95 research outputs found

    Robust Peak Recognition in Intracranial Pressure Signals

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    <p>Abstract</p> <p>Background</p> <p>The waveform morphology of intracranial pressure pulses (ICP) is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.</p> <p>Methods</p> <p>This paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse). Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.</p> <p>Results</p> <p>Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.</p> <p>Conclusion</p> <p>The proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.</p

    Regression analysis for peak designation in pulsatile pressure signals

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    Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm

    Human Astrocytes Exhibit Tumor Microenvironment-, Age-, and Sex-Related Transcriptomic Signatures

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    : Astrocytes are critical for the development and function of synapses. There are notable species differences between human astrocytes and commonly used animal models. Yet, it is unclear whether astrocytic genes involved in synaptic function are stable or exhibit dynamic changes associated with disease states and age in humans, which is a barrier in understanding human astrocyte biology and its potential involvement in neurological diseases. To better understand the properties of human astrocytes, we acutely purified astrocytes from the cerebral cortices of over 40 humans across various ages, sexes, and disease states. We performed RNA sequencing to generate transcriptomic profiles of these astrocytes and identified genes associated with these biological variables. We found that human astrocytes in tumor-surrounding regions downregulate genes involved in synaptic function and sensing of signals in the microenvironment, suggesting involvement of peri-tumor astrocytes in tumor-associated neural circuit dysfunction. In aging, we also found downregulation of synaptic regulators and upregulation of markers of cytokine signaling, while in maturation we identified changes in ionic transport with implications for calcium signaling. In addition, we identified subtle sexual dimorphism in human cortical astrocytes, which has implications for observed sex differences across many neurological disorders. Overall, genes involved in synaptic function exhibit dynamic changes in the peritumor microenvironment and aging. This data provides powerful new insights into human astrocyte biology in several biologically relevant states, that will aid in generating novel testable hypotheses about homeostatic and reactive astrocytes in humans.SIGNIFICANCE STATEMENTAstrocytes are an abundant class of cells playing integral roles at synapses. Astrocyte dysfunction is implicated in a variety of human neurological diseases. Yet our knowledge of astrocytes is largely based on mouse studies. Direct knowledge of human astrocyte biology remains limited. Here, we present transcriptomic profiles of human cortical astrocytes, and we identified molecular differences associated with age, sex, and disease state. We found that peritumor and aging astrocytes downregulate genes involved in astrocyte-synapse interactions. These data provide necessary insight into human astrocyte biology that will improve our understanding of human disease

    Improved Noninvasive Intracranial Pressure Assessment With Nonlinear Kernel Regression

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