Effects of clamping end-tidal CO2 on neurofluidic low-frequency oscillations.

In recent years, low-frequency oscillations (LFOs) (0.01-0.1 Hz) have been a subject of interest in resting-state functional magnetic resonance imaging research. They are believed to have many possible driving mechanisms, from both regional and global sources. Internal fluctuations in the partial pressure of CO2 (PCO2) has long been thought of as one of these major driving forces, but its exact contributions compared with other mechanisms have yet to be fully understood. This study examined the effects of end-tidal PCO2 (PetCO2) oscillations on LF cerebral hemodynamics and cerebrospinal fluid (CSF) dynamics under "clamped PetCO2" and "free-breathing" conditions. Under clamped PetCO2, a participant's PetCO2 levels were fixed to their baseline average, whereas PetCO2 was not controlled in free breathing. Under clamped PetCO2, the fractional amplitude of hemodynamic LFOs in the occipital and sensorimotor cortex and temporal lobes were found to be significantly reduced. Additionally, the fractional amplitude of CSF LFOs, measured at the fourth ventricle, was found to be reduced by almost one-half. However, the spatiotemporal distributions of blood and CSF delay times, as measured by cross-correlation in the LF domain, were not significantly altered between conditions. This study demonstrates that, while PCO2 oscillations significantly mediate LFOs, especially those observed in the CSF, other mechanisms are able to maintain LFOs, with high correlation, even in their absence.

Real-time quantification of in vivo cerebrospinal fluid velocity using the functional magnetic resonance imaging inflow effect.

In vivo estimation of cerebrospinal fluid (CSF) velocity is crucial for understanding the glymphatic system and its potential role in neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease. Current cardiac or respiratory-gated approaches, such as 4D flow magnetic resonance imaging (MRI), cannot capture CSF movement in real time because of limited temporal resolution and, in addition, deteriorate in accuracy at low fluid velocities. Other techniques like real-time phase-contrast-MRI or time-spatial labeling inversion pulse are not limited by temporal averaging but have limited availability, even in research settings. This study aims to quantify the inflow effect of dynamic CSF motion on functional MRI (fMRI) for in vivo, real-time measurement of CSF flow velocity. We considered linear and nonlinear models of velocity waveforms and empirically fit them to fMRI data from a controlled flow experiment. To assess the utility of this methodology in human data, CSF flow velocities were computed from fMRI data acquired in eight healthy volunteers. Breath-holding regimens were used to amplify CSF flow oscillations. Our experimental flow study revealed that CSF velocity is nonlinearly related to inflow effect-mediated signal increase and well estimated using an extension of a previous nonlinear framework. Using this relationship, we recovered velocity from in vivo fMRI signal, demonstrating the potential of our approach for estimating CSF flow velocity in the human brain. This novel method could serve as an alternative approach to quantifying slow flow velocities in real time, such as CSF flow in the ventricular system, thereby providing valuable insights into the glymphatic system's function and its implications for neurological disorders.

Paravascular fluid dynamics reveal arterial stiffness assessed using dynamic diffusion-weighted imaging.

Paravascular cerebrospinal fluid (pCSF) surrounding the cerebral arteries within the glymphatic system is pulsatile and moves in synchrony with the pressure waves of the vessel wall. Whether such pulsatile pCSF can infer pulse wave propagation-a property tightly related to arterial stiffness-is unknown and has never been explored. Our recently developed imaging technique, dynamic diffusion-weighted imaging (dynDWI), captures the pulsatile pCSF dynamics in vivo and can explore this question. In this work, we evaluated the time shifts between pCSF waves and finger pulse waves, where pCSF waves were measured by dynDWI and finger pulse waves were measured by the scanner's built-in finger pulse oximeter. We hypothesized that the time shifts reflect brain-finger pulse wave travel time and are sensitive to arterial stiffness. We applied the framework to 36 participants aged 18-82 years to study the age effect of travel time, as well as its associations with cognitive function within the older participants (N = 15, age > 60 years). Our results revealed a strong and consistent correlation between pCSF pulse and finger pulse (mean CorrCoeff = 0.66), supporting arterial pulsation as a major driver for pCSF dynamics. The time delay between pCSF and finger pulses (TimeDelay) was significantly lower (i.e., faster pulse propagation) with advanced age (Pearson's r = -0.44, p = 0.007). Shorter TimeDelay was further associated with worse cognitive function in the older participants. Overall, our study demonstrated pCSF as a viable pathway for measuring intracranial pulses and encouraged future studies to investigate its relevance with cerebrovascular functions.

CO2 as an engine for neurofluid flow: Exploring the coupling between vascular reactivity, brain clearance, and changes in tissue properties.

The brain relies on an effective clearance mechanism to remove metabolic waste products for the maintenance of homeostasis. Recent studies have focused on elucidating the forces that drive the motion of cerebrospinal fluid (CSF), responsible for removal of these waste products. We demonstrate that vascular responses evoked using controlled manipulations of partial pressure of carbon dioxide (PaCO2 ) levels, serve as an endogenous driver of CSF clearance from the brain. To demonstrate this, we retrospectively surveyed our database, which consists of brain metastases patients from whom blood oxygen level-dependent (BOLD) images were acquired during targeted hypercapnic and hyperoxic respiratory challenges. We observed a correlation between CSF inflow signal around the fourth ventricle and CO2 -induced changes in cerebral blood volume. By contrast, no inflow signal was observed in response to the nonvasoactive hyperoxic stimulus, validating our measurements. Moreover, our results establish a link between the rate of the hemodynamic response (to elevated PaCO2 ) and peritumoral edema load, which we suspect may affect CSF flow, consequently having implications for brain clearance. Our expanded perspective on the factors involved in neurofluid flow underscores the importance of considering both cerebrovascular responses, as well as the brain mechanical properties, when evaluating CSF dynamics in the context of disease processes.

Assessing pulsatile waveforms of paravascular cerebrospinal fluid dynamics within the glymphatic pathways using dynamic diffusion-weighted imaging (dDWI)

Cerebrospinal fluid (CSF) in the paravascular spaces of the surface arteries (sPVS) is a vital pathway in brain waste clearance. Arterial pulsations may be the driving force of the paravascular flow, but its pulsatile pattern remains poorly characterized, and no clinically practical method for measuring its dynamics in the human brain is available. In this work, we introduce an imaging and quantification framework for in-vivo non-invasive assessment of pulsatile fluid dynamics in the sPVS. It used dynamic Diffusion-Weighted Imaging (dDWI) at a lower b-values of 150s/mm2 and retrospective gating to detect the slow flow of CSF while suppressing the fast flow of adjacent arterial blood. The waveform of CSF flow over a cardiac cycle was revealed by synchronizing the measurements with the heartbeat. A data-driven approach was developed to identify sPVS and allow automatic quantification of the whole-brain fluid waveforms. We applied dDWI to twenty-five participants aged 18-82 y/o. Results demonstrated that the fluid waveforms across the brain showed an explicit cardiac-cycle dependency, in good agreement with the vascular pumping hypothesis. Furthermore, the shape of the CSF waveforms closely resembled the pressure waveforms of the artery wall, suggesting that CSF dynamics is tightly related to artery wall mechanics. Finally, the CSF waveforms in aging participants revealed a strong age effect, with a significantly wider systolic peak observed in the older relative to younger participants. The peak widening may be associated with compromised vascular compliance and vessel wall stiffening in the older brain. Overall, the results demonstrate the feasibility, reproducibility, and sensitivity of dDWI for detecting sPVS fluid dynamics of the human brain. Our preliminary data suggest age-related alterations of the paravascular pumping. With an acquisition time of under six minutes, dDWI can be readily applied to study fluid dynamics in normal physiological conditions and cerebrovascular/neurodegenerative diseases.

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.

Cerebral circulation time derived from fMRI signals in large blood vessels.

BACKGROUND: The systemic low-frequency oscillation (sLFO) functional (f)MRI signals extracted from the internal carotid artery (ICA) and the superior sagittal sinus (SSS) are found to have valuable physiological information. PURPOSE: 1) To further develop and validate a method utilizing these signals to measure the delay times from the ICAs and the SSS. 2) To establish the delay time as an effective perfusion biomarker that associates with cerebral circulation time (CCT). 3) To explore within subject variations, and the effects of gender and age on the delay times. STUDY TYPE: Prospective. SUBJECTS: In all, 100 healthy adults (Human Connectome Project [HCP], age range 22-36 years, 54 females and 46 males), 56 healthy children (Adolescent Brain Cognitive Development project) were included. FIELD STRENGTH/SEQUENCE: Echo planar imaging (EPI) sequence at 3T. ASSESSMENT: The sLFO fMRI signals from the ICAs and the SSSs were extracted from the resting state fMRI data. The maximum cross-correlation coefficients and their corresponding delay times were calculated. The gender and age differences of delay times were assessed statistically. STATISTICAL TESTS: T-tests were conducted to measure the gender differences. The Kruskal-Wallis test was used to detect age differences. RESULTS: Consistent and robust results were found from 80% of the 400 HCP scans included. Negative correlations (-0.67) between the ICA and the SSS signals were found with the ICA signal leading the SSS signal by ∼5 sec. Within subject variation was 2.23 sec at the 5% significance level. The delay times were not significantly different between genders (P = 0.9846, P = 0.2288 for the left and right ICA, respectively). Significantly shorter delay times (4.3 sec) were found in the children than in the adults (P < 0.01). DATA CONCLUSION: We have shown that meaningful perfusion information (ie, CCT) can be derived from the sLFO fMRI signals of the large blood vessels. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1504-1513.

Comparison of peripheral near-infrared spectroscopy low-frequency oscillations to other denoising methods in resting state functional MRI with ultrahigh temporal resolution.

PURPOSE: Functional MRI (fMRI) blood-oxygen level-dependent (BOLD) signals result not only from neuronal activation, but also from nonneuronal physiological processes. These changes, especially in the low-frequency domain (0.01-0.2 Hz), can significantly confound inferences about neuronal processes. It is crucial to effectively identify these nuisance low-frequency oscillations (LFOs). METHOD: A high temporal resolution (repetition time, ∼0.5 s) fMRI resting state study was conducted with simultaneous physiological measurements to compare LFOs measured directly by near-infrared spectroscopy (NIRS) in the periphery and three methods that model LFOs from the respiration or cardiac signal: 1) the respiration volume per time (RVT), 2) the respiratory variation (RVRRF), and 3) the cardiac variation method (HRCRF). The LFO noise regressors from these methods were compared temporally and spatially as well as in their denoising efficiency. RESULTS: Methods were not highly correlated with one another, temporally or spatially. The set of two NIRS LFOs combined explained over 13% of BOLD signal variance and explained equal or more variance than HRCRF and RVRRF or RVT combined (in 14 of 16 participants). CONCLUSION: LFOs collected using NIRS in the periphery contain distinct temporal and spatial information about the LFOs in BOLD fMRI that is not contained in current low-frequency denoising methods derived from respiration and cardiac pulsation. Magn Reson Med 76:1697-1707, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

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.

Necrostatin-1 inhibits Hmgb1-IL-23/IL-17 pathway and attenuates cardiac ischemia reperfusion injury.

Ischemia reperfusion (IR) injury is a major issue in cardiac transplantation and inflammatory processes play a major role in myocardial IR injury. Necrostatin-1 (Nec-1) is a small molecule capable of inhibiting RIP1 kinase activity and attenuates inflammation-mediated tissue injury. In our study, hearts of C57Bl/6 mice were flushed and stored in cold Bretschneider solution for 8 h and then transplanted into syngeneic recipients. We found that Nec-1 decreased cardiomyocyte necrosis and recruitment of neutrophils and macrophages. Troponin T (TnT) production on 24 h after myocardial IR injury was reduced by Nec-1 administration. Cardiac output at 60 mmHg of afterload pressure was significantly increased in hearts with Nec-1 administration and the cardiac allograft survival in Nec-1-treated animals was significantly prolonged (MST = 90 days in IR + Nec-1 group, P < 0.05 as compared with IR group, MST = 83.5 days). Nec-1 treatment attenuated ROS generation and increased expression of NOS2 and COX-2. The expression of Hmgb1, IL-23, and IL-17A were also decreased with Nec-1 administration. Furthermore, the decreased TnT expression induced by Nec-1 was abrogated with exogenous Hmgb1 administration. In conclusion, Nec-1 played a protective role in cardiomyocyte IR injury, and this was associated with inhibited Hmgb1-IL-23/IL-17 pathway.

Brain network hypersensitivity underlies pain crises in sickle cell disease.

Sickle cell disease (SCD) is a genetic disorder causing painful and unpredictable Vaso-occlusive crises (VOCs) through blood vessel blockages. In this study, we propose explosive synchronization (ES) as a novel approach to comprehend the hypersensitivity and occurrence of VOCs in the SCD brain network. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) as well as VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited lower alpha frequency than controls. SCD patients showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, stronger FDA was observed in SCD patients with a higher frequency of VOCs and EEG recording near VOC. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Our model demonstrated that a stronger FDA could be linked to increased sensitivity and frequency of VOCs. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

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.

Whole body measurements using near-infrared spectroscopy in a rat spinal cord contusion injury model.

Background: Spinal cord injuries cause great damage to the central nervous system as well as the peripheral vasculature. While treatments for spinal cord injury typically focus on the spine itself, improvements in the function of the peripheral vasculature after spinal cord injury have shown to improve overall neurological recovery.Objective: This study focused on the use of near-infrared spectroscopy (NIRS) as a mode to monitor cerebral and peripheral vascular condition non-invasively during the recovery process.Design: Animal research study.Methods: Rats underwent spinal contusion or sham injury and relative concentrations of de-/oxyhemoglobin (Δ[HbO]/Δ[Hb]) over time were measured over the cerebral, spinal, and pedal regions via NIRS. Correlational relationships across the body were determined. Rats received 1 NIRS measurement before injury and 3 after injury: 4, 7, and 14 days post.Results: Correlational relationships between signals across the body, between animals with and without spinal cord injury, indicate that NIRS was able to detect patterns of vascular change in the spine and the periphery occurring secondary to spinal cord injury and evolving during subsequent recovery. Additionally, NIRS determined an overall correlational decrease within the central nervous system, between spinal and cerebral measurements.Conclusion: NIRS was able to closely reflect physiologic changes in the rat during recovery, demonstrating a promising method to monitor whole body hemodynamics after spinal cord injury.

Age-dependent neurovascular coupling characteristics in children and adults during general anesthesia.

General anesthesia is an indispensable procedure in clinical practice. Anesthetic drugs induce dramatic changes in neuronal activity and cerebral metabolism. However, the age-related changes in neurophysiology and hemodynamics during general anesthesia remain unclear. Therefore, the objective of this study was to explore the neurovascular coupling between neurophysiology and hemodynamics in children and adults during general anesthesia. We analyzed frontal electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals recorded from children (6-12 years old, n = 17) and adults (18-60 years old, n = 25) during propofol-induced and sevoflurane-maintained general anesthesia. The neurovascular coupling was evaluated in wakefulness, maintenance of a surgical state of anesthesia (MOSSA), and recovery by using correlation, coherence and Granger-causality (GC) between the EEG indices [EEG power in different bands and permutation entropy (PE)], and hemodynamic responses the oxyhemoglobin (Δ[HbO]) and deoxy-hemoglobin (Δ[Hb]) from fNIRS in the frequency band in 0.01-0.1 Hz. The PE and Δ[Hb] performed well in distinguishing the anesthesia state (p > 0.001). The correlation between PE and Δ[Hb] was higher than those of other indices in the two age groups. The coherence significantly increased during MOSSA (p < 0.05) compared with wakefulness, and the coherences between theta, alpha and gamma, and hemodynamic activities of children are significantly stronger than that of adults' bands. The GC from neuronal activities to hemodynamic responses decreased during MOSSA, and can better distinguish anesthesia state in adults. Propofol-induced and sevoflurane-maintained combination exhibited age-dependent neuronal activities, hemodynamics, and neurovascular coupling, which suggests the need for separate rules for children's and adults' brain states monitoring during general anesthesia.

Neurofluid coupling during sleep and wake states.

BACKGROUND: In clinical populations, the movement of cerebrospinal fluid (CSF) during sleep is a growing area of research with potential mechanistic connections in both neurodegenerative (e.g., Alzheimer's Disease) and neurodevelopmental disorders. However, we know relatively little about the processes that influence CSF movement. To inform clinical intervention targets this study assesses the coupling between (a) real-time CSF movement, (b) neuronal-driven movement, and (c) non-neuronal systemic physiology driven movement. METHODS: This study included eight young, healthy volunteers, with concurrently acquired neurofluid dynamics using functional Magnetic Resonance Imaging (MRI), neural activity using Electroencephalography (EEG), and non-neuronal systemic physiology with peripheral functional Near-Infrared Spectroscopy (fNIRS). Neuronal and non-neuronal drivers were assessed temporally; wherein, EEG measured slow wave activity that preceded CSF movement was considered neuronally driven. Similarly, slow wave oscillations (assessed via fNIRS) that coupled with CSF movement were considered non-neuronal systemic physiology driven. RESULTS AND CONCLUSIONS: Our results document neural contributions to CSF movement were only present during light NREM sleep but low-frequency non-neuronal oscillations were strongly coupled with CSF movement in all assessed states - awake, NREM-1, NREM-2. The clinical/research implications of these findings are two-fold. First, neuronal-driven oscillations contribute to CSF movement outside of deep sleep (NREM-3); therefore, interventions aimed at increasing CSF movement may yield meaningful increases with the promotion of NREM sleep more generally - a focus on NREM S3 may not be needed. Second, non-neuronal systemic oscillations contribute across wake and sleep stages; therefore, interventions may increase CSF movement by manipulating systemic physiology.

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics.

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral trade-off. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from a red-green-blue (RGB) image without complete hyperspectral measurements. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral trade-off, offering simple hardware requirements and potential applications of various machine learning techniques.

Real-time Quantification of in vivo cerebrospinal fluid velocity using fMRI inflow effect.

In vivo estimation of cerebrospinal fluid (CSF) velocity is crucial for understanding the glymphatic system and its potential role in neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease. Current cardiac or respiratory gated approaches, such as 4D flow MRI, cannot capture CSF movement in real time due to limited temporal resolution and in addition deteriorate in accuracy at low fluid velocities. Other techniques like real-time PC-MRI or time-spatial labeling inversion pulse are not limited by temporal averaging but have limited availability even in research settings. This study aims to quantify the inflow effect of dynamic CSF motion on functional magnetic resonance imaging (fMRI) for in vivo, real-time measurement of CSF flow velocity. We considered linear and nonlinear models of velocity waveforms and empirically fit them to fMRI data from a controlled flow experiment. To assess the utility of this methodology in human data, CSF flow velocities were computed from fMRI data acquired in eight healthy volunteers. Breath holding regimens were used to amplify CSF flow oscillations. Our experimental flow study revealed that CSF velocity is nonlinearly related to inflow effect-mediated signal increase and well estimated using an extension of a previous nonlinear framework. Using this relationship, we recovered velocity from in vivo fMRI signal, demonstrating the potential of our approach for estimating CSF flow velocity in the human brain. This novel method could serve as an alternative approach to quantifying slow flow velocities in real time, such as CSF flow in the ventricular system, thereby providing valuable insights into the glymphatic system's function and its implications for neurological disorders.

Brain network hypersensitivity underlies pain crises in sickle cell disease.

Sickle cell disease (SCD) is a genetic disorder causing blood vessel blockages and painful Vaso-occlusive crises (VOCs). VOCs, characterized by severe pain due to blocked blood flow, are recurrent and unpredictable, posing challenges for preventive strategies. In this study we propose explosive synchronization (ES), a phenomenon characterized by abrupt brain network phase transitions, as a novel approach to address this challenge. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) and VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited significantly lower alpha wave frequency than controls. SCD patients under painful pressure stimulation showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, patients who had a higher frequency of VOCs in the preceding 12 months presented with stronger FDA. The timing of VOC occurrence relative to EEG recordings was significantly associated to FDA. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Stronger FDA correlated with higher responsivity and complexity in our model. Simulation under noisy environment showed that higher FDA could be linked to increased occurrence frequency of crisis. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

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.