Vascular effects of caffeine found in BOLD fMRI.

The blood oxygen level-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) measures neuronal activation indirectly. Previous studies have found aperiodic, systemic low-frequency oscillations (sLFOs, ~0.1 Hz) in BOLD signals from resting state (RS) fMRI, which reflects the non-neuronal cerebral perfusion information. In this study, we investigated the possibility of extracting vascular information from the sLFOs in RS BOLD fMRI, which could provide complementary information to the neuronal activations. Two features of BOLD signals were exploited. First, time delays between the sLFOs of big blood vessels and brain voxels were calculated to determine cerebral circulation times and blood arrival times. Second, voxel-wise standard deviations (SD) of LFOs were calculated to represent the blood densities. We explored those features on the publicly available Myconnectome data set (a 2-year study of an individual subject (Male)), which contains 45 RS scans acquired after the subject had coffee, and 45 coffee-free RS scans, acquired on different days. Our results showed that shorter time delays and smaller SDs were detected in caffeinated scans. This is consistent with the vasoconstriction effects of caffeine, which leads to increased blood flow velocity. We also compared our results with previous findings on neuronal networks from the same data set. Our finding showed that brain regions with the significant vascular effect of caffeine coincide with those with a significant neuronal effect, indicating close interaction. This study provides methods to assess the physiological information from RS fMRI. Together with the neuronal information, we can study simultaneously the underlying correlations and interactions between vascular and neuronal networks, especially in pharmacological studies.

Impact of vessel wall lesions and vascular stenoses on cerebrovascular reactivity in patients with intracranial stenotic disease.

PURPOSE: To compare cerebrovascular reactivity (CVR) and CVR lagtimes in flow territories perfused by vessels with vs. without proximal arterial wall disease and/or stenosis, separately in patients with atherosclerotic and nonatherosclerotic (moyamoya) intracranial stenosis. MATERIALS AND METHODS: Atherosclerotic and moyamoya patients with >50% intracranial stenosis and <70% cervical stenosis underwent angiography, vessel wall imaging (VWI), and CVR-weighted imaging (n = 36; vessel segments evaluated = 396). Angiography and VWI were evaluated for stenosis locations and vessel wall lesions. Maximum CVR and CVR lagtime were contrasted between vascular territories with and without proximal intracranial vessel wall lesions and stenosis, and a Wilcoxon rank-sum was test used to determine differences (criteria: corrected two-sided P < 0.05). RESULTS: CVR lagtime was prolonged in territories with vs. without a proximal vessel wall lesion or stenosis for both patient groups: moyamoya (CVR lagtime = 45.5 sec ± 14.2 sec vs. 35.7 sec ± 9.7 sec, P < 0.001) and atherosclerosis (CVR lagtime = 38.2 sec ± 9.1 sec vs. 35.0 sec ± 7.2 sec, P = 0.001). For reactivity, a significant decrease in maximum CVR in the moyamoya group only (maximum CVR = 9.8 ± 2.2 vs. 12.0 ± 2.4, P < 0.001) was observed. CONCLUSION: Arterial vessel wall lesions detected on noninvasive, noncontrast intracranial VWI in patients with intracranial stenosis correlate on average with tissue-level impairment on CVR-weighted imaging. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1167-1176.

Default Mode Network Functional Reorganization During Early Abstinence in Polysubstance-Using Emerging Adults Treated for Opioid Dependence.

This study examined default mode network connectivity within the first 30 days of abstinence in emerging adults entering treatment for opioid dependence. There were significant associations between abstinence duration and coupling strength with brain regions within and outside of the network.

Tracking cerebral blood flow in BOLD fMRI using recursively generated regressors.

BOLD functional MRI (fMRI) data are dominated by low frequency signals, many of them of unclear origin. We have recently shown that some portions of the low frequency oscillations found in BOLD fMRI are systemic signals closely related to the blood circulation (Tong et al. [2013]: NeuroImage 76:202-215). They are commonly treated as physiological noise in fMRI studies. In this study, we propose and test a novel data-driven analytical method that uses these systemic low frequency oscillations in the BOLD signal as a tracer to follow cerebral blood flow dynamically. Our findings demonstrate that: (1) systemic oscillations pervade the BOLD signal; (2) the temporal traces evolve as the blood propagates though the brain; and, (3) they can be effectively extracted via a recursive procedure and used to derive the cerebral circulation map. Moreover, this method is independent from functional analyses, and thus allows simultaneous and independent assessment of information about cerebral blood flow to be conducted in parallel with the functional studies. In this study, the method was applied to data from the resting state scans, acquired using a multiband EPI sequence (fMRI scan with much shorter TRs), of seven healthy participants. Dynamic maps with consistent features resembling cerebral blood circulation were derived, confirming the robustness and repeatability of the method.

Short repetition time multiband echo-planar imaging with simultaneous pulse recording allows dynamic imaging of the cardiac pulsation signal.

PURPOSE: Recently developed simultaneous multislice echo-planar imaging (EPI) sequences permit imaging of the whole brain at short repetition time (TR), allowing the cardiac fluctuations to be fully sampled in blood-oxygen-level dependent functional MRI (BOLD fMRI). A novel low computational analytical method was developed to dynamically map the passage of the pulsation signal through the brain and visualize the whole cerebral vasculature affected by the pulse signal. This algorithm is based on a simple combination of fast BOLD fMRI and the scanner's own built-in pulse oximeter. METHODS: Multiple, temporally shifted copies of the pulse oximeter data (with 0.08 s shifting step and coverage of a 1-s span) were downsampled and used as cardiac pulsation regressors in a general linear model based analyses (FSL) of the fMRI data. The resulting concatenated z-statistics maps show the voxels that are affected as the cardiac signal travels through the brain. RESULTS: Many voxels were highly correlated with the pulsation regressor or its temporally shifted version. The dynamic and static cardiac pulsation maps obtained from both the task and resting state scans, resembled cerebral vasculature. CONCLUSION: The results demonstrated: (i) cardiac pulsation significantly affects most voxels in the brain; (ii) combining fast fMRI and this analytical method can reveal additional clinical information to functional studies.

Widespread Autonomic Physiological Coupling Across the Brain-Body Axis.

The brain is closely attuned to visceral signals from the body's internal environment, as evidenced by the numerous associations between neural, hemodynamic, and peripheral physiological signals. We show that these brain-body co-fluctuations can be captured by a single spatiotemporal pattern. Across several independent samples, as well as single-echo and multi-echo fMRI data acquisition sequences, we identify widespread co-fluctuations in the low-frequency range (0.01 - 0.1 Hz) between resting-state global fMRI signals, neural activity, and a host of autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems. The same brain-body co-fluctuations observed at rest are elicited by arousal induced by cued deep breathing and intermittent sensory stimuli, as well as spontaneous phasic EEG events during sleep. Further, we show that the spatial structure of global fMRI signals is maintained under experimental suppression of end-tidal carbon dioxide (PETCO2) variations, suggesting that respiratory-driven fluctuations in arterial CO2 accompanying arousal cannot explain the origin of these signals in the brain. These findings establish the global fMRI signal as a significant component of the arousal response governed by the autonomic nervous system.

Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks.

Independent component analysis (ICA) is widely used in resting state functional connectivity studies. ICA is a data-driven method, which uses no a priori anatomical or functional assumptions. However, as a result, it still relies on the user to distinguish the independent components (ICs) corresponding to neuronal activation, peripherally originating signals (without directly attributable neuronal origin, such as respiration, cardiac pulsation and Mayer wave), and acquisition artifacts. In this concurrent near infrared spectroscopy (NIRS)/functional MRI (fMRI) resting state study, we developed a method to systematically and quantitatively identify the ICs that show strong contributions from signals originating in the periphery. We applied group ICA (MELODIC from FSL) to the resting state data of 10 healthy participants. The systemic low frequency oscillation (LFO) detected simultaneously at each participant's fingertip by NIRS was used as a regressor to correlate with every subject-specific IC time course. The ICs that had high correlation with the systemic LFO were those closely associated with previously described sensorimotor, visual, and auditory networks. The ICs associated with the default mode and frontoparietal networks were less affected by the peripheral signals. The consistency and reproducibility of the results were evaluated using bootstrapping. This result demonstrates that systemic, low frequency oscillations in hemodynamic properties overlay the time courses of many spatial patterns identified in ICA analyses, which complicates the detection and interpretation of connectivity in these regions of the brain.

Using carpet plots to analyze blood transit times in the brain during hypercapnic challenge magnetic resonance imaging.

Blood arrival time and blood transit time are useful metrics in characterizing hemodynamic behaviors in the brain. Functional magnetic resonance imaging in combination with a hypercapnic challenge has been proposed as a non-invasive imaging tool to determine blood arrival time and replace dynamic susceptibility contrast (DSC) magnetic resonance imaging, a current gold-standard imaging tool with the downsides of invasiveness and limited repeatability. Using a hypercapnic challenge, blood arrival times can be computed by cross-correlating the administered CO2 signal with the fMRI signal, which increases during elevated CO2 due to vasodilation. However, whole-brain transit times derived from this method can be significantly longer than the known cerebral transit time for healthy subjects (nearing 20 s vs. the expected 5-6 s). To address this unrealistic measurement, we here propose a novel carpet plot-based method to compute improved blood transit times derived from hypercapnic blood oxygen level dependent fMRI, demonstrating that the method reduces estimated blood transit times to an average of 5.32 s. We also investigate the use of hypercapnic fMRI with cross-correlation to compute the venous blood arrival times in healthy subjects and compare the computed delay maps with DSC-MRI time to peak maps using the structural similarity index measure (SSIM). The strongest delay differences between the two methods, indicated by low structural similarity index measure, were found in areas of deep white matter and the periventricular region. SSIM measures throughout the remainder of the brain reflected a similar arrival sequence derived from the two methods despite the exaggerated spread of voxel delays computed using CO2 fMRI.

Concurrent fNIRS and fMRI processing allows independent visualization of the propagation of pressure waves and bulk blood flow in the cerebral vasculature.

Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) measures changes in blood oxygenation, which is affected by physiological processes, including cardiac pulsation, breathing, and low frequency oscillations (LFO). It is challenging to identify spatial and temporal effects of these processes on the BOLD signal because the low sampling rate of BOLD leads to aliasing of higher frequency physiological signal components. In this study, we used concurrent functional near infrared spectroscopy (fNIRS) and fMRI on 6 subjects during a resting state scan. To reduce aliasing, the BOLD fMRI acquisition was repeatedly performed on a set of sequentially acquired slice stacks to lower the TR to 0.5s while retaining high spatial resolution. Regressor interpolation at progressive time delays (RIPTiDe) method was used, in which physiological signal acquired by fNIRS (without aliasing) and its temporal shifts were used as regressors in the fMRI analysis to determine the magnitude and timing of the effects of various physiological processes on the BOLD signal. The details of the timing of the passage of the cardiac pulsation wave and of the cerebral blood itself were mapped. The result suggests that the cardiac signal affects the voxels near large blood vessels (arteries and veins) most strongly, while LFO mostly affected the drainage veins. We hypothesize that this could be the result of differences in the cerebral blood path lengths, and differences in the dynamics of the propagation of the signals. Together these results validate and extend a novel imaging technique to dynamically track the pulse-wave and bulk blood flow with concurrent fMRI and fNIRS.

Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS).

Confounding noise in BOLD fMRI data arises primarily from fluctuations in blood flow and oxygenation due to cardiac and respiratory effects, spontaneous low frequency oscillations (LFO) in arterial pressure, and non-task related neural activity. Cardiac noise is particularly problematic, as the low sampling frequency of BOLD fMRI ensures that these effects are aliased in recorded data. Various methods have been proposed to estimate the noise signal through measurement and transformation of the cardiac and respiratory waveforms (e.g. RETROICOR and respiration volume per time (RVT)) and model-free estimation of noise variance through examination of spatial and temporal patterns. We have previously demonstrated that by applying a voxel-specific time delay to concurrently acquired near infrared spectroscopy (NIRS) data, we can generate regressors that reflect systemic blood flow and oxygenation fluctuations effects. Here, we apply this method to the task of removing physiological noise from BOLD data. We compare the efficacy of noise removal using various sets of noise regressors generated from NIRS data, and also compare the noise removal to RETROICOR+RVT. We compare the results of resting state analyses using the original and noise filtered data, and we evaluate the bias for the different noise filtration methods by computing null distributions from the resting data and comparing them with the expected theoretical distributions. Using the best set of processing choices, six NIRS-generated regressors with voxel-specific time delays explain a median of 10.5% of the variance throughout the brain, with the highest reductions being seen in gray matter. By comparison, the nine RETROICOR+RVT regressors together explain a median of 6.8% of the variance in the BOLD data. Detection of resting state networks was enhanced with NIRS denoising, and there were no appreciable differences in the bias of the different techniques. Physiological noise regressors generated using Regressor Interpolation at Progressive Time Delays (RIPTiDe) offer an effective method for efficiently removing hemodynamic noise from BOLD data.

Estimating and mitigating the effects of systemic low frequency oscillations (sLFO) on resting state networks in awake non-human primates using time lag dependent methodology.

Aim: Resting-state fMRI (rs-fMRI) is often used to infer regional brain interactions from the degree of temporal correlation between spontaneous low-frequency fluctuations, thought to reflect local changes in the BOLD signal due to neuronal activity. One complication in the analysis and interpretation of rs-fMRI data is the existence of non-neuronal low frequency physiological noise (systemic low frequency oscillations; sLFOs) which occurs within the same low frequency band as the signal used to compute functional connectivity. Here, we demonstrate the use of a time lag mapping technique to estimate and mitigate the effects of the sLFO signal on resting state functional connectivity of awake squirrel monkeys. Methods: Twelve squirrel monkeys (6 male/6 female) were acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Rs-fMRI data was preprocessed using an in-house pipeline and sLFOs were detected using a seed regressor generated by averaging BOLD signal across all voxels in the brain, which was then refined recursively within a time window of -16-12 s. The refined regressor was then used to estimate the voxel-wise sLFOs; these regressors were subsequently included in the general linear model to remove these moving hemodynamic components from the rs-fMRI data using general linear model filtering. Group level independent component analysis (ICA) with dual regression was used to detect resting-state networks and compare networks before and after sLFO denoising. Results: Results show sLFOs constitute ~64% of the low frequency fMRI signal in squirrel monkey gray matter; they arrive earlier in regions in proximity to the middle cerebral arteries (e.g., somatosensory cortex) and later in regions close to draining vessels (e.g., cerebellum). Dual regression results showed that the physiological noise was significantly reduced after removing sLFOs and the extent of reduction was determined by the brain region contained in the resting-state network. Conclusion: These results highlight the need to estimate and remove sLFOs from fMRI data before further analysis.

An improved method for mapping cerebrovascular reserve using concurrent fMRI and near-infrared spectroscopy with Regressor Interpolation at Progressive Time Delays (RIPTiDe).

Cerebrovascular reserve (CVR) reflects the compensatory dilatory capacity of cerebral vasculature to a dilatory stimulus. Blood oxygen-level dependent (BOLD) fMRI has been proven to be an effective imaging technique to obtain CVR maps when subjects perform CO(2) inhalation or a breath-holding (BH) task. Here we propose a novel way to process the fMRI data obtained during a blocked BH task by using simultaneously collected near-infrared spectroscopy (NIRS) data as regressors to estimate the vascular contribution to the BOLD signal. Six healthy subjects underwent a 6min 30s resting state (RS) fMRI scan, followed by a scan of the same duration with a blocked BH task (5 breath holds with 20s durations separated by ~50s of regular breathing). NIRS data were recorded from a probe over the subjects' right prefrontal area. For each scan, the time course of changes in total hemoglobin (Δ[tHb]) was calculated from the NIRS data, time shifted by various amounts, and resampled to the fMRI acquisition rate. Each shifted time course was used as regressor in a general linear model analysis. The maximum parameter estimate across all time shifts was calculated at all voxels in both the BH and RS scans, and then converted into signal percentage changes. The ratio of these signal changes generates a CVR map of the BH response, normalized to the resting state. The NIRS regressor method makes no assumptions about the shape (or presence) of the BH response, and allows direct, quantitative comparison of the vascular BOLD response to BH to the baseline map obtained in the resting state.

Using carpet plots to analyze transit times of low frequency oscillations in resting state fMRI.

A "carpet plot" is a 2-dimensional plot (time vs. voxel) of scaled fMRI voxel intensity values. Low frequency oscillations (LFOs) can be successfully identified from BOLD fMRI and used to study characteristics of neuronal and physiological activity. Here, we evaluate the use of carpet plots paired with a developed slope-detection algorithm as a means to study LFOs in resting state fMRI (rs-fMRI) data with the help of dynamic susceptibility contrast (DSC) MRI data. Carpet plots were constructed by ordering voxels according to signal delay time for each voxel. The slope-detection algorithm was used to identify and calculate propagation times, or "transit times", of tilted vertical edges across which a sudden signal change was observed. We aim to show that this metric has applications in understanding LFOs in fMRI data, possibly reflecting changes in blood flow speed during the scan, and for evaluating alternative blood-tracking contrast agents such as inhaled CO2. We demonstrate that the propagations of LFOs can be visualized and automatically identified in a carpet plot as tilted lines of sudden intensity change. Resting state carpet plots produce edges with transit times similar to those of DSC carpet plots. Additionally, resting state carpet plots indicate that edge transit times vary at different time points during the scan.

An MRI protocol for anatomical and functional evaluation of the California sea lion brain.

BACKGROUND: Domoic acid (DOM) is a neurotoxin produced by some harmful algae blooms in coastal waters. California sea lions (Zalophus californianus) exposed to DOM often strand on beaches where they exhibit a variety of symptoms, including seizures. These animals typically show hippocampal atrophy on MRI scans. NEW METHOD: We describe an MRI protocol for comprehensive evaluation of DOM toxicosis in the sea lion brain. We intend to study brain development in pups exposed in utero. The protocol depicts the hippocampal formation as the primary region of interest. We include scans for quantitative morphometry, functional and structural connectivity, and a cerebral blood flow map. RESULTS: High-resolution 3D anatomical scans facilitate post hoc slicing in arbitrary planes and accurate morphometry. We demonstrate the first cerebral blood flow map using MRI, and the first structural tractography from a live sea lion brain. COMPARISON WITH EXISTING METHODS: Scans were compared to prior anatomical and functional studies in live sea lions, and structural connectivity in post mortem specimens. Hippocampal volumes were broadly in line with prior studies, with differences likely attributable to the 3D approach used here. Functional connectivity of the dorsal left hippocampus matched that found in a prior study conducted at a lower magnetic field, while structural connectivity in the live brain agreed with findings observed in post mortem studies. CONCLUSIONS: Our protocol provides a comprehensive, longitudinal view of the functional and anatomical changes expected to result from DOM toxicosis. It can also screen for other common neurological pathologies and is suitable for any pinniped that can fit inside an MRI scanner.

Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain.

Low frequency oscillations (LFOs), characterized by frequencies in the range 0.01-0.1 Hz are commonly observed in blood-related brain functional measurements such as near-infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI). While their physiological origin and implications are not fully understood, these signals are believed to reflect some types of neuronal signaling, systemic hemodynamics, and/or cerebral vascular auto-regulation processes. Here, we examine a new method of integrated processing of concurrent NIRS and fMRI data collected on six human subjects during a whole brain resting state acquisition. The method combines the high spatial resolution offered by fMRI (approximately 3mm) and the high temporal resolution offered by NIRS (approximately 80 ms) to allow for the quantitative assessment of temporal relationships between the LFOs observed at different spatial locations in fMRI data. This temporal relationship allowed us to infer that the origin of a large proportion of the LFOs is independent of the baseline neural activity. The spatio-temporal pattern of LFOs detected by NIRS and fMRI evolves temporally through the brain in a way that resembles cerebral blood flow dynamics. Our results suggest that a major component of the LFOs arise from fluctuations in the blood flow and hemoglobin oxygenation at a global circulatory system level.

Asymmetry of peripheral vascular biomarkers in ischemic stroke patients, assessed using NIRS.

SIGNIFICANCE: Low-frequency oscillations (LFOs) ranging from 0.01 to 0.15 Hz are common in functional imaging studies. Some of these LFOs are non-neuronal and are correlated with autonomic physiological processes. AIM: We investigate the relationships between systemic low-frequency oscillations (sLFOs) measured at different peripheral sites during resting states in ischemic stroke patients. APPROACH: Twenty-seven ischemic stroke patients (ages 44 to 90; 20 male and 7 female) were recruited for the study. During the experiments, fluctuations in oxyhemoglobin concentration were measured in the left and right toes, fingertips, and earlobes using a multichannel near-infrared spectroscopy instrument. We applied cross-correlation and frequency component analyses on the sLFO data. RESULTS: The results showed that embolization broke the symmetry of the sLFO transmission and that the damage was not limited to the local area but spread throughout the body. Among six peripheral sites, the power spectrum width of the earlobes was significantly larger than that of the fingers and toes. This indicates that the earlobes may contain more physiological information. Finally, the results of fuzzy clustering verified that sLFOs can serve as perfusion biomarkers to differentiate stroke from healthy subjects. CONCLUSIONS: The high correlation values and corresponding delays in sLFOs support the hypothesis that (1) the correlation characteristics of sLFOs in stroke patients are different from those of healthy subjects. These characteristics can reflect patient condition, to an extent. Embolization in ischemic stroke patients breaks the symmetry of the body's sLFO transmission, disrupting the balance of blood circulation. (2) sLFOs can be used as perfusion biomarkers to differentiate ischemic stroke patients from healthy subjects. Studying these signals can explicate the overall feedback/influence of pericentral interactions. Finally, peripheral sLFOs have been shown to be an effective and accurate tool for assessing peripheral blood circulation and vascular integrity in ischemic stroke patients.

The resting-state fMRI arterial signal predicts differential blood transit time through the brain.

Previous studies have found that aperiodic, systemic low-frequency oscillations (sLFOs) are present in blood-oxygen-level-dependent (BOLD) data. These signals are in the same low frequency band as the "resting state" signal; however, they are distinct signals which represent non-neuronal, physiological oscillations. The same sLFOs are found in the periphery (i.e. finger tips) as changes in oxy/deoxy-hemoglobin concentration using concurrent near-infrared spectroscopy. Together, this evidence points toward an extra-cerebral origin of these sLFOs. If this is the case, it is expected that these sLFO signals would be found in the carotid arteries with time delays that precede the signals found in the brain. To test this hypothesis, we employed the publicly available MyConnectome dataset (a two-year longitudinal study of a single subject) to extract the sLFOs in the internal carotid arteries (ICAs) with the help of the T1/T2-weighted images. Significant, but negative, correlations were found between the LFO BOLD signals from the ICAs and (1) the global signal (GS), (2) the superior sagittal sinus, and (3) the jugulars. We found the consistent time delays between the sLFO signals from ICAs, GS and veins which coincide with the blood transit time through the cerebral vascular tree.

Clinical Use of Cerebrovascular Compliance Imaging to Evaluate Revascularization in Patients With Moyamoya.

BACKGROUND: Surgical revascularization is often performed in patients with moyamoya, however routine tools for efficacy evaluation are underdeveloped. The gold standard is digital subtraction angiography (DSA); however, DSA requires ionizing radiation and procedural risk, and therefore is suboptimal for routine surveillance of parenchymal health. OBJECTIVE: To determine whether parenchymal vascular compliance measures, obtained noninvasively using magnetic resonance imaging (MRI), provide surrogates to revascularization success by comparing measures with DSA before and after surgical revascularization. METHODS: Twenty surgical hemispheres with DSA and MRI performed before and after revascularization were evaluated. Cerebrovascular reactivity (CVR)-weighted images were acquired using hypercapnic 3-Tesla gradient echo blood oxygenation level-dependent MRI. Standard and novel analysis algorithms were applied (i) to quantify relative CVR (rCVRRAW), and decompose this response into (ii) relative maximum CVR (rCVRMAX) and (iii) a surrogate measure of the time for parenchyma to respond maximally to the stimulus, CVRDELAY. Measures between time points in patients with good and poor surgical outcomes based on DSA-visualized neoangiogenesis were contrasted (signed-rank test; significance: 2-sided P < .050). RESULTS: rCVRRAW increases (P = .010) and CVRDELAY decreases (P = .001) were observed pre- vs post-revascularization in hemispheres with DSA-confirmed collateral formation; no difference was found pre- vs post-revascularization in hemispheres with poor revascularization. No significant change in rCVRMAX post-revascularization was observed in either group, or between any of the MRI measures, in the nonsurgical hemisphere. CONCLUSION: Improvement in parenchymal compliance measures post-revascularization, primarily attributed to reductions in microvascular response time, is concurrent with collateral formation visualized on DSA, and may be useful for longitudinal monitoring of surgical outcomes.

Denoising of neuronal signal from mixed systemic low-frequency oscillation using peripheral measurement as noise regressor in near-infrared imaging.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive functional imaging technique measuring hemodynamic changes including oxygenated ( O 2 Hb ) and deoxygenated (HHb) hemoglobin. Low frequency (LF; 0.01 to 0.15 Hz) band is commonly analyzed in fNIRS to represent neuronal activation. However, systemic physiological artifacts (i.e., nonneuronal) likely occur also in overlapping frequency bands. We measured peripheral photoplethysmogram (PPG) signal concurrently with fNIRS (at prefrontal region) to extract the low-frequency oscillations (LFOs) as systemic noise regressors. We investigated three main points in this study: (1) the relationship between prefrontal fNIRS and peripheral PPG signals; (2) the denoising potential using these peripheral LFOs, and (3) the innovative ways to avoid the false-positive result in fNIRS studies. We employed spatial working memory (WM) and control tasks (e.g., resting state) to illustrate these points. Our results showed: (1) correlation between signals from prefrontal fNIRS and peripheral PPG is region-dependent. The high correlation with peripheral ear signal (i.e., O 2 Hb ) occurred mainly in frontopolar regions in both spatial WM and control tasks. This may indicate the finding of task-dependent effect even in peripheral signals. We also found that the PPG recording at the ear has a high correlation with prefrontal fNIRS signal than the finger signals. (2) The systemic noise was reduced by 25% to 34% on average across regions, with a maximum of 39% to 58% in the highly correlated frontopolar region, by using these peripheral LFOs as noise regressors. (3) By performing the control tasks, we confirmed that the statistically significant activation was observed in the spatial WM task, not in the controls. This suggested that systemic (and any other) noises unlikely violated the major statistical inference. (4) Lastly, by denoising using the task-related signals, the significant activation of region-of-interest was still observed suggesting the manifest task-evoked response in the spatial WM task.

Systemic low-frequency oscillations observed in the periphery of healthy human subjects.

This study investigated the relationships of systemic low-frequency oscillations (sLFOs) measured at different peripheral sites in resting state, during passive leg raising (PLR), and during a paced breathing (PB) test. Twenty-five healthy subjects (21 to 57 years old; males: 13 and females: 12) were recruited for these experiments. During the experiments, the fluctuations of oxyhemoglobin concentration were measured at six peripheral sites (left and right toes, fingertips, and earlobes) using a multichannel near-infrared spectroscopy instrument developed by our group. We applied cross-correlation and frequency component analyses on the data. The results showed that the sLFO signals in the symmetric peripheral sites were highly correlated, with time delays close to zero, whereas the correlation coefficients decreased between the sLFO signals of asymmetric sites, with delays up to several seconds. Furthermore, in PLR/PB tests, we found that PB caused wider and more robust changes in hemoglobin concentrations at peripheral sites compared to PLR. Among six peripheral sites, earlobes were the most sensitive to these perturbations, followed by fingertips, and then toes. Lastly, we showed that the perturbation signals may have different coupling mechanisms than the sLFO signals. The study deepened our understanding of the sLFO signals and establishes baseline measures for developing perfusion biomarkers to assess peripheral vascular integrity.