Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2015 Feb 15.
Published in final edited form as: Neuroimage. 2013 Nov 1;87:323–331. doi: 10.1016/j.neuroimage.2013.10.044

Direct, intraoperative observation of ∼0.1 Hz hemodynamic oscillations in awake human cortex: Implications for fMRI

Aleksandr Rayshubskiy 1, Teresa J Wojtasiewicz 2, Charles B Mikell 2, Matthew B Bouchard 1, Dmitriy Timerman 1, Brett E Youngerman 2, Robert A McGovern 2, Marc L Otten 2, Peter D Canoll 3, Guy M McKhann II 2, Elizabeth MC Hillman 1,*
PMCID: PMC3961585  NIHMSID: NIHMS552871  PMID: 24185013

Abstract

An almost sinusoidal, large amplitude ∼0.1 Hz oscillation in cortical hemodynamics has been repeatedly observed in species ranging from mice to humans. However, the occurrence of ‘slow sinusoidal hemodynamic oscillations’ (SSHOs) in human functional magnetic resonance imaging (fMRI) studies is rarely noted or considered. As a result, little investigation into the cause of SSHOs has been undertaken, and their potential to confound fMRI analysis, as well as their possible value as a functional biomarker has been largely overlooked.

Here, we report direct observation of large-amplitude, sinusoidal ∼0.1 Hz hemodynamic oscillations in the cortex of an awake human undergoing surgical resection of a brain tumor. Intraoperative multispectral optical intrinsic signal imaging (MS-OISI) revealed that SSHOs were spatially localized to distinct regions of the cortex, exhibited wave-like propagation, and involved oscillations in the diameter of specific pial arterioles, confirming that the effect was not the result of systemic blood pressure oscillations. fMRI data collected from the same subject 4 days prior to surgery demonstrates that ∼0.1 Hz oscillations in the BOLD signal can be detected around the same region. Intraoperative optical imaging data from a patient undergoing epilepsy surgery, in whom sinusoidal oscillations were not observed, is shown for comparison.

This direct observation of the ‘0.1 Hz wave’ in the awake human brain, using both intraoperative imaging and pre-operative fMRI, confirms that SSHOs occur in the human brain, and can be detected by fMRI. We discuss the possible physiological basis of this oscillation and its potential link to brain pathologies, highlighting its relevance to resting-state fMRI and its potential as a novel target for functional diagnosis and delineation of neurological disease.

Introduction

It is well known that the brain exhibits baseline variations in blood flow with temporal frequencies between ∼0.01 - 0.15 Hz (Fox and Raichle, 2007). Assumed to have a relatively featureless 1/f-type spectrum, and to be tightly coupled to neuronal activity, these fluctuations are the foundation of so-called ‘resting state functional connectivity mapping’ (RSFC); an increasingly popular approach to functional magnetic resonance imaging (fMRI). The basis of RSFC mapping is the idea that spatially distinct correlations in the time-course of spontaneous hemodynamic fluctuations (detected as changes in the fMRI blood oxygen level dependent signal, BOLD) can be interpreted as maps of large-scale functional network organization. Such mapping has been demonstrated in mice (White et al., 2011), rats (Pawela et al., 2008), nonhuman primates (Mantini et al., 2011) and humans (Buckner et al., 2008; Fox and Raichle, 2007) and is increasingly being applied to study the difference between normal and diseased brain states including early Alzheimer's disease (Buckner et al., 2009; Sheline and Raichle, 2013), study of motor deficits in brain tumor patients (Otten et al., 2012), predicting surgical outcome of epilepsy (Negishi et al., 2011), traumatic brain injury (Mayer et al., 2011) and schizophrenia (Garrity et al., 2007).

Distinct from these 1/f-type fluctuations, there have also been reports of a so called ∼0.1 Hz, ‘vasomotor’ or slow sinusoidal hemodynamic oscillation (SSHO) in the brain (Mayhew et al., 1996). These large oscillations, with amplitudes comparable to the hemodynamic response to sensory stimulus, are sporadic, but have been shown to occur in the rat (Golanov et al., 1994; Grosberg et al., 2012; Majeed et al., 2009; Mayhew et al., 1996; Saka et al., 2010), cat (Spitzer et al., 2001), awake rabbit (Hundley et al., 1988) and non-invasively in humans using near-infrared spectroscopy (Elwell et al., 1999; Kolyva et al., 2013; Näsi et al., 2011; Obrig et al., 2000; Sassaroli et al., 2012; Schroeter et al., 2005), and fMRI BOLD (Mitra et al., 1997). In some cases the appearance of SSHOs in the brain has been linked to distinct physiological conditions or states of arousal (Näsi et al., 2011). However, a factor that has confused interpretation of these observations is the known occurrence of sinusoidal oscillations in systemic blood pressure, also at around 0.1 Hz, in this case being called ‘Mayer waves’ (Julien, 2006; Mayer, 1876). The possible influence of systemic blood pressure oscillations on measurements of cortical hemodynamics (Kvernmo et al., 1998) has made the true prevalence, cause, mechanisms and neural correlates of SSHOs unclear.

In this study we present the direct observation of a ∼0.1 Hz, spatially distinct cortical hemodynamic oscillation in an awake human undergoing brain tumor resection, acquired using intraoperative multi-spectral optical intrinsic signal imaging (MS-OISI). Intraoperative imaging data collected on a second subject, who was undergoing epilepsy surgery and did not exhibit SSHOs, is shown for comparison. We characterize the spatiotemporal properties of the SSHOs observed in subject 1, identifying correlated oscillations in the tone of pial arterioles, and demonstrating that the SSHOs exhibit wave-like properties within affected regions of the cortex. We further show that a similar ∼0.1 Hz oscillation was detected in fMRI BOLD data acquired in the same subject, four days prior to surgery.

We conclude that although only observed here in one subject, SSHOs can occur in the human, awake brain and are measurable via fMRI. We discuss the possible mechanistic underpinnings of SSHOs and their potential importance for diagnostics and RSFC mapping.

Methods

Multi-spectral optical intrinsic signal imaging (MS-OISI)

This study utilized MS-OISI, a technique which uses a camera to acquire images while the cortical surface is illuminated with specific wavelengths of light. Changes in measured light intensity correspond to changes in absorption due to changes in the concentrations of oxy- and deoxy-hemoglobin ([HbO] and [HbR] respectively) (Bouchard et al., 2009; Hillman, 2007). MS-OISI images represent a 2D, superficially weighted sum of these hemodynamic signals from the pial vasculature and the deeper microvasculature of the parenchyma, and can also be used to calculate dynamic changes in vascular tone (Tian et al., 2011). Since the first human intraoperative OISI study (Haglund et al., 1992) researchers have primarily focused on human intraoperative mapping of eloquent cortex (Prakash et al., 2009; Suh et al., 2010), perfusion assessment (Hecht et al., 2013), and neurovascular coupling changes in epileptogenic cortex (Haglund and Hochman, 2004; Zhao et al., 2007). One distinction of our method relative to earlier human work was our choice not to utilize glass to immobilize the cortex. All of our recordings were made quickly and simply via a standard Leica M720 surgical microscope without compromising the sterile surgical field. Brain motion was overcome by using highly uniform illumination and post-hoc image registration as described further below.

Optical data acquisition

All human subject procedures were reviewed and approved by the Columbia University Institutional Review Board. In both patients, a craniotomy was performed to expose the brain under propofol anesthesia. The region of brain exposed was determined by the patient's medical condition, but in both cases was centered on the posterior frontal lobe.

Human subject 1

Images were acquired using sequentially strobed, filtered blue, green, and red light emitting diodes (LEDs, 470, 530, and 625 nm, respectively: M470L2, M530L2, and M625L2; Thorlabs, filters: NT67-027 (blue), NT67-031 (green), Edmund Optics). Strobed illumination was synchronized with high-speed monochrome camera acquisition (AVT Pike F-032B) mounted onto the surgical microscope (Leica M720). Data was recorded using our open-source SPLASSH optical imaging software (Sun et al., 2010), and was collected at 20 frames per second (fps) equivalent to 6.67 fps per wavelength with 480 × 640 pixel resolution over a ∼ 3 × 3 cm field of view.

Human subject 2

Images were acquired using a color camera (Ikegami MKC 505) mounted on the surgical microscope (Leica M720). White light illumination was provided by the microscope's built in xenon arc-lamp (400W). To gain spectral isolation for blue, green and red channels, we placed a triple-bandpass filter (Semrock FF01-457/530/628-25) before the camera. Filter cutoff properties were chosen to match the spectral response of the camera. Color data was recorded using the microscope's acquisition software (Med × Digital Recording System V6) and was collected at 30 fps per wavelength with a 480 × 640 pixel resolution over a ∼3 × 3 cm field of view. This system corresponded to a later, improved version of our first intrasurgical MS-OISI set-up, but has been independently tested to ensure that it yields equivalent data to the system used for subject 1.

Optical Data analysis

Post-operatively, ‘elastix’ image registration software (Klein et al., 2010) was applied to MS-OISI data to correct for motion artifacts due to the patient's head movement, brain motion caused by heart rate and respiration, and slight microscope motion. Elastix was used to apply non-rigid, bspline-based registration, or where appropriate rigid registration transformations with supervision to ensure that registration-related errors were not introduced into the data. All subsequent analysis and visualization was performed using Matlab™. Monotonic trends resulting from evaporation of moisture from the cortical surface (Lavine et al., 2011), were removed from the raw data prior to conversion into ΔHbO and ΔHbR as detailed below. Correlation images were generated by using corr, Fourier maps show the log of the time-course power spectrum calculated using pwelch, and plotted ‘power spectra’ are shown as the absolute values of the Fourier transform using abs and fft Matlab functions (R2012a).

All optical imaging data was converted to ΔHbO and ΔHbR and ΔHbT = ΔHbO+ΔHbR by using the modified Beer–Lambert law with wavelength-dependent path-length factors derived from Monte Carlo modeling (Bouchard et al., 2009; Hillman, 2007). Hemoglobin extinction coefficients were calculated for each system's measured blue, green and red spectral bandwidths based on data collated by (Prahl, 1998).

fMRI data acquisition

fMRI data was acquired on Subject 1, four days prior to surgery, using a 3.0 T magnetic resonance scanner (GE Healthcare Signa HDxt with 8 channel HD Brain Coil). Head movement was restricted using a pillow and foam, and earplugs were used to attenuate scanner noise and maximize subject comfort. During functional imaging, the subject was instructed to close her eyes, but to stay awake and remain as still as possible while performing a left hand motor task during acquisition consisting of a 20 second hand grasp followed by 20 second rest. Data consisted of a total of 80 multi-slice T2*-weighted volumes acquired with a gradient echo-planar sequence using axial slice orientation (39 slices, slice thickness = 5 mm, spacing = 0 mm, field of view = 21 cm2, phase field of view 1.00 cm2, frequency = 64 Hz, phase = 64, repetition time = 2000 ms, echo time = 30 ms, flip angle = 90°). A T1 magnetization prepared rapid gradient echo sequence was also acquired in the same session for co-registration with functional data.

fMRI data analysis

Acquired T2* data was pre-processed using FEAT (fMRI expert analysis tool, version 6.00, part of FSL) (Jenkinson et al., 2002) with processing limited to time-interpolation between slices and motion correction. Co-registration between functional and anatomical data was performed manually to avoid registration confounds due to low T1 contrast in the tumor region. Power spectra are shown as the absolute values of the Fourier transform.

Results

Direct observation of ∼0.1 Hz oscillation in the awake human cortex

Subject 1 was a 35 year old female undergoing a repeat craniotomy for resection of a right posterior medial frontal lobe oligodendroglioma. During intrasurgical optical imaging, the subject was awake and engaged in performing a hand motor task by pushing a button for the duration of an auditory cue (3 seconds on, 3 seconds off). Although the subject was performing a task during imaging, pre-operative fMRI data (Figure 4) and intrasurgical somatosensory evoked potential monitoring via a strip electrode confirmed that the receptive field for this task was not within the surgical field of view (see Figure 4).

Figure 4. Observation of 0.1 Hz oscillation in pre-operative fMRI BOLD signal in subject 1.

Figure 4

Open in a new tab

(A) Optical field of view and (B) correspondingly oriented field of view on the MRI outlined by a white dashed region showing 2 ROIs. (C) Additional deeper horizontal section showing an ROI over the left hand region in the right motor cortex. Sagittal view showing the location of the horizontal sections is displayed on the top right for B and C. A = anterior, P = posterior, R = right. (D) BOLD signal responses to left hand task in ROI 1. (E,F) BOLD signal time course from ROI 2 and ROI 3. Black crosses mark measured BOLD time-points. A spline interpolated trace is also shown in each case. (G) Power spectra (abs(FFT)) showing a ∼0.1 Hz peak in ROI 2 (indicated by the green arrow). Peak frequency corresponding to the left hand task is indicated by the brown arrow. (inset) Power spectra (abs(FFT)) from (G) plotted on a log-log scale, showing 1/f behavior. (H,I,J) Saggital, coronal and horizontal views of subject 1 respectively, showing the location and extent of the tumor.

The subject was imaged three times over the course of the MS-OISI imaging session. The optical imaging field of view is shown in Figure 1A. Figure 1B shows time courses of changes in HbO, HbR and HbT extracted from the 4 regions of interest (ROI) shown in Figure 1A for one run (data for the other two runs are shown in supplemental figures S1 and S2). A distinct 0.1 Hz, sinusoidal oscillation in ΔHbO, ΔHbR and ΔHbT is observed in two of these four ROIs, with the strongest oscillation towards the bottom right of the field of view. The power spectra of the ΔHbT time-courses show a prominent peak at ∼0.1 Hz (Figures 2C-D). Additional peaks in the power spectrum correspond to the patient's heart rate frequency (∼2.0 Hz), respiration (∼0.4 Hz), expected for an awake patient during neurosurgery and confirmed by anesthesia physiological monitoring.

Figure 1. Direct observation of the 0.1 Hz hemodynamic oscillations in the awake human brain.

Figure 1

Open in a new tab

(A) Grayscale image of subject 1 craniotomy under 530 nm illumination with 4 regions of interest (ROIs) indicated. (B) 4 corresponding time courses showing ΔHbT, ΔHbO and ΔHbR dynamics for each ROI indicated in A. (C) Power spectrum (abs(FFT)) of ΔHbT time courses shown in B. Green arrow indicates SSHO at ∼0.1 Hz. (D) Log-log abs(FFT) plot over wider frequency range showing otherwise 1/f shape. Heart-rate (2 Hz) is indicated by the thick blue arrow, breathing (0.4 Hz) with a thin blue arrow and the SSHO with a green arrow. (E) Fourier image of the field of view at 0.1 Hz, delineating 3 distinct vascular networks, outlined with white dashed ovals (stars indicate artifacts from specular reflections). (F) Fourier image at 0.15 Hz, away from the 0.1 Hz peak in the power spectrum, showing no delineated regions.

Figure 2. Non-sinusoidal low frequency hemodynamics in a second human subject.

Figure 2

Open in a new tab

(A) Grayscale image of subject 2 craniotomy under 530 nm illumination with 4 ROIs indicated. (B) 4 corresponding time courses showing ΔHbT, ΔHbO and ΔHbR dynamics for each ROI indicated in A. (C) Power spectrum (abs(FFT)) of HbT time courses shown in B with 0.17 Hz breathing rate indicated by thin blue arrow. (D) Log-log abs(FFT) plot of wider frequency range with thicker blue arrows indicating heart rate (1 Hz) and its harmonic (2 Hz). (E) Fourier image of the field of view at 0.1 Hz, and (F) a Fourier image at 0.15 Hz. Stars indicate artifacts from specular reflections. No distinct vascular networks are apparent in either image. A movie of ΔHbT dynamics in subject 2 is shown in supplemental movie M2.

Figure 1E shows a ‘Fourier image’, produced by calculating the spectral power of the time-course of each pixel at a given frequency, in this case: 0.1 Hz (Kiviniemi et al., 2000). The resulting image reveals 3 distinct oscillating regions, highlighted with white dashed ovals in Figure 1E. A Fourier image away from the 0.1 Hz peak, at 0.15 Hz (Figure 1F), shows no discernible regions, and confirms that regional localization is specific to the 0.1 Hz SSHO.

Non-sinusoidal hemodynamic fluctuations in human cortex (subject 2)

Subject 2 was a 36 year old male undergoing electrode grid placement surgery to locate epileptic foci in the right posterior frontal lobe. The patient was sedated with propofol anesthesia during imaging and no seizure activity was noted during optical recordings. Figure 2B shows time-courses of ΔHbR, ΔHbO and ΔHbT for 4 ROIs, along with their power spectra (Figure 2C-D). As confirmed by anesthesia records, a heart rate peak at ∼1 Hz, and its harmonic at ∼2 Hz, along with a respiration related peak at ∼0.17 Hz can be seen for all ROIs, but no 0.1 Hz or similar peak is present. Time courses are markedly different form the time courses in Figure 1B for subject 1, showing clear low frequency fluctuations, but without a periodic sinusoidal shape. Figures 2E-F show Fourier images generated at 0.1 Hz and 0.15 Hz. No defined regions oscillating at ∼0.1 Hz are discernible. This data confirms that the 0.1 Hz oscillation observed in subject 1 was not a function of an exposed-cortex craniotomy, optical imaging or analysis techniques.

Spatiotemporal and vascular properties of the 0.1 Hz hemodynamic oscillation

Intraoperative MS-OISI provides the opportunity to closely examine the features of the 0.1 Hz hemodynamic oscillation in the human brain. To ascertain the spatial properties of the oscillation observed in subject 1, a Pearson correlation coefficient image was generated (Figure 3A) by correlating the ΔHbT time course seen in ROI 3 in Figure 1B with the time course of each pixel in the ΔHbT image time-series. This time course was chosen because of its highest spectral power at 0.1 Hz. The resulting correlation image (Figure 3A) reveals the distinct spatial localization and extent of the seeded 0.1 Hz oscillation. Note that the relative phase of the oscillation in this region causes it to be isolated from the other SSHO regions identified in the Fourier image figure 1E. Magnifying the area with the highest correlation reveals fine vascular structure shown in Figure 3B. Notably, small vessels under the large central vein are seen to be a continuous part of the ∼0.1 Hz oscillating vascular network (arrows in Figure 3B,C).

Figure 3. Properties of the ∼0.1 Hz oscillation in the human brain.

Figure 3

Open in a new tab

(A) Pearson correlation coefficient image using the ΔHbT time course from ROI 3 in Figure 1. (B,C) Magnification and corresponding comparison of the correlation image with raw green channel grayscale image of the region indicated by the white dashed box in A. Note the large vascular network delineated by the correlation image extends under the large vein (black arrow). (D) Change in vessel diameter relating to change in ΔHbT over vessel indicated by white arrow in C. Light brown indicates raw vessel diameter time course, dark brown is the raw vessel diameter time course low pass filtered at 0.2 Hz. (E) Wave-like propagation of the 0.1 Hz oscillation depicted ασ a ΔHbT kymograph image of time courses extracted along the long dashed yellow ROI in A and in yellow in the inset gray scale image below (F). Red arrows mark pixels corresponding to blood vessels. Black arrow indicates the direction of a single wave's propagation. Equivalent data from two more trials is shown in supplementary figures S1 and S2. White arrow indicates a point of merge. (G) Using cross-correlation analysis, time lags between ∼0.1 Hz oscillating time courses extracted from 4 ROIs shown on the grayscale image to the left demonstrate directional propagation of the SSHO wave.

To explore these vessels further, we analyzed whether the correlated fluctuations in ΔHbT corresponded to variations in vessel diameter. Vessel diameter calculations were carried out on the raw 530 nm reflectance data, registered using the rigid instead of the non-rigid transformations to avoid vessel motion removal caused by non-rigid registration. Time courses of the changes in HbT and vessel diameter are plotted in Figure 3D, and show a close correlation of 0.83 between the two signals. We conclude that these vessels are pial arterioles undergoing rhythmic vasomotor dilation and constriction (Grosberg et al., 2012).

Further exploring the observed SSHOs, upon inspection of movies of ΔHbT, distinct epochs were noted in which the 0.1 Hz oscillation appeared to propagate across the cortex as a spatial wave (see supplemental movie M1, supplemental figures S1-2). This wave behavior is visible in the kymograph in figure 3E, which shows the detected ΔHbT signal along a line within the field of view as a function of time. For the first few cycles of the SSHO, the wave can be seen to be propagating in a medial to lateral direction, as is most common across all three runs acquired in this subject. However, the kymograph also shows that at certain times, oscillations can travel and merge in different ways. Figure 3G shows the relative phase delays of SSHOs in four different ROIs during ∼50-60 second epochs for all three trials showing clear directional propagation. Phase delays were calculated by cross correlating HbT time courses from ROIs 2,3 and 4 with the HbT time course from ROI 1 and extracting the time value corresponding to the largest correlation in the nearest cycle around 0, for each of the 4 time course pairs.

Pre-operative fMRI in subject 1

fMRI data was acquired on subject 1 four days prior to surgery to assess the location of the left hand motor region in relation to the tumor. The patient performed a similar left hand motor task during fMRI acquisition as during intrasurgical imaging, although at a slower repetition frequency (20 seconds on, 20 seconds off).

The approximate location of the optical imaging field of view, as indicated in Figure 4B, was determined from examination of anatomical T1 and T2* MRI scans and intraoperative MR registration using the BrainLAB® system (Brainlab, Inc. Westchester, IL). Regions responding to the motor task were confirmed to be outside of the intrasurgical imaging field of view by awake intraoperative somatosensory evoked potential mapping using a cortical strip electrode placed along the posterior edge of the cranial window. Examination of the anatomical MRI scan shows that the anterior edge of our optical imaging field of view is located over superficial tumor, but that the posterior side (where most prominent SSHOs were observed) may have some residual cortex overlaying the tumor. Related orthogonal views of the subject's T1 anatomical scan are shown in Figures 4 K-J.

fMRI BOLD data was minimally pre-processed, as described in the methods section, and then examined using Fourier analysis and manual inspection of voxel time-courses to identify the ROIs and corresponding time-courses shown in Figures 4D, E and F. ROI 1 shows the primary right motor cortical region for the left hand, where well-correlated responses to the stimulus were seen (figure 4D). Both T1 and T2* signals within the tumor were low, which combined with large voxels made it difficult to extract time-courses from the uppermost surface of the tumor / cortex corresponding to the exact area of our intraoperative recordings. However, ROI 2 was selected from the superior edge of the left hand motor area, at the posterior edge of our optical imaging field of view, and in addition to exhibiting a small stimulus response exhibits very similar 0.1 Hz BOLD oscillations to those observed intraoperatively (Figure 4E, power spectrum 4G). ROI 3 is a region from the cortex contralateral to the tumor, and while exhibiting slow fluctuations in BOLD (Figure 4F) can be seen to have a featureless 1/f spectrum with no peak at 0.1 Hz (Figure 4G).

Discussion

Slow sinusoidal 0.1 Hz hemodynamic oscillations, or SSHOs in humans have been reported previously using both NIRS and fMRI BOLD (Mitra et al., 1997; Obrig et al., 2000). However, the prevalence and importance of SSHOs have been questioned, particularly in relation to contamination from systemic blood flow changes (Ferguson, 2003; Kvernmo et al., 1998). Using intraoperative MS-OISI we recorded optical reflectance signals directly from the superficial layers of the awake human cortex, unobstructed by confounds from the scalp and skull. The substantially higher spatiotemporal resolution of MS-OISI compared to NIRS and fMRI measurements allowed analysis of time courses of ΔHbO, ΔHbR, ΔHbT and vasomotion dynamics, demonstrating that observed SSHOs were associated with distinct networks of pial arterioles. Visualization of SSHOs propagating as a traveling wave within discrete regions of the cortex suggests that the oscillation is not a manifestation of systemic blood pressure ‘Mayer waves’. fMRI data acquired in the same patient prior to surgery revealed that similar ∼0.1 Hz SSHOs could be observed as fluctuations in the raw BOLD signal.

Since our data demonstrates that localized 0.1 Hz oscillations in cortical hemodynamics can occur in the awake human brain, and that these oscillations can be detected by fMRI, the physiological basis of these oscillations, and how to interpret them, becomes a key question. Of particular importance is whether 0.1 Hz hemodynamic oscillations in the brain are neurogenic; driven by a specific neuronal state or sequence of activity, or whether oscillations are myogenic; driven by the vasculature itself independent of neuronal activity. We propose that our findings, combined with the evidence detailed below, suggest that 0.1 Hz oscillations are primarily myogenic.

Prior studies suggest neurovascular uncoupling during SSHOs

Despite numerous reports of SSHOs in animal models, the physiological basis of SSHOs in the brain has barely been explored. While several recent studies have found electrophysiological correlates to slow hemodynamic fluctuations in fMRI BOLD at <0.1 Hz (Drew et al., 2008; Leopold et al., 2003; Lőrincz et al., 2009; Nir et al., 2008), only a small number of studies have specifically noted the relationship between neuronal activity and the ∼0.1 Hz hemodynamic oscillation. One recent study using combined spectrophotometry and electrophysiology to study neurovascular coupling in the visual thalamus of a cat, and reported spontaneous SSHOs at 0.14 Hz, which were inducible by chloralose anesthesia and could be temporarily blocked by systemic administration of adrenaline or acetylcholine (Rivadulla et al., 2011). The authors demonstrated a marked breakdown in the relationship between local neural activity and blood flow when oscillations were present. Another study using laser Doppler to measure blood flow combined with electroencephalography (EEG) in rats during isoflurane-induced neuronal bursting, noted that in instances when ‘strong sinusoidal’ hemodynamic fluctuations at around 0.1 Hz arose following sharp blood pressure changes, correlations between neuronal and hemodynamic measures were abolished (Golanov et al., 1994).

Is there something special about 0.1 Hz?

It is well known that isolated blood vessels, from the brain and peripheral vasculature across species can exhibit spontaneous rhythmic ‘vasomotor’ oscillations in tone in the frequency range of 0.05 – 0.2 Hz (Aalkjær et al., 2011; Aalkjær and Nilsson, 2005; Fujii et al., 1990; Oishi et al., 2002; Peng et al., 2001; Stefanovska, 2007). In the past 10 years, this phenomenon has been studied in detail (Aalkjær et al., 2011), and ∼0.1 Hz has been identified as a characteristic frequency for arterioles from diverse locations and species (Bouskela and Grampp, 1992; Haddock et al., 2002; Setchell et al., 1995). In isolated vessels, vasomotion at ∼0.1 Hz has been shown to correspond to synchronization of the intermittent release of calcium within vascular smooth muscle cells (Aalkjær et al., 2011). While the mechanisms initiating this synchronization, and the cyclic process that sustains the oscillation are not well understood (Aalkjær and Nilsson, 2005; Haddock and Hill, 2005; Kapela et al., 2012), a range of different factors have been shown to lead to this synchronous state. These include conditions where microcirculation is at the lower end of the systemic autoregulatory pressure range (Ren et al., 1994; Schmidt et al., 1992), peripheral arterial occlusion disease (Schmidt et al., 1993), and in patients after compression therapy (Pekanmäki et al., 1991). The amplitude and frequency of vasomotion has been shown to be temperature dependent (Setchell et al., 1995), and affected by local changes in PO2 and pH (Bouskela and Grampp, 1992). Most reports of vasomotion in peripheral vessels conclude that it is myogenic in origin, driven by the properties of the vessels themselves in response to external conditions (Bouskela and Grampp, 1992).

Are all brain SSHOs the same?

SSHOs have been observed in the brain in a wide range of conditions. Some have dismissed SSHOs as a consequence of anesthesia, abnormal blood pressure, sudden blood pressure changes, or an artifact of non-physiological open cortex surgical conditions (Golanov et al., 1994; Jones et al., 2005; Julien, 2006; Nakai and Maeda, 1999). Nitric oxide (NO) blockers and ketamine anesthesia have also been noted to induce marked enhancement of SSHOs in animal models (Ances et al., 2010; Dirnagl et al., 1993; Spitzer et al., 2001). Further studies have reported anomalous, low frequency periodic oscillations in brain blood flow in a range of pathological conditions including neuroleukemia, hydrocephalus and glioma (Kiviniemi et al., 2000; Wang et al., 2008). However, SSHOs have also been observed non-invasively in healthy awake humans using both NIRS and fMRI (Elwell et al., 1999; Mitra et al., 1997; Obrig et al., 2000).

The distinctive frequency of SSHOs suggest that each of these diverse conditions shares some common property. Based on the considerations above, we hypothesize that SSHOs might be an outward indication that some factor of normal hemodynamic regulation in the brain has been altered, whether by a transient physiological or attentional state, a pharmacological agent or a local or global pathology. We posit that 0.1 Hz may be a uniquely important frequency, noting that the time-constant of its cycle is highly consistent with the functional ‘hemodynamic response function’, which typically peaks 3-4 seconds after the onset of a stimulus (Kruggel and von Cramon, 1999); suggesting that 0.1 Hz might be the brain's hemodynamic natural frequency. Observations of SSHOs in the brain during periods of low blood pressure or flow, or sudden changes in blood pressure would be consistent with observations of vasomotion in the peripheral vasculature (Sakurai and Terui, 2006; Schmidt et al., 1992). NO inhibitors and ketamine could both be expected to induce an imbalance in the normal regulation of cerebrovascular tone (Anis et al., 1983; Attwell et al., 2010; Harrison and Simmonds, 1985). In the case of pathologies; situations in which any component of the neurovascular unit or autoregulatory system including endothelial cells, smooth muscle cells, astrocytes or neurons themselves might be damaged or impaired could potentially result in SSHOs. For example, glioma cells have been shown to spread via migration along the vasculature (Cretu et al., 2005), a disruptive action that could easily interfere with the local vasculature's ability to maintain normal tone. We note that the patient in whom we observed SSHOs was undergoing resection of a recurrent oligodendroglioma. Both the cellular presence of the tumor, and perfusion deficits caused by compression or stretching of overlying cortex (depicted in Figure 4H-J) could potentially account for the presence of SSHOs.

While requiring further investigation, evidence to date points to SSHOs having little direct neuronal origin, instead suggesting that the appearance of SSHOs might represent a departure from normal neurovascular coupling.

Implications for Interpretation of Functional Imaging Studies and Functional Diagnostics

From the first demonstration of functional connectivity MRI (fcMRI) in the human sensorimotor cortex (Biswal et al., 1995), RSFC algorithms have commonly applied a low-pass cutoff filter at ∼0.08 Hz (Fox et al., 2005) with the intent of removing artifacts from human breathing frequencies. However, the proximity of this cut-off to 0.1 Hz makes it possible that SSHOs, if present, could have a major influence on RSFC related analysis. To date, the presence of this oscillation in resting state fMRI has rarely been noted or specifically analyzed, so its prevalence and relevance to RSFC results in different pathologies has not been explored (Murphy et al., 2013; Wang et al., 2008). fcMRI is increasingly being applied to pathological states including acute and chronic stroke (Grefkes and Fink, 2011), Alzheimer's disease (Sheline and Raichle, 2013), and traumatic brain injury (Mayer et al., 2011); all conditions in which SSHOs could feasibly arise, and all conditions in which alterations in resting state networks have been demonstrated.

While SSHOs could potentially confound conventional interpretation of resting state fMRI data, our findings present the exciting opportunity that SSHOs themselves could be markers of neurovascular abnormalities, and thus early markers of disease or prognosis. Our observation of SSHOs in a patient with a recurrent oligodendroglioma both presurgically via fMRI and then intraoperatively using MS-OISI suggests that SSHOs could provide important new, previously unexplored biomarkers, detectable non-invasively via conventional fMRI BOLD techniques (Wang et al., 2008).

Since BOLD is primarily sensitive to HbR, it is important to note that in our optical recordings, HbR represented a smaller contribution than changes in HbT (Figure 1B), likely due to the strong arteriolar contribution to SSHOs (Figure 2B). We also note that identifying regions exhibiting 0.1 Hz oscillations in fMRI data acquired at 0.5 Hz required careful examination of the data's power spectra since at low sample rates, it can be challenging to distinguish a sinusoidal 0.1 Hz wave from both normal baseline fluctuations and aliasing. Nevertheless, regions exhibiting a distinct 0.1 Hz wave were clearly detected in our data, in some cases prominently, confirming that it is possible to detect SSHOs using fMRI.

Supplementary Material

01
02
03
04
Download video file (18.3MB, mpg)
05
Download video file (18.3MB, mpg)

Highlights.

  • Direct observation of ∼0.1 Hz oscillation in awake human cortex.

  • Oscillation in frequency range of resting state function connectivity analysis.

  • Oscillation delineates vascular networks.

  • ∼0.1 Hz oscillations are also observed in pre-operative fMRI BOLD signal.

  • Potential novel target for functional diagnosis of neurological disease.

Acknowledgments

We thank Drs. Catherine Schevon, Angela Lignelli-Dipple, Jack Grinband, Sameer Sheth, John Sheehy, Hani Malone, Daniel Stoyanov, Daniel Chow, Mariel Kozberg and Jason Berwick for helpful discussions; Drs. Michael Sisti and Jeffrey Bruce, Columbia University neurosurgery residents, and neurosurgical operating room staff for their help in collecting the data; Keith Yeager for machining assistance; and members of the Laboratory for Functional Optical Imaging for support and helpful discussions. We acknowledge support from National Institutes of Health: UL1 RR024156 National Center for Advancing Translational Sciences, Irving Institute CTSA, National Institute of Neurological Disorders and Stroke, 1R01NS063226, 1R01NS076628, and R21NS053684 (to E.M.C.H.); National Science Foundation Grants CAREER 0954796 (to E.M.C.H.); Graduate Fellowship (M.B.B.); National Defense Science and Engineering Graduate Fellowship (to M.B.B.), Doris Duke Foundation research fellowship (T.J.W.); the Human Frontier Science Program and the Kavli Foundation.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Aalkjær C, Boedtkjer D, Matchkov V. Vasomotion – what is currently thought? Acta Physiologica. 2011;202:253–269. doi: 10.1111/j.1748-1716.2011.02320.x. [DOI] [PubMed] [Google Scholar]
  2. Aalkjær C, Nilsson H. Vasomotion: cellular background for the oscillator and for the synchronization of smooth muscle cells. British Journal of Pharmacology. 2005;144:605–616. doi: 10.1038/sj.bjp.0706084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ances BM, Greenberg JH, Detre JA. Interaction between nitric oxide synthase inhibitor induced oscillations and the activation flow coupling response. Brain Research. 2010;1309:19–28. doi: 10.1016/j.brainres.2009.09.119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Anis NA, Berry SC, Burton NR, Lodge D. The dissociative anaesthetics, ketamine and phencyclidine, selectively reduce excitation of central mammalian neurones by N-methyl-aspartate. Br J Pharmacol. 1983;79:565–575. doi: 10.1111/j.1476-5381.1983.tb11031.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Attwell D, Buchan AM, Charpak S, Lauritzen M, MacVicar BA, Newman EA. Glial and neuronal control of brain blood flow. Nature. 2010;468:232–243. doi: 10.1038/nature09613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–541. doi: 10.1002/mrm.1910340409. [DOI] [PubMed] [Google Scholar]
  7. Bouchard MB, Chen BR, Burgess SA, Hillman EMC. Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics. Opt Express. 2009;17:15670–15678. doi: 10.1364/OE.17.015670. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bouskela E, Grampp W. Spontaneous vasomotion in hamster cheek pouch arterioles in varying experimental conditions. American Journal of Physiology - Heart and Circulatory Physiology. 1992;262:H478–H485. doi: 10.1152/ajpheart.1992.262.2.H478. [DOI] [PubMed] [Google Scholar]
  9. Buckner RL, Andrews-Hanna JR, Schacter DL. The Brain's Default Network. Annals of the New York Academy of Sciences. 2008;1124:1–38. doi: 10.1196/annals.1440.011. [DOI] [PubMed] [Google Scholar]
  10. Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, Andrews-Hanna JR, Sperling RA, Johnson KA. Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease. The Journal of Neuroscience. 2009;29:1860–1873. doi: 10.1523/JNEUROSCI.5062-08.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cretu A, Fotos J, Little B, Galileo D. Human and Rat Glioma Growth, Invasion, and Vascularization in a Novel Chick Embryo Brain Tumor Model. Clinical & Experimental Metastasis. 2005;22:225–236. doi: 10.1007/s10585-005-7889-x. [DOI] [PubMed] [Google Scholar]
  12. Dirnagl U, Lindauer U, Villringer A. Nitric Oxide Synthase Blockade Enhances Vasomotion in the Cerebral Microcirculation of Anesthetized Rats. Microvascular Research. 1993;45:318–323. doi: 10.1006/mvre.1993.1028. [DOI] [PubMed] [Google Scholar]
  13. Drew PJ, Duyn JH, Golanov E, Kleinfeld D. Finding coherence in spontaneous oscillations. Nat Neurosci. 2008;11:991–993. doi: 10.1038/nn0908-991. [DOI] [PubMed] [Google Scholar]
  14. Elwell CE, Springett R, Hillman E, Delpy DT. Oscillations in cerebral haemodynamics. Implications for functional activation studies. Adv Exp Med Biol. 1999;471:57–65. [PubMed] [Google Scholar]
  15. Ferguson A. A review of the physiology of cranial osteopathy. Journal of Osteopathic Medicine. 2003;6:74–84. [Google Scholar]
  16. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8:700–711. doi: 10.1038/nrn2201. [DOI] [PubMed] [Google Scholar]
  17. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America. 2005;102:9673–9678. doi: 10.1073/pnas.0504136102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fujii K, Heistad DD, Faraci FM. Vasomotion of basilar arteries in vivo. American Journal of Physiology - Heart and Circulatory Physiology. 1990;258:H1829–H1834. doi: 10.1152/ajpheart.1990.258.6.H1829. [DOI] [PubMed] [Google Scholar]
  19. Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD. Aberrant “Default Mode” Functional Connectivity in Schizophrenia. American Journal of Psychiatry. 2007;164:450–457. doi: 10.1176/ajp.2007.164.3.450. [DOI] [PubMed] [Google Scholar]
  20. Golanov EV, Yamamoto S, Reis DJ. Spontaneous waves of cerebral blood flow associated with a pattern of electrocortical activity. American Journal of Physiology - Regulatory, Integrative and Comparative Physiology. 1994;266:R204–R214. doi: 10.1152/ajpregu.1994.266.1.R204. [DOI] [PubMed] [Google Scholar]
  21. Grefkes C, Fink GR. Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain. 2011 doi: 10.1093/brain/awr033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Grosberg LE, Chen BR, Hillman EMC. Simultaneous multiplane in vivo nonlinear microscopy using spectral encoding. Opt Lett. 2012:37, 2967–2969. doi: 10.1364/OL.37.002967. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Haddock RE, Hill CE. Rhythmicity in arterial smooth muscle. The Journal of Physiology. 2005;566:645–656. doi: 10.1113/jphysiol.2005.086405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Haddock RE, Hirst GDS, Hill CE. Voltage independence of vasomotion in isolated irideal arterioles of the rat. The Journal of Physiology. 2002;540:219–229. doi: 10.1113/jphysiol.2001.013698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Haglund MM, Hochman DW. Optical Imaging of Epileptiform Activity in Human Neocortex. Epilepsia. 2004;45:43–47. doi: 10.1111/j.0013-9580.2004.04010.x. [DOI] [PubMed] [Google Scholar]
  26. Haglund MM, Ojemann GA, Hochman DW. Optical imaging of epileptiform and functional activity in human cerebral cortex. Nature. 1992;358:668–671. doi: 10.1038/358668a0. [DOI] [PubMed] [Google Scholar]
  27. Harrison NL, Simmonds MA. Quantitative Studies on Some Antagonists of N-Methyl D-Aspartate in Slices of Rat Cerebral-Cortex. British Journal of Pharmacology. 1985;84:381–391. doi: 10.1111/j.1476-5381.1985.tb12922.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hecht N, Woitzik J, Konig S, Horn P, Vajkoczy P. Laser speckle imaging allows real-time intraoperative blood flow assessment during neurosurgical procedures. J Cereb Blood Flow Metab. 2013;33:1000–1007. doi: 10.1038/jcbfm.2013.42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hillman EMC. Optical brain imaging in vivo: techniques and applications from animal to man. Journal of Biomedical Optics. 2007;12:051402–051402. doi: 10.1117/1.2789693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hundley WG, Renaldo GJ, Levasseur JE, Kontos HA. Vasomotion in cerebral microcirculation of awake rabbits. American Journal of Physiology - Heart and Circulatory Physiology. 1988;254:H67–H71. doi: 10.1152/ajpheart.1988.254.1.H67. [DOI] [PubMed] [Google Scholar]
  31. Jenkinson M, Bannister P, Brady M, Smith S. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage. 2002;17:825–841. doi: 10.1016/s1053-8119(02)91132-8. [DOI] [PubMed] [Google Scholar]
  32. Jones M, Berwick J, Hewson-Stoate N, Gias C, Mayhew J. The effect of hypercapnia on the neural and hemodynamic responses to somatosensory stimulation. NeuroImage. 2005;27:609–623. doi: 10.1016/j.neuroimage.2005.04.036. [DOI] [PubMed] [Google Scholar]
  33. Julien C. The enigma of Mayer waves: Facts and models. Cardiovascular Research. 2006;70:12–21. doi: 10.1016/j.cardiores.2005.11.008. [DOI] [PubMed] [Google Scholar]
  34. Kapela A, Parikh J, Tsoukias Nikolaos M. Multiple Factors Influence Calcium Synchronization in Arterial Vasomotion. Biophysical journal. 2012;102:211–220. doi: 10.1016/j.bpj.2011.12.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kiviniemi V, Jauhiainen J, Tervonen O, Pääkkö E, Oikarinen J, Vainionpää V, Rantala H, Biswal B. Slow vasomotor fluctuation in fMRI of anesthetized child brain. Magnetic Resonance in Medicine. 2000;44:373–378. doi: 10.1002/1522-2594(200009)44:3<373::aid-mrm5>3.0.co;2-p. [DOI] [PubMed] [Google Scholar]
  36. Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. elastix: A Toolbox for Intensity-Based Medical Image Registration. Medical Imaging, IEEE Transactions on 29. 2010:196–205. doi: 10.1109/TMI.2009.2035616. [DOI] [PubMed] [Google Scholar]
  37. Kolyva C, Kingston H, Tachtsidis I, Mohanty S, Mishra S, Patnaik R, Maude R, Dondorp A, Elwell C. Oscillations in Cerebral Haemodynamics in Patients with Falciparum Malaria. In: Welch WJ, Palm F, Bruley DF, Harrison DK, editors. Oxygen Transport to Tissue XXXIV. Springer; New York: 2013. pp. 101–107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kruggel F, von Cramon DY. Temporal properties of the hemodynamic response in functional MRI. Human Brain Mapping. 1999;8:259–271. doi: 10.1002/(SICI)1097-0193(1999)8:4&#x0003c;259::AID-HBM9&#x0003e;3.0.CO;2-K. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kvernmo HD, Stefanovska A, Bracic M, Kirkebøen KA, Kvernebo K. Spectral Analysis of the Laser Doppler Perfusion Signal in Human Skin before and after Exercise. Microvascular Research. 1998;56:173–182. doi: 10.1006/mvre.1998.2108. [DOI] [PubMed] [Google Scholar]
  40. Lavine M, Haglund MM, Hochman DW. Dynamic linear model analysis of optical imaging data acquired from the human neocortex. Journal of Neuroscience Methods. 2011;199:346–362. doi: 10.1016/j.jneumeth.2011.05.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Leopold DA, Murayama Y, Logothetis NK. Very Slow Activity Fluctuations in Monkey Visual Cortex: Implications for Functional Brain Imaging. Cerebral Cortex. 2003;13:422–433. doi: 10.1093/cercor/13.4.422. [DOI] [PubMed] [Google Scholar]
  42. Lőrincz ML, Geall F, Bao Y, Crunelli V, Hughes SW. ATP-Dependent Infra-Slow (<0.1 Hz) Oscillations in Thalamic Networks. PLoS ONE. 2009;4:e4447. doi: 10.1371/journal.pone.0004447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Majeed W, Magnuson M, Keilholz SD. Spatiotemporal dynamics of low frequency fluctuations in BOLD fMRI of the rat. Journal of Magnetic Resonance Imaging. 2009;30:384–393. doi: 10.1002/jmri.21848. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mantini D, Gerits A, Nelissen K, Durand JB, Joly O, Simone L, Sawamura H, Wardak C, Orban GA, Buckner RL, Vanduffel W. Default Mode of Brain Function in Monkeys. The Journal of Neuroscience. 2011;31:12954–12962. doi: 10.1523/JNEUROSCI.2318-11.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Mayer AR, Mannell MV, Ling J, Gasparovic C, Yeo RA. Functional connectivity in mild traumatic brain injury. Human Brain Mapping. 2011;32:1825–1835. doi: 10.1002/hbm.21151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Mayer S. Studien zur physiologie des herzens und blutgeflaesse V. Ueber spontane blutdruckschwankungen. Akademie der Wissenschaften in Wien. Mathematisch-naturwissenschaftliche Classe. 1876;74:281–307. [Google Scholar]
  47. Mayhew JEW, Askew S, Zheng Y, Porrill J, Westby GWM, Redgrave P, Rector DM, Harper RM. Cerebral Vasomotion: A 0.1-Hz Oscillation in Reflected Light Imaging of Neural Activity. NeuroImage. 1996;4:183–193. doi: 10.1006/nimg.1996.0069. [DOI] [PubMed] [Google Scholar]
  48. Mitra PP, Ogawa S, Hu X, Uğ urbil K. The nature of spatiotemporal changes in cerebral hemodynamics as manifested in functional magnetic resonance imaging. Magnetic Resonance in Medicine. 1997;37:511–518. doi: 10.1002/mrm.1910370407. [DOI] [PubMed] [Google Scholar]
  49. Murphy K, Birn RM, Bandettini PA. Resting-state fMRI confounds and cleanup. NeuroImage. 2013 doi: 10.1016/j.neuroimage.2013.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Nakai M, Maeda M. Scopolamine-sensitive and resistant components of increase in cerebral cortical blood flow elicited by periaqueductal gray matter of rats. Neuroscience Letters. 1999;270:173–176. doi: 10.1016/s0304-3940(99)00491-7. [DOI] [PubMed] [Google Scholar]
  51. Näsi T, Virtanen J, Noponen T, Toppila J, Salmi T, Ilmoniemi RJ. Spontaneous Hemodynamic Oscillations during Human Sleep and Sleep Stage Transitions Characterized with Near-Infrared Spectroscopy. PLoS ONE. 2011;6:e25415. doi: 10.1371/journal.pone.0025415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Negishi M, Martuzzi R, Novotny EJ, Spencer DD, Constable RT. Functional MRI connectivity as a predictor of the surgical outcome of epilepsy. Epilepsia. 2011;52:1733–1740. doi: 10.1111/j.1528-1167.2011.03191.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Nir Y, Mukamel R, Dinstein I, Privman E, Harel M, Fisch L, Gelbard-Sagiv H, Kipervasser S, Andelman F, Neufeld MY, Kramer U, Arieli A, Fried I, Malach R. Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nat Neurosci. 2008;11:1100–1108. doi: 10.1038/nn.2177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Obrig H, Neufang M, Wenzel R, Kohl M, Steinbrink J, Einhäupl K, Villringer A. Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults. NeuroImage. 2000;12:623–639. doi: 10.1006/nimg.2000.0657. [DOI] [PubMed] [Google Scholar]
  55. Oishi H, Schuster A, Lamboley M, Stergiopulos N, Meister JJ, Bény JL. Role of membrane potential in vasomotion of isolated pressurized rat arteries. Life Sciences. 2002;71:2239–2248. doi: 10.1016/s0024-3205(02)02014-3. [DOI] [PubMed] [Google Scholar]
  56. Otten ML, Mikell CB, Youngerman BE, Liston C, Sisti MB, Bruce JN, Small SA, McKhann GM. Motor deficits correlate with resting state motor network connectivity in patients with brain tumours. Brain. 2012;135:1017–1026. doi: 10.1093/brain/aws041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Pawela CP, Biswal BB, Cho YR, Kao DS, Li R, Jones SR, Schulte ML, Matloub HS, Hudetz AG, Hyde JS. Resting-state functional connectivity of the rat brain. Magnetic Resonance in Medicine. 2008;59:1021–1029. doi: 10.1002/mrm.21524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Pekanmäki K, Kolari PJ, Kiistala U. Laser Doppler vasomotion among patients with post-thrombotic venous insufficiency: effect of intermittent pneumatic compression. VASA Zeitschrift fur Gefasskrankheiten. 1991;20:394–397. [PubMed] [Google Scholar]
  59. Peng H, Matchkov V, Ivarsen A, Aalkjær C, Nilsson H. Hypothesis for the Initiation of Vasomotion. Circulation Research. 2001;88:810–815. doi: 10.1161/hh0801.089603. [DOI] [PubMed] [Google Scholar]
  60. Prahl SA. 1998 Online resource: http://omlc.ogi.edu/spectra/hemoglobin/summary.html.
  61. Prakash N, Uhlemann F, Sheth SA, Bookheimer S, Martin N, Toga AW. Current trends in intraoperative optical imaging for functional brain mapping and delineation of lesions of language cortex. NeuroImage. 2009;47(2):T116–T126. doi: 10.1016/j.neuroimage.2008.07.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Ren TY, Brown JN, Nuttall AL, Miller JM. Cochlear Flux Motion and Arterial Pressure Reduction in Guinea Pig. Acta Oto-laryngologica. 1994;114:254–258. doi: 10.3109/00016489409126052. [DOI] [PubMed] [Google Scholar]
  63. Rivadulla C, de Labra C, Grieve KL, Cudeiro J. Vasomotion and Neurovascular Coupling in the Visual Thalamus In Vivo. PLoS ONE. 2011;6:e28746. doi: 10.1371/journal.pone.0028746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Saka M, Berwick J, Jones M. Linear superposition of sensory-evoked and ongoing cortical hemodynamics. Frontiers in Neuroenergetics. 2010;2 doi: 10.3389/fnene.2010.00023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Sakurai T, Terui N. Effects of sympathetically induced vasomotion on tissue-capillary fluid exchange. American Journal of Physiology - Heart and Circulatory Physiology. 2006;291:H1761–H1767. doi: 10.1152/ajpheart.00280.2006. [DOI] [PubMed] [Google Scholar]
  66. Sassaroli A, Pierro M, Bergethon PR, Fantini S. Low-Frequency Spontaneous Oscillations of Cerebral Hemodynamics Investigated With Near-Infrared Spectroscopy: A Review. Selected Topics in Quantum Electronics, IEEE Journal of 18. 2012:1478–1492. [Google Scholar]
  67. Schmidt JA, Borgström P, Firestone GP, von Wichert P, Intaglietta M, Fronek A. Periodic hemodynamics (flow motion) in peripheral arterial occlusive disease. Journal of Vascular Surgery. 1993;18:207–215. doi: 10.1067/mva.1993.42773. [DOI] [PubMed] [Google Scholar]
  68. Schmidt JA, Intaglietta M, Borgstrom P. Periodic hemodynamics in skeletal muscle during local arterial pressure reduction. Journal of Applied Physiology. 1992;73:1077–1083. doi: 10.1152/jappl.1992.73.3.1077. [DOI] [PubMed] [Google Scholar]
  69. Schroeter ML, Bucheler MM, Preul C, Scheid R, Schmiedel O, Guthke T, von Cramon DY. Spontaneous slow hemodynamic oscillations are impaired in cerebral microangiopathy. J Cereb Blood Flow Metab. 2005;25:1675–1684. doi: 10.1038/sj.jcbfm.9600159. [DOI] [PubMed] [Google Scholar]
  70. Setchell BP, Bergh A, Widmark A, Damber JE. Effect of testicular temperature on vasomotion and blood flow. International Journal of Andrology. 1995;18:120–126. doi: 10.1111/j.1365-2605.1995.tb00397.x. [DOI] [PubMed] [Google Scholar]
  71. Sheline YI, Raichle ME. Resting State Functional Connectivity in Preclinical Alzheimer's Disease. Biol Psychiatry. 2013 doi: 10.1016/j.biopsych.2012.11.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Spitzer MW, Calford MB, Clarey JC, Pettigrew JD, Roe AW. Spontaneous and Stimulus-Evoked Intrinsic Optical Signals in Primary Auditory Cortex of the Cat. Journal of Neurophysiology. 2001;85:1283–1298. doi: 10.1152/jn.2001.85.3.1283. [DOI] [PubMed] [Google Scholar]
  73. Stefanovska A. Coupled Oscillatros: Complex But Not Complicated Cardiovascular and Brain Interactions. Engineering in Medicine and Biology Magazine, IEEE. 2007;26:25–29. doi: 10.1109/emb.2007.907088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Suh M, Ma H, Zhao M, Geneslaw A, Schwartz T. Intraoperative Optical Imaging of Human Cortex. In: Roe AW, editor. Imaging the Brain with Optical Methods. Springer; New York: 2010. pp. 135–157. [Google Scholar]
  75. Sun R, Bouchard MB, Hillman EMC. SPLASSH: Open source software for camera-based high-speed, multispectral in-vivo optical image acquisition. Biomed Opt Express. 2010;1:385–397. doi: 10.1364/BOE.1.000385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Tian P, Devor A, Sakadžić S, Dale AM, Boas DA. Monte Carlo simulation of the spatial resolution and depth sensitivity of two-dimensional optical imaging of the brain. Journal of Biomedical Optics. 2011;16:016006, 016006–016013. doi: 10.1117/1.3533263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Wang HH, Menezes NM, Zhu MW, Ay H, Koroshetz WJ, Aronen HJ, Karonen JO, Liu Y, Nuutinen J, Wald LL, Sorensen AG. Physiological noise in MR images: An indicator of the tissue response to ischemia? Journal of Magnetic Resonance Imaging. 2008;27:866–871. doi: 10.1002/jmri.21007. [DOI] [PubMed] [Google Scholar]
  78. White BR, Bauer AQ, Snyder AZ, Schlaggar BL, Lee JM, Culver JP. Imaging of Functional Connectivity in the Mouse Brain. PLoS ONE. 2011;6:e16322. doi: 10.1371/journal.pone.0016322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Zhao M, Suh M, Ma H, Perry C, Geneslaw A, Schwartz TH. Focal Increases in Perfusion and Decreases in Hemoglobin Oxygenation Precede Seizure Onset in Spontaneous Human Epilepsy. Epilepsia. 2007;48:2059–2067. doi: 10.1111/j.1528-1167.2007.01229.x. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

01
NIHMS552871-supplement-01.eps (1.6MB, eps)
02
NIHMS552871-supplement-02.eps (1.6MB, eps)
03
NIHMS552871-supplement-03.eps (3.3MB, eps)
04
Download video file (18.3MB, mpg)
05
Download video file (18.3MB, mpg)

RESOURCES