Detecting Interactions between the Renal Autoregulation Mechanisms in Time and Space

<italic>Objective:</italic> Our objective is to identify localized interactions between the renal autoregulation mechanisms over time. <italic>Methods:</italic> A time-varying phase-randomized wavelet bicoherence detector for quadratic phase coupling between tubuloglomerular feedback and the myogenic response is presented. Through simulations we show its ability to interrogate quadratic phase coupling. The method is applied to kidney blood flow and laser speckle imaging sequences of cortical perfusion from anesthetized rats before and after nonselective inhibition of nitric-oxide synthase. <italic>Results:</italic> Quadratic phase coupling in kidney blood flow data was present in four out of nine animals during the control period for 13.0 ± 5.6% (mean ± SD) of time and in five out of nine animals during inhibition of nitric-oxide synthase for 15.8 ± 8.2% of time. Approximately 60% of time-series extracted from laser speckle imaging pixels of the renal cortex showed significant quadratic phase coupling. Pixels with significant coupling had a median coupling length of 10.8 ± 2.2% and 12.1 ± 3.1% of time with the 95th percentile of pixels being coupled for 25.5 ± 4.4% and 30.9 ± 6.4% of time during control and inhibition of nitric-oxide synthase, respectively. <italic>Conclusion:</italic> These results indicate quadratic phase coupling exists in short time intervals between tubuloglomerular feedback and the myogenic response and is detected more often in local renal perfusion signals than whole kidney blood flow in anesthetized rats. <italic>Significance:</italic> Combining the detector and laser speckle imaging provides identification of coordination between renal autoregulation mechanisms that is localized in time and space.

[1]  K. Chon,et al.  Nonlinear interactions in renal blood flow regulation. , 2005, American journal of physiology. Regulatory, integrative and comparative physiology.

[2]  Yuru Zhong,et al.  Autonomic nervous nonlinear interactions lead to frequency modulation between low- and high-frequency bands of the heart rate variability spectrum. , 2007, American journal of physiology. Regulatory, integrative and comparative physiology.

[3]  R. Loutzenhiser,et al.  Renal autoregulation: new perspectives regarding the protective and regulatory roles of the underlying mechanisms. , 2006, American journal of physiology. Regulatory, integrative and comparative physiology.

[4]  Xiaoli Li,et al.  The comodulation measure of neuronal oscillations with general harmonic wavelet bicoherence and application to sleep analysis , 2009, NeuroImage.

[5]  Niels-Henrik Holstein-Rathlou,et al.  Frequency encoding in renal blood flow regulation. , 2005, American journal of physiology. Regulatory, integrative and comparative physiology.

[6]  Benjamin A. Carreras,et al.  Wavelet bicoherence: A new turbulence analysis tool , 1995 .

[7]  Niels-Henrik Holstein-Rathlou,et al.  Nephron blood flow dynamics measured by laser speckle contrast imaging. , 2011, American journal of physiology. Renal physiology.

[8]  S. Elgar,et al.  Statistics of bicoherence and biphase , 1989 .

[9]  Ki H. Chon,et al.  Time–Frequency Approaches for the Detection of Interactions and Temporal Properties in Renal Autoregulation , 2012, Annals of Biomedical Engineering.

[10]  Steve McLaughlin,et al.  Quadratic phase coupling detection using higher order statistics , 1995 .

[11]  K. Chon,et al.  Interactions of TGF-dependent and myogenic oscillations in tubular pressure. , 2005, American journal of physiology. Renal physiology.

[12]  E Mosekilde,et al.  Double-wavelet approach to study frequency and amplitude modulation in renal autoregulation. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Thomas M van Gulik,et al.  Real-time assessment of renal cortical microvascular perfusion heterogeneities using near-infrared laser speckle imaging. , 2010, Optics express.

[14]  K. Chon,et al.  On the Efficacy of the Combined Use of the Cross-Bicoherence with Surrogate Data Technique to Statistically Quantify the Presence of Nonlinear Interactions , 2009, Annals of Biomedical Engineering.

[15]  Ki H. Chon,et al.  Statistical Approach to Quantify the Presence of Phase Coupling Using the Bispectrum , 2008, IEEE Transactions on Biomedical Engineering.

[16]  K. Chon,et al.  Detecting physiological systems with laser speckle perfusion imaging of the renal cortex. , 2013, American journal of physiology. Regulatory, integrative and comparative physiology.

[17]  B. Braam,et al.  Assessment of renal autoregulation. , 2007, American journal of physiology. Renal physiology.

[18]  K. Chon,et al.  Interactions between TGF-dependent and myogenic oscillations in tubular pressure and whole kidney blood flow in both SDR and SHR. , 2006, American journal of physiology. Renal physiology.

[19]  Steve Elgar,et al.  Statistics of bicoherence , 1988, IEEE Trans. Acoust. Speech Signal Process..

[20]  O. Sosnovtseva,et al.  Electrotonic vascular signal conduction and nephron synchronization. , 2009, American journal of physiology. Renal physiology.

[21]  D J Marsh,et al.  Detection of interactions between myogenic and TGF mechanisms using nonlinear analysis. , 1994, The American journal of physiology.

[22]  R. Loutzenhiser,et al.  Systolic pressure and the myogenic response of the renal afferent arteriole. , 2004, Acta physiologica Scandinavica.

[23]  W. Cupples Interactions contributing to kidney blood flow autoregulation , 2007, Current opinion in nephrology and hypertension.

[24]  E. Mosekilde,et al.  The effect of L-NAME on intra- and inter-nephron synchronization. , 2009, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[25]  D J Marsh,et al.  Mechanisms of temporal variation in single-nephron blood flow in rats. , 1993, The American journal of physiology.

[26]  H. Jacob,et al.  Interaction between Rf-1 and Rf-4 quantitative trait loci increases susceptibility to renal damage in double congenic rats. , 2005, Kidney international.

[27]  E. Lewis,et al.  Renal autoregulation and vulnerability to hypertensive injury in remnant kidney. , 1987, The American journal of physiology.

[28]  A. Arapostathis,et al.  A Novel QPC Detector for the Health Monitoring of Rotating Machines , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[29]  K. Chon,et al.  Laser speckle contrast imaging reveals large-scale synchronization of cortical autoregulation dynamics influenced by nitric oxide. , 2015, American journal of physiology. Renal physiology.

[30]  Janez Jam,et al.  Nonlinear cardio-respiratory interactions revealed by time-phase bispectral analysis , 2004 .

[31]  Aneta Stefanovska,et al.  Wavelet bispectral analysis for the study of interactions among oscillators whose basic frequencies are significantly time variable. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  K. Chon,et al.  Detection of low-frequency oscillations in renal blood flow. , 2009, American journal of physiology. Renal physiology.