1. 封面页
ESGCO2016 Oral Presentation
Coherence analysis of cerebral tissue oxygenation and arterial blood pressure signals in post-stroke subjects
Speaker: Zengyong LI1,2
1.Shandong University, Jinan, P.R. China
2. National Research Center for Rehabilitation Technical Aids, Beijing 1

2. Contents
Background 1
Methods 2
Results 3
Discussion 4

3. I. Background SDU Cerebral Autoregulation(CA)：
Contact Author Name: Zengyong Li
I. Background
SDU
Cerebral Autoregulation(CA)：
The brain has a high metabolic demand and therefore requires adequate and timely nutrient and oxygen supply.
Cerebrovasculature is able to maintain constant global blood flow despite variations in regional flow and systemic arterial pressure (termed ‘cerebral autoregulation’).
Shandong University 3

4. Background
The relationship between spontaneous cerebral oscillations (i.e. [O2Hb]) and cardiovascular parameters (i.e. arterial blood pressure (ABP)) is a promising technique for non-invasively assessing the status of CA.
CA is a frequency-dependent phenomenon that operates most effectively in the frequency range below 0.1 Hz (termed as a ‘high-pass filter’ )(Panerai et al. 1998; Zhang et al. 1998; Hamner et al. 2004).

5. Background
Wavelet analysis provides the possibility for identification the signals in the time domain and the frequency domain (Stefanovska et al., 1999 ).
Spontaneous oscillations of NIRS and ABP signals in various characteristic frequency bands have been identified by means of the wavelet analysis (Cui et al., 2014; Li et al., 2010, 2012, 2014)

6. Near-Infrared Spectroscopy
Near-Infrared Spectroscopy (NIRS) is an increasingly popular technology for studying brain function. It can measure concentration changes of oxygenated (oxy-Hb) and deoxygenated (deoxy-Hb) hemoglobin in local cerebral tissues non-invasively and continuously.
Izzetoglu M, et al IEEE Eng Med Biol Mag.

7. Objective
Pathological conditions (e.g. stroke) may alter the normal CA.
What’s the effects of stroke on CA in various frequency bands ?
It can be hypothesized that the dynamic relationship between the Delta [O2Hb] and ABP signals would be altered because of CI.
Aim to assess the coherence of cerebral tissue oxyhemoglobin concentrations changes (Delta [O2Hb]) and ABP signals using wavelet-based coherence method in elderly subjects with CI.

8. II. Subjects and Methods
Contact Author Name: Zengyong Li
II. Subjects and Methods
SDU
Subjects in Two Group
A total of 31 subjects were recruited, in which 16 subjects healthy subjects (Group Health) and 15 were patients with CI (Group CI).
1.Measured stroke subjects from two "nursing home for elderly" in Jinan. (20min resting state)
2.Measured healthy elderly subjects from the Shandong University. (20min resting state)
3.Excluded from the diabetes mellitus; subarachnoid hemorrhage; insufficiency of heart, lungs, kidneys; smoking and drinking; additional medications
Shandong University 8

9. Methods
Subjects

10. Methods
The Delta [O2Hb] signals in the left and right prefrontal cortex (PFC), the left and right motor areas with 8 channels bilaterally by referring to the international 10–20 electrode system. Sampling rate:10 Hz
The continuous ABP waveform was monitored with a pressure sensor attached to the wrist to get the ABP signal using an ABP colleting system.

11. Methods
Experiments

12. Methods Wavelet Transform
A method that provides for the complex transformation of a time series from the time to the time-frequency domain and can provide appropriate time and frequency resolution by using the adjustable filter band lengths.
Raw time series
Wavelet Transform

13. Methods
The power spectra of Oxy-Hb signals exhibit oscillations in various frequency bands.
Interval
Frequency (Hz)
Physiological origin I
0.6-2
Cardiac activity II
Respiration
III
Myogenic activity IV
Neurogenic activity V
– 0.021
Nitric oxide dependent endothelial metabolic activity VI
0.005 –
Nitric oxide independent endothelial activity
Time-averaged Wavelet Transform
Li ZY et al., 2010, Microvascular Research, 80 (1) 142–147 ;
Shiogai et al.,2010, Physics Reports 488 (2010)

14. Methods
Wavelet Transform
Raw time series
Wavelet Transform

15. Wavelet Coherence
Wavelet coherence was used to determine the coherence of two signals in the time-frequency domain (Sheppard et al., 2012).
An example of wavelet coherence of Δ[HbO2] signals measured from the left and right prefrontal lobes in a healthy elderly subject. The level of 0.5 suggests a significant coherence between two signals based on 50% shared variance.

16. Wavelet Coherence
An example of wavelet coherence of Δ[HbO2] and ABP signals in a healthy elderly subject. The level of 0.5 suggests a significant coherence between two signals based on 50% shared variance.

17. Wavelet Phase Coherence
Wavelet Phase Coherence identifies possible relationships by evaluating the match between the instantaneous phases of two signals (Bernjak, Stefanovska,et al., 2012).
An example of wavelet phase coherence Δ[HbO2] signals measured from the left and right prefrontal lobes in a healthy elderly subject. The upper and lower dotted lines show the mean, and two standard deviations above the mean, respectively, for the coherence calculated from 100 surrogate signals per subject.

18. Wavelet Phase Coherence
An example of wavelet phase coherence of the ABP and the Delta [O2Hb] signals in the six frequency intervals in a typical healthy elderly subject.

19. Results (a) Delta [O2Hb] signal of Ch.1 and ABP signal;
Contact Author Name: Zengyong Li
Results
(a) Delta [O2Hb] signal of Ch.1 and ABP signal;
(b) Delta [O2Hb] signal of Ch.10 and ABP signal.
*p<0.05, **p<0.01.
Shandong University 19

20. SDU (a) Delta [O2Hb] signal of Ch.1 and ABP signal;
Contact Author Name: Zengyong Li
SDU
(a) Delta [O2Hb] signal of Ch.1 and ABP signal;
(b) Delta [O2Hb] signal of Ch.10 and ABP signal.
*p<0.05 or **p<0.01.
Shandong University 20

21. Results Functional connectivity revealed by wavelet coherence
Group Health
Group CI
Blue line —— Low connectivity Green line —— Middle connectivity
Red line —— High connectivity Purple line —— Very high connectivity

22. Results Functional connectivity revealed by wavelet phase coherence
Group Health
Group CI
Tan Q , Li Z et al., 2015, Med Phys. 2015

23. Results Comparison of wavelet coherence Fronto-posterior connectivity
Homologous connectivity
Motor-contralateral connectivity
Motor-homolateral connectivity

24. Result Comparison of wavelet phase coherence
Fronto-posterior connectivity
Homologous connectivity
Motor-contralateral connectivity
Motor-homolateral connectivity

25. Discussion
NIRS and wavelet-based methods can be used to investigate the CA of brain.
Lower WCO and WPCO in subjects with CI suggest an enhanced synchronization between Delta [O2Hb] and ABP.
This enhancement might be an indicative of compensatory mechanism.
This study provides new insight into the CA mechanisms in elderly subjects with CI and may be used to assess motor rehabilitation and brain plasticity after stroke.

26. Acknowledgment
Ming Zhang, Professor, The Hong Kong Polytechnic University Qitao tan, Shandong University Manyu Zhang , Shandong University Qingyu Han, Shandong University Qing Xin, Hospital of Shandong University

27. This project was supported by the National Natural Science Foundation of China (Grant No. 31371002).

28. Thanks for your attention!