Abstract The circulation of cerebrospinal fluid (CSF) within the brain parenchyma and its exchange with interstitial fluid (ISF) are essential for maintaining homeostasis and clearing interstitial waste. However, the driving forces behind parenchymal hydrodynamics remain unclear. Cardiac pulsation alone may be insufficient to drive parenchymal flow, while respiration has emerged as an additional contributor. To date, however, most respiration-related studies have focused on the ventricular system and spinal canal, leaving its influence on parenchymal hydrodynamics—the site of waste production—largely unexplored. In this study, we investigated whether parenchymal hydrodynamics are coupled with respiration during normal breathing, and how this coupling compares to that driven by cardiac pulsation. We used dynamic diffusion-weighted imaging (dynDWI) with a b-value of 150 s/mm² to capture incoherent fluid motion, quantified using the apparent diffusion coefficient (ADC). Respiration and cardiac signals were simultaneously recorded using a respiratory belt and finger photoplethysmography (PPG). Temporal coupling strength and time delay between parenchymal hydrodynamics and physiological signals was assessed using TRACC-PHYSIO (Time-domain Resolution-Aligned Cross-Correlation) for physiological coupling). We found that temporal ADC fluctuations in the parenchyma were coupled to respiration, with a latency of 0.7 seconds, and exhibited a gray-to-white matter propagation pattern. Spatial coupling patterns differed between respiration and cardiac pulsation: respiration coupling was stronger in gray and white matter than in the perivascular subarachnoid space (PVSAS) and lateral ventricles (LV), whereas cardiac coupling predominated in the PVSAS and LV. Rebinning dynDWI into respiration cycles revealed parenchymal ADC peaked during inhalation and reached a minimum during exhalation. Together, these findings suggest that respiration contributes to driving parenchymal hydrodynamics and fluid-driving forces vary spatially across brain regions, with respiration and cardiac pulsation play complementary roles in regulating brain fluid dynamics.

PURPOSE:To validate a method to assess cardiac and respiratory brain pulsations in slowly sampled dynamic MR scans. Cardiac and respiratory pulsations are key drivers of neurofluid circulation. However, resolving their temporal dynamics requires fast imaging, which is not achievable in many dynamic MR acquisitions. METHODS:We systematically validated TRACC-PHYSIO, a Time-domain Resolution-Aligned Cross-Correlation framework for estimating physiological coupling and time delays in dynamic MRI. This method uses simultaneously recorded cardiac and respiratory waveforms as external references and applies time-shifted cross-correlation to estimate physiological coupling strength and relative pulse delay in dynamic MRI data. Two metrics are derived: Peak Coupling Coefficient (Peak CorrCoeff), which quantifies coupling strength, and TimeDelay, which estimates the relative arrival time of the physiological impulse in the brain. TRACC-PHYSIO was first evaluated with in vivo fMRI (TR = 363 ms) by comparing the Peak CorrCoeff with physiological bandpower derived from spectral analysis. Performance was further assessed using simulations of realistically modeled dynamic MR signals across repetition time (TR = 50-3000 ms) and acquisition durations (60-360 s). RESULTS:In fMRI, TRACC-derived cardiac and respiratory Peak CorrCoeff were strongly associated with their respective spectrum-derived physiological bandpower (all tests Pearson r > 0.90). In simulations, both Peak CorrCoeff and TimeDelay were estimated with no mean bias and low temporal errors across TRs and acquisition durations. CONCLUSION:TRACC-PHYSIO is a validated time-domain framework for quantifying cardiac and respiratory coupling strength and estimating millisecond-scale relative pulse delays in standard dynamic MR acquisitions.

Brief eyes-closed rest (e.g., 30 min) during the day promotes rejuvenation and enhances mental clarity. However, its effect on neurofluid dynamics-including cerebral hemodynamics and cerebrospinal fluid (CSF) oscillations-remains largely unexplored. These neurofluid dynamics, driven by low-frequency oscillations (LFOs), respiration, and cardiac pulsation, play a critical role in the brain's waste clearance and may serve as a key mechanism underlying the cognitive benefits of rest. This study used fast functional MRI (TR=363msec) to assess neurofluid dynamics across three frequency bands in five key regions of interest (ROI): cerebral arteries, superior sagittal sinus, grey matter, white matter, and fourth ventricle CSF. Measurements were taken before and after 30 min of eyes-closed rest in a cohort of 38 participants aged 35-82. After rest, we observed a significant increase in LFO power across all five ROIs, suggesting that enhanced LFO may promote neurofluid clearance and contribute to the rest's restorative effects. Concurrently, cardiac power significantly decreased across all ROIs, indicating cerebrovascular relaxation, consistent with reduced cardiovascular activity during drowsiness. Lastly, older participants exhibited significantly smaller LFO and cardiac power changes than younger individuals, reflecting an age-related decline in neurofluid modulation that may diminish the benefits of rest. By simultaneously examining arterial, venous, and parenchymal hemodynamics, this study offers a integrated view of how brief rest influences brain pulsations and how these effects change with aging.

Physiological brain pulsations, primarily driven by cardiac and respiratory activity, play a key role in driving neurofluid circulation and waste clearance. Capturing the temporal dynamics of cardiac- and respiratory-driven brain pulsations (0.2-1.5 Hz) requires fast imaging with TRs near 100 ms, which is often unachievable in functional MRI or dynamic diffusion MRI. As a result, valuable physiological information remains hidden in these datasets. Here, we introduce TRACC-PHYSIO, a time-domain analytical framework designed to quantify physiological coupling and pulse time delays in dynamic MRI without requiring a fast acquisition. TRACC-PHYSIO uses cross-correlation to detect co-fluctuations between slowly sampled dynamic MRI data and simultaneously recorded physiological waveforms. It measures two key metrics: the peak Coupling Coefficient (peak CorrCoeff), quantifying the strength of co-fluctuations, and the TimeDelay, reflecting the relative arrival time of the physiological impulse in the brain with millisecond-level temporal resolution. The primary aim of this study is to validate TRACC-PHYSIO through systematic simulations that model realistic dynamic MR signals with mixed physiological components. We comprehensively evaluate TRACC-PHYSIO's performance under a wide range of conditions, including varying cardiac-to-respiratory composition ratios, TRs, and acquisition times. Results demonstrate that TRACC-PHYSIO can robustly assess coupling strengths and time delays for both cardiac (TRACC-Cardiac) and respiratory (TRACC-Respiratory) components, even in datasets with long TRs up to 3 seconds. By enabling a reliable time-domain coupling analysis, TRACC-PHYSIO opens new avenues for revealing brain pulsation mechanisms and elucidating the physiological drivers of neurofluid dynamics in health and disease. This stimulation study provides a valuable reference for interpreting TRACC-PHYSIO results and understanding associated uncertainties in future applications.

Fluctuations in cerebral blood volume (CBV) are a dominant mechanism aiding cerebrospinal fluid (CSF) movement in the brain during wakefulness and non-rapid eye movement (NREM) sleep. However, it is unclear if the amplitudes of CBV oscillations also change in proportion to the changes in amplitude of CSF movement across specific NREM sleep states. It is also not known if the coupling strength between them varies between NREM sleep states. To investigate these relationships, we measured cerebral hemodynamics and craniad CSF movement at the fourth ventricle simultaneously during wakefulness and NREM sleep states using concurrent Electroencephalography and functional Magnetic Resonance Imaging. We found that the amplitude fluctuations of cerebral hemodynamics and CSF oscillations desynchronize from one another only during deep NREM3 state, despite the strong mechanical coupling between CBV changes and CSF movement, which was consistent across all states. This suggests the existence of a different mechanism, linked to the cortical interstitial volume/resistance change, that regulates the NREM3 CSF inflow into the brain.

Resting-state functional MRI (fMRI) signals capture physiological processes, including systemic low-frequency oscillations (LFOs), respiration, and cardiac pulsations. These physiological oscillations-often treated as noise in functional connectivity analysis-reflect fundamental aspects of brain physiology and have recently been recognized as key drivers of brain waste clearance. However, these critical physiological signals are obscured in fMRI data due to slow sampling rates (typical repetition time (TR) > 0.8 s), which cause cardiac signal to alias into lower frequencies. To resolve physiological signals in fMRI datasets, we leveraged fast cross-slice sampling within each TR to hypersample the fMRI signal. A key novelty of this study is the development of a region-specific hypersampling approach, called HyPER (Hypersampling for Physiological signal Extraction in a Region-specific manner). HyPER enhances temporal resolution within coherently pulsating vascular and tissue compartments, including the major cerebral arteries, the superior sagittal sinus (SSS), gray matter (GM), and white matter (WM). This study is structured in three parts: (1) We developed and validated the HyPER approach using fast fMRI from a local dataset in four regions of interest: the major cerebral arteries, SSS, GM, and WM. (2) We applied this approach to the publicly available Human Connectome Project-Aging (HCP-A) dataset (ages 36-90 years), increasing the resolvable frequency by ninefold-from 0.625 Hz to 5.625 Hz-enabling clear separation of cardiac, respiration, and LFO oscillations. (3) We investigated how brain physiological pulsations change with age. Our findings revealed an age-related increase in cardiac and respiratory pulsations across all brain regions, likely reflecting an increased vessel stiffness and reduced dampening of high-frequency pulsations along the vascular network. In contrast, LFO pulsations generally declined with age, suggesting reduced vasomotion in the older brain. In summary, we demonstrated the feasibility and reliability of a region-specific hypersampling technique to resolve physiological pulsations in fMRI. This method can be broadly applied to existing fMRI datasets to uncover hidden physiological pulsations and advance our understanding of brain physiology and disease-related alterations.

Each heartbeat generates a cardiac pressure wave that propagates through the brain and travels from large arteries through cerebrospinal fluid and brain tissue, compressing the venous sinuses and producing venous blood pulsatility. The delay between arterial and venous pulsation (A-V delay) is an insightful marker of intracranial compliance and the intracranial mechanical environment. We developed a novel approach to extract A-V delay from conventional resting-state functional MRI (fMRI) scans, leveraging fMRI's sensitivity to vessel pulsations in large cerebral arteries and the superior sagittal sinus (SSS). This fully automated method was applied to the Human Connectome Project - Aging dataset to analyze 578 participants aged 35 to 90 years. The mean A-V delay was 78 ± 32 msec; it shortened by 4 msec for every decade of aging and was 12 msec faster in men than women, highlighting age-related and sex-specific differences. We also identified a within-SSS pattern of pulsations, characterized by an earlier posterior pulsation and a later anterior pulsation. This pattern opposes the direction of blood flow, supporting that the SSS is passively compressed and tied to a distinct intracranial pulse transmission. Overall, this work demonstrates the feasibility of extracting an fMRI-based A-V delay, uncovering a previously unexplored capability of fMRI. This approach broadens the potential applications of fMRI by adding a biomechanical dimension to fMRI's established roles in evaluating neuronal and hemodynamic function. Given the widespread availability of fMRI, this approach can be applied in future studies to investigate biomechanical changes in various disease conditions.

BACKGROUND:Diffusion imaging holds great potential for the non-invasive assessment of the glymphatic system in humans. One technique, diffusion tensor imaging along the perivascular space (DTI-ALPS), has introduced the ALPS-index, a novel metric for evaluating diffusivity within the perivascular space. However, it still needs to be established whether the observed reduction in the ALPS-index reflects axonal changes, a common occurrence in neurodegenerative diseases. PURPOSE:To determine whether axonal alterations can influence change in the ALPS-index. STUDY TYPE:Retrospective. POPULATION:100 participants (78 cognitively normal and 22 with mild cognitive impairments) aged 50-90 years old. FIELD STRENGTH/SEQUENCE:3T; diffusion-weighted single-shot spin-echo echo-planar imaging sequence, T1-weighted images (MP-RAGE). ASSESSMENT:The ratio of two radial diffusivities of the diffusion tensor (i.e., λ2/λ3) across major white matter tracts with distinct venous/perivenous anatomy that fulfill (ALPS-tracts) and do not fulfill (control tracts) ALPS-index anatomical assumptions were analyzed. STATISTICAL TESTS:To investigate the correlation between λ2/λ3 and age/cognitive function (RAVLT) while accounting for the effect of age, linear regression was implemented to remove the age effect from each variable. Pearson correlation analysis was conducted on the residuals obtained from the linear regression. Statistical significance was set at p < 0.05. RESULTS:λ2 was ~50% higher than λ3 and demonstrated a consistent pattern across both ALPS and control tracts. Additionally, in both ALPS and control tracts a reduction in the λ2/λ3 ratio was observed with advancing age (r = -0.39, r = -0.29, association and forceps tract, respectively) and decreased memory function (r = 0.24, r = 0.27, association and forceps tract, respectively). DATA CONCLUSIONS:The results unveil a widespread radial asymmetry of white matter tracts that changes with aging and neurodegeration. These findings highlight that the ALPS-index may not solely reflect changes in the diffusivity of the perivascular space but may also incorporate axonal contributions. LEVEL OF EVIDENCE:3 TECHNICAL EFFICACY: Stage 2.

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.

Existing methods for evaluating in vivo placental function fail to reliably detect pregnancies at-risk for adverse outcomes prior to maternal and/or fetal morbidity. Here we report the results of a prospective dual-site longitudinal clinical study of quantitative placental T2* as measured by blood oxygen-level dependent magnetic resonance imaging (BOLD-MRI). The objectives of this study were: 1) to quantify placental T2* at multiple time points across gestation, and its consistency across sites, and 2) to investigate the association between placental T2* and adverse outcomes. 797 successful imaging studies, at up to three time points between 11 and 38 weeks of gestation, were completed in 316 pregnancies. Outcomes were stratified into three groups: (UN) uncomplicated/normal pregnancy, (PA) primary adverse pregnancy, which included hypertensive disorders of pregnancy, birthweight <5th percentile, and/or stillbirth or fetal death, and (SA) secondary abnormal pregnancy, which included abnormal prenatal conditions not included in the PA group such as spontaneous preterm birth or fetal anomalies. Of the 316 pregnancies, 198 (62.6%) were UN, 70 (22.2%) PA, and 48 (15.2%) SA outcomes. We found that the evolution of placental T2* across gestation was well described by a sigmoid model, with T2* decreasing continuously from a high plateau level early in gestation, through an inflection point around 30 weeks, and finally approaching a second, lower plateau in late gestation. Model regression revealed significantly lower T2* in the PA group than in UN pregnancies starting at 15 weeks and continuing through 33 weeks. T2* percentiles were computed for individual scans relative to UN group regression, and z-scores and receiver operating characteristic (ROC) curves calculated for association of T2* with pregnancy outcome. Overall, differences between UN and PA groups were statistically significant across gestation, with large effect sizes in mid- and late- pregnancy. The area under the curve (AUC) for placental T2* percentile and PA pregnancy outcome was 0.71, with the strongest predictive power (AUC of 0.76) at the mid-gestation time period (20–30 weeks). Our data demonstrate that placental T2* measurements are strongly associated with pregnancy outcomes often attributed to placental insufficiency. Trial registration: ClinicalTrials.gov: NCT02749851.

The placenta is a remarkable organ that coordinates and regulates maternal-fetal interactions during pregnancy to optimize fetal development. A host of obstetric complications are associated with placental dysfunction, and existing methods for evaluating in vivo placental function fail to reliably detect at-risk pregnancies prior to maternal or fetal morbidity. Although routinely used as a monitoring tool, the predictive power of ultrasound for identifying compromised pregnancies is poor. Recent preclinical studies performed in our laboratory, using blood oxygen-level dependent magnetic resonance imaging (BOLD-MRI) in the pregnant nonhuman primate (NHP), established a strong correlation between placental T2* values and maternal-fetal oxygen transport. Here we extend this work to a large, longitudinal, two-site study of quantitative in vivo T2* mapping in human pregnancies across 11 to 38 weeks of gestation to characterize the evolution of placental oxygenation in uncomplicated pregnancies and to elucidate the relationship between aberrant placental T2* and adverse obstetric outcomes attributable to placental dysfunction. This methodology has high discriminatory power and strong potential diagnostic utility.

INTRODUCTION:Heterogeneity of nephrotic diseases and a lack of validated biomarkers limits interventions and reduces the ability to examine outcomes. Urinary CD80 is a potential biomarker for minimal change disease (MCD) steroid-sensitive nephrotic syndrome (NS). We investigated and validated a CD80 enzyme-linked immunosorbent assay (ELISA) in urine in a large cohort with a variety of nephrotic diseases. METHODS:A commercial CD80 ELISA was enhanced and analytically validated for urine. Patients were from Mayo Clinic (307) and Nephrotic Syndrome Study Network Consortium (NEPTUNE; 104) as follows: minimal change disease (MCD, 56), focal segmental glomerulosclerosis (FSGS, 92), lupus nephritis (LN, 25), IgA nephropathy (IgAN, 20), membranous nephropathy (MN, 49), autosomal dominant polycystic kidney disease (ADPKD, 10), diabetic nephropathy (DN; 106), pyuria (19), and controls (34). Analysis was by Kruskal-Wallis test, generalized estimating equation (GEE) models, and receiver operating characteristic (AUC) curve. RESULTS:Urinary CD80/creatinine values were highest in MCD compared to other glomerular diseases and were increased in DN with proteinuria >2 compared to controls (control = 36 ng/g; MCD = 139 ng/g, P < 0.01; LN = 90 ng/g, P < 0.12; FSGS = 66 ng/g, P = 0.18; DN = 63, P = 0.03; MN = 69 ng/g, P = 0.33; ng/g, P = 0.07; IgA = 19 ng/g, P = 0.09; ADPKD = 42, P = 0.36; and pyuria 31, P = 0.20; GEE, median, P vs. control). In proteinuric patients, CD80 concentration appears to be independent of proteinuria levels, suggesting that it is unrelated to nonspecific passage across the glomeruli. CD80/creatinine values were higher in paired relapse versus remission cases of MCD and FSGS (P < 0.0001, GEE). CONCLUSION:Using a validated ELISA, urinary CD80 levels discriminate MCD from other forms of NS (FSGS, DN, IgA, MN) and primary from secondary FSGS.

[This corrects the article DOI: 10.1371/journal.pone.0235840.].