Literature DB >> 19142194

Inferring cerebrovascular changes from latencies of systemic and intracranial pulses: a model-based latency subtraction algorithm.

Xiao Hu1, Andrew W Subudhi, Peng Xu, Shadnaz Asgari, Robert C Roach, Marvin Bergsneider.   

Abstract

Changes in cerebral blood flow velocity (CBFV) pulse latency reflect pathophysiological changes of the cerebral vasculature based on the theory of pulse wave propagation. Timing CBFV pulse onset relative to electrocardiogram QRS is practical. However, it introduces confounding factors of extracranial origins for characterizing the cerebral vasculature. This study introduces an approach to reducing confounding influences on CBFV latency. This correction approach is based on modeling the relationship between CBFV latency and systemic arterial blood pressure (ABP) pulse latency. It is tested using an existing data set of CBFV and ABP from 14 normal subjects undergoing pressure cuff tests under both normoxic and acute hypoxic states. The results show that the proposed CBFV latency correction approach produces a more accurate measure of cerebral vascular changes, with an improved positive correlation between beat-to-beat CBFV and the CBFV latency time series, for example, correlation coefficient increased from 0.643 to 0.836 for group-averaged cuff deflation traces at normoxia. In conclusion, this study suggests that subtraction of systemic ABP latency improves CBFV latency measurements, which in turn improve the characterization of cerebral vascular changes.

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Year:  2009        PMID: 19142194      PMCID: PMC2664398          DOI: 10.1038/jcbfm.2008.160

Source DB:  PubMed          Journal:  J Cereb Blood Flow Metab        ISSN: 0271-678X            Impact factor:   6.200


  22 in total

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6.  An algorithm for extracting intracranial pressure latency relative to electrocardiogram R wave.

Authors:  Xiao Hu; Peng Xu; Darrin J Lee; Paul Vespa; Kevin Baldwin; Marvin Bergsneider
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  7 in total

1.  An extended model of intracranial latency facilitates non-invasive detection of cerebrovascular changes.

Authors:  Shadnaz Asgari; Andrew W Subudhi; Robert C Roach; David S Liebeskind; Marvin Bergsneider; Xiao Hu
Journal:  J Neurosci Methods       Date:  2011-02-15       Impact factor: 2.390

2.  Intracranial pressure pulse morphological features improved detection of decreased cerebral blood flow.

Authors:  Xiao Hu; Thomas Glenn; Fabien Scalzo; Marvin Bergsneider; Chris Sarkiss; Neil Martin; Paul Vespa
Journal:  Physiol Meas       Date:  2010-03-26       Impact factor: 2.833

3.  Cardiac-gated steady-state multifrequency magnetic resonance elastography of the brain: Effect of cerebral arterial pulsation on brain viscoelasticity.

Authors:  Felix Schrank; Carsten Warmuth; Heiko Tzschätzsch; Bernhard Kreft; Sebastian Hirsch; Jürgen Braun; Thomas Elgeti; Ingolf Sack
Journal:  J Cereb Blood Flow Metab       Date:  2019-05-29       Impact factor: 6.200

4.  Consistent changes in intracranial pressure waveform morphology induced by acute hypercapnic cerebral vasodilatation.

Authors:  Shadnaz Asgari; Marvin Bergsneider; Robert Hamilton; Paul Vespa; Xiao Hu
Journal:  Neurocrit Care       Date:  2011-08       Impact factor: 3.210

5.  Steady-state indicators of the intracranial pressure dynamic system using geodesic distance of the ICP pulse waveform.

Authors:  Xiao Hu; Nestor Gonzalez; Marvin Bergsneider
Journal:  Physiol Meas       Date:  2012-11-15       Impact factor: 2.833

6.  Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach.

Authors:  Paria Rashidinejad; Xiao Hu; Stuart Russell
Journal:  Physiol Meas       Date:  2020-11-06       Impact factor: 2.833

7.  Continuous detection of cerebral vasodilatation and vasoconstriction using intracranial pulse morphological template matching.

Authors:  Shadnaz Asgari; Nestor Gonzalez; Andrew W Subudhi; Robert Hamilton; Paul Vespa; Marvin Bergsneider; Robert C Roach; Xiao Hu
Journal:  PLoS One       Date:  2012-11-30       Impact factor: 3.240

  7 in total

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