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Abstract

Cephalad fluid shift is strongly implicated in the brain-structural and craniovascular disturbances observed after long-duration spaceflight. It is part of what has been termed the cranial microgravity response (CMR). However, the physiological mechanisms underlying these changes remain unclear. We employed a ~2-h, −10° head-down tilt (HDT) paradigm to investigate the immediate effects of cephalad fluid shift on cranial venous outflow, cephalic venous vasculature, and brain temperature. Twenty-one adults (26.6 ± 2.4 years) underwent four MRI sessions combining 4D-flow measurements, susceptibility-weighed imaging, Time-of-Flight angiography, and MR Spectroscopy. HDT resulted in a significant increase in internal jugular vein cross-sectional area (p < 0.001), decreased jugular vein flow velocity (p < 0.001) and normalized mean flowrate (p < 0.001), a volumetric expansion of the main cervical arteries (p < 0.001), and a reduction in superior sagittal sinus flow velocity (p = 0.03). We also found a significant increase in global brain temperature after HDT (0.33°C ± 0.28°C; p < 0.001). These findings provide converging evidence for compromised cranial venous outflow in simulated microgravity, suggesting an upstream impact on the cerebral venous vasculature and thermal homeostasis of the head. Our setup now offers a comprehensive and fast-turnaround framework for the testing of countermeasures against the CMR.

Keywords

cerebral blood flow cerebral hemodynamics cerebral autoregulation MRI thrombosis

Introduction

Despite comprising only 2% of body weight, the brain is one of the most highly perfused organs, receiving upwards of 20% of the total cardiac output regardless of body position.¹ This makes it particularly susceptible to the effects of microgravity, given that the human cardiovascular system is evolutionarily adapted to the gravitational hydrostatic pressure gradient on Earth. During weightlessness, this gradient disappears and fluid redistributes more uniformly throughout the body, producing a net “cephalic” fluid shift toward the upper torso and head.² This shift has been increasingly implicated in a range of neurovascular changes observed during long-duration spaceflight (LDSF), including disruptions to arterial structure and function, increased microbleed risk, volumetric expansion of the superior sagittal sinus (SSS), internal jugular vein (IJV) distension, and a rare case of obstructive venous thrombosis in the left IJV.^3–8 Further investigations of the IJVs have revealed strong evidence of stagnant or reversed flow patterns and reduced peak flow velocities.^9–11 Our group has also previously reported an expansion of cerebrospinal fluid (CSF) spaces and perivascular spaces (PVS) in several astronauts after LDSF, coupled with reductions in gray- and white-matter (GM and WM) volume.^12–14 Collectively, these observations have been termed the Cranial Microgravity Response (CMR).^15,16 A fluid shift-induced impairment of cerebral venous drainage may correspondingly inhibit CSF absorption capacity through the PVS and SSS, offering a potential explanation for the CMR as well as fluid-based ophthalmic disturbances observed in astronauts with the Space-Associated Neuro-ocular Syndrome (SANS).^17–21

Given the close coupling of cerebral blood flow (CBF) and brain temperature (T_Br) through cerebral autoregulation,²² any disruption to the arterial or venous circulation during LDSF is likely to influence temperature homeostasis.^23–26 In fact, the T_Br would theoretically increase by 0.28°C/min without an adequate heat offloading system (i.e., intracranial venous pathways).²⁷ Elevated head temperatures have been previously reported in astronauts at rest and during exercise, with both rising faster and reaching higher levels than on Earth.^28,29 However, aside from one study measuring suborbital skin temperature during ultra-short dry immersion,³⁰ the impact of microgravity on T_Br remains largely unexplored. Progress has been partly limited by methodological constraints, as traditional thermometry methods (e.g., intracerebral thermistors) are invasive and restricted to focal measurements.^31,32 This has encouraged the adoption of Magnetic Resonance (MR) Spectroscopy thermometry (MRS) as a non-invasive alternative. MRS can estimate T_Br by measuring the chemical-shift difference between water and certain metabolite peaks and has already been validated across several clinical and research settings.^25,32–35

Much of the current evidence on hemodynamic changes during spaceflight comes from Doppler ultrasound, which records real-time flow and pressure data but is constrained by poor spatial resolution, inconsistent velocity measurements, and operator-dependent expertise.³⁶ 4D-flow MR Imaging (MRI) is a phase-contrast (PC) imaging technique that implements three-directional velocity encoding in addition to a time-resolved acquisition using electrocardiogram (ECG)-gating. Thus, it can provide time-resolved flow data from multiple vessels in a single acquisition that can be analysed post-hoc to derive complex hemodynamic parameters.³⁷ While 4D-flow MRI has previously been used in a ground-based experiment to investigate cardiovascular deconditioning in the aorta,³⁸ no 4D-flow study has examined the cervical or cerebral veins in the context of spaceflight.

MR-based approaches are currently limited to pre- and post-flight measurements due to logistical difficulties in performing imaging aboard the International Space Station (ISS). Head-down tilt (HDT) bed-rest offers a reliable ground-based solution, reproducing key physiological effects of spaceflight such as cephalad fluid shifts and lower body offloading without the cost or difficulty of in-flight experiments.³⁹ Indeed, several MR-based HDT studies have directly replicated astronaut findings from the ISS in healthy populations, including venous and ocular changes.^38,40–47 However, most bed-rest studies suffer from long protocols requiring significant time and financial investment, limiting throughput and feasibility for speedy countermeasure development.⁴⁸

Here, we sought to address these gaps by using a multimodal neuroimaging approach to comprehensively characterise the structural and functional changes of the cephalic vasculature under HDT and supine positioning. We additionally aimed to investigate the effect of HDT on brain temperature. We hypothesised that acute HDT would result in (i) an increased area and/or volume of the IJVs and other cranial veins, (ii) reduced flow velocity and/or flowrate through these vessels, and (iii) a slight elevation in T_Br, particularly within superficial cortical regions. To our knowledge, this study is the first to concurrently evaluate venous outflow and cerebral thermodynamics under simulated microgravity, as well as the first to include multiple supine control sessions.

Material and methods

Participants and study design

An initial cohort of 30 healthy adults (i.e., without any chronic medical illness) was recruited for this study. All subjects were screened for standard MR-related exclusion criteria through self-reports, for example, MRI-incompatible implants, pregnancy, unstable cardiovascular or neurological conditions, claustrophobia, etc. Additional exclusion criteria included age ⩾ 18 years, a body-mass-index (BMI) < 30 (due to the limited size of the scanner bore during HDT) and head size fitting within the field-of-view (FOV) such that the first four cervical vertebrae were clearly visible. This experiment was carried out in accordance with the Declaration of Helsinki and approved by the ethics committee of the Ludwig-Maximilians-University Hospital. All participants gave written informed consent and received a payment for participation. After initial screening, 27 subjects were deemed suitable for the experiment. Each subject underwent four MRI scanning sessions, divided into two conditions or session-pairs (Supine-Supine and Supine-HDT) to explore the effects of supine and HDT positioning on the variables of interest (Figure 1(a)). The −10° HDT was achieved using a custom-made laser-cut foam incline placed directly onto the scanner bed. Subjects were instructed to rest with their back upon the incline, extended head-down at a >5° orientation with respect to the infraorbitomeatal line cushioned within the head and neck coil for the entire duration of the HDT manipulation. The 10° elevation of the body and legs was the maximum achievable for a scanner with a 60 cm bore. A detailed description of the experimental design can be found in the Supplemental material. Of the screened subjects, four did not complete all sessions and two dropped out due to adverse effects during HDT. Thus, the final cohort consisted of 21 subjects, 10 males, and 11 females aged 24–34 (median ± IQR: 26.6 ± 2.4) with BMI ranging from 16.8 to 26.5 (21.3 ± 2.5).

Figure 1.

Overview of the experimental design and experimental protocol. (a) Participants underwent four MRI sessions in total: three in the supine position (sessions 1–3) and one after 2 h of −10° head-down tilt (session 4), split into two conditions (Supine-Supine and Supine-HDT). Each condition consisted of a baseline and follow-up session; sessions 1 and 2 were part of the Supine-Supine condition while sessions 3 and 4 were part of Supine-HDT. Both baseline sessions occurred at the same time of day (e.g., morning, afternoon, evening) to control for circadian variation. (b) Eight different scans were collected in each session (total scan time ~1.5 h). A high-resolution T1-weighted (T1w) scan served as the anatomical reference for all subsequent scans. Blood flow and vessel geometry were assessed using 2D phase contrast (2D-PC), Time-of-Flight (across the intracranial veins, cervical arteries, and cervical veins—bTOF, aTOF, and vTOF, respectively), Susceptibility-weighted imaging (SWI), as well as 4D-flow MRI. Brain temperature (T_Br) was quantified from magnetic resonance spectroscopy (MRS) data.

Alongside demographic variables, we considered additional subject-independent factors that could act as potential confounds. To evaluate changes in core body temperature (T_Bo) within conditions, tympanic temperature was measured at the start and end of each scanning day using an infrared (IR) ear thermometer (ThermoScan^® 7 IRT6520, Braun, Kronberg, Germany). Another laser-based IR thermometer recorded the ambient temperature in the scanning room, inside the scanner bore between same-day sessions, and the facial surface temperature (T_Su) from the cheek (Raynger ST, Raytek, Santa Cruz, USA). Outdoor temperature, interscan time, and total scanning duration were also noted down.

MRI data acquisition

MRI data acquisition was conducted on a 3T MAGNETOM Prisma Scanner (Siemens Healthineers, Erlangen, Germany) with a 64-channel head and neck coil. Each scanning session consisted of 11 separate image acquisitions, spanning six different imaging modalities and lasting approximately 90 min per session (Figure 1(b)). Each session began with the acquisition of a high-resolution T1-weighted (T1w) anatomical image (repetition time (TR) = 2060 ms, echo time (TE) = 2.17 ms, flip angle (FA) = 12°, bandwidth = 230 Hz, voxel size = 0.51 mm³ isotropic).

Structural angiography/venography

To create structural angiograms and venograms of the cervical blood vessels, 3D arterial (TE/TR = 3.42/21 ms, FA = 18°, voxel size = 0.3 × 0.3 × 0.5 mm³) and venous (TE/TR = 4.17/23 ms, FA = 18°, voxel size = 0.3 × 0.3 × 0.5 mm³) time-of-flight (TOF) sequences were obtained (aTOF and vTOF, respectively). The middle slab of both scans was centered on the C2/C3 boundary across all subjects. A susceptibility-weighted image (SWI) of the brain was separately acquired to characterize the small venous vasculature (i.e., venules) of the brain (TR = 28 ms, TE = 20 ms, FA = 15°, voxel size = 0.6 mm × 0.6 mm × 1.2 mm, FOV = 220 mm). Lastly, an intracranial venous TOF or bTOF (TE/TR = 4.56/19 ms, FA = 60°, voxel size = 1.0 mm × 1.0 × 2.5 mm) and a 2D-PC-MRI (FOV = 240 mm², TR = 81.7 ms, TE = 9.89 ms, FA = 15°, velocity-encoding (venc) = 10 cm/s) sequence were performed to investigate structural and functional changes of the main cerebral veins, including the SSS and rectal sinus.

4D-flow MRI

To evaluate time-resolved changes in the blood flow of the major cervical veins, 4D-flow MRI was carried out using a kt-GRAPPA accelerated dual-venc sequence.⁴⁹ Dual-venc imaging mitigates the effects of velocity aliasing by combining low- and high-venc acquisitions to accurately capture a broader range of velocities.⁴⁹ The scan was also positioned at the C2/C3 level, with a FOV = 175 mm², TR/TE = 7.5/4.6 ms, slice thickness = 0.9 mm, FA = 15°, venc_low = 20 cm/s and venc_high = 40 cm/s. For each cardiac cycle, 6–10 timepoints were reconstructed using prospective ECG-gating; the total acquired number of cardiac time frames depended on the individual heartrate (HR), which ranged from 39 to 100 beats-per-min, leading to scan times between 20 and 7 min (i.e., longer scan times for slower heartrates).

Magnetic resonance spectroscopy

To investigate variations in brain temperature, four single-voxel ¹H MRS scans were obtained using a Point RESolved Spectroscopy (PRESS) pulse sequence (TR = 1500 ms, TE = 135 ms, voxel size = 15 mm³ isotropic, FA = 90° and 170 excitations). Each scan was performed in a different region of interest (ROI) to characterize regional temperature gradients. We positioned one ROI in the deep brain (right thalamus, rTha) and three near the cortical surface (left temporal pole (lTPC), left occipital cortex (lOcC), and right prefrontal cortex (rPFC)).

Data analysis

A general quality assessment of all T1w structural scans was conducted using MRIQC and can be found in the Supplement.^50,51 All 4D-flow magnitude, SWI, 2D-PC-MRI, and TOF images were registered in native space to the corresponding T1w image using ANTs linear registration.⁵²

4D-flow MRI

4D-flow MRI data was analyzed using a recently developed semi-automated pipeline in MATLAB.⁵³ After preprocessing (including eddy current correction, noise masking, phase unwrapping, and velocity anti-aliasing), the low and high-venc data were combined to reconstruct a dual-venc dataset and a corresponding 3D-PC-MR angiogram (PC-MRA).⁴⁹ A maximum intensity projection (MIP) of this 3D-PC-MRA image served as the anatomical basis for segmentation of the cervical veins.

An overview of the segmentation pipeline can be seen in Figure 2(a). Briefly, pre-processed dual-venc 4D-flow MR images underwent region growing, centreline identification, and cut-plane extraction to obtain hemodynamic information for the left and right IJVs (lIJV and rIJV, respectively). Six hemodynamic variables were quantified based on previous work^9,42: cross-sectional area (CSA, mm²), stroke volume (SV, mL per cardiac cycle), mean flowrate (MFR, mL/s), peak and mean velocity over the cardiac cycle (v_peak and v_mean, cm/s), and the time-averaged volumetric flow per unit area, dubbed normalized MFR (nMFR, mL/s per mm²). Color-coded peak systolic 3D streamlines were generated from velocity data in MATLAB to visualize overall flow pattern changes across sessions.

Figure 2.

Segmentation, streamline visualization, and selected flow metrics from the internal jugular veins. (a) Representative pipeline for IJV segmentation and 4D-flow analysis, showing: (i) PCMRA-derived 3D vascular geometry, (ii) region growing and centerpoints after skeletonization, (iii) centerline identification (iv) cut-plane overlays, (v) ROI overlay, and (vi) sample flow profile. (b–c) Streamline visualization of velocity vectors from the right (rIJV) and left (lIJV) IJVs of a representative subject. A clear reduction in flow velocity can be appreciated during (b) HDT (session 4) compared to (a) supine baseline (session 3). (d) Group-level changes across all sessions for the cross-sectional area (CSA), stroke volume (SV), peak velocity (v_peak), and normalized mean flow rate (nMFR), derived from time-averaged 4D-flow data. Data are plotted as paired values per subject with boxplots overlaid for group metrics.

2D-PC and SWI

Both 2D-PC-MRI and SWI images were analyzed using a custom pipeline in Python v3.12.7, starting with N4 bias field correction, non-local means denoising, registration, and skull-stripping using SynthStrip.⁵⁴ After intensity normalization within a pre-defined window, Otsu thresholding and morphological operations were applied to the 2D-PC data to isolate the cranial venous sinuses of interest. The resulting binary mask was quantified to obtain a total combined volume (in cm³) of the SSS and rectal sinus followed by a pairwise analysis using an in-house MATLAB-based tool. The maximum velocity of the SSS (v_sss) was calculated from the masked phase image based on the pre-determined venc (10 cm/s). A modified version of Braincharter in Python v3.12.7 was used for segmentation of the SWI data,⁵⁵ specific details on which can be found in the original publication and Supplemental material. The binary vessel masks created by this pipeline were similarly quantified to obtain a total volume (in cm³) and voxel-wise statistics for the cortical venules and veins.

TOF angiography

All TOF images were bias field corrected, denoised, and smoothed with a 1 mm Gaussian filter. A contour-based morphological approach removed extraneous skin and subcutaneous fat signals for better vessel identification and segmentation. The resulting stripped images were intensity-thresholded to obtain MIPs of the major cervical veins and arteries in the imaging slab. These vessels included the facial, jugular, and vertebral veins in the vTOF, and the vertebral and carotid arteries in the aTOF. For the bTOF, we used SynthStrip to remove extraneous signals from the skull before thresholding and MIP creation to obtain a mask of the cerebral veins. All TOF images were quantified in a similar fashion to the SWI and 2D-PC images to obtain a total volume (in cm³).

MRS thermometry

After conversion into NIfTI format using spec2nii,⁵⁶ eddy-current-corrected scans were batch processed using jMRUI v7.0.⁵⁷ All spectra underwent a visual quality inspection for motion artifacts, spurious peaks, excessive noise, and/or insufficient water suppression.⁵⁸ Preprocessing consisted of signal conjugation, phase and frequency correction, zero-filling, and apodization denoising of the of the free induction decay signal.⁵⁹ To determine the frequency shift difference (Δδ, ppm) between water (H₂O) and N-acetylaspartate (NAA), time-domain fitting of the NAA and H₂O resonance with a Gaussian lineshape model was carried out using the in-built jMRUI AMARES algorithm.⁶⁰ The algorithm was fitted using 5 peaks such that the H2O, NAA, Cho, and both Cr signals were captured; if this failed, fitting was attempted with additional peaks. Temperature per ROI (T_lOcC, T_lTPC, T_rPFC, T_rTha) was calculated using the equation most recently described by Rzechorzek et al.³⁴ calibrated in human brain tissue (T_Rz):

T_{R z} (° C) = 37 - 100 (Δ δ - 2.665)

(1)

As an additional validation step, temperatures were estimated with two other commonly used equations in the literature, the original proposed by Cady et al.⁶¹ (T_Ca) and the default jMRUI formula from Zhu et al.⁶² (T_Zh).

T_{C a} (° C) = 286.9 - 94 (Δ δ)

(2)

T_{Z h} (° C) = 36 - 103.8 (Δ δ - 2.676)

(3)

Global T_Br was calculated by taking the simple average of the temperature readings from the four ROIs. Spectral linewidth and signal-to-noise ratio (SNR) were recorded as quality checks.

Statistical analysis

Data visualization and preliminary descriptive statistics were performed in R v4.4.1^63,64; final statistical analyses were performed using IBM SPSS Statistics v29.0.⁶⁵ All data were first assessed for normality using the Shapiro–Wilk test, followed by a repeated-measures analysis-of-variance (rmANOVA) on the normally-distributed primary measures of interest, with session as the main within-subject factor. The ROI was considered as an additional factor in the analysis of 4D-flow variables (lIJV or rIJV) and temperature (lOcC, lTPC, rPFC, rTha). In instances where the assumption of sphericity did not hold, Greenhouse-Geiser-corrected F-statistics were reported instead.

To account for between-subject variance, age, HR, and BMI were considered as covariates in secondary analyses of most variables. Correlations between the covariates and variables of interest were assessed through Pearson and Spearman correlations, with strong correlations (r > 0.5) further analyzed using simple linear regressions. Follow-up pairwise comparisons within and across conditions were investigated with paired-sample t-tests for each variable of interest. The Wilcoxon Signed Rank-test was used as the non-parametric equivalent. Normally distributed continuous variables are reported with mean ± SD; non-normal variables are reported as median ± IQR. Statistical significance was defined as p < 0.05 (two-sided), with trends considered as 0.05 ⩽ p ⩽ 0.1. Correction for multiple comparison testing was performed using the Benjamini–Hochberg False Discovery Rate (FDR) procedure.⁶⁶

Results

Subjects were randomly split based on start time, with seven subjects per time block (morning, afternoon, evening); there were no differences in age or BMI between blocks (p > 0.5). No significant changes in tympanic (T_Bo), scanner, or ambient room temperature were observed across sessions (Table 1). However, there was a significantly larger increase in T_Su in the Supine-HDT condition compared to Supine-Supine (+2.4°C vs +1.6°C, difference: 0.7°C ± 0.9°C). An overview of the rmANOVA and baseline versus follow-up pairwise comparisons for all major variables of interest can be found in Table 2. Further demographic characteristics, comparisons with Supine-Supine sessions, and covariate analyses can be found in the Supplemental material.

Table 1.

External temperature and scanning-related variables (n = 21).

Variable	Supine-Supine			Supine-HDT			Difference
Variable	Mean	Median	Range	Mean	Median	Range	Difference
Ambient room temperature (°C)	20.2 ± 0.3	20.2 ± 0.2	19.9–21.2	20.4 ± 0.5	20.3 ± 0.3	19.8–22.2	0.2 ± 0.5
Scanner temperature (°C)	22.6 ± 0.8	22.7 ± 0.7	20.8–23.9	22.9 ± 0.6	22.8 ± 0.8	22.1–24	0.3 ± 1
Tympanic temperature change (°C)	0.4 ± 0.5	0.4 ± 0.7	−0.2 to 1.4	0.6 ± 0.5	0.5 ± 0.5	−0.1 to 1.6	0.2 ± 0.5
Surface temperature change (°C)	1.6 ± 1.2	1.4 ± 1.6	−0.1 to 4.4	2.4 ± 1.2	2.2 ± 1.4	0.4–4.7	0.7 ± 0.9**
Outdoor temperature (°C)	14 ± 6	15 ± 8	4–23	15 ± 8	17 ± 9	−1 to 30	−1 ± 6
Interscan time (min)	113 ± 5	114 ± 5	102–123	113 ± 5	112 ± 9	105–121	0 ± 7
Total scan time (min)	207 ± 11	207 ± 10	193–241	309 ± 10	307 ± 11	297–329	100 ± 11***
Average heart rate (bpm)	70 ± 14	69 ± 13	39–100	68 ± 13	68 ± 16	43–93	1 ± 14

HDT: head-down tilt; BPM: beats per minute.

Values are presented as mean ± standard deviation (SD) unless otherwise indicated (±IQR for median values). Interscan Time refers to the duration between the start of sessions 1 and 2 in the Supine-Supine condition, or from the onset of HDT to the start of scanning in session 4 of the Supine-HDT condition. Total Scan Time for the Supine-HDT condition includes the 2-h acclimatization period under HDT outside of the bore. Heart Rate was averaged within each condition. The outdoor temperature was obtained from local weather reports at the time of the first scan (session 1 or session 3). Significant session differences are depicted as bold font.

p < 0.01 or ***p < 0.001 indicates statistical significance.

Table 2.

Group-level means and statistical comparisons for venous flow and temperature metrics.

Variable	F (df)	Partial η²	Supine-Supine					Supine-HDT
Variable	F (df)	Partial η²	Session 1 (Supine) mean ± SD	Session 2 (Supine) mean ± SD	Difference ± SD (95% CI)	% Change	p _FDR	Session 3 (Supine) mean ± SD	Session 4 (HDT) mean ± SD	Difference ± SD (95% CI)	% Change	p _FDR
CSA (mm²)^a	F(1.48, 26.5) = 56.332***	0.76	22.7 ± 6.2	23.8 ± 7.1	1.2 ± 3.4 (−0.43, 2.8)	+4.8%	0.21	23.6 ± 6.6	40.2 ± 7.8	16.6 ± 8.8 (12.6, 20.6)	+70%	<0.001
Mean velocity (cm/s)	F(3, 57) = 15.832***	0.35	16.0 ± 2.7	15.3 ± 2.5	−0.64 ± 2.2 (−0.39, 0.49)	−4.4%	0.31	15.7 ± 2.3	11.9 ± 2.4	−3.8 ± 2.8 (−5.1, −2.5)	−24%	<0.001
Peak velocity (cm/s)^a	F(2.27, 40.9) = 14.581***	0.45	34.5 ± 6.4	34.0 ± 6.7	−0.46 ± 4.5 (−2.6, 1.6)	−1.4%	0.65	33.3 ± 6.4	26.5 ± 4.0	−6.8 ± 6.3 (−9.6, −3.9)	−20%	<0.001
Stroke volume (mL)	F(3, 57) = 10.667***	0.37	2.96 ± 1.00	3.12 ± 1.21	0.16 ± 0.77 (−0.20, 0.53)	−5.4%	0.53	3.04 ± 1.12	4.04 ± 1.11	1.00 ± 1.03 (0.53, 1.47)	+33%	0.001
MFR (mL/s)^a	F(2.13, 38.2) = 9.671***	0.35	3.50 ± 1.32	3.40 ± 1.22	−0.10 ± 0.67 (−0.41, 0.22)	−2.9%	0.74	3.42 ± 1.20	4.41 ± 0.81	1.00 ± 0.94 (0.57, 1.42)	+29%	<0.001
Normalized MFR (mL/s/mm²)	F(3, 57) = 8.951***	0.33	14.3 ± 3.3	13.5 ± 2.5	−0.79 ± 2.7 (−2.1, 0.47)	−5.6%	0.31	14.1 ± 2.7	10.8 ± 2.5	−3.2 ± 2.8 (−4.5, −1.9)	−23%	<0.001
lOcC temperature (°C)	F(3, 60) = 3.868*	0.16	38.1 ± 0.4	38.4 ± 0.5	−0.07 ± 0.56 (−0.33, 0.19)	+ 0.8%	0.81	38.1 ± 0.5	38.4 ± 0.5	0.34 ± 0.62 (0.05, 0.62)	+0.9%	0.047
lTPC temperature (°C)	F(3, 60) = 4.407**	0.18	37.6 ± 0.9	37.9 ± 0.8	0.27 ± 0.76 (−0.07, 0.62)	+ 0.8%	0.14	37.6 ± 0.8	38.2 ± 0.7	0.58 ± 0.74 (0.25, 0.92)	+1.6%	0.078
rPFC temperature (°C)	F(3, 60) = 0.739	0.04	37.7 ± 0.5	37.6 ± 0.4	−0.08 ± 0.44 (−0.28, 0.12)	−0.2%	0.65	37.7 ± 0.5	37.8 ± 0.5	0.10 ± 0.60 (−0.17, 0.38)	+0.3%	0.65
rTha temperature (°C)	F(3, 60) = 3.589*	0.15	39.1 ± 0.5	38.8 ± 0.7	−0.23 ± 0.68 (−0.54, 0.08)	−0.6%	0.20	39.0 ± 0.6	39.3 ± 0.6	0.32 ± 0.78 (−0.03, 0.68)	+0.8%	0.073
Global brain temperature (°C)	F(3, 60) = 9.232***	0.32	38.1 ± 0.4	38.1 ± 0.4	−0.03 ± 0.29 (−0.16, 0.10)	−0.1%	0.81	38.1 ± 0.4	38.4 ± 0.3	0.33 ± 0.28 (0.21, 0.46)	+0.9%	<0.001
2DPC sinus velocity (cm/s)^a	F(1.55, 26.3) = 5.505*	0.24	6.53 ± 1.05	6.52 ± 1.02	−0.01 ± 0.36 (−0.18, 0.17)	−0.1%	0.93	6.68 ± 0.98	6.04 ± 0.71	−0.64 ± 0.92 (−1.07, −0.21)	−10%	0.034
2DPC sinus volume (cm³)^a	F(2.17, 39.1) = 1.033	0.05	26.9 ± 6.0	26.5 ± 5.7	−0.40 ± 1.9 (−1.32, 0.51)	−1.5%	0.66	27.5 ± 6.7	27.9 ± 6.9	0.44 ± 3.3 (−1.05, 1.92)	+1.5%	0.66
SWI venous volume (cm³)^a	F(2.12, 40.4) = 0.643	0.03	47.3 ± 1.6	47.0 ± 1.7	−0.28 ± 1.07 (−0.79, 0.22)	−0.6%	0.61	47.1 ± 1.3	46.9 ± 1.6	−0.20 ± 1.0 (−0.65, 0.25)	−0.4%	0.37
TOF neck arterial volume (cm³)	F(3, 60) = 13.859***	0.42	10.2 ± 1.7	10.0 ± 1.5	−0.2 ± 1.1 (−0.66, 0.32)	−2.0%	0.58	10.0 ± 1.6	11.4 ± 1.9	1.4 ± 1.3 (0.77, 1.96)	+14%	<0.001
TOF neck venous volume (cm³)	F(3, 60) = 8.252***	0.29	9.9 ± 2.3	9.8 ± 3.1	−0.1 ± 2.2 (−1.1, 0.9)	−0.1%	0.87	10.0 ± 2.2	12.7 ± 4.2	2.6 ± 3.2 (1.2, 4.1)	+27%	0.004
TOF brain venous volume (cm³)^a	F(2.1, 37.7) = 0.643	0.04	31.6 ± 8.0	30.7 ± 8.4	−0.95 ± 2.7 (−2.23, 0.34)	−2.8%	0.60	32.6 ± 9.2	32.2 ± 8.5	−0.35 ± 4.7 (−2.5, 1.8)	−1.2%	0.76

A repeated-measures ANOVA for session-level effects was run for each of the 17 variables of interest. Six pairwise comparisons between the four sessions were performed for each variable. All brain temperature values were estimated using the Rzechorzek et al.³⁴ equation. Bold letters indicate statistical significance defined p < 0.05, FDR-corrected using the Benjamini and Hochberg procedure.

FDR: false-discovery rate; CSA: cross-sectional area; MFR: mean flowrate; lOcC: left occipital cortex; lTPC: left temporal pole; rPFC: right prefrontal cortex; rTha: right thalamus; PC: phase contrast; SWI: susceptibility-weighted imaging; TOF: time-of-flight.

Variables for which the assumption of sphericity did not hold, where Greenhouse-Geiser-corrected F-statistics and degrees of freedom are reported instead.

***

p < 0.001, **p < 0.01, and *p < 0.05 indicates statistical significance.

Flow dynamics of the internal jugular veins

The 4D-flow MRI data for session 2 was motion corrupted in two subjects and thus excluded from the respective within-subject analyses. Streamline visualizations of IJV flow revealed extensive inter-individual variability in jugular hemodynamics, but largely confirmed the general trends seen in the quantitative analysis (Figure 2(b) and (c)). No significant main effects of vessel (i.e., lIJV vs rIJV) were observed for any flow variables (all p > 0.05), and session by vessel interactions were also statistically insignificant for CSA (p = 0.846), SV (p = 0.811), MFR (p = 0.824), nMFR (p = 0.380), v_mean (p = 0.576), and v_peak (p = 0.513). Accordingly, rmANOVA results are reported as an average across both IJVs unless otherwise specified.

We observed a significant cross-sectional expansion of the IJVs following head-down tilt (HDT) compared to all supine sessions (Table 2 and Supplement; p < 0.001). When averaged across both IJVs, CSA increased by approximately 70% between sessions 3 and 4 (Table 2; p_FDR < 0.001). There was a larger percent increase in the lIJV compared to the rIJV (80.7% vs 61.2%); however, this difference was not statistically significant (t₂₀ = 0.425, p = 0.675). Overall, rIJV-CSA was ~20% larger on average than lIJV-CSA across all sessions (31.3 ± 13.0 mm² ± vs 24.5 ± 12.2 mm²; Z = 3.779, p < 0.001). Interestingly, the degree of change in CSA between sessions 3 (supine) and 4 (HDT) correlated with BMI (ρ = 0.444, p = 0.044).

There was a significant slowdown in jugular vein flow velocity during HDT, with v_peak and v_mean decreasing by ~20% and ~24%, respectively (Table 2; p < 0.001). Compared to session 3, the v_peak in the lIJV was 9.21 ± 7.95 cm/s slower in session 4 (p_FDR = 0.001), with a similar, but insignificant, trend seen in the rIJV (−4.66 ± 9.89 cm/s; p_FDR = 0.09). There was a slightly smaller asymmetry when looking at the v_mean, with HDT leading to a similar slowdown in both vessels (lIJV: −4.43 ± 4.53 cm/s, rIJV: −3.19 ± 2.82 cm/s; both p_FDR < 0.001). SV was on average ~33% higher during the Supine-HDT condition (lIJV: 0.80 ± 1.34 mL, t₂₀ = 2.714, p_FDR = 0.027 and rIJV: 1.14 ± 1.55 mL; t₂₀ = 3.350, p_FDR = 0.019). We observed a similar 29% average increase in the MFR between sessions 3 and 4 (Table 2; p_FDR < 0.001) with a 0.70 ± 1.35 mL/s increase in the lIJV (p_FDR = 0.047) and 1.22 ± 1.57 mL/s increase in the rIJV (p_FDR = 0.01). Across all sessions, the SV and MFR were consistently higher in the rIJV at the group level (SV: 0.75 ± 2.72 mL/beat; Z = 2.124, p = 0.034; MFR: 0.92 ± 3.05 mL/s; p = 0.008).

On the other hand, we observed a significant reduction in the per second flow of blood during session 4 when controlling for the IJV-CSA. Namely, nMFR was on average 23% lower during HDT compared to the same-day supine baseline (Table 2, p_FDR < 0.001). While there were no contralateral differences in nMFR during session 4 (lIJV: 0.106 ± 0.04 mL/s/mm² vs rIJV: 0.112 ± 0.03 mL/s/mm², p = 0.48), a more significant drop in nMFR was seen on the left side (lIJV: −0.044 mL/s/mm² vs rIJV: −0.018 mL/s/mm²; t₂₀ = 2.651, p = 0.015).

Structural changes in the other cephalic vessels

2D-PC data was missing for three subjects, and they were excluded from the group-level analysis. Across all subjects, mean v_SSS decreased by 10% between sessions 3 and 4 (Table 2; p_FDR = 0.034). Correlation analysis with 4D-flow metrics revealed that v_SSS was directly related with nMFR (r = 0.28, p = 0.014), v_peak (r = 0.29, p = 0.011), and v_mean (r = 0.31, p = 0.007). While we found no significant differences between sessions in the group-level volumetric analysis of the 2D-PC or SWI masks (Table 2), voxel-wise analysis suggested a significant expansion of the cortical venules after HDT (Figure 3). Furthermore, correlations with IJV flow parameters suggest that the volume of the major venous sinuses covaried with some 4D-flow variables, including CSA (ρ = 0.26, p = 0.02), SV (ρ = 0.30, p = 0.008), and MFR (ρ = 0.36, p = 0.001).

Figure 3.

Cranial venous vascular segmentation findings across subjects. (a) Mean superior sagittal and rectal sinus volume across supine sessions (blue) showed only minimal increase for the HDT session (increase in yellow). (b) Cortical venules segmented from SWI on the other hand showed a substantial increase (red) in caliber and visible number compared to the general venous vasculature (blue) found during the supine sessions across all subjects.

Quantitative analysis of the vascular masks derived from TOF images revealed a volumetric expansion of both the arterial and venous cervical vasculature during HDT (Figure 4). Specifically, we saw an increase in arterial vessel volume by 14% between sessions 3 and 4 (Table 2; p_FDR < 0.001), while venous volume increased by 27% (p_FDR = 0.004). However, no significant changes were seen across sessions in the volume of the cranial veins obtained from the bTOF mask (Table 2, p = 0.76). Cervical arterial volume was strongly related to the CSA (r = 0.417, p = 0.002), SV (r = 0.393, p < 0.001), and MFR (r = 0.453, p < 0.001). Similarly, the volume of the vTOF mask was positively correlated with the CSA across all sessions (r = 0.339, p = 0.002), with the degree of change during the Supine-HDT condition closely related to the increase in IJV-CSA as obtained from the 4D-flow measurements (r = 0.563, p = 0.008).

Figure 4.

Volumetric changes in the cervical vasculature across sessions. (a) Volume of the venous time-of-flight (vTOF) mask across all sessions, centered on the C2/C3 vertebral boundary. Individual subject values are shown with overlaid boxplots and insets display a representative vTOF segmentation used for quantification. A significant expansion in volume was seen during HDT, mirroring 4D-flow results. (b) Volume of the arterial time-of-flight (aTOF) mask across sessions, where a post-HDT increase in arterial volume was also observed. Inset shows a representative aTOF mask.

Elevation of global brain temperature

A two-way rmANOVA on T_Br revealed a significant main effect of session, regardless of the calibration equation used (T_Rz: F_{2.12, 42.5} = 9.232; T_Ca: F_{3, 60} = 9.211; T_Zh: F_{3, 60} = 9.356; all p_FDR < 0.001). Nonetheless, the absolute temperature estimates produced by each calibration equation were significantly different across all sessions (T_Zh > T_Rz > T_Ca, 38.4 ± 0.4°C vs 38.2 ± 0.4°C vs 37.5 ± 0.4°C; p < 0.001). As T_Rz values generally fell between T_Ca and T_Zh, we report findings here according to this equation³⁴; results for the other two equations can be found in the Supplement. Overall, our data suggests a mean T_Br of 38.1 ± 0.4°C in the supine position (averaged across sessions 1, 2, and 3 from all subjects). MRS-derived T_Br across all sessions was significantly higher relative to T_Bo and T_Su and displayed much less variability (Figure 5(c); 38.2 ± 0.3°C vs 36.7 ± 0.3°C and 33.2 ± 0.5°C, respectively; t₁₉ = 14.4, p < 0.001; t₁₉ = 46.2, p < 0.001).

Figure 5.

Temperature changes across sessions. (a) MRS voxel placements for four regions of interest (ROIs): left temporal pole (lTPC), left occipital cortex (lOcC), right thalamus (rTha), and right prefrontal cortex (rPFC). (b) There was a significant elevation in mean global brain temperature (T_Br) following HDT (session 4), with generally no differences between supine sessions. Individual subject trajectories are overlaid. (c) Change in surface, core body (tympanic), and brain temperature from baseline. While surface temperature increased significantly across both conditions, the change in Supine-HDT was greater than Supine-Supine. Overall, there was much greater variation in surface and tympanic temperatures than in the brain.

The global T_Br in session 4 was ~0.3°C higher than all supine sessions (Figure 5; all p_FDR < 0.01). Further analysis revealed that the elevation in global T_Br between sessions 3 and 4 was closely tied to the change in the T_lOcC (r = 0.61, p = 0.003) and T_rTha (r = 0.77, p < 0.001). However, only the increase in T_lOcC was significant after correction for multiple comparisons (Table 2; t₂₀ = 2.58, p_FDR = 0.047). While trends were occasionally seen for the other ROIs, all pairwise comparisons between sessions were statistically insignificant (p_FDR > 0.05). Notably, regression analysis showed that the CSA, MFR, v_peak, and v_mean all had a significant effect on global T_Br (F_{4, 78} = 3.41, R² = 0.149, p = 0.013).

Discussion

To our knowledge, this is the first study to investigate the effects of HDT on the hemodynamic and thermal regulation of the brain. By combining phase-contrast, spectroscopy, and angiography techniques, we demonstrate that even ~2 h of −10° HDT can lead to noticeable changes in the dynamic and structural properties of the IJVs, cervical veins and arteries, intracranial veins, and major venous sinuses. We additionally report a global elevation in T_Br without consistent regional changes. These effects were largely independent of the time spent in the supine position and varied considerably between individuals. The findings support the role of impeded cranial venous outflow as a driver for CMR.

As a principal finding, we observed significant changes in all the hemodynamic parameters of interest following HDT. Most notably, the left and right IJV-CSA increased by 81% and 61%, respectively, replicating previous findings in space and using ground-based analogues.^{9,42,45,67–70} We saw a comparative volumetric expansion in the TOF-derived venous masks highly correlated with the change in CSA, serving as an anatomical proof of concept.^4,71–73 Since the IJVs are thin-walled vessels, they are prone to changes in central venous pressure (CVP) and will expand in situations where venous flow is inhibited.^74,75 Thus, this vasodilatory response also implies an elevation of CVP (or intracranial pressure (ICP) more generally) due to a cephalad fluid shift-mediated stagnation of venous outflow.^70,71,76,77 Moreover, the obtained values for SV and MFR suggest an increased total blood volume in both IJVs as predicted by mathematical modelling⁷⁸ in similar acute HDT settings.^44,45,79 At first glance, greater venous outflow from the head without a commensurate increase in arterial inflow appears to violate the Monro–Kellie doctrine.^80,81 Although dynamic flow information for the cervical arteries was not acquired, the TOF-derived arterial mask provides strong evidence that arterial inflow did in fact increase during HDT, mirroring results from earlier studies.^{38,79,82–84} Since the cranium naturally has a lower vascular resistance than most other peripheral areas, this result likely indicates both an exacerbated hydrostatic pressure gradient from heart to head in HDT as well as central vasodilatory adjustments to maintain cerebral perfusion.⁸⁵ Lastly, the increased MFR could also reflect a further transfer of venous outflow toward the IJVs similar to the shift of primary venous drainage when going from an upright to supine posture; as the other cervical veins were not included in the 4D-flow analysis, we cannot rule out this possibility.^44,86

Given that the increase in IJV-CSA was nearly twice the drop in v_mean, we would expect a much larger volume flowrate than observed from a fluid dynamics standpoint (Q = A × v). This mismatch indicates less efficient venous blood offloading and is supported by our analysis with nMFR, which reflects changes in flowrate independent of vessel CSA. Specifically, we saw a 23% drop in nMFR during HDT, consistent with the notion of cranial blood pooling and observations of facial edema (“puffy faces”) in astronauts during early spaceflight.¹⁸ Both v and v also decreased by ~20% post-HDT, with patches of near-static flow near vessel edges in many subjects, mirroring earlier results.^{10,44,45,87,88}

Considering that slow venous flow is a known risk factor for thrombosis in the lower extremities,⁸⁹ these results provide additional evidence for a link between IJV flow stasis in microgravity and elevated blood clot risk.^8–11 Furthermore, our results show that this slowdown in the IJVs is closely tied to a reduction in v_SSS. As the brain’s major site of venous and CSF drainage, a HDT-mediated congestion of SSS outflow would naturally slow return downstream; this is corroborated by both mathematical modelling and empirical observations.^72,90–94 Although correlation results suggested a direct relationship between IJV-CSA and SSS volume, there were no group-level volumetric changes of the 2DPC- and TOF-derived sinus masks. Since the major dural sinuses sit in rigid connective tissue that limits their distensibility,¹⁶ a measurable enlargement in an acute setting is quite unlikely.⁷⁰ In fact, Rosenberg et al.⁵ showed that the SSS only expands after weeks of microgravity exposure, and moreover, exclusively in astronauts who developed SANS. Lastly, the voxel-wise expansion of the SWI-derived masks during HDT suggests venous congestion in major cranial veins may also be having an upstream effect on drainage from the cortical venules. Since smaller cerebral veins are generally more vulnerable to changes in venous pressure, this may lead to an elevated risk of microbleeds and associated cerebrovascular complications during LDSF.⁶

Previous work suggests the lIJV is anatomically more vulnerable to flow disruption in microgravity due to its relatively perpendicular angle of entry into the left subclavian vein.^78,95,96 Our results further support this claim, with both v_peak and nMFR showing more significant decreases in the lIJV compared to the rIJV during HDT. Even the consistently greater SV, MFR, and CSA of the rIJV across sessions can be attributed to more efficient outflow resulting from differences in lower IJV resistance,^92,97 resembling findings from other microgravity experiments.^10,45,69,98 However, we did not find any vessel-based effect at the level of vertebra C2, suggesting the response to the rather short HDT manipulation eclipsed any bilateral differences so close to the skull.^92,97

These individual responses were likely modulated by factors such as BMI, since higher BMI was linked to greater CSA expansion during HDT. In fact, greater intrathoracic pressures in heavier individuals could lead to increased CVP and IJV expansion, likely intensifying the effects of HDT compared to lower BMI subjects.^99,100 Additionally, we observed that older participants tended to have lower stroke volumes, possibly reflecting age-related declines in vessel compliance.^75,101 Lastly, the strong correlation between HR and SV demonstrates that 4D-flow-derived volumetric flow metrics are often confounded by HR variability.^102–104 Since astronauts are typically younger, fitter, and male compared to the civilian population,^16,105,106 future studies must account for such individual heterogeneity in the next generation of spacefarers.

To our knowledge, this protocol is the first to implement 4D-flow MRI with MRS to jointly examine venous blood flow and brain temperature. Our major finding is a discrete but consistent ~0.3°C rise in global T_Br following HDT, as suggested by the data of Stahn et al.²⁹ upon arrival in microgravity. This unprecedented result suggests that thermoregulatory impairment in microgravity extends beyond the body to include the brain as well.^{28–30,107,108} There is compelling evidence that compromised CBF leads to an increase in T_Br,^{22,24,26,109,110} and that the venous system plays a crucial role in cerebral autoregulation.^111–113 Hence, stagnant venous flow due to a microgravity-mediated cephalad fluid shift would likely prevent an adequate offloading of metabolic heat through the cerebral veins and sinuses, contributing to an elevation in T_Br as we observe here and supported by the regression results. This increase may have been driven by the temperature changes in the lOcC and rTha as they correlated most strongly with global T_Br during HDT. The higher CSF and GM fractions in these regions could have rendered them more vulnerable to venous stasis-induced impairment of glymphatic heat clearance, particularly given the elevated metabolic activity of GM.^22,114,115

By demonstrating that HDT had no effect on T_Bo and that T_Bo remained significantly lower than T_Br across all sessions, we illustrate how surface-level temperature readings cannot be used as a reliable proxy for brain tissue temperature changes.^{29,30,34,116,117} Furthermore, while the significant increase in T_Su during both conditions can be partially explained by the confined scanner environment, the additional 0.7°C difference after HDT points to vascular adjustments (e.g., facial vasodilation) related to the cephalad fluid shift and impaired venous heat offloading.^{29,107,108,111}

Given that central nervous tissue is particularly sensitive to heat,¹¹⁸ these findings raise significant concerns about overall brain health during space travel. Prolonged increases in brain temperature are linked to neuronal injury, parenchymal edema, and blood-brain barrier leakage.¹¹⁹ These effects would likely contribute to an accumulation of fluid in the brain, increased ICP, and an exacerbation of symptoms associated with obstructed venous outflow. It also remains unclear whether brain temperature continues to rise in space or stabilizes, similar to CBT,²⁹ or if the observed changes are simply artifacts of immediate exposure to microgravity, highlighting the need for further research in this area.

The absence of significant changes across all measured parameters during the Supine-Supine condition provides strong evidence that the neurovasculature is well adapted to uniform gravitational gradients.⁴⁵ In contrast, HDT introduces a sudden gravitational challenge in the form of a headward fluid shift that resembles the primary physiological mechanisms contributing to the CMR. The strongest evidence for this comes from longer-duration HDT bed-rest studies, which reliably reproduce key neurovascular changes associated with prolonged exposure to microgravity such as cranial venous congestion, IJV distension, PVS expansion, and elevated ICP.^{39,42–46,120–123} Generally, the longer the duration of bed-rest, the more robust the replication of these effects. However, even SANS-related ophthalmic findings such as optic nerve sheath distension and increased choroidal thickness have been observed after only 1 h of −6° HDT,^124,125 further supporting the ability of short-duration protocols like ours to elicit CMR-like responses.

Study limitations and future directions

This study has a few limitations worth considering. First, the short HDT exposure duration (~2 h) and small sample size, while sufficient to detect acute group-level effects, may have limited our ability to observe slower-developing physiological changes.^126–128 Second, our 4D-flow analysis did not examine collateral venous outflow (e.g., vertebral venous plexus) and arterial inflow, preventing a proper inflow-outflow comparison, and resolution of Monro–Kellie discrepancies. Additionally, the selected venc range may have been suboptimal for capturing very slow or stagnant flow, potentially leading to underestimation of IJV-CSA or false instances of aliasing.^75,129 While temperature changes were largely independent of equation used, the accuracy of absolute temperature estimates with MRS remains limited by the assumptions of each calibration.⁶⁰ Spectral quality in deeper ROIs, especially the lTPC, was occasionally reduced, likely due to its high vascularization and corresponding motion artifacts.³³

As mission durations increase and spaceflight becomes more accessible to the general population, the development of tailored countermeasures to enhance astronaut health and safety will become increasingly important.¹³⁰ Lower body negative pressure (LBNP) is a leading candidate, proven to improve blood flow, reduce ICP, and decrease venous pulsatility in both HDT and spaceflight settings.^{73,93,122,131,132} However, the effects of LBNP often dissipate rapidly,^67,133 necessitating repeated or prolonged use to sustain benefit and heightening the risk of cardiovascular events like syncope and orthostatic intolerance.^134,135 Thus, research into other viable alternatives is urgently needed. Given that the present setup induced observable structural and functional changes within just a few hours, it presents a rapid-turnaround model for the evaluation of future countermeasures against CMR. This paradigm may therefore serve as a practical platform to screen, compare, and optimize interventions, identifying important biomarkers in the process.

Conclusion

As humans have only spent extended month-long periods in space for the past 15 years, data on long-term neurological health remains scarce.¹⁶ Additionally, prevention and management strategies for the adverse effects of spaceflight can only be developed in combination with active astronaut surveillance and analogue-based experiments on Earth.^136,137 Such data will be essential in evaluating the long-term impact of weightlessness on the brain, particularly in light of the present findings suggesting impeded cranial venous outflow and elevated T_Br in the early phases of microgravity exposure. In conclusion, this work contributes to the growing body of literature on the CMR and introduces a novel experimental model for countermeasure development.¹⁵ Ongoing efforts to safeguard astronaut health will be vital as we set our sights towards the Moon, Mars and beyond.

Supplemental Material

sj-docx-1-jcb-10.1177_0271678X251400470 – Supplemental material for The immediate functional and structural consequences of head-down tilt on the cranial venous vasculature and brain temperature

Supplemental material, sj-docx-1-jcb-10.1177_0271678X251400470 for The immediate functional and structural consequences of head-down tilt on the cranial venous vasculature and brain temperature by Mehul Nimpal, Patrick Winter, Susanne Schnell and Peter zu Eulenburg in Journal of Cerebral Blood Flow & Metabolism

Supplemental Material

sj-docx-2-jcb-10.1177_0271678X251400470 – Supplemental material for The immediate functional and structural consequences of head-down tilt on the cranial venous vasculature and brain temperature

Supplemental material, sj-docx-2-jcb-10.1177_0271678X251400470 for The immediate functional and structural consequences of head-down tilt on the cranial venous vasculature and brain temperature by Mehul Nimpal, Patrick Winter, Susanne Schnell and Peter zu Eulenburg in Journal of Cerebral Blood Flow & Metabolism

Footnotes

Acknowledgements

We thank all volunteers for their participation.

Author contributions

Mehul Nimpal: Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – Original Draft, Writing – Review & Editing. Patrick Winter: Methodology, Resources, Software, Writing – Review & Editing. Susanne Schnell: Software, Methodology, Resources, Writing – Review & Editing. Peter zu Eulenburg: Conceptualization, Methodology, Supervision, Funding Acquisition, Validation, Visualization, Writing – Review & Editing.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the German Space Agency (DLR) on behalf of the Federal Ministry of Economics and Technology/Energy (50WB2027 to PzE).

Ethical considerations

This experiment was carried out in accordance with the Declaration of Helsinki and approved by the ethics committee of the Ludwig-Maximilians-University Hospital.

Consent to participate

All human participants provided written and verbal informed consent to the experiment before participation, and could withdraw at any time before, during, and after the study without consequence.

Consent for publication

Informed consent for publication of images and data was provided from all participants. Identifying details are omitted wherever possible, and all data was anonymized prior to analysis.

ORCID iDs

Mehul Nimpal

Peter zu Eulenburg

Data availability statement

Processed data and code supporting the findings of this experiment can be made available upon reasonable request to the corresponding author (MN).

Supplemental material

Supplemental material for this article is available online.

References

Williams

Leggett

RW.

Reference values for resting blood flow to organs of man. Clin Phys Physiol Meas 1989; 10: 187–217.

Moore

Thornton

WE.

Space shuttle inflight and postflight fluid shifts measured by leg volume changes. Aviat Space Environ Med 1987; 58: A91–A96.

Iwasaki

Ogawa

Kurazumi

, et al. Long-duration spaceflight alters estimated intracranial pressure and cerebral blood velocity. J Physiol 2021; 599: 1067–1081.

Lee

SMC

Ribeiro

Martin

, et al. Arterial structure and function during and after long-duration spaceflight. J Appl Physiol 2020; 129: 108–123.

Rosenberg

Coker

Taylor

, et al. Comparison of dural venous sinus volumes before and after flight in astronauts with and without spaceflight-associated neuro-ocular syndrome. JAMA Netw Open 2021; 4: e2131465.

Burles

Willson

Townes

, et al. Preliminary evidence of high prevalence of cerebral microbleeds in astronauts with spaceflight experience. Front Physiol 2024; 15: 1360353.

Zu Eulenburg

Buchheim

J-I

Ashton

, et al. Changes in blood biomarkers of brain injury and degeneration following long-duration spaceflight. JAMA Neurol 2021; 78: 1525–1527.

Auñón-Chancellor

Pattarini

Moll

, et al. Venous thrombosis during spaceflight. N Engl J Med 2020; 382: 89–90.

Marshall-Goebel

Laurie

Alferova

, et al. Assessment of jugular venous blood flow stasis and thrombosis during spaceflight. JAMA Netw Open 2019; 2: e1915011.

10.

Pavela

Sargsyan

Bedi

, et al. Surveillance for jugular venous thrombosis in astronauts. Vasc Med 2022; 27: 365–372.

11.

Lan

Van Akin

Anderson

, et al. Microgravity-induced reduced jugular vein flow is more pronounced on the non-dominant side. Acta Astronaut 2022; 198: 746–755.

12.

van Ombergen

Jillings

Jeurissen

, et al. Brain tissue–volume changes in cosmonauts. N Engl J Med 2018; 379: 1678–1680.

13.

Van Ombergen

Jillings

Jeurissen

, et al. Brain ventricular volume changes induced by long-duration spaceflight. Proc Natl Acad Sci 2019; 116: 10531–10536.

14.

Barisano

Sepehrband

Collins

, et al. The effect of prolonged spaceflight on cerebrospinal fluid and perivascular spaces of astronauts and cosmonauts. Proc Natl Acad Sci 2022; 119: e2120439119.

15.

Zu Eulenburg

Petersen

Bailey

. Watching the eye with Mars in sight. Exp Physiol. DOI: 10.1113/EP092974. Online ahead of print.

16.

Seidler

Mao

Tays

, et al. Effects of spaceflight on the brain. Lancet Neurol 2024; 23: 826–835.

17.

Marshall-Bowman

Barratt

Gibson

CR.

Ophthalmic changes and increased intracranial pressure associated with long duration spaceflight: an emerging understanding. Acta Astronaut 2013; 87: 77–87.

18.

Zu Eulenburg

Van Ombergen

Tomilovskaya

, et al. Reply to Ludwig et al.: a potential mechanism for intracranial cerebrospinal fluid accumulation during long-duration spaceflight. Proc Natl Acad Sci U S A 2019; 116: 20265–20266.

19.

Lee

Mader

Gibson

, et al. Spaceflight associated neuro-ocular syndrome (SANS) and the neuro-ophthalmologic effects of microgravity: a review and an update. NPJ Microgravity 2020; 6: 1–10.

20.

Mader

Gibson

Pass

, et al. Optic disc edema, globe flattening, choroidal folds, and hyperopic shifts observed in astronauts after long-duration space flight. Ophthalmology 2011; 118: 2058–2069.

21.

Mader

Gibson

Otto

, et al. Persistent asymmetric optic disc swelling after long-duration space flight: implications for pathogenesis. J Neuroophthalmol 2017; 37: 133.

22.

Nunneley

Nelson

DA.

Limitations on arteriovenous cooling of the blood supply to the human brain. Eur J Appl Physiol 1994; 69: 474–479.

23.

Conroy

Spielman

Scott

RQ.

Daily rhythm of cerebral blood flow velocity. J Circadian Rhythms 2005; 3(1): 3.

24.

Sung

Kottke

Risk

, et al. Personalized predictions and non-invasive imaging of human brain temperature. Commun Phys 2021; 4: 68.

25.

Tan

Stephenson

Alhadad

, et al. Elevated brain temperature under severe heat exposure impairs cortical motor activity and executive function. J Sport Health Sci 2024; 13: 233–244.

26.

Zhu

Ackerman

JJH

Sukstanskii

, et al. How the body controls brain temperature: the temperature shielding effect of cerebral blood flow. J Appl Physiol 2006; 101: 1481–1488.

27.

Rango

Arighi

Bresolin

Brain temperature: what do we know?

NeuroReport 2012; 23: 483.

28.

Fortney

Mikhaylov

Lee

, et al. Body temperature and thermoregulation during submaximal exercise after 115-day spaceflight. Aviat Space Environ Med 1998; 69: 137–141.

29.

Stahn

Werner

Opatz

, et al. Increased core body temperature in astronauts during long-duration space missions. Sci Rep 2017; 7: 16180.

30.

Gerasimova-Meigal

Meigal

Gerasimova

, et al. Cerebral circulation and brain temperature during an ultra-short session of dry immersion in young subjects. Pathophysiology 2023; 30: 209–218.

31.

Childs

Human brain temperature: regulation, measurement and relationship with cerebral trauma: part 1. Br J Neurosurg 2008; 22: 486–496.

32.

Corbett

Laptook

Weatherall

Noninvasive measurements of human brain temperature using volume-localized proton magnetic resonance spectroscopy. J Cereb Blood Flow Metab 1997; 17: 363–369.

33.

Childs

Hiltunen

Vidyasagar

, et al. Determination of regional brain temperature using proton magnetic resonance spectroscopy to assess brain–body temperature differences in healthy human subjects. Magn Reson Med 2007; 57: 59–66.

34.

Rzechorzek

Thrippleton

Chappell

, et al. A daily temperature rhythm in the human brain predicts survival after brain injury. Brain 2022; 145: 2031–2048.

35.

Zou

Heyn

Grigorian

, et al. Measuring brain temperature in youth bipolar disorder using a novel magnetic resonance imaging approach: a proof-of-concept study. Curr Neuropharmacol 2023; 21: 1355–1366.

36.

Dyverfeldt

Bissell

Barker

, et al. 4D flow cardiovascular magnetic resonance consensus statement. J Cardiovasc Magn Reson 2015; 17: 72.

37.

Youn

Lee

From 2D to 4D phase-contrast MRI in the neurovascular system: will it be a quantum jump or a fancy decoration?

J Magn Reson Imaging 2022; 55: 347–372.

38.

Rabineau

Issertine

Hoffmann

, et al. Cardiovascular deconditioning and impact of artificial gravity during 60-day head-down bed rest—insights from 4D flow cardiac MRI. Front Physiol 2022; 13: 944587.

39.

Pavy-Le Traon

Heer

Narici

, et al. From space to Earth: advances in human physiology from 20 years of bed rest studies (1986–2006). Eur J Appl Physiol 2007; 101: 143–194.

40.

Ong

Lee

Moss

HE.

Head-down tilt bed rest studies as a terrestrial analog for spaceflight associated neuro-ocular syndrome. Front Neurol 2021; 12: 648958.

41.

Arbeille

Fomina

Roumy

, et al. Adaptation of the left heart, cerebral and femoral arteries, and jugular and femoral veins during short- and long-term head-down tilt and spaceflights. Eur J Appl Physiol 2001; 86: 157–168.

42.

Zahid

Martin

Collins

, et al. Quantification of arterial, venous, and cerebrospinal fluid flow dynamics by magnetic resonance imaging under simulated micro-gravity conditions: a prospective cohort study. Fluids Barriers CNS 2021; 18: 8.

43.

Kramer

Hasan

Sargsyan

, et al. Quantitative MRI volumetry, diffusivity, cerebrovascular flow, and cranial hydrodynamics during head-down tilt and hypercapnia: the SPACECOT study. J Appl Physiol 2017; 122: 1155–1166.

44.

Ishida

Miyati

Ohno

, et al. MRI-based assessment of acute effect of head-down tilt position on intracranial hemodynamics and hydrodynamics. J Magn Reson Imaging 2018; 47: 565–571.

45.

Marshall-Goebel

Ambarki

Eklund

, et al. Effects of short-term exposure to head-down tilt on cerebral hemodynamics: a prospective evaluation of a spaceflight analog using phase-contrast MRI. J Appl Physiol 2016; 120: 1466–1473.

46.

Ogoh

Sato

de Abreu

, et al. Arterial and venous cerebral blood flow responses to long-term head-down bed rest in male volunteers. Exp Physiol 2020; 105: 44–52.

47.

Bleeker

MWP

De Groot

PCE

Pawelczyk

, et al. Effects of 18 days of bed rest on leg and arm venous properties. J Appl Physiol 2004; 96: 840–847.

48.

Hargens

Vico

Long-duration bed rest as an analog to microgravity. J Appl Physiol 2016; 120: 891–903.

49.

Schnell

Ansari

, et al. Accelerated dual-venc 4D flow MRI for neurovascular applications. J Magn Reson Imaging 2017; 46: 102–114.

50.

Dietrich

Raya

Reeder

, et al. Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging 2007; 26: 375–385.

51.

Magnotta

Friedman

and FIRST BIRN. Measurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study. J Digit Imaging 2006; 19: 140–147.

52.

Tustison

Cook

Holbrook

, et al. The ANTsX ecosystem for quantitative biological and medical imaging. Sci Rep 2021; 11: 9068.

53.

Vali

Aristova

Vakil

, et al. Semi-automated analysis of 4D flow MRI to assess the hemodynamic impact of intracranial atherosclerotic disease. Magn Reson Med 2019; 82: 749–762.

54.

Hoopes

Mora

Dalca

, et al. SynthStrip: skull-stripping for any brain image. NeuroImage 2022; 260: 119474.

55.

Bernier

Cunnane

Whittingstall

The morphology of the human cerebrovascular system. Hum Brain Mapp 2018; 39: 4962–4975.

56.

Clarke

Bell

Emir

, et al. NIfTI-MRS: a standard data format for magnetic resonance spectroscopy. Magn Reson Med 2022; 88: 2358–2370.

57.

Naressi

Couturier

Devos

, et al. Java-based graphical user interface for the MRUI quantitation package. MAGMA 2001; 12: 141–152.

58.

Weerasekera

Coelho

DRA

Ratai

E-M

, et al. Dose-dependent effects of transcranial photobiomodulation on brain temperature in patients with major depressive disorder: a spectroscopy study. Lasers Med Sci 2024; 39: 249.

59.

Goryawala

Sullivan

Maudsley

AA.

Effects of apodization smoothing and denoising on spectral fitting. Magn Reson Imaging 2020; 70: 108–114.

60.

Thrippleton

Parikh

Harris

, et al. Reliability of MRSI brain temperature mapping at 1.5 and 3 T. NMR Biomed 2014; 27: 183–190.

61.

Cady

D’Souza

Penrice

, et al. The estimation of local brain temperature by in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 1995; 33: 862–867.

62.

Zhu

Bashir

Ackerman

, et al. Improved calibration technique for in vivo proton MRS thermometry for brain temperature measurement. Magn Reson Med 2008; 60: 536–541.

63.

Wickham

. ggplot2. Wires Comput Stat 2011; 3: 180–185.

64.

Wickham

François

Henry

, et al. dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://dplyr.tidyverse.org, 2025.

65.

IBM Corp. IBM SPSS Statistics for Windows (Version 28.0) [Computer software]. IBM Corp, 2022.

66.

Benjamini

Hochberg

Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Ser B Methodol 1995; 57: 289–300.

67.

Arbeille

Herault

Fomina

, et al. Influences of thigh cuffs on the cardiovascular system during 7-day head-down bed rest. J Appl Physiol 1999; 87: 2168–2176.

68.

Boschert

Elmenhorst

Gauger

, et al. Sleep is compromised in −12° head down tilt position. Front Physiol 2019; 10: 00397.

69.

Whittle

Diaz-Artiles

Gravitational effects on carotid and jugular characteristics in graded head-up and head-down tilt. J Appl Physiol 2023; 134: 217–229.

70.

Lawley

Petersen

Howden

, et al. Effect of gravity and microgravity on intracranial pressure. J Physiol 2017; 595: 2115–2127.

71.

Arbeille

Provost

Zuj

, et al. Measurements of jugular, portal, femoral, and calf vein cross-sectional area for the assessment of venous blood redistribution with long duration spaceflight (Vessel Imaging Experiment). Eur J Appl Physiol 2015; 115: 2099–2106.

72.

Arbeille

Avan

Treffel

, et al. Jugular and portal vein volume, middle cerebral vein velocity, and intracranial pressure in dry immersion. Aerosp Med Hum Perform 2017; 88: 457–462.

73.

Arbeille

Zuj

Macias

, et al. Lower body negative pressure reduces jugular and portal vein volumes and counteracts the elevation of middle cerebral vein velocity during long-duration spaceflight. J Appl Physiol 2021; 131: 1080–1087.

74.

Tartière

Seguin

Juhel

, et al. Estimation of the diameter and cross-sectional area of the internal jugular veins in adult patients. Crit Care 2009; 13: R197.

75.

Jeon

Choi

Lee

, et al. Anatomical morphology analysis of internal jugular veins and factors affecting internal jugular vein size. Medicina (Mex) 2020; 56: 135.

76.

Holmlund

Eklund

Koskinen

L-OD

, et al. Venous collapse regulates intracranial pressure in upright body positions. Am J Physiol-Regul Integr Comp Physiol 2018; 314: R377–R385.

77.

Martin

Lee

SMC

Matz

, et al. Internal jugular pressure increases during parabolic flight. Physiol Rep 2016; 4: e13068.

78.

Mohammadyari

Gadda

Taibi

Modelling physiology of haemodynamic adaptation in short-term microgravity exposure and orthostatic stress on Earth. Sci Rep 2021; 11: 4672.

79.

Boschert

Gauger

Bach

, et al. External to internal cranial perfusion shifts during simulated weightlessness: results from a randomized cross-over trial. NPJ Microgravity 2023; 9: 25.

80.

Mokri

The Monro–Kellie hypothesis. Neurology 2001; 56: 1746–1748.

81.

Wilson

MH.

Monro-Kellie 2.0: the dynamic vascular and venous pathophysiological components of intracranial pressure. J Cereb Blood Flow Metab 2016; 36: 1338–1350.

82.

Sato

Fisher

Seifert

, et al. Blood flow in internal carotid and vertebral arteries during orthostatic stress. Exp Physiol 2012; 97: 1272–1280.

83.

Serrador

Freeman

Enhanced cholinergic activity improves cerebral blood flow during orthostatic stress. Front Neurol 2017; 8: 00103.

84.

Norsk

Asmar

Damgaard

, et al. Fluid shifts, vasodilatation and ambulatory blood pressure reduction during long duration spaceflight. J Physiol 2015; 593: 573–584.

85.

Muccio

Sun

Chu

, et al. The impact of body position on neurofluid dynamics: present insights and advancements in imaging. Front Aging Neurosci 2014; 16: 1454282.

86.

Valdueza

von Münster

Hoffman

, et al. Postural dependency of the cerebral venous outflow. Lancet 2000; 355: 200–201.

87.

Zhao

Xiao

, et al. Different responses of cerebral vessels to -30 degrees head-down tilt in humans. Aviat Space Environ Med 1999; 70: 674–680.

88.

Sun

Pavy-LeTraon

Gharib

[Change of cerebral blood flow velocity during 4 d head-down tilt bed rest]. Space Med Med Eng (Beijing) 2002; 15: 163–166.

89.

Jensen

Chahin

Amin

, et al. Qualitative slow blood flow in lower extremity deep veins on doppler sonography: quantitative assessment and preliminary evaluation of correlation with subsequent deep venous thrombosis development in a tertiary care oncology center. J Ultrasound Med 2017; 36: 1867–1874.

90.

Bateman

Barber

The relationship between cerebral blood flow and venous sinus pressure: can hyperemia induce idiopathic intracranial hypertension?

Fluids Barriers CNS 2021; 18(1): 5.

91.

Gadda

Taibi

Sisini

, et al. A new hemodynamic model for the study of cerebral venous outflow. Am J Physiol Heart Circ Physiol 2015; 308: H217–H231.

92.

Gadda

Taibi

Sisini

, et al. Validation of a hemodynamic model for the study of the cerebral venous outflow system using MR imaging and echo-color doppler data. Am J Neuroradiol 2016; 37: 2100–2109.

93.

Stolz

Fox

Hoffmann

, et al. Cranial venous outflow under lower body positive and negative pressure conditions and head-up and -down tilts. J Neuroimaging 2009; 19: 31–36.

94.

Stoquart-ElSankari

Lehmann

Villette

, et al. A phase-contrast MRI study of physiologic cerebral venous flow. J Cereb Blood Flow Metab 2009; 29: 1208–1215.

95.

Elias

Weber

Green

, et al. Systematic review of the use of ultrasound for venous assessment and venous thrombosis screening in spaceflight. NPJ Microgravity 2024; 10: 14.

96.

Sagirov

Sergeev

Shabrov

, et al. Postural influence on intracranial fluid dynamics: an overview. J Physiol Anthropol 2023; 42: 5.

97.

Simka

Czaja

Kowalczyk

Collapsibility of the internal jugular veins in the lateral decubitus body position: a potential protective role of the cerebral venous outflow against neurodegeneration. Med Hypotheses 2019; 133: 109397.

98.

Marshall-Goebel

Lee

SMC

Lytle

, et al. Jugular venous flow dynamics during acute weightlessness. J Appl Physiol 2024; 136: 1105–1112.

99.

Bloomfield

Ridings

Blocher

, et al. A proposed relationship between increased intra-abdominal, intrathoracic, and intracranial pressure. Crit Care Med 1997; 25: 496.

100.

Magnano

Belov

Krawiecki

, et al. Internal jugular vein narrowing and body mass index in healthy individuals and multiple sclerosis patients. Veins Lymphat 2014; 3(1). DOI: 10.4081/vl.2014.4632.

101.

Magnano

Belov

Krawiecki

, et al. Internal jugular vein cross-sectional area enlargement is associated with aging in healthy individuals. PLoS One 2016; 11: e0149532.

102.

Stankovic

Allen

Garcia

, et al. 4D flow imaging with MRI. Cardiovasc Diagn Ther 2014; 4: 173–192.

103.

Takehara

Sekine

Obata

Why 4D flow MRI? Real advantages. Magn Reson Med Sci 2022; 21: 253–256.

104.

Casas

Tunedal

Viola

, et al. Observer- and sequence variability in personalized 4D flow MRI-based cardiovascular models. Sci Rep 15, 1352 (2025). https://doi.org/10.1038/s41598-024-84390-4.

105.

Lee

SMC

Feiveson

Stein

, et al. Orthostatic intolerance after ISS and space shuttle missions. Aerosp Med Hum Perform 2015; 86: A54–A67.

106.

Jones

Overbey

Lacombe

, et al. Molecular and physiological changes in the SpaceX Inspiration4 civilian crew. Nature 2024; 632: 1155–1164.

107.

Crandall

Johnson

Convertino

, et al. Altered thermoregulatory responses after 15 days of head-down tilt. J Appl Physiol 1994; 77: 1863–1867.

108.

Lee

SMC

Williams

Schneider

SM.

Role of skin blood flow and sweating rate in exercise thermoregulation after bed rest. J Appl Physiol 2002; 92: 2026–2034.

109.

Dietrich

Busto

Halley

, et al. The importance of brain temperature in alterations of the blood-brain barrier following cerebral ischemia. J Neuropathol Exp Neurol 1990; 49: 486–497.

110.

Wang

Kim

Normoyle

, et al. Thermal regulation of the brain—an anatomical and physiological review for clinical neuroscientists. Front Neurosci 2016; 9: 00528.

111.

Tho

Hai

Duong

, et al. Numerical simulation of human head temperature: new data and comparison. J Adv Res Numer Heat Transf 2025; 30: 108–119.

112.

Nybo

Secher

Nielsen

Inadequate heat release from the human brain during prolonged exercise with hyperthermia. J Physiol 2002; 545: 697–704.

113.

Jia

Guo

Z-N

Jin

, et al. Venous sinus stenting improves cerebral autoregulation in a patient with venous sinus stenosis: a case report. BMC Neurol 2020; 20: 9.

114.

Agnati

Marcoli

Leo

, et al. Homeostasis and the concept of ‘interstitial fluids hierarchy’: relevance of cerebrospinal fluid sodium concentrations and brain temperature control (Review). Int J Mol Med 2017; 39: 487–497.

115.

Harris

Attwell

The energetics of CNS white matter. J Neurosci 2012; 32: 356–371.

116.

Soukup

Zauner

Doppenberg

EMR

, et al. The importance of brain temperature in patients after severe head injury: relationship to intracranial pressure, cerebral perfusion pressure, cerebral blood flow, and outcome. J Neurotrauma 2002; 19: 559–571.

117.

Tan

Liu

, et al. Measurement of healthy adult brain temperature using 1H magnetic resonance spectroscopy thermometry. Clin Neuroradiol 2025; 35: 159–164.

118.

Haveman

Sminia

Wondergem

, et al. Effects of hyperthermia on the central nervous system: what was learnt from animal studies? Int J Hyperthermia 2005; 21: 473–487.

119.

Wang

Normoyle

, et al. Brain temperature and its fundamental properties: a review for clinical neuroscientists. Front Neurosci 2014; 8: 307.

120.

Tidwell

Taylor

Collins

, et al. Longitudinal changes in cerebral perfusion, perivascular space volume, and ventricular volume in a healthy cohort undergoing a spaceflight analog. Am J Neuroradiol 2023; 44: 1026–1031.

121.

Laurie

Lytle

Pickering

, et al. Daily use of countermeasures to prevent headward fluid shifts throughout 30 days of strict head-down tilt bed rest. J Appl Physiol 2025; 139: 394–413.

122.

Petersen

Lawley

Lilja-Cyron

, et al. Lower body negative pressure to safely reduce intracranial pressure. J Physiol 2019; 597: 237–248.

123.

Caldwell

Halpern

Binsted

, et al. Cephalad fluid shift induced nasal tissue swelling during 70-day 6° head- down tilt in exerciser and control subjects, https://www.semanticscholar.org/paper/Cephalad-fluid-shift-induced-nasal-tissue-swelling-Caldwell-Halpern/390edea990a6d85b8720cdbb93c1d6d10e2e2ba5 (2014, accessed 18 October 2025).

124.

Laurie

Vizzeri

Taibbi

, et al. Effects of short-term mild hypercapnia during head-down tilt on intracranial pressure and ocular structures in healthy human subjects. Physiol Rep 2017; 5: e13302.

125.

Laurie

Lee

SMC

Macias

, et al. Optic disc edema and choroidal engorgement in astronauts during spaceflight and individuals exposed to bed rest. JAMA Ophthalmol 2020; 138: 165–172.

126.

Pavez Loriè

Baatout

Choukér

, et al. The future of personalized medicine in space: from observations to countermeasures. Front Bioeng Biotechnol 2021; 9: 739747.

127.

Daners

Knobloch

Soellinger

, et al. Age-specific characteristics and coupling of cerebral arterial inflow and cerebrospinal fluid dynamics. PLoS One 2012; 7: e37502.

128.

Seghier

Price

CJ.

Interpreting and utilising intersubject variability in brain function. Trends Cogn Sci 2018; 22: 517–530.

129.

Morgan

Thrippleton

Wardlaw

, et al. 4D flow MRI for non-invasive measurement of blood flow in the brain: a systematic review. J Cereb Blood Flow Metab 2021; 41: 206–218.

130.

Schmidt

Goodwin

TJ.

Personalized medicine in human space flight: using Omics based analyses to develop individualized countermeasures that enhance astronaut safety and performance. Metabolomics 2013; 9: 1134–1156.

131.

Kramer

Hasan

Gabr

, et al. Cerebrovascular effects of lower body negative pressure at 3T MRI: implications for long-duration space travel. J Magn Reson Imaging 2022; 56: 873–881.

132.

Watkins

Hargens

Seidl

, et al. Lower-body negative pressure decreases noninvasively measured intracranial pressure and internal jugular vein cross-sectional area during head-down tilt. J Appl Physiol 2017; 123: 260–266.

133.

Sun

X-Q

Yao

Y-J

Yang

C-B

, et al. Effect of lower-body negative pressure on cerebral blood flow velocity during 21 days head-down tilt bed rest. Med Sci Monit 2005; 11: CR1–CR5.

134.

Campbell

Charles

JB.

Historical review of lower body negative pressure research in space medicine. Aerosp Med Hum Perform 2015; 86: 633–640.

135.

Harris

Petersen

Weber

Reviving lower body negative pressure as a countermeasure to prevent pathological vascular and ocular changes in microgravity. NPJ Microgravity 2020; 6: 1–8.

136.

Rahill

Mulavara

George

, et al. Increasing fidelity in lunar and martian analogs for behavioral health and performance research. Front Space Technol 2025; 6: 1505823.

137.

Seidler

Stern

Basner

, et al. Future research directions to identify risks and mitigation strategies for neurostructural, ocular, and behavioral changes induced by human spaceflight: a NASA-ESA expert group consensus report. Front Neural Circuits 2022; 16: 876789.

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