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Abstract

Low-intensity transcranial ultrasound stimulation (TUS) can modulate the coupling of high-frequency (160–200 Hz) neural oscillations and cerebral blood oxygen metabolism (BOM); however, the correlation of low-frequency (0–2 Hz) neural oscillations with BOM in temporal and frequency domains under TUS remains unclear. To address this, we monitored the TUS-evoked neuronal calcium oscillations and BOM simultaneously in the mouse visual cortex by using multimodal optical imaging with a high spatiotemporal resolution. We demonstrated that TUS can significantly increase the intensity of the neuronal calcium oscillations and BOM; the peak value, peak time, and duration of calcium oscillations are functionally related to stimulation duration; TUS does not significantly increase the neurovascular coupling strength between calcium oscillations and BOM in the temporal domain; the time differences of the energy peaks between TUS-induced calcium oscillations and BOM depend on their spectral ranges; the frequency differences of the energy peaks between TUS-induced calcium oscillations and BOM depend on their time ranges; and TUS can significantly change the phase of calcium oscillations and BOM from uniform distribution to a more concentrated region. In conclusion, ultrasound stimulation can evoke the time–frequency cross-coupling between the cortical low-frequency neuronal calcium oscillations and BOM in mouse.

ultrasound stimulation, neuronal calcium oscillations, blood oxygen metabolism, low-frequency, time–frequency cross-coupling

Introduction

Brain functional activities include several processes such as neuronal activity and local energy metabolism (Bélanger et al. 2011; Magistretti and Allaman 2015) and allow the accumulation of information from multiple modalities, most importantly the neuronal electrical activity and changes in blood oxygen metabolism (BOM) in activated regions (Mintun et al. 2001; Bruinsma et al. 2018). Neurovascular coupling is a neural activation support mechanism that involves synergy among the nervous, metabolic, and vascular systems. Hemodynamics (dynamics of blood flow) can undergo local changes in response to the changes produced by local neural activity via the neurovascular coupling mechanism (Lauritzen et al. 2014; Zhao et al. 2022). The magnitude and spatial location of hemodynamic changes are closely related to changes in the neural activity via a complex series of coordinated events involving neurons, glia, and vascular cells (Petzold and Murthy 2011; Iadecola 2017).

The physical stimulation techniques of neuromodulation, including deep brain, optogenetic, transcranial magnetic, and transcranial direct current stimulations, have been shown to cause changes in neurovascular coupling. For example, the BOM responses evoked by deep brain stimulation were associated with broadband power changes in synthetic evoked potentials and multiunit activity (Noor et al. 2020). Optogenetic stimulation can increase the blood flow velocity locally in the barrel cortex and ipsilateral motor cortex, and these changes have a unique coupling feature with stimulation-evoked electrophysiological activity (Anenberg et al. 2015). Transcranial magnetic stimulation disrupts the temporal structure of activity by altering the phase relationship of neural signals, which are reflected in hemodynamic signals and are quantitatively coupled across a range of stimulation parameters (Allen et al. 2007). Anodal transcranial direct current stimulation can disrupt the coupling between local neural activity and hemodynamics (Jindal et al. 2015). Studying the relationship between stimulus-evoked neural activity and vascular changes is critical for revealing that physical stimulation techniques modulate the brain activity processes.

Low-intensity transcranial ultrasound stimulation (TUS) is another noninvasive technique but provides high penetration depth and spatial resolution in comparison with other invasive and noninvasive physical stimulation techniques (Bystritsky et al. 2011; Chang et al. 2020; Yu et al. 2020; Meng et al. 2021; Huang et al. 2022; Wang et al. 2022). Previous studies have demonstrated that TUS has a strong modulating effect on cortical neural activity, cerebral hemodynamic/BOM, and neurovascular coupling. TUS in the motor cortex of mice and rats can induce limb and tail motor responses, and the responses are affected by ultrasound parameters (such as ultrasound intensity and fundamental frequency). Electrophysiology results showed that TUS can alter the relative power and entropy of theta (4–8 Hz), gamma (30–45 Hz), and high-gamma (55–100 Hz) oscillations in the motor cortex, and the modulation effect was closely related to the ultrasonic intensity and stimulation duration (SD) (Legon et al. 2014; Wang et al. 2020). Studies have also shown that TUS can evoke action potentials (>200 Hz) in the rat cortex, and the excitatory and inhibitory neurons that generate action potentials respond differently to ultrasound pulse repetition frequency (PRF) (Yu et al. 2021). Therefore, TUS can significantly modulate the neural oscillations (with frequencies >4 Hz), and the modulation effect is closely related to ultrasound parameters. Ironically, the TUS-mediated modulation of the low-frequency neural oscillations (0–2 Hz), as well as the dependency of such a modulation on ultrasound parameters, is not unclear.

TUS can induce global and local cerebral hemodynamic changes in mouse cortex, and the peak cerebral blood flow velocity elicited by ultrasound stimulation increased with stimulation intensity and duration; however, the stimulation duty cycle had little effect on the peak response (Yuan et al. 2020). The TUS of the sensory cortex in mice can cause a transient increase and decrease in oxyhemoglobin and deoxyhemoglobin concentrations, respectively, and the magnitude of hemodynamic changes is related to the ultrasound intensity (Kim et al. 2017; Kim et al. 2019). Previously, we recorded the neural activity and cerebral blood flow velocity induced by TUS in the mouse visual cortex and found that the relative power and sample entropy of the neural oscillations in the ripple band (160–200 Hz) were significantly related to the relative cerebral blood flow in the time domain. We also found a strong coupling relationship between the neural oscillations in the ripple band and BOM in the time domain under TUS (Yuan et al. 2021); the coupling strength depended on the level of anesthesia and increased linearly with ultrasound intensity. These studies have shown that TUS can effectively modulate the temporal coupling between high-frequency neural activity (160–200 Hz) and hemodynamics/BOM (Yuan et al. 2019). However, until now, only a few studies have investigated the neurovascular coupling relationship (between TUS-induced cortical low-frequency neural activity [0–2 Hz] and BOM), and the coupling in the frequency domain remains unclear.

The frequency-domain coupling relationship between low-frequency neural activity and hemodynamics plays a key role in studying stimulus-evoked functional brain responses (Chalak et al. 2017; Das et al. 2021). Studies have demonstrated an interplay between the brain responses evoked by stimulus tasks and spontaneous cortical oscillations (Du et al. 2014; Ma, Shaik, Kozberg et al. 2016; Chen et al. 2020). For example, sustained neuronal oscillations under forepaw stimulation negatively modulate the stimulus-evoked calcium oscillations (Chen et al. 2020). Cerebral hemodynamic responses correlate with transient changes in neuronal activity (low-frequency calcium oscillations). The frequency-domain analysis of neural activity and hemodynamics revealed that low-frequency calcium oscillations (~0.07 Hz) in tissues were similar to the low-frequency spontaneous oscillations of deoxyhemoglobin and oxyhemoglobin in large vessels and capillaries (Du et al. 2014). Furthermore, oxygenated and deoxygenated hemoglobin fluctuations within tissues were closely related to calcium oscillations (frequency < 1 Hz). Studies have also found that hemodynamic low-frequency spontaneous oscillations are closely related to low-frequency calcium oscillations, thus suggesting that they directly reflect neuronal activity. Hence, stimulation-induced neurovascular response represents strong coupling in the low-frequency domain, which can used be to reveal the mechanism of stimulation-regulated brain functions.

To address the unresolved questions on low-frequency neural oscillations and neurovascular coupling, we stimulated (TUS) healthy mice and monitored calcium activity and BOM in the visual cortex by using multimodal optical imaging, and the coupling coefficient between calcium oscillations and BOM in the time domain was calculated to analyze the relationship between neuronal activity and blood oxygenation.

Materials and methods

Animal

Adult Thy1-GCaMP6f transgenic mice were used in the experiments (total, n = 15; normal hearing mice, n = 9; deaf male mice, n = 6; body weight, 20–25 g; age, 10–12 weeks; Jackson Laboratory). All experimental procedures involving mice were approved by the Animal Ethics and Administrative Council of Yanshan University. The mice were housed in standard cages with a 12-h light/dark cycle and were provided with food and water ad libitum.

Surgery

Mice were anesthetized (0.5% isoflurane), and the oxygen level was set to a delivery rate of 0.5 L/min before the experiments. Thereafter, the animals were placed on a mouse adapter (68,030; RWD, Shenzhen, China). Hairs on the mouse head were shaved, and the area was disinfected with iodine. The animal’s eyes were covered using Vaseline eye ointment. A craniotomy was performed to obtain a circular window (4 mm diameter) on the visual cortex, and this part of the dura mater was tucked up with a surgical dual hook and covered with a cover glass (4 mm diameter; dental cement was used to seal the periphery of the cover glass and fix the 3D printing head cover) to reduce exposure to the brain exercise. After the operation, animals were housed individually in their respective cages with an ambient temperature of 24 ± 1°C and allowed rest for 1 week. To chemically deafen animals (Sato et al. 2018), an injection of kanamycin (1 g/kg subcutaneously, 030201211, 100 mg/mL; North China Pharmaceutical Group Corporation Ltd, Shijiazhuang, China) was administered first, followed by the administration of furosemide (200 mg/kg intraperitoneally, 041101181, 10 mg/mL; Shanxi Zhaoyi Biology Ltd, Yuncheng, China) and saline (1.5 mL subcutaneously) 30 min later.

Ultrasound experimental setup

An ultrasound transducer (V323-SU, Olympus, USA) connected to the mouse skull with a conical coupling cone filled with ultrasound coupling gel was used to perform ultrasound stimulation (Fig. 1a). In the ultrasound system, the fundamental frequency, pulsed repetition frequency, and duty cycle were 500 kHz, 0.4 s, 1 kHz, and 10%, respectively (Fig. 1b). To compare the effects of different stimulus durations, SDs were varied with an interval of 0.1 s (0.3–0.7 s). The ultrasound intensity was 0.45 MPa, and the corresponding spatial peak and pulse-average intensity (Isppa) values were 6.75 W/cm2, respectively. A calibrated needle-type hydrophone (HNR500; Onda, USA) was moved under the skull by using a 3D electric translation platform. The reconstruction profile was placed along the dotted white lines in the x–y plane. The diameter of the focal area measured at the full width at half maximum (FWHM) was ~ 6.6 mm. We referred to the mouse brain atlas and distribution of the ultrasound field to determine the placement of the coupling cone to ensure that the maximum ultrasound intensity was targeted to the desired area.

Experimental setup and protocol. (a) Schematic of the timing synchronization integrated system of ultrasound stimulation, fluorescence imaging, and intrinsic signal optical imaging. (b) Time sequence of ultrasound stimulation and imaging experiments. (c) Ultrasound field distribution under the skull in the x–y plane. The reconstruction profile along the blue dotted lines.
Fig. 1

Experimental setup and protocol. (a) Schematic of the timing synchronization integrated system of ultrasound stimulation, fluorescence imaging, and intrinsic signal optical imaging. (b) Time sequence of ultrasound stimulation and imaging experiments. (c) Ultrasound field distribution under the skull in the x–y plane. The reconstruction profile along the blue dotted lines.

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Optical hemodynamic and fluorescence imaging

A time-synchronized integrated system consisting of ultrasound stimulation, fluorescence imaging, and intrinsic signal optical imaging was established (Du et al. 2014; Chen et al. 2019; Chen et al. 2020). In this system, a 3D-printed head fixture was attached to a machined head fixture integrated with an XY stage, and the dental cement fragments on the surface of the cover glass were carefully cleaned. The LabVIEW program and camera acquisition software were subsequently started. An acquisition card (NI USB-6251) collected the camera EXSYNC signal to switch the light at wavelengths of 488, 530, and 630 nm per 15 photos. According to the camera frame rate and the number of EXSYNC signals, a 5 V digital signal output was obtained every 10 s to externally trigger the function generator to transmit ultrasound waves. The ultrafast imaging of cranial windows using multispectral illumination allowed us to simultaneously acquire the neuronal calcium and BOM signals at 45 fps (equivalent tricolor frame rate of 15 Hz in each channel) in a single imaging session. The average data of 8 different trials were acquired. The above experiment was repeated after 30 min. We covered the mouse eyes with black cloth to avoid LED interference in the experiment.

Calculation of HbO, HbR, and HbT

We used a modified Beer–Lambert law with a Monte Carlo-derived wavelength-dependent path length and standard HbO and HbR absorption spectra to convert image sets acquired under 530 and 630 nm illuminations to dynamic images of HbO and HbR concentrations. The HbO and HbR concentration changes were obtained from the following equations (Ma, Shaik, Kim et al. 2016; Hillman 2007):
(1)
(2)
(3)
where |${\xi}_{\textrm{HbO}}$| and|${\xi}_{\textrm{HbR}}$| are the molar extinction coefficients of oxyhemoglobin and deoxyhemoglobin under different wavelengths of illumination, respectively, and |$\Delta{\mu}_a$| is the change in the absorption coefficients at different wavelengths.

Calculation of calcium signal intensity

The changes in calcium signal intensity in Thy1-GCaMP6f mice were expressed as follows:
(4)
where |${F}_t$| is the fluorescence signal intensity at different time points, and |${F}_{\textrm{baseline}}$| is the average value of the data 2 s before TUS.

Pearson’s correlation coefficient

We performed a correlation analysis between calcium oscillations and HbO, HbR, and HbT by using Pearson’s correlation coefficient in the temporal domain. For column Xa in matrix X and column Yb in matrix Y, having means |$\overline{X_a}=\sum_{i=1}^n({X}_{a,i})/\textrm{n},$| and |$\overline{Y_b}=\sum_{i=1}^n({X}_{b,i})/n$|⁠, the Pearson’s correlation coefficient (PCC) was defined as follows:
(5)
where n denotes the length of each column. The PCC values ranged from −1 to 1. Values of −1 and + 1 indicated the perfect negative and positive correlations, respectively, whereas a value of zero indicated no correlation between the columns.

Time–frequency analysis of calcium oscillation and BOM

The wavelet transform of signal |$x(t)$| with respect to wavelet |$\varPsi$| is a series of convolutions:
(6)
In this study, we used the complex Morlet wavelet to perform a time–frequency analysis of the signal. The Morlet wavelet was defined as a sine wave tapered by a Gaussian function (Tanllon Baudry and Bertrand 1999). A complex Morlet wavelet was used in which the real-valued Gaussian tapers a complex-valued sine wave. The complex Morlet wavelet was then convolved with the time-series signal. For a time series, a complex Morlet wavelet |$\varPsi$| can be defined as the product of a complex sine wave and a Gaussian window:
(7)
where i is the imaginary operator (⁠|$i=\sqrt{-1}$|⁠), and f is the frequency in Hz. Additionally, t is the time in seconds. To avoid introducing a phase shift, t should be centered at |$t=0$|⁠. σ is the width of the Gaussian distribution and is defined as |$\sigma =\frac{n}{2\pi f}$|⁠. The parameter n defines the time–frequency precision trade-off.
By discretizing the time series, it can be transformed into the following:
(8)
where |${f}_b$| is the bandwidth parameter, |${f}_c$| is the wavelet center frequency. Owing to the low frequency of the data analyzed in this study, we set fb and fc to 3 and 0.3 Hz, respectively.
Thereafter, |W|, which is the norm of the wavelet spectrum, was calculated. The time–frequency analysis of the signal in this study had a good resolution effect and could identify the time–frequency variation information expressed in the signal. The calculation method for the wavelet spectrum is as follows:
(9)
where imag(W(a,b)) and real(W(a,b)) indicate the imaginary and real parts of W, respectively.

Calculation of ridge of wavelet

For the wavelet spectrum |W|, we computed the ridges of the wavelet spectrum on the time and frequency scales. To evaluate the time changes of the neuronal calcium oscillations and BOM at different frequencies, we calculated the temporal changes in the main features of the wavelet spectrum with increasing frequency as a time-scale wavelet ridge (TWR).
(10)
where |$t\in [0,8]$|⁠. We calculated the relative time changes in the wavelet ridge of the neuronal calcium oscillations to the BOM in different frequency bands after TUS:
(11)
where |${\textrm{TWR}}_{\textrm{BOM}}$| is the TWR of BOM (HbO, HbR, and HbT), and |${\textrm{TWR}}_{\textrm{Ca}}$| is the TWR of calcium oscillations.
To evaluate the changes in the neuronal calcium oscillations and BOM at different times, we calculated the frequency of the main features of the wavelet spectrum with an increase in time as a frequency-scale wavelet ridge:
(12)
where |$f\in [0,2]$|⁠. We calculated the relative frequency changes in the wavelet ridge of the neuronal calcium oscillations to the BOM at different time bands before and after TUS:
(13)
where |${\textrm{TWR}}_{\textrm{Before}\_\textrm{TUS}}$|and TUS |${\textrm{FWR}}_{\textrm{After}\_\textrm{TUS}}$|represent the frequency-scale wavelet ridges of the calcium oscillations and BOM (HbO, HbR, and HbT) before and after TUS, respectively. The extraction of wavelet ridges is an important content in the field of time–frequency analysis of non-stationary signals. Using the information contained in the wavelet ridge line can realize the estimation of the transient characteristics of the signal. In this study, the wavelet ridge method was used to observe the fine changes of the main components of BOM by ultrasound in the 2D of frequency and time.

Calculation of phase distribution

First, we performed the Hilbert transform of the BOM and calcium oscillation signals within 1.5 s before and after TUS in 8 stimulus trials and obtained the phases of the signals. Thereafter, we divided the phase in the range of –pi to pi into 13 parts. Finally, we calculated the percentage of the above 13 parts of the phase distribution.

Statistical analysis

The results were analyzed using the paired t-test, and the differences were considered significant at P < 0.05. Further, Rayleigh’s test was used to evaluate the phase distribution. All statistical analyses were performed using MATLAB software.

TUS-induced calcium oscillation and BOM in mouse visual cortex. (a) The spatiotemporal responses of neuronal calcium oscillation and HBO, HBR, and HBT under TUS. (b) The response curve of neuronal calcium oscillation, HbO, HbR, and HbT under 8 stimuli trials from 1 typical mouse. (c) Time-dependent curves of neuronal calcium oscillations for all samples with different SDs from 0.3 to 0.7 s. (d) Peak value of ΔF/F under different SD, (e) peak time of ΔF/F, (f) FWHM of response duration (mean ± SEM, n = 9 for each group, *P < 0.05, **P < 0.01; paired t-test).
Fig. 2

TUS-induced calcium oscillation and BOM in mouse visual cortex. (a) The spatiotemporal responses of neuronal calcium oscillation and HBO, HBR, and HBT under TUS. (b) The response curve of neuronal calcium oscillation, HbO, HbR, and HbT under 8 stimuli trials from 1 typical mouse. (c) Time-dependent curves of neuronal calcium oscillations for all samples with different SDs from 0.3 to 0.7 s. (d) Peak value of ΔF/F under different SD, (e) peak time of ΔF/F, (f) FWHM of response duration (mean ± SEM, n = 9 for each group, *P < 0.05, **P < 0.01; paired t-test).

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Results

TUS-induced calcium oscillations and BOM in the mouse visual cortex

To assess stimulus-evoked brain responses, we used a multimodal imaging platform (Fig. 1a) to simultaneously acquire images of the neuronal calcium signals and BOM (HbO, HbR, and HbT) with a high temporal resolution. Figure 2a shows the spatiotemporal responses of the neuronal calcium oscillations and the HbO, HbR, and HbT under TUS. TUS enhanced the intensity of calcium oscillations, increased HbO and HbT, and decreased HbR; the results were consistent with those previous studies (Kim et al. 2017; Fisher and Gumenchuk 2018). Figure 2b shows the response curve of neuronal calcium oscillation, HbO, HbR, and HbT under 8 stimulus trials from a typical mouse. We observed that the onset of the neuronal calcium oscillations and transient responses of BOM were remarkably synchronized, thus reflecting a neurovascular coupling process. We calculated the time-dependent curves (Fig. 2c) of the neuronal calcium oscillations for all samples with different SDs (0.3–0.7). When spatiotemporal properties (peak intensity, peak time, and FWHM of response duration) of the neuronal calcium oscillations were evoked by TUS at SDs of 0.3, 0.4, 0.5, 0.6, and 0.7 s, the mean ± SEM peak intensity (0.007 ± 0.002, 0.008 ± 0.002, 0.012 ± 0.003, 0.019 ± 0.005, and 0.022 ± 0.004), peak time (0.30 ± 0.03, 0.35 ± 0.05, 0.36 ± 0.04, 0.41 ± 0.05, and 0.50 ± 0.06), and FWHM of response duration (0.53 ± 0.04, 0.60 ± 0.08, 0.69 ± 0.08, 0.78 ± 0.07, and 0.85 ± 0.09) increased significantly (Fig. 2d–f), thus suggesting that the neuronal calcium oscillations have a positive function in increasing SD. By contrast, the peak intensity, peak time, and FWHM of the response duration of BOM did not change with increasing SD. The above results demonstrated that TUS can modulate the neuronal calcium oscillations and BOM, and the change in calcium oscillations is a function of SD.

TUS-evoked coupling of calcium oscillations and BOM in the time domain

We recorded fluorescence and BOM signals 1 min before ultrasound stimulation. The calcium oscillations and HbO, HbR, and HbT signals within 30 s before stimulation from one of the mice are shown in Fig. 3a. Figure 3b shows the normalized curves of the neuronal calcium oscillations and the changes in HbO, HbR, and HbT over time for all samples. Furthermore, the Pearson’s correlation coefficients of the neuronal calcium oscillations and the HbO, HbR, and HbT are shown in Fig. 3c–e. Compared with 30 s before TUS, the coupling strength between the neuronal calcium oscillations and the HbO, HbR, or HbT has an increasing trend within 1.5 s after TUS, but there was no statistical significance (Ca2 + –HbO: 0.13 ± 0.05; Ca2 + –HbR: −0.05 ± 0.06; Ca2 + –HbT: 0.13 ± 0.05; mean ± SEM, n = 9; mean ± SEM, n = 9, n.s.: no significance; paired t-test). These results indicated that ultrasound stimulation does not significantly increase the neurovascular coupling strength between calcium oscillations and BOM in the temporal domain.

TUS-evoked coupling of calcium oscillations and cerebral hemodynamic in time-domain. (a) The normalized curves of neuronal calcium oscillations, HbO, HbR, and HbT during 30 s before TUS for 1 mouse. (b) The normalized curves of neuronal calcium oscillations, HbO, HbR, and HbT over time for all samples. (c)–(e) Pearson’s correlation coefficients between neuronal calcium oscillations, HbO, HbR, and HbT (mean ± SEM, n = 9; n.s.: no significance; paired t-test).
Fig. 3

TUS-evoked coupling of calcium oscillations and cerebral hemodynamic in time-domain. (a) The normalized curves of neuronal calcium oscillations, HbO, HbR, and HbT during 30 s before TUS for 1 mouse. (b) The normalized curves of neuronal calcium oscillations, HbO, HbR, and HbT over time for all samples. (c)–(e) Pearson’s correlation coefficients between neuronal calcium oscillations, HbO, HbR, and HbT (mean ± SEM, n = 9; n.s.: no significance; paired t-test).

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TUS-evoked time–frequency cross-coupling of low-frequency calcium oscillations and BOM

We constructed the time–frequency diagram (Fig. 4a) of the neuronal calcium oscillations and BOM under ultrasound stimulation (as in Fig. 3a) and marked the time-dependent wavelet ridges with frequency as the main axis (see solid white line in the figure). The energy intensity of the neuronal calcium activity under ultrasound stimulation was mainly distributed from 0 to 2 Hz, whereas that for BOM was mainly concentrated in the relatively low-frequency band (<0.5 Hz). To quantitatively evaluate the time–frequency characteristics of the neural oscillations and BOM induced by TUS, we calculated the average TWR at different frequency bands (0–0.15 Hz, 0.15–1 Hz, and 1–2 Hz). The average TWR of the neuronal calcium oscillations in the 3 frequency bands was much shorter than that of the BOM (Fig. 4b–d). We subsequently calculated the relative changes in the TWR (ΔTWR/TWR) of HbO, HbR, HbT and the neuronal calcium oscillations at different frequency bands. We found that ΔTWR/TWR increased significantly with increasing frequency (Fig. 4e–g) (for 0–0.15 Hz, 0.15–1 Hz, and 1–2 Hz; mean ± SEM HbO, 36.8 ± 4.9%, 64.9 ± 3.4%, and 93.8 ± 5.5%; HbR, 38.5 ± 5.0%, 65.0 ± 3.5%, and 102.8 ± 5.1%; HbT, 45.1 ± 5.9%, 73.3 ± 5.6%, and 96.5 ± 7.8%), thus suggesting that the time of the wavelet ridge distribution was frequency dependent. Given that wavelet ridges represent the main energy components of the signal, the time differences of energy peaks between TUS-induced calcium oscillations and BOM depend on their spectral ranges.

TUS-evoked time–frequency cross-coupling of low-frequency calcium oscillations and BOM. (a) The time–frequency diagram of neuronal calcium oscillations and BOM under ultrasound stimulation in Fig. 3(b). (b)–(d) The average TWR of neuronal calcium oscillations and BOM at different frequency bands (0–0.15, 0.15–1, 1–2 Hz). (e)–(g) The relative changes of TWR(ΔTWR/TWR) between HbO, HbR, HbT and neuronal calcium oscillation at different frequency bands, (e) Ca2+ vs. HbO, (f) Ca2+ vs. HbR, (g) Ca2+ vs. HbT (mean ± SEM, n = 9 for each group, *P < 0.05, **P < 0.01, ***P < 0.001; paired t-test).
Fig. 4

TUS-evoked time–frequency cross-coupling of low-frequency calcium oscillations and BOM. (a) The time–frequency diagram of neuronal calcium oscillations and BOM under ultrasound stimulation in Fig. 3(b). (b)–(d) The average TWR of neuronal calcium oscillations and BOM at different frequency bands (0–0.15, 0.15–1, 1–2 Hz). (e)–(g) The relative changes of TWR(ΔTWR/TWR) between HbO, HbR, HbT and neuronal calcium oscillation at different frequency bands, (e) Ca2+ vs. HbO, (f) Ca2+ vs. HbR, (g) Ca2+ vs. HbT (mean ± SEM, n = 9 for each group, *P < 0.05, **P < 0.01, ***P < 0.001; paired t-test).

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Figure 5a shows the plot of the frequency-dependent wavelet ridges with time as the main axis (solid white line in the figure) in the time–frequency diagram of the neuronal calcium oscillations and BOM. We quantitatively calculated the spectral distributions of the neuronal calcium oscillations and the HbO, HbR, and HbT signals at different time points before and after TUS. Figure 5b shows the average spectrum distribution of the neuronal calcium oscillation signals. The average spectrum of calcium oscillation signals was mainly concentrated at a frequency of 0.08 ± 0.01 Hz before TUS (−2 to 0 s). Furthermore, the spectrum of calcium oscillations was significantly blue-shifted after TUS, and the range was concentrated at 0.16 ± 0.03 Hz (0–2 s); over time, the spectral range red-shifted to close to the level before TUS (0.09 ± 0.02, 0.07 ± 0.01, and 0.07 ± 0.01 Hz at 2–4, 4–6, and 6–8 s, respectively). The average spectra of BOM were 0.07 ± 0.003 Hz (HbO), 0.07 ± 0.004 Hz (HbR), and 0.08 ± 0.005 Hz (HbT) before TUS. The spectrum of BOM exhibited a significant blue shift after TUS at 0–2 s and 2–4 s. (0–2 s: 0.10 ± 0.004 Hz [HbO], 0.1 ± 0.007 Hz [HbR], and 0.11 ± 0.011 Hz [HbT]; 2–4 s: 0.11 ± 0.004 Hz [HbO], 0.11 ± 0.008 [HbR], and 0.12 ± 0.012 Hz [HbT]). The blue shift of the spectrum peaked at 2–4 s, which was ~2 s later than that for the neuronal calcium oscillations. The spectrum of the BOM red-shifted with time until it returned to the level before TUS at 6–8 s. (6–8 s: 0.07 ± 0.006 Hz [HbO], 0.06 ± 0.005 [HbR], 0.08 ± 0.007 Hz [HbT]). The above results showed that ultrasound stimulation can modulate the spectral distribution of BOM, and the maximum change in the spectrum occurs within 2–4 s after TUS.

TUS-evoked time–frequency cross-coupling of low-frequency calcium oscillations and BOM. (a) Frequency-dependent wavelet ridges with time as the main axis (solid white line) in the time–frequency diagram of neuronal calcium oscillations and BOM. (b)–(e) The average spectrum distribution of neuronal calcium oscillation, HbO, HbR, and HbT, (b) neuronal calcium oscillation, (c) HbO, (d) HbR (e) HbT. (f)–(h) The relative changes of FWR(ΔFWR/FWR) between HbO, HbR, HbT and neuronal calcium oscillation at different time bands, (e) Ca2+ vs. HbO, (f) Ca2+ vs. HbR, (g) Ca2+ vs. HbT (mean ± SEM, n = 9 for each group, *P < 0.05, **P < 0.01, ***P < 0.001; paired t-test).
Fig. 5

TUS-evoked time–frequency cross-coupling of low-frequency calcium oscillations and BOM. (a) Frequency-dependent wavelet ridges with time as the main axis (solid white line) in the time–frequency diagram of neuronal calcium oscillations and BOM. (b)–(e) The average spectrum distribution of neuronal calcium oscillation, HbO, HbR, and HbT, (b) neuronal calcium oscillation, (c) HbO, (d) HbR (e) HbT. (f)–(h) The relative changes of FWR(ΔFWR/FWR) between HbO, HbR, HbT and neuronal calcium oscillation at different time bands, (e) Ca2+ vs. HbO, (f) Ca2+ vs. HbR, (g) Ca2+ vs. HbT (mean ± SEM, n = 9 for each group, *P < 0.05, **P < 0.01, ***P < 0.001; paired t-test).

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Thereafter, we calculated the relative change in the frequency of wavelet ridges (ΔFWR/FWR) before and after TUS and compared the relative changes in the neuronal calcium oscillations and in the HbO, HbR, and HbT. The ΔFWR/FWR of the neuronal calcium signals was significantly higher than (0–2 s), lower than (2–4 s and 4–6 s), and identical (6–8 s) to that of the BOM after ultrasound stimulation (Fig. 5f–h), thus indicating that the spectral changes of the calcium oscillations after TUS was more intense than that of the BOM. Furthermore, the spectra of calcium oscillations quickly returned to the baseline over time; however, the spectrum of BOM continued to shift until it returned to baseline levels. Hence, the frequency difference of the energy peak between TUS-induced calcium oscillations and BOM depends on their time range, thus demonstrating that ultrasound stimulation can significantly modulate the time–frequency cross-coupling of low-frequency calcium oscillations and BOM in the mouse visual cortex.

TUS-evoked phase distribution of calcium oscillations and BOM

By using the Hilbert transform, we analyzed the phase distribution of the neuronal calcium oscillations and the HbO, HbR, and HbT curves before and after TUS. We found that the phases of spontaneous calcium oscillations and the HbO, HbR, and HbT were uniformly distributed between –π and π before TUS (Fig. 6a–d). A transient change in the phase of the neuronal calcium oscillations was observed after TUS, thus causing a significant deviation of the phase from a uniform distribution to a more concentrated 0–1 rad phase region (P < 0.05) (Fig. 6e). The phase of BOM also changed transiently after TUS, and a significant change in the phase from a uniform distribution to a more concentrated phase region was observed (HbO at −1 to 1 rad, HbR at −3 to 2 rad and 2–3 rad, and HbT at −1 to 1 rad) (Fig. 6f–h). These results suggested that TUS can significantly alter the phase distribution of the neuronal calcium oscillations and BOM.

TUS-evoked phase distribution of calcium oscillations and BOM. (a)–(d) Phase distribution of neuronal calcium oscillations, HBO, HBR, HBT curves before TUS, (a) neuronal calcium oscillations, (b) HBO, (c) HBR, (d) HBT. (e)–(h) Phase distribution of neuronal calcium oscillations, HBO, HBR, HBT curves after TUS, (e) neuronal calcium oscillations, (f) HBO, (g) HBR, (h) HBT (*P < 0.05, Rayleigh’s test).
Fig. 6

TUS-evoked phase distribution of calcium oscillations and BOM. (a)–(d) Phase distribution of neuronal calcium oscillations, HBO, HBR, HBT curves before TUS, (a) neuronal calcium oscillations, (b) HBO, (c) HBR, (d) HBT. (e)–(h) Phase distribution of neuronal calcium oscillations, HBO, HBR, HBT curves after TUS, (e) neuronal calcium oscillations, (f) HBO, (g) HBR, (h) HBT (*P < 0.05, Rayleigh’s test).

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Auditory effects for TUS-evoked calcium and BOM

Previous studies have shown that TUS activates the cortical neurons via nonspecific auditory responses (Guo et al. 2018; Sato et al. 2018). We chemically stimulated deaf mice (n = 6) by using ultrasound and recorded the neuronal calcium oscillations and BOM. When we compared the peak values of ΔF/F and ΔHbO/HbO, no statistically significant differences were observed between the normal hearing and deaf mouse groups (Fig. 7a and b). Hence, TUS-evoked neuronal calcium oscillations and BOM were not due to nonspecific auditory effects.

Auditory effects for TUS-evoked calcium and BOM. (a) Peak value of ΔF/F in normal hearing and deaf mice. (b) Peak value of ΔHbO/HbO in normal hearing and deaf mice.
Fig. 7

Auditory effects for TUS-evoked calcium and BOM. (a) Peak value of ΔF/F in normal hearing and deaf mice. (b) Peak value of ΔHbO/HbO in normal hearing and deaf mice.

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Discussion

In this study, we established a time synchronization integrated system of ultrasound stimulation, fluorescence imaging, and intrinsic signal optical imaging and used it to study the time–frequency coupling of low-frequency neuronal calcium oscillations and BOM in the mouse visual cortex. We found that TUS can induce the spatiotemporal morphology of the neuronal calcium oscillations and BOM, and this finding is similar to those of previous studies (Kim et al. 2017; Fisher and Gumenchuk 2018). We also found that the characteristic parameters of the neuronal calcium oscillations, including the peak intensity, peak time, and duration increased significantly with increasing SD. Furthermore, we also found that TUS can alter the time (frequency) differences in energy peaks between TUS-induced calcium oscillations and BOM depending on their spectral (time) ranges. We also detected a significant phase deviation of the neuronal calcium oscillations and BOM from a uniform distribution before stimulation to a more concentrated phase region after TUS. To the best of our knowledge, this is the first study to demonstrate that TUS can modulate time–frequency cross-coupling between low-frequency neural oscillations and BOM in the mouse visual cortex.

Our observation that TUS enhanced the neuronal calcium oscillation signals may be related to the effect of TUS on the neuronal calcium channels. Previous studies have demonstrated that ultrasound stimulation can modulate calcium signaling by activating voltage-gated calcium channels (Tyler et al. 2008). In addition, recent studies have shown that focused ultrasound activates specific calcium-selective mechanosensitive ion channels, thus resulting in the gradual accumulation of calcium, amplification of calcium-gated and voltage-gated channels, and a burst discharge response (Yoo et al. 2022). In addition to directly acting on the neurons, a previous study has shown that ultrasound stimulation can also open TRPA1 channels in glial cells, and Ca2+ entry through TRPA1 leads to the release of glutamate in astrocytes. The released glutamate activates NMDA receptors in the neighboring neurons to trigger action potentials (Oh et al. 2019). Therefore, we speculate that the biophysical mechanism associated with ultrasound stimulation is responsible for the TUS-evoked neuronal calcium oscillations. The coupled analysis of the neuronal calcium oscillations and BOM in our study was mainly reflected in the time and frequency domains. We observed a strong correlation between the neuronal calcium oscillations and BOM after TUS; the neuronal calcium oscillations significantly advance BOM. We speculate that ultrasound first induces neuronal excitation via the biophysical mechanistic route, and the neuronal excitatory activities require a large amount of oxygen and energy consumption, which alters the cerebral BOM. In the current study, we also found that ultrasound stimulation significantly altered the spectral shift (blue shift) of spontaneous oscillations in the neuronal calcium signals and BOM. We speculate that the opening of the neuronal calcium ion channels causes the originally slow-changing calcium ion signal to change rapidly in a short period of time under the influence of ultrasonic waves. Hence, the frequency of calcium oscillations showed a significant blue shift. Similar observations were made for BOM, and the frequencies of the neuronal calcium oscillations and BOM returned to a spontaneous oscillatory state over time. We also found that the phase of spontaneous neuronal calcium oscillations and BOM changed from a uniform distribution to a concentrated distribution after TUS. A similar phenomenon was observed in forepaw stimulation in rats. Researchers speculated that this phase reset effect may indicate that the spontaneous neuronal activity adapts to sensory stimuli by adjusting its phase to an unpredictable excitatory state (Chen et al. 2020). This may also explain why ultrasound stimulation modulates the phase of spontaneous oscillations.

Neurovascular coupling is the basis of neurological activity and metabolism; hence, its dysfunction may be closely related to the occurrence and development of neurological diseases (Girouard and Iadecola 2006; Montgomery et al. 2020; Stackhouse and Mishra 2021). The coupling relationship between the nerves and blood vessels changes because of physiological states such as disease or aging. This abnormal state can alter the cellular and molecular mechanisms underlying the neural activity. In conditions of disease and old age, the dynamics of the coupling relationship between chemical transmitters and the vasculature itself changes. Neurovascular dysfunction may contribute to the brain dysfunction by decreasing the blood flow below the threshold required for the normal cerebral oxygenation, resulting in hypoxia (Blicher et al. 2012; Shabir et al. 2018). A certain degree of neurovascular coupling dysfunction in the early stages of many neurological diseases, such as stroke, small vessel disease, and Alzheimer’s diseases, have been observed (D'Esposito et al. 2003; Iadecola 2004; Jessen et al. 2017). Therefore, it is particularly important to examine and modulate abnormal neurovascular coupling functions for the diagnosis and treatment of neurological diseases. Our study found that TUS modulated the neuronal activity and BOM in local brain regions and their low-frequency coupling strength. Previous studies have demonstrated that TUS can promote the recovery from ischemic stroke, Alzheimer’s disease, and other neurovascular diseases (Blicher et al. 2012; Jessen et al. 2017). We speculate that the TUS-mediated strengthening of neurovascular coupling may be useful in the treatment of neurovascular coupling–related neurological diseases, such as ischemic stroke and Alzheimer’s disease. Further studies are required to demonstrate the role of modulating neurovascular coupling in promoting neurological recovery.

To stimulate the mouse visual cortex and monitored the TUS-evoked neuronal calcium oscillations and BOM simultaneously, we used the ultrasound with the following parameters: FF, 500 kHz; peak pressure, 0.45 MPa; Isppa, 6.75 W/cm2; PRF, 1 kHz; and SD, 400 ms. We found that TUS can significantly increase the intensity of the neuronal calcium oscillations and BOM, and the peak value of calcium oscillations increase as the SD increases. In terms of neural activity, previous studies used ultrasound with different parameters to stimulate the cortex or hippocampus of mice/rat and found that ultrasound stimulation could effectively induce cortical neural oscillatory responses (such as neuronal action potentials, local field potentials, and neural calcium signaling) and alter the spatiotemporal features of cortical neural oscillations (Fisher et al. 2018; Yang et al. 2021; Tseng et al. 2021; Yu et al. 2021). Fisher et al. (2018) used the following parameters: FF, 510 kHz; Isppa, 0.69 W/cm2; PRF, 1 kHz; and SD, 1 s. Yu et al. (2021) used the following parameters: FF, 500 kHz; Isppa, 175.80 mW/cm2; PRF, 30 to 4500 Hz; and SD, 67 ms. Yang et al. (2021) used the following parameters: FF, 1.7 MHz; peak negative pressure, 1.3 MPa; and SD, 7 s. Tseng et al. (2021) used the following parameters: FF, 350 kHz; peak pressure, 0.44 MPa; PRF, 2 kHz; and SD, 100 ms. In terms of hemodynamics and cerebral BOM, previous studies have found that ultrasound stimulation with different parameters can induce hemodynamic/cerebral BOM responses and lead to continued recovery to baseline levels. Kim et al. (2017) used the following parameters: FF, 425 kHz; PRFs, 375, 750, and 1.5 kHz; N cycles, 80, 40, and 20; and Isppa, 1.84 W/cm2. Yuan et al. (2020) used the following parameters: FF, 500 kHz; Isppa: 1.1 W/cm2; and SD: 400 ms. In another study, Yuan et al. (2021) used the following parameters, FF: 500 kHz, Isppa: 10.1 W/cm2; PRF, 1 kHz; and SD: 400 ms. A previous study also found that the firing rate of neuronal action potentials induced by TUS increased significantly with SD from 0 to 500 ms (Ye et al. 2018). This is consistent with our finding that the response strength of in vivo neural activity (peak calcium oscillations) increases with stimulation time. The above comparisons show that trends in neural activity and hemodynamics in previous studies are similar over a wide range of parameters. However, because our research and literature cannot guarantee that all parameters are close and because of the selectivity of neurons to ultrasound parameters and the different effects caused by different ultrasound parameters (Yu et al. 2021), the specific parameter values ​​of the response, such as amplitude, strength, and duration, exhibit differences.

Previous studies have shown that different ultrasound parameters (frequency, intensity, pulse time, and duty cycle) can achieve different neuromodulation functions. For example, ultrasound at lower frequencies (0.25–0.35 MHz) can more effectively induce EMG responses in anesthetized mice than ultrasound at higher frequencies (0.5–0.6 MHz) (Ye et al. 2016). The firing rate of action potentials and the amplitude of the local field potentials are closely related to the ultrasound intensity (Kubanek et al. 2018). The ultrasound duty cycle determines the excitatory or inhibitory effects on the neural functions (Yoo et al. 2011). The above studies show that the macroscopic brain function activity induced by ultrasound modulation has ultrasound-related parameter selectivity. In the current study, we found that the characteristic parameters of the neuronal calcium oscillations evoked by TUS increased significantly with increasing SD. However, clarity on the effect of other parameters (such as ultrasound intensity, frequency, and duty cycle) on the modulation of the neuronal calcium oscillations could not be obtained. Furthermore, although we also demonstrated that the modulatory effects of TUS on the neural activity and cerebral hemodynamics also depend on the state of the animal, the dependency of the time–frequency cross-coupling on the state of the animal, such as different depths of anesthesia or awake state, is still unclear.

Conclusion

This study revealed that TUS can effectively alter the spatiotemporal features and phase distribution of calcium oscillations and BOM and modulate the time–frequency cross-coupling between the low-frequency neuronal calcium oscillations and BOM in the mouse visual cortex. The findings of this study may potentially be useful in devising treatments for neuropsychiatric diseases associated with abnormal neurovascular coupling.

Funding

This research was supported by Natural Science Foundation of Hebei Province (F2022203050). National Natural Science Foundation of China (62273291, 61827811, 81871029). Scientific and Technological Innovation 2030 (No. 2021ZD0204300).

Conflict of interest statement

The author(s) declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Author notes

Zhaocheng Su and Jiaqing Yan have contributed equally to this work.

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