Cerebral autoregulation in response to posture change in elderly subjects-assessment by wavelet phase coherence analysis of cerebral tissue oxyhemoglobin concentrations and arterial blood pressure signals

Behavioural Brain Research 278 (2015) 330–336

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Cerebral autoregulation in response to posture change in elderly subjects-assessment by wavelet phase coherence analysis of cerebral tissue oxyhemoglobin concentrations and arterial blood pressure signals Yuanjin Gao a , Ming Zhang b , Qingyu Han a , Wenhao Li a , Qing Xin c , Yan Wang b , Zengyong Li a,∗ a

Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, PR China Interdisciplinary Division of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR, PR China c Hospital of Shandong University, Jinan 250061, PR China b

h i g h l i g h t s • • • •

Phase coherence between cerebral Delta [HbO2 ] and blood pressure was analyzed. Sit-to-stand change induces low wavelet phase coherence in elderly subjects. Stand-to-sit change induces high wavelet phase coherence (WPCO) in elderly subjects. Difference in WPCO indicates an altered cerebral autoregulation due to aging.

a r t i c l e

i n f o

Article history: Received 23 August 2014 Received in revised form 7 October 2014 Accepted 12 October 2014 Available online 18 October 2014 Keywords: Cerebral tissue oxygenation Spontaneous oscillations Near-infrared spectroscopy Wavelet phase coherence Aging

a b s t r a c t This study aims to assess the dynamic cerebral autoregulation (dCA) in response to posture change using wavelet phase coherence (WPCO) of cerebral tissue oxyhemoglobin concentrations (Delta [HbO2 ]) and arterial blood pressure (ABP) signals in healthy elderly subjects. Continuous recordings of near-infrared spectroscopy (NIRS) and ABP signals were obtained from simultaneous measurements in 16 healthy elderly subjects (age: 68.9 ± 7.1 years) and 19 young subjects (age: 24.9 ± 3.2 years). The phase coherence between Delta [HbO2 ] and ABP oscillations in six frequency intervals (I, 0.6–2 Hz; II, 0.15–0.6 Hz; III, 0.05–0.15 Hz; IV, 0.02–0.05 Hz, V, 0.0095–0.02 Hz and VI, 0.005–0.0095 Hz) was analyzed using WPCO. The sit-to-stand posture change induces significantly lower WPCO in interval III (F = 5.50 p = 0.025) in the elderly subjects than in the young subjects. However, the stand-to-sit posture change induces higher WPCO in intervals II (F = 5.25 p = 0.028) and V (F = 6.22 p = 0.018) in the elderly subjects than in the young subjects. The difference of WPCO in response to posture change between the elderly and the young subjects indicates an altered CA due to aging. This study provides new insight into the dynamics of CA and may be useful in identifying the risk for dCA processes. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Dynamic studies of cerebral autoregulation (CA) quantify the rapid changes in cerebral blood flow velocity (CBFV) in a major cerebral artery in relation to the rapid alterations in arterial blood pressure (ABP) [1]. Posture change induces a transient change in mean ABP [2]. The brain vasculature must respond to changes in

∗ Corresponding author. Tel.: +86 531 8839 5624; fax: +86 531 8839 2863. E-mail address: [email protected] (Z. Li). http://dx.doi.org/10.1016/j.bbr.2014.10.019 0166-4328/© 2014 Elsevier B.V. All rights reserved.

ABP or intracranial pressure to maintain stable cerebral blood flow (CBF) by the protective mechanism of the brain of CA [1]. Therefore, cerebral blood vessels have an inherent ability to keep the CBF fluctuation around certain value through myogenic, neurogenic, or metabolic mechanisms [1,3]. However, the CA can become impaired with ageing and thus resulting in orthostatic hypotension and related cerebral symptoms, such as lightheadedness, dizziness, falls or even syncope in elderly subjects [4]. It has been demonstrated that supine-to-standing posture change has significant effect on the frontal cortical HbO2 in healthy elderly subjects. Mehagnoul-Schippe [5] found that elderly subjects

Y. Gao et al. / Behavioural Brain Research 278 (2015) 330–336 Table 1 Frequency intervals and their possible physiological origins [17]. Interval I II III IV V VI

Frequency (Hz) 0.6–2 0.15–0.6 0.05–0.15 0.02–0.05 0.0095–0.02 0.005–0.0095

331

Table 2 Characteristics of the participants.

Physiological origin

Characteristic

Young

Elderly

Cardiac activity Respiration Myogenic activity Neurogenic activity Endothelial metabolic activity Endothelial activity

p For difference

Age (years) Body mass index (BMI) Female sex Systolic blood pressure (mm Hg) Diastolic blood pressure(mm Hg)

24.9 (3.2) 21.3 (2.5) 31.5% 116.9 (12.6) 68.7 (6.3)

68.9 (7.1) 24.0 (2.9) 37.5% 122.4 (10.5) 72.8 (8.1)

0.000** 0.016* 0.374 0.460 0.520

Values are presented as means and standard deviations and percentages. p Values for differences are calculated using t-test for means and standard deviations, and Chi-square test for percentages, * <0.05, ** <0.01.

experienced significant declines in frontal cortical oxyhemoglobin concentration [HbO2 ] during supine-to-standing posture change, whereas these variables did not change significantly in the young subjects. Edlow [6] found that healthy aging alters the magnitude of change in frontal cortical HbO2 , but not cerebral blood flow (rCBF), total hemoglobin concentration (THC) or Hb during supineto standing posture change. A single sit-to-stand posture change is a straightforward technique to assess dynamic cerebral autoregulation (dCA), which is well tolerated in elderly subjects [1]. The sit-to-stand procedure is a useful and feasible method to test dCA in elderly subjects, which represents a physiologic challenge that occurs in daily life [1]. It was demonstrated to induce a depressor change in BP and CBFV [7]. Although the response of CA to posture change has been the subject of many studies, the interaction between the cerebral regulation and cardiovascular mechanisms is still far from comprehensive during sit-to-stand posture change. Near-infrared spectroscopy (NIRS) is a promising technique for studying brain function during rest or task [8,9]. Spontaneous oscillations are generally found in the spectral analysis of changes in cerebral tissue oxyhemoglobin concentrations (Delta [HbO2 ]) signals measured using near-infrared spectroscopy (NIRS) [10,11] as well as arterial blood pressure (ABP) signals [12,13]. Tachtsidis [14] found that posture change induced a significant increase in oscillatory changes in oxyhemoglobin concentration [HbO2 ] and diastolic blood pressure (DBP). However, the power spectra of Delta [HbO2 ] and ABP signals exhibit oscillations in various frequency bands. Wavelet analysis via the Morlet wavelet can detect these oscillations with logarithmic frequency resolution [11,15–18]. Different characteristic frequencies of cardiovascular signals, which indicate possible regulatory mechanisms, have been identified using wavelet analysis [17] (Table 1). The oscillations in intervals I and II reflect the effects of cardiac and respiratory activities, respectively [11,16,17]. Within the brain, interval IV is closely regulated through tight neurovascular coupling and partial autonomic control [3]. The cerebral oscillations in interval III (0.05–0.15 Hz) were suggested to originate locally from intrinsic myogenic activity of smooth muscle cells in resistance vessels and this myogenic mechanism may be partly under autonomic control [13,17]. The oscillations in frequency intervals V and VI were identified and investigated by Stefanovska [19] and Kvandal [20,21], which correspond to nitric oxide (NO)-related endothelial activity and NO-independent endothelial activity, respectively. The wavelet phase coherence (WPCO) can reveal possible relationships by evaluating the match between the instantaneous phases of two signals [22,23]. WPCO analysis has been used to analyze the relationships between oscillations in skin blood flow, temperature and oxygen saturation within certain frequency ranges [18,22]. WPCO analyses here were used to test the hypothesis that changes in ABP are transmitted into changes in Delta [HbO2 ] during posture change, and that this dynamic relationship is altered in elderly subjects because of aging. This study can provide new insight into the dynamics of CA and may be useful in identifying the risk for dCA processes.

2. Methods 2.1. Subjects A total of 35 healthy subjects were studied: 16 elderly (age: 68.9 ± 7.1 years) and 19 young (age: 24.9 ± 3.2 years). Table 2 shows the characteristics of the participants. Excluded from the study were subjects with hypertension; diabetes mellitus; subarachnoid hemorrhage; insufficiency of the heart, lungs, kidneys and liver; smoking or drinking habits, and additional medications (angiotensin-converting enzyme, inhibitors/angiotensin II-receptor blockers, and calcium-channel blockers). A diagnosis of hypertension was made when systolic blood pressure (SBP) ≥ 140 mm Hg or diastolic blood pressure (DBP) ≥ 90 mm Hg [24]. A diagnosis of diabetes mellitus was based on clinical assessment or fasting serum glucose level. The study was approved by the Human Ethics Committee of Shandong University and was in accordance with the ethical standards specified by the Helsinki Declaration of 1975 (revised in 1983). 2.2. Experimental procedures Prior to the experiment, basic subject information, including age, weight, height, and ABP was recorded. Informed consent was obtained from all subjects. No alcohol and caffeine drinks were permitted 12 h prior to experimental testing. Subjects were instructed to be familiarized with the study protocol. After instrumentation and when a stable ABP signal was obtained, the subjects assumed a sitting position for 15 min, after which they stood up within 10 s and remained standing for 15 min and a further 15 min of sitting rest. When changing position from sitting to standing, the ABP probe remains in the same spatial in relation to the heart, and thus avoiding the issue of a possible hydrostatic effect on cerebral perfusion pressure [1]. The subject was instructed to sit down if a subject developed syncope or presyncopal symptoms. 2.3. Measurement Data for the NIRS and ABP signals were obtained from simultaneous measurements. After the age, height and body mass of the participants were recorded, NIRS measurements were performed on the subjects in a comfortable sitting posture using the tissue saturation An Heng monitor (TAH-100, developed by Tsinghua University, China). This equipment has been previously described in detail by Li [11,15,16]. In brief, the TSAH-100 sensor consisted of a two-wavelength LED and two PIN diodes. The LED component served as the source of emitted light at 760 and 850 nm, whereas the PIN diodes served as the detectors. Photons can penetrate the overlying tissues into the cerebral cortex (gray matter) when the distance between the detector and the source is ≥30 mm. Moreover, the penetration depth can reach the maximum value when the distance is 40 mm [25]. Therefore, the distances between the light

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source and the two detectors were set to 30 mm (S1) and 40 mm (S2), respectively. The differential signal (S1–S2) in the optical density (OD) was recorded by the two detectors and used to obtain the cortical signal. This configuration was validated by Teng [25]. The forehead of each subject was cleaned using isopropyl alcohol. Afterward, the sensors were carefully fixed using a flexible adhesive fixation pad and an elastic band. A sensor was placed on the left forehead 1.5 cm lateral to the cerebral midline to avoid the sagittal sinus and at least 2 cm above the eyebrow to avoid the frontal sinus. The sensor was carefully secured with a tensor bandage wrapped around the forehead while ensuring no admission of background light. The sampling rate of the NIRS-derived signals was set to 20 Hz. The Delta [HbO2 ] signal was monitored at the frontal lobe for 15 min using NIRS. The continuous ABP waveform was monitored noninvasively by a transducer attached to the wrist and using an ABP analysis system (FDP-I, Shanghai Science Teaching Co., China) at a sampling of 1000 Hz. This system continuously measure ABP by a sensor located over the subject’s radial artery. A special designed device positions the sensor to the best position to monitor the radial artery pulse. The measured ABP data were resampled to 20 Hz. During the ABP recordings, the wrist was held at the heart level. Changes in the mean ABP measured at the heart level were used to estimate changes in cerebral perfusion pressure in the sitting position. 2.4. Data pre-processing Wavelet transform was applied to the NIRS and ABP time series to decompose them into signal and uncorrelated noise components in distinct scales. The wavelet transform was calculated in the 0.005 Hz to 2 Hz frequency intervals. The wavelet transform at a given scale s can be interpreted as band-pass filtering, which gives an estimation of the contribution of the frequencies in this band [26]. Very slow variations defined as below 0.005 Hz and uncorrelated noise components above 2 Hz were removed. The average Delta [HbO2 ] and ABP of all recorded segments were subtracted for signal normalization to avoid systematic differences between subjects and groups. 2.5. Wavelet transform Wavelet transform is a method that allows the complex transformation of a time series from the time domain to the timefrequency domain. It involves convolving the time series g(u) with a family of generally nonorthogonal basis functions, that are generated from the mother wavelet [18,19]:



+∞

1 W (s, t) = √ s



u − t  s

g (u) du,

(1)

−∞

where W(s, t) is a wavelet coefficient and  is the Morlet mother wavelet, scaled by the factor s and translated in time by t. The Morlet mother wavelet is a complex sinusoid modulated by the Gaussian function with basic frequency ω0 : 1 2  (u) = √ × e−iω0 u × e−(u /2) , 4 

(2)

√ where i = −1. The continuous wavelet transform is a mapping of the function g(u) onto the time-frequency plane. Wavelet scaling enables the detection of oscillations with different frequencies, whereas wavelet translation in time allows the monitoring of spectra evolution over time. The translation from scale to frequency depends upon the particular choice of wavelet. An approximate

relationship between wavelet scale and translated frequency, pseudo-frequency, fs , was computed as [27]: fs =

fc , s × ıt

(3)

where fc is the center frequency and ıt is the sampling period. The choice of ω0 is a compromise between localization in time and in frequency. For smaller ω0 , the shape of the wavelet favors localization of singular time events, whilst for larger ω0 more periods of the sine wave in the window improve the frequency localization [26]. To detect a frequency, the signal must be observed over at least one period of this frequency. In this study, we choose ω0 = 5, in time, approximately six to seven periods. The wavelet transform was calculated in the frequency interval of 0.005 Hz to 2 Hz. The upper limit of 2 Hz was set to include the heart rate frequency, whereas the lower limit was selected to include possible regulatory mechanisms of the tissue oxygenation signal [11,15,17]. 2.6. WPCO The wavelet coefficients are complex numbers with the complex Morlet wavelet. These values define the absolute amplitude and instantaneous relative phase for each frequency and time. Phase information can be used to investigate the relationships between oscillations from different signals [18]. The relationship between the phases of two oscillatory processes at a specific frequency is defined as the phase coherence. If a characteristic phase difference is maintained between two signals, they have high phase coherence [28]. WPCO identifies possible relationships by evaluating the match between the instantaneous phases of two signals [18]. The instantaneous phases, named ϕ1k,n and ϕ2k,n , are calculated at each time tn and frequency fk for both signals. The relative phase difference is obtained using the formula ϕk,n = ϕ2k,n − ϕ1k,n . The sine and cosine components of the phase differences are calculated and averaged in time for the entire length of the signal. The phase coherence function is then defined as [18]: Cϕ (fk ) =



cosϕk,n

2



+ sinϕk,n

2

.

(4)

The value of the phase coherence function Cϕ (fk ) is between 0 and 1. This function quantifies the tendency of the phase difference between the two signals to remain constant at a particular frequency [18]. When two oscillations are unrelated, their phase difference continuously changes with time; thus, their phase coherence approaches zero. Significant coherence was determined during the evaluation of the coherence of two oscillatory time series that may have variable amplitude and frequency. Amplitude-adjusted Fourier transform (AAFT) surrogate signals were generated by shuffling the phases of the original time series to create a new time series with the same means, variances, and autocorrelation functions as the original sequences but without any phase relations [18]. We then averaged 100 WPCOs from surrogate signals. A WPCO from the original recording was considered statistically significant when it was two standard deviations above the mean surrogate coherence. In order to indicate the relative change of WPCO in response to posture change among the subjects, the WPCO was normalized. The normalized WPCO was defined as the ratio of the WPCO values at standing/post standing to the values at rest. It reveals the sole effects of posture change on dCA among the subjects. 2.7. Statistical analysis The values were expressed as the median (standard deviations) or percentages. The data of each subject were tested for the normality (Kolmogorov–Smirnov test) at the group level and homogeneity

Y. Gao et al. / Behavioural Brain Research 278 (2015) 330–336

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Fig. 1. Typical time series of the simultaneous recordings of Delta [HbO2 ] signal and arterial blood pressure (ABP) signals from one subject; (a) Delta [HbO2 ] signal and (b) the average wavelet amplitude, (c) ABP signal and (d) the average wavelet amplitude. The vertical lines indicate the outer limits of the frequency intervals: I (0.6–2 Hz), II (0.15–0.6 Hz), III (0.05–0.15 Hz), IV (0.02–0.05 Hz), V (0.0095–0.02 Hz), VI (0.005–0.0095 Hz).

of variance (Levene test) to ensure the values fulfilled the assumption required by the parameter analysis. Significant differences between the characteristics of the elderly and of the young subjects were determined using t-test for means and standard deviations and a chi-square test (for percentages). Two-way ANOVA was used to analyze the effects of age and posture change on the coherence. Post-hoc analyses of the two groups were performed using Bonferroni comparison tests. A difference with an adjusted p < 0.05 was considered statistically significant.

3. Results In this study, periodic oscillations in the Delta [HbO2 ] and ABP signals were identified at six frequencies intervals (Fig. 1): I (0.6–2 Hz), II (0.15–0.6 Hz), III (0.05–0.15 Hz), IV (0.02–0.05 Hz), V (0.0095–0.02 Hz) and VI (0.005–0.0095) Hz. Fig. 2 shows an example of wavelet phase coherence of ABP and Delta [HbO2 ] signals in response to posture change in a young and an elderly subject. The ABP and Delta [HbO2 ] oscillations showed evident WPCO peaks across a wide spectrum of oscillations in intervals II–VI during rest. Posture change did not induce significant difference in WPCO in the young subjects (Fig. 3). However, for the elderly subjects, the WPCO showed significant increase in frequency interval II (F = 4.007 p = 0.026) during the post standing posture (Fig. 4). Fig. 5 shows a comparison of the WPCO in response to posture change between the young and the elderly subjects. The WPCO was significantly lower in interval III (F = 5.50 p = 0.025) during the standing posture in the elderly subjects than in the young subjects. However, the WPCO was significantly higher in intervals II (F = 5.25 p = 0.028) and V (F = 6.22 p = 0.018) during the post standing posture in the elderly subjects than in the young subjects.

4. Discussion In this study, the phase coherence between simultaneously measured ABP and Delta [HbO2 ] signals of healthy elderly and young subjects during sit-to-stand procedure was assessed using wavelet-based phase coherence analysis. Our results show that sit-to-stand posture change induced significant lower WPCO in interval III, and stand-to-sit posture change induce higher WPCO in intervals II and V in the elderly subjects than in the young subjects. In this study, the wavelet transform with logarithmic frequency resolution was used to extract phase information from signals. The WPCO can reveal possible relationships by evaluating the match between the instantaneous phases of two signals [22,23]. It allowed the identification of significant coherence even at low common power; this capability particularly important when where low-frequency components significantly contribute cardiovascular signals [18]. CA functions like a high-pass filter, allowing rapid BP changes to be transmitted to cerebral blood flow velocity (CBFV), whereas slow BP changes are filtered [29]. The cerebrovascular system is a part of the systemic circulation and is thus mediated by both central sympathetic activation and local myogenic or metabolic mechanisms [2]. The ABP and Delta [HbO2 ] oscillations exhibited evident WPCO peaks in intervals III to VI in the young and elderly subjects. These results further verified the high-pass filter function of CA in healthy subjects. Posture change has been demonstrated to have significant effect on the frontal cortical HbO2 in healthy elderly subjects. MehagnoulSchipper [5] found that supine-to-standing posture change induced significant declines in frontal cortical oxyhemoglobin concentration [HbO2 ] in the elderly subjects. Edlow [6] found that healthy aging alters the magnitude of change in frontal cortical [HbO2 ] during supine-to standing posture change. Tachtsidis [14] assessed the dynamic responses of the cerebral and systemic circulation upon standing up and the posture dependence of spontaneous

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1

Wavelet Phase Coherence

VI

V

IV

III II I Orignal Mean Surrogate Two Standard Deviations Above Mean

0.8 0.6 0.4 0.2 0 0.005 0.0095

0.02

0.05

0.15

0.6

2

Frequency(Hz)

0.6

Wavelet Phase Coherence

VI

V

IV

Fig. 4. Comparison of the wavelet phase coherence of the elderly subjects between rest, standing posture and post standing posture in the six frequency intervals. Significant differences are marked with $ p < 0.05 between the young and elderly subjects. Frequency intervals: I (0.6–2 Hz), II (0.15–0.6 Hz), III (0.05–0.15 Hz), IV (0.02–0.05 Hz), V (0.0095–0.02 Hz), VI (0.005–0.0095 Hz).

III II I Orignal Mean Surrogate Two Standard Deviations Above Mean

0.5 0.4 0.3 0.2 0.1 0 0.005 0.0095

0.02

0.05

0.15

0.6

2

Frequency(Hz) Fig. 2. Examples of wavelet phase coherence of arterial blood pressure (ABP) and Delta [HbO2 ] signals in the six frequency intervals in a young subject (a) and elderly subject (b) during rest. The dashed lines show the mean and two standard deviations above the mean for the coherence calculated from 100 surrogate signals per subject. The vertical lines indicate the outer limits of the frequency intervals: I (0.6–2 Hz), II (0.15–0.6 Hz), III (0.05–0.15 Hz), IV (0.02–0.05 Hz), V (0.0095–0.02 Hz), VI (0.005–0.0095 Hz).

revealed by the synchronization between arterial blood pressure and cerebral oxyhaemoglobin concentration in interval III, this component reflects neural control of the cerebral circulation [13]. The results in this study indicate a low consistency of the phase delay between two signals in the elderly subjects. These might partly explain why the cerebral perfusion and oxygen supply become insufficient in response to postural change. In healthy humans, upon standing, neural reflexes are activated rapidly in order to regulate blood pressure and maintain adequate cerebral perfusion [14,30]. The CA broadly describes the mechanism which couples systemic variables (ABP) to cerebral

oscillations. They found oscillatory changes in diastolic blood pressure (DBP) and oxyhemoglobin concentration [HbO2 ] showed a significant increase when subjects were standing [14]. A single sit-to-stand posture change is a straightforward technique to assess dCA [1]. In this study, sit-to-stand posture induces significant lower normalized WPCO in intervals III in the elderly subjects than in the young subjects. The cerebral oscillations in interval III were suggested to originate locally from intrinsic myogenic activity of smooth muscle cells [17]. This myogenic mechanism may be partly under autonomic control [13]. As

Fig. 3. Comparison of the wavelet phase coherence of the young subjects between rest, standing posture and post standing posture in the six frequency intervals. I (0.6–2 Hz), II (0.15–0.6 Hz), III (0.05–0.15 Hz), IV (0.02–0.05 Hz), V (0.0095–0.02 Hz), VI (0.005–0.0095 Hz).

Fig. 5. Comparison of the normalized wavelet phase coherence of standing posture (a) and post standing posture (b) in the six frequency intervals between the young and the elderly subjects. Significant differences are marked with * p < 0.05 between the young and elderly subjects. I (0.6–2 Hz), II (0.15–0.6 Hz), III (0.05–0.15 Hz), IV (0.02–0.05 Hz), V (0.0095–0.02 Hz), VI (0.005–0.0095 Hz).

Y. Gao et al. / Behavioural Brain Research 278 (2015) 330–336

hemodynamic variables (O2 Hb). This coupling is due to the cooperative action of both sympathetically mediated and local myogenic mechanisms. It is thought that myogenic mechanisms act to buffer small changes in cerebral blood flow due to changes in systemic variables and that the sympathetic nervous system is most active during large pressure changes [31]. In elderly subjects it is possible that the sympathetic mechanisms of cerebral autoregulation are impaired as indicated by the low WPCO. Interestingly, the WPCO in intervals II and V was significantly higher level in the elderly subjects than in the young subjects during the post standing period. The oscillations in interval II reflect the effects of respiratory activity [11,15]. The oscillations in frequency intervals V correspond to NO-related endothelial activity [19]. In generally, CA is caused by the cooperative action of both sympathetically mediated and local myogenic or metabolic mechanisms [2]. The WPCO in the low frequency (0.02–0.05 Hz) has been found to be higher in elderly subjects than in young subjects at rest [32]. Changes in CBFV recover faster than the changes in BP, which causes a displacement of CBFV relative to BP in such a manner that CBFV oscillations appear to lead BP oscillations. The higher WPCO indicated that the CA did not recover from the cerebrovascular adaptations to the posture changes in the elderly subjects. This might suggest a reduced inherent ability to adapt to the change of ABP in elderly subjects. When using the WPCO measure to detect a causal relationship between signals, the null hypothesis that the coherence value is due to a chance relationship preserved over a limited number of correlated measurements must be considered [33]. In finite-length signals, low-frequency components are represented by fewer periods than high-frequency components. Consequently, less variation in the phase difference occurs at low-frequencies and result in artificially increased phase coherence. Significant WPCO was tested during the evaluating of the coherence of two oscillatory time series that may have variable amplitude and frequency. The test was performed by generating AAFT surrogate signals via shuffling of the phases of the original time series to create a new time series with the same means, variances, and autocorrelation functions (and therefore the same power spectra) as the original sequences but without any phase relations. When the coherence value is equal to the standard deviations above the mean surrogate coherence, a causal relationship possible exists between the signals regardless of their spectral similarities or differences [18]. 4.1. Methodological considerations NIR light must first pass through the superficial tissue layers (scalp and skull) before reaching the cortex. Therefore, these superficial layers may provide noise as well as nonspecific hemodynamic variations and this would contaminate the measured signal. The interferences from superficial layers are often referred to as “global interference” [34,35]. As several authors have pointed out, depth-resolved measurements can be effectively achieved using detectors at short (∼1 cm) and long (>3 cm) distances from the source [35–37]. In the present study, we used one light source and two detectors placed at 30 and 40 mm from the source to separate extracerebral (scalp and skull) and brain hemodynamic signals. For such configurations, the differences in the optical density (OD) as detected by the two detectors were mainly attributed to the tissue (cortex) absorption. In addition, as spontaneous oscillations are posture-dependent [14]. NIRS and ABP measurements were collected in their comfortable sitting posture. In this study, the normalized WPCO was used to compare the difference of dCA in response to posture change between the elderly and the young subjects. Compared to the absolute values of WPCO, the normalized WPCO indicates a relative change in response to posture change and thereby revealing the sole effects of posture

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change on dCA regardless of the variability of WPCO among the subjects. A limitation of current study was the recordings period. The recordings lasted 900 s and oscillations with frequency below 0.005 Hz would be represented with fewer than 5 cycles. This may results in an unreliable detection of both the power and the amplitude within the interval. Recordings are usually recommended to last ten times the period of the lower frequency boundary of the investigated component [38]. 5. Conclusions The phase coherence between simultaneously measured Delta [HbO2 ] and ABP signals in elderly and young subjects was assessed using wavelet-based coherence analysis. Our results show that the Delta [HbO2 ] and ABP oscillations exhibited significant WPCO in intervals II, III and VI for the young subjects and intervals II and IV for the elderly subjects. Most important, the sit-to-stand posture change induced significant lower WPCO in interval III in the elderly subjects than in the young subjects. The differences in WPCO between the elderly and the young subjects indicate an aging-related change in CA in response to posture change. This study provide new insight into the dynamics of Delta [HbO2 ] and ABP oscillations and maybe useful in identifying risk for dynamic CA. Conflict of interest statement The authors declare that they have no conflict of interest. Acknowledgement This project was supported by the National Natural Science Foundation of China (Grant nos. 31371002, 81071223, 11272273). References [1] Van Beek AH, Claassen JA, Rikkert MGO, Jansen RW. Cerebral autoregulation: an overview of current concepts and methodology with special focus on the elderly. J Cereb Blood Flow Metab 2008;28:1071–85. [2] Priebe H. Cardiovascular physiology fundamentals of anaesthesia and acute medicine. London: Wiley-Blackwell; 2000. [3] Zhang R, Zuckerman JH, Iwasaki K, Wilson TE, Crandall CG, Levine BD. Autonomic neural control of dynamic cerebral autoregulation in humans. Circulation 2002;106:1814–20. [4] Peng T, Ainslie PN, Cotter JD, Murrell C, Thomas K, Williams MJ, et al. The effects of age on the spontaneous low-frequency oscillations in cerebral and systemic cardiovascular dynamics. Physiol Meas 2008;29:1055. [5] Mehagnoul-Schipper DJ, Vloet LC, Colier WN, Hoefnagels WH, Jansen RW. Cerebral oxygenation declines in healthy elderly subjects in response to assuming the upright position. Stroke 2000;31:1615–20. [6] Edlow BL, Kim MN, Durduran T, Zhou C, Putt ME, Yodh AG, et al. The effects of healthy aging on cerebral hemodynamic responses to posture change. Physiol Meas 2010;31:477. [7] Basso Moro S, Bisconti S, Muthalib M, Spezialetti M, Cutini S, Ferrari M, et al. A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study. NeuroImage 2014;85(Pt 1):451–60. [8] Sprangers RL, van Lieshout JJ, Karemaker JM, Wesseling KH, Wieling W. Circulatory responses to stand up: discrimination between the effects of respiration, orthostasis and exercise. Clin Physiol (Oxford, England) 1991;11:221–30. [9] Boecker M, Buecheler MM, Schroeter ML, Gauggel S. Prefrontal brain activation during stop-signal response inhibition: an event-related functional near-infrared spectroscopy study. Behav Brain Res 2007;176:259–66. [10] Cheng R, Shang Y, Hayes Jr D, Saha SP, Yu G. Noninvasive optical evaluation of spontaneous low frequency oscillations in cerebral hemodynamics. NeuroImage 2012;62:1445–54. [11] Li Z, Wang Y, Li Y, Wang Y, Li J, Zhang L. Wavelet analysis of cerebral oxygenation signal measured by near infrared spectroscopy in subjects with cerebral infarction. Microvasc Res 2010;80:142–7. [12] Peng T, Rowley AB, Ainslie PN, Poulin MJ, Payne SJ. Wavelet phase synchronization analysis of cerebral blood flow autoregulation. IEEE Trans Biomed Eng 2010;57:960–8.

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