and left-centroparietal-to-right-occipital connections. In the middle-late stage (271–420 ms), the audiovisual network presented a right hemispheric dominance, involving more connections to right frontal and occipital areas. More inter-hemispheric connections, including bilateral connections from parietal to frontal cortex, were activated for audiovisual mismatched information processing and decision making.

Stroke could result in both structural and functional impairments. Sensory remapping is believed to play a special role in cortical plasticity, which contributes to the recovery after ischemic stroke. Previous studies using imaging and electrophysiological methods have found that sensory representations after stroke can remap not only to the surrounding areas, but also to remote areas. Based on a rodent photothrombotic stroke model using optical intrinsic signal (OIS) imaging, the present study attempts to investigate the remapping of sensory representation in contralesional cortex to ipsilesional hindlimb stimulation. Quantitative analysis revealed an overall expansion of hindlimb representation in contralesional cortex after stroke. Moreover, results indicated that hindlimb representation became less correlated to different stimulation intensity in contrast to the previous positive correlation before stroke. We speculate that diaschisis in the acute stage of stroke might account for such a change in contralesional sensory representation.

This study investigated mental rotation for identical stimuli and mirrored stimuli by both behavior response and event-related desynchronization (ERD) of EEG signals. Results showed that subjects had longer response time for mirrored stimuli than identical stimuli. Beta-band desynchronization appeared in whole brain with the parietal-occipital dominance. The ERD in beta band recovered slowly in an angular order after 450-600 ms of stimulus onset except the sharp rebound in the case of identical stimuli with rotation at 0 °. This temporal difference of beta ERD between the identical and the mirrored stimuli at 0° rotation and the ERD topographic difference in left fronto-parietal regions, together with the behavior difference, may imply an extra flip process in mirrored condition.

Middle cerebral artery occlusion (MCAO) by intraluminal suture is one of the most commonly used stroke models. Our previous study has indicated that the intraoperative cerebral blood flow (CBF) immediately after the stroke is prognostic for long-term permanent injury. The area of more than 50% CBF drop at the first minute after the stroke was found significantly correlated with the lesion size at 24 hours after the stroke. In order to compare the prognostic of different vessels, in this study, we further analyzed the correlation between the CBF levels in major artery, vein and the capillary bed and the lesion volume at 24 hours respectively. The results show that ipsilesional artery blood flow is of more prognostic value in MCAO lesion than the CBF in veins and capillaries.

The quantification of Magnetic Resonance Spectroscopy (MRS) signal remains challenging due to the low signal-to-noise ratio (SNR) of data. All time-domain quantification methods highly require user interactions, which reduce the reproducibility of the data quantification. The goal of our work is to design a systematic methodology for automated quantification of a weak signal in MRS data. We used Hankel Singular Value Decomposition (HSVD) algorithm in our signal estimation step, along with frequency selective preprocessing step using ER-filter to improve the computational efficiency. On the model order selection problem of HSVD, we investigated two strategies for optimal choice of model order K: 1) Gaussian line-shape fitting (GLF) method; 2) Kurtosis analysis (KA) method. The detection and estimation performance of both methodologies have been evaluated in terms of detection rate and relative root mean square error (RRMSE), in comparison to traditional strategies with fixed model order. The synthesis and semi-synthesis simulation results both show that GLF and KA outperform the traditional K fixed methods. Moreover, GLF works the best among all, and its performance is worsened when the signal damping factor increases.

Recent studies on Alzheimer's disease (AD) have shown disrupted topological organizations of both functional and anatomical brain networks in AD patients, suggesting that AD might be a disease related to disconnection between brain regions. White matter (WM)-based anatomical network can be weighted by fractal anisotropy (FA), which reflects the integrity of axons and myelin sheaths. Thus, study on the WM-based brain network from the perspectives of topological organization and pair-wise edges might provide information about global organization alteration and WM integrity impairment in AD. In the current work, in addition to the topological characteristics of brain networks, we further investigated the disconnection of edges in anatomical brain networks of AD patients via diffusion tensor tractography. The results showed that both the brain networks of AD patients and normal controls (NCs) exhibited small-world property and such small-world property tended to decline with cognitive impairment. Some network metrics (e.g., clustering coefficient, and characteristic path length and mean FA of all edges) were found to be correlated with the cognitive performance. More importantly, compared to NCs, AD patients had decreased FA weight of edges between certain cortical regions, indicating network disconnection and damage of WM integrity. In summary, our results provided further support for the existence of small-world property in anatomical brain networks of AD patients, and revealed reduced edge weights in AD patients' anatomical brain networks.

Intraluminal middle cerebral artery occlusion (MCAO) model in rats has been widely used to mimic human ischemic stroke and serves as an indispensable tool in the stroke research field. One limitation of this model is its high variability in infarct volume. The cerebral blood flow (CBF) information after cerebrovascular occlusion may reflect the availability of collateral circulation, which serves as a key factor for brain infarct volume. Laser speckle contrast imaging (LSCI) is a valuable tool for full-field imaging of CBF with high spatial and temporal resolution. In this paper, we investigated the spatio-temporal changes of CBF in rat MCAO stroke model using our self-developed real-time LSCI system. CBF images of adult male Sprague Dawley rats (n=13) were recorded before surgery, during first 1.5 hours after surgery, and 24 hours after stroke. We compared the CBF changes of different functional vessels during this period. In the ipsilateral hemisphere, CBF of veins and arteries both decreased as expected, while CBF of veins increased after occlusion in the contralateral hemisphere. Moreover, we found a linear correlation between early-stage CBF after occlusion and brain infarct volume, which can be utilized for surgery guidance to improve the uniformity of rat MCAO stroke models.

People nowadays are usually involved in computer work within a required time, in which the subjects are under time pressure (TP). It has been reported that TP can impact the autonomic neural regulation system and cognitive behavior during and after the commitment. However, there are few studies on the influence of TP on the cortical information processing. In this study, we designed an experiment with three sessions of computer-based tasks under different background to investigate how TP affect the cortical information processing pattern by large-scale cortical synchrony analysis of the multichannel electroencephalogram (EEG) signals. Results showed that compared with task without TP, task under TP could only cause the decrease of inter-hemispheric cortical synchrony. Synchronization analysis of multichannel EEG provides a new insight into the effect of TP on the functional cortical connection of brain.

Our research aims to develop a novel method for quantitative measurement of hCG concentration in human serum. Based on digital imaging processing technique, the relationship between hCG concentration and image information of reaction dot on hCG test sample can be built and be described as a standard curve. According to this standard curve, concentration of hCG sample can be measured. The image information of hCG test sample is captured by CCD mini-camera and is processed by digital signal processor (DSP). The sensitivity can be under 10mIU/ml. The high accuracy makes our system significantly potential application in clinical diagnosis.

Stroke has been one of the leading causes of mortality and long-term morbidity around the world. Describing the cortical synchrony has been useful in understanding of central nervous system disorders after brain injury. In this paper, we investigated the large scale cortical phase synchronization derived from multichannel electroencephalogram (EEG) recordings of bilateral ischemic stroke patients(n = 9). Compared with the results from the age- and gender-matched control subjects (n = 8), stroke patients showed 1) there were not significant changes of the intra-hemispheric synchronization after the bilateral stroke; and however, 2) both global and inter-hemispheric synchronization are vulnerable to the ischemic injury. Our preliminary results indicated that synchrony analysis of the spontaneous scalp EEG during at resting state provided new insight into cortical functional disorder following stroke.

Stroke has remained as a leading cause of death and neurological disability worldwide in the past decades. Previous structural and functional studies reported little information regarding cortical neural network after stroke. Using the causality measure based on multi-channel electroencephalograph (EEG), i.e. partial directed coherence (PDC) in this paper, we investigated the different network patterns involved in pre-motor and parietal areas (F3, F4, C3, C4, P3 and P4) in three groups of patients who suffered unilateral or bilateral hemispheric stroke in basal ganglia with extension into corona radiate. Compared with the results in the control group, stroke patients showed: 1) more vulnerable long-distance intra- and inter-hemispheric interactions due to the ischemic injury; 2) strengthened short-distance interactions between the central areas in the intact hemisphere with the injured counterpart, which implied a functional compensation after unilateral stroke; 3) more suppression of cortical connections after bilateral hemispheric stroke than those with unilateral stroke. Causal interdependence by PDC analysis provides a new insight of cortical functional network following stroke.

Laser speckle imaging (LSI) and optical intrinsic signal (OIS) imaging are shown to produce high resolution information of cerebral blood flow(CBF) and de-oxygen hemoglobin(dHb) respectively. However, the cortex curvature and non-uniform illumination always result in the inhomo-geneous impact and thus distort the CBF and dHb results. In this paper, we propose to extract both CBF and dHb images from raw speckle images. A parametric model for such inhomogeneity is estimated by adaptive window median-mean filtering and curve fitting technique to reconstruct the CBF and dHb image.

Laser speckle imaging (LSI) has been implemented as a tool of acquiring two dimensional blood flow information. Using vascular segmentation techniques, we are able to obtain the cortical vascular blood flow distribution. Nevertheless, the conventional LSI methods have limitations of small imaging area and low dynamic range of contrast due to the ambient background field, dark current and anomalies in the circuits of CCD camera. In this study, we introduced an enhanced laser speckle contrast analysis (eLASCA) to improve the dynamic range of LASCA, then a high resolution of cerebral blood flow (CBF) was obtained by image mosaicing technique, including image registration and image fusion. The eLASCA-based LSI mosaicing provides global anatomic details of cortical microvessels as well as the cortical vascular blood flow information.

The aim of this study is to compare the brain activities in depression patients and healthy controls by a quantitative method. In the present study, the wavelet entropy (WE) and subband segmentation analysis are proposed to characterize the degree of disorder and complexity of the electroencephalographic (EEG) signals recorded from 16 scalp electrodes in 20 depression patients and 20 normal controls at rest and during mental arithmetic. The WE analysis demonstrates the EEGs of the depression subjects have higher WE than those of the controls at rest, which indicates a less rhythmic and ordered status in depression, whereas such difference is not significant during mental arithmetic. These results provide evidence that depression patients have more regular brain wave during mental arithmetic than rest. Furthermore, the WE of the depression patients is higher during rest than mental arithmetic at almost all electrodes, and this phenomenon is not found in the controls. It may suggest that larger parts of the brain of the patients are active during rest than performing a cognitive task. In addition, marginal effect of hemisphere is detected for the patients during mental arithmetic. Our results show that the we could be a useful tool in cognitive process analysis, especially in depression.

Center-surround spatiotemporal (ST) filter is a powerful tool to simulate the spatial and temporal properties of retina ganglion cells and encode visual information with electric spikes. This paper introduces the application of particle swarm optimization (PSO) algorithm to tune the parameters in the retina model consisting of a ST filter module and a back-propagation (BP) neural network module. Images are converted into electric spikes by the ST filters whose outputs are then fed into the BP neural network to reconstruct the output images. The parameters of the ST filters determine the electric spike sequences as well as the output image from the BP network. In order to get the expected output images, we employ PSO to iteratively tune the parameters. Euclidean distance between output and input image is used as scalar criteria to optimize the ST filter. The tuning process stops until the similarity between output and input images no longer improves. The results show that 62.3 % of the images trained by PSO have better output image quality and less iteration time compared with those trained by the current evolution strategy (ES).

Video acquisition and processing is always the first stage in all prosthetics. We consider the problem of video stabilization applied in visual prosthetics: removing unwanted image perturbations due to unstable camera motions caused by walking subjects. In this paper, we propose a software video stabilization algorithm based on feature block searching (FBS) approach to remove the large amplitude of moving and shaking in video. Compared with the conventional approach of video stabilization, FBS is scene content oriented, and can be easily implemented due to its computational simplicity, real-time processing speed and reasonable memory requirements by tracking the motion vector locally. The experimental results show that the method results in a greatly visual quality of the reconstructed sequence and a significant saving in terms of bit rate.

Hypoxic-ischemic (HI) brain injury after cardiac arrest (CA) causes the main morbidity and mortality of the patients. The mechanism of HI neurological injury and its real time detection and monitoring have been extensively studied in the past years. The electroencephalogram (EEG) following CA has been investigated to provide a noninvasive monitoring strategy. In this present work, we studied the complexity of the EEG signal after HI injury. Sample entropy (SampEn) is applied to analyze the EEG in different frequency bands (delta, theta, alpha and beta). The "alpha vulnerability" in rhythmic complexity is observed, which indicated that among different EEG rhythmic subbands, alpha wave shows a distinct SampEn decrease during the early recovery period after HI injury. The alpha vulnerability is confirmed by our previous research on the vulnerability in thalamic somatosensory pathway. The results may help us develop means to restore electrical function recovery after HI.

The neurological injury after resuscitation from cardiac arrest (CA) is a major cause of morbidity and mortality. The unavailability of primary brain directed measures of injury and effective therapy worsens the problem. We have embarked on better understanding the process of injury and recovery by developing an animal model in our laboratory. Using this model, we have discovered that the use of quantitative EEG (Q-EEG) analysis will allow us to titrate the brain injury, monitor the recovery process, study the electrophysiological and biochemical mechanisms of injury and recovery and detect the neuroprotective effect afforded by a drug that prevents neuronal death after injury. With ideas derived from the laboratory research, we have designed a study to test the applicability of QEEG analysis in human subjects resuscitated from cardiac arrest. Our preliminary analysis shows that our measure is able to track the neurological changes of these patients. With this very promising start of this QEEG based measure, we can utilize this technology to develop more rational and effective therapeutic strategies that will improve the neurological outcome in this patient population.

Previous study has shown that the scalp electrical activity (e.g. EEG) could provide information on hypoxic-ischemic (HI) brain injury. The time dependent entropy (TDE) of EEG was applied as a means to segment the injury and recovery phases. A reduction in entropy during the injury was explained as the result of the spiking or bursting activity during the recovery of HI injury. The aim of this study is to investigate the spiking activity in the cortex during (HI) brain injury. The multiunit activity recorded from the cortex (CTX MUA) is analyzed with TDE methods. The spike sensitive TDE tracks the electric activity at the cellular level. The CTX MUA has high TDE value after the HI injury, which gradually returns to the baseline level as the brain recovers. This is the first report of cellular electrical response to global ischemic injury.

The high frequency of neurological dysfunction after cardiac arrest emphasizes the need for new approaches able to provide an early evaluation of the amount of initial injury as well as the presence of ongoing injury. In the present paper, we apply two synchronization measures to the study of EEG data, namely phase synchronization and mutual information, and evaluate their potential usefulness in the assessment of the degree of brain injury following the cardiac arrest injury. Our preliminary results reveal that phase synchronization in the theta frequency range and the patterns of information flow across the cerebral cortex changes during the one hour after the cardiac arrest event. Therefore, measures of synchronization applied on the brain's electrical signaling can provide a new quantitative assessment of the brain response to injury, as well as the recovery process of neurological function after cardiac arrest.

In the basic and clinical research on brain's response to injury, electrical signals from the brain, namely EEG, is useful in providing an immediate signaling of the dysfunction. However, EEG signals have proven to be difficult to analyze and interpret due it its complex signal characteristic. There is a critical need for developing robust and reliable measures that can be correlated with injury as well as survival. In this paper, we address a unique problem of characterizing quantitatively the electrical measures of brain injury for analysis of brain activity in animal and human subjects. The key objective is to model EEG spectra and its features so that signaling changes due to injury can be discovered. We do so with the method of autoregressive modeling and dominant frequency analysis. The trends in the electrical signaling following injury and following resuscitation are modeled using the cepstral distance derived from the AR model.