Exploring the link between white matter hyperintensities volume and cerebral blood flow in normal-appearing white matter using dual post label delays of arterial spin labeling

doi:10.21203/rs.3.rs-5285915/v1

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Research Article

Exploring the link between white matter hyperintensities volume and cerebral blood flow in normal-appearing white matter using dual post label delays of arterial spin labeling

https://doi.org/10.21203/rs.3.rs-5285915/v1

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Objective

To explore the link between white matter hyperintensities (WMH) volume and cerebral blood flow (CBF) in normal-appearing white matter (NAWM) using dual-post label delays (PLD) arterial spin labeling (ASL) techniques.

Methods

Imaging and clinical data were analyzed from 83 patients with cerebral small vessel disease (CSVD) collected between November 2022 and July 2023. WMH volume was segmented using ITK-SNAP software. The difference in CBF between the two PLDs, ΔCBF, was calculated as CBF(PLD = 2.5s) - CBF(PLD = 1.6s). CBF values for both PLD times (PLD = 1.6s, PLD = 2.5s) and ΔCBF were quantified for NAWM regions.

Results

Pearson correlation analysis revealed significant associations between WMH volume and both CBF (PLD = 2.5s) (r = 0.298, P < 0.05) and ΔCBF (r = 0.287, P < 0.05) in NAWM. When WMH volume was stratified into tertiles, significant differences in CBF (PLD = 2.5s) and ΔCBF were observed among the tertile groups (P < 0.05). Multivariate regression analysis showed that CBF (PLD = 2.5s) and ΔCBF in NAWM were independently associated with WMH volume after adjusting for age, sex, and vascular risk factors (P < 0.05). The β coefficient for CBF (PLD = 2.5s) was 0.258 (95% CI: 0.063, 0.453), and for ΔCBF, it was 0.327 (95% CI: 0.109, 0.545).

Conclusions

Elevated WMH volume is linked to increased CBF (PLD = 2.5s) and ΔCBF in NAWM, suggesting that prolonged arterial transit time may contribute to WMH development in CSVD patients.

White matter hyperintensities (WMHs), also referred to as leukoaraiosis, are widely recognized as a hallmark of cerebral small vessel disease (CSVD)(1). These lesions are highly prevalent, particularly in the elderly population, and contribute significantly to the global burden of cerebrovascular and neurodegenerative diseases, including dementia, stroke, and Alzheimer's disease(2–4). Despite their clinical importance, the pathophysiological mechanisms underlying the development and progression of WMHs remain inadequately understood. A comprehensive understanding of these mechanisms is critical for the development of effective prevention and intervention strategies.

Emerging evidence suggests that reduced cerebral blood flow (CBF) may play a pivotal role in the pathogenesis of WMHs. Several studies(5–7), including those by Wong et al.(5), have reported decreased CBF not only in WMH regions but also in normal-appearing white matter (NAWM), indicating that CBF reduction may precede the visible formation of WMHs. Conversely, other research has challenged the notion that CBF alterations are the primary driver of WMHs, proposing instead that these lesions may arise from a complex interplay of factors, including vascular dysfunction and impaired cerebral autoregulation(8).

One potential explanation for these conflicting findings is that WMHs are not static entities but rather evolve over time through a dynamic process. In the early stages of WMHs development, the primary change may be a prolongation of arterial arrival time (ATT) rather than an outright reduction in CBF. This hypothesis is supported by studies, such as those by Zhu et al.(9), which have demonstrated an age-related increase in ATT, correlating with the heightened risk of CSVD in older adults. The extension of ATT could reflect early microvascular changes(10) that eventually contribute to the formation of WMHs.

Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for the quantification of CBF without the need for contrast agents. By utilizing dual post-labeling delay (PLD) times, ASL can provide estimates of ATT(6, 11), offering a window into the early microcirculatory alterations associated with WMHs. Recent research has begun to explore the utility of dual-PLD ASL in the context of cerebrovascular diseases(12, 13), yet studies specifically targeting the evaluation of WMHs using this approach remain sparse.

Therefore, the current study aims to bridge this gap by employing dual-PLD ASL to assess the relationship between ATT and WMHs. By investigating this association, we seek to enhance our understanding of the early vascular changes that contribute to WMHs formation and progression, ultimately informing strategies for early detection and prevention of CSVD-related complications.

Participants

This study was approved by the Ethics Committee of Zhejiang Provincial Tongde Hospital (Approval No. 2024 Research 097-JY), and informed consent was obtained from all participants. Patients were recruited from our hospital between November 2022 and July 2023, with clinical and imaging findings fully meeting the diagnostic criteria for cerebral small vessel disease (CSVD). The inclusion criteria were: (1) age ≥ 40 years; (2) MRI revealing typical imaging markers of CSVD according to standards for reporting vascular changes on neuroimaging(1), specifically the presence of white matter hyperintensities (WMHs); (3) presence of at least one cerebrovascular risk factor. The exclusion criteria included: (1) contraindications to MRI; (2) a diagnosis of secondary WMHs caused by metabolic or infectious encephalopathy; (3) history of head trauma, or the presence of a known intracranial tumor or vascular malformation; (4) severe stenosis or occlusion of major supplying vessels as confirmed by MRA and carotid ultrasound.

Protocol

All MRI scans were performed using a 1.5T Siemens MAGNETOM Area scanner with a 20-channel head coil. Patients were positioned supine on the MRI examination bed, and their heads were immobilized in the head coil using foam pads to minimize movement. Noise-canceling headphones were used to reduce patient discomfort during scanning. The scanning direction was head-to-foot, and the sequences included routine MRI protocols (T1-weighted imaging, T2-weighted imaging, T2 FLAIR), 3D time-of-flight magnetic resonance angiography (3D-TOF-MRA), 3D T1-weighted imaging, and dual PLD ASL with PLD = 1.6 s and PLD = 2.5 s. Scanning parameters for ASL are with repetition time of 4600 ms, echo time of 17.4 ms, filed of view of 24×24 cm², slice thickness of 5 mm.

Image Analysis

White Matter Hyperintensity Volume

T2 FLAIR images were exported in DICOM format and subsequently converted to NIFTI format. The WMHs were manually segmented using ITK-SNAP software, generating WMHs labels and three-dimensional regions of interest (ROIs). The total volume of WMHs was then calculated based on these segmentations.

Cerebral Blood Flow (CBF) and ΔCBF

The ASL sequence was processed using Siemens' post-processing software to generate cerebral blood flow (CBF) maps, with the M0 image approximated by the control image(14, 15). The difference in CBF between the two post-labeling delay times, ΔCBF, was defined as CBF(PLD = 2.5s) - CBF(PLD = 1.6s). The 3D T1-weighted images, CBF, and ΔCBF maps were exported in DICOM format and converted to NIFTI format. Using Statistical Parametric Mapping (SPM) software, the 3D T1-weighted structural images were segmented to obtain white matter (WM) labels. NAWM labels were obtained by subtracting the WMH label from the WM label. Then, NAWM labels were registered to the CBF and ΔCBF images. Finally, the CBF and ΔCBF values for NAWM regions were calculated.

Statistical analyses

Data analysis was conducted using SPSS version 20.0. Continuous variables with a normal distribution were expressed as mean ± standard deviation (x̅±s), while non-normally distributed data were expressed as median (IQR). Categorical variables were presented as frequency and percentage (n, %). The differences in CBF and ΔCBF among WMH volume tertiles were calculated using analysis of variance. The relationship between CBF and ΔCBF values in NAWM regions and WMHs volume was assessed using Pearson rank correlation analysis and multivariate analysis. Statistical significance was set at P < 0.05.

A total of 83 patients with CSVD were included in the study, consisting of 37 males (44.6%) with an average age of 64.90 ± 13.47 years. Among these patients, 41 (49.4%) had hypertension, 12 (14.5%) had diabetes, 13 (15.7%) had hyperlipidemia, 8 (9.6%) had coronary artery disease, and 17 (20.5%) were smokers. The mean CBF in NAWM was 23.85 ± 6.64 ml/100 g/min for PLD = 1.6s and 15.65 ± 4.21 ml/100 g/min for PLD = 2.5s, resulting in a mean ΔCBF of -8.20 ± 5.69 ml/100 g/min.

Univariate Analysis of Factors Associated with WMH Volume

Pearson correlation analysis revealed a significant correlation between WMH volume and both CBF (PLD = 2.5s) in NAWM (r = 0.298, P < 0.05) and ΔCBF in NAWM (r = 0.287, P < 0.05). In contrast, no significant correlation was found between WMH volume and CBF (PLD = 1.6s) in NAWM (P > 0.05). When WMH volume was categorized into tertiles, significant differences were observed in CBF (PLD = 2.5s) in NAWM and ΔCBF in NAWM across the tertile groups (P < 0.05, see Table 1 and Fig. 1). However, no significant differences were found in sex, age, vascular risk factors, or CBF (PLD = 1.6s) in NAWM among the groups (P > 0.05).

Table 1

Baseline clinical and imaging characteristics among participants according to WMH volume tertiles.
	WMH volume Tertiles			P value
	Q1(n = 28)	Q2(n = 28)	Q3(n = 27)	P value
Male, n(%)	15 (53.6)	12 (42.9)	10 (37.0)	0.456
Age, years	65.61 ± 14.22	61.07 ± 13.06	68.15 ± 12.57	0.142
Hypertension, n(%)	13 (46.4)	14 (50.0)	14 (51.9)	0.919
Diabetes mellitus, n(%)	4 (14.3)	5 (17.9)	3 (11.1)	0.776
Hyperlipidemia, n(%)	7 (25.0)	2 (7.1)	4 (14.8)	0.183
Coronary Heart Disease, n(%)	2 (7.1)	2 (7.1)	4 (14.8)	0.54
Current smoking, n(%)	9 (32.1)	5 (17.9)	3 (11.1)	0.141
WMH volume, ml	3.65 ± 1.21	8.40 ± 2.42	25.44 ± 11.34	< 0.001
CBF in NAWM, ml/100 g/min
PLD = 1.6s	24.95 ± 5.79	23.38 ± 7.01	23.19 ± 7.15	0.562
PLD = 2.5s	15.34 ± 3.46	14.29 ± 2.85	17.38 ± 5.46	0.020
ΔCBF	-9.61 ± 4.71	-9.09 ± 5.27	-5.81 ± 6.42	0.026
WMH, white matter hyperintensities; CBF, cerebral blood flow; NAWM, normal-appearing white matter; PLD, post label delays.

Multivariate Analysis of Factors Associated with WMH Volume

After adjusting for sex, age, and vascular risk factors, multivariate analysis demonstrated that both CBF (PLD = 2.5s) in NAWM and ΔCBF in NAWM were independently associated with WMH volume. The β coefficient for CBF (PLD = 2.5s) was 0.258 (95% CI: 0.063, 0.453), and for ΔCBF, it was 0.327 (95% CI: 0.109, 0.545), both with P < 0.05. Detailed results are presented in Table 2.

Table 2

Multivariate analysis for factors associated with WMH volume.
	B value	P value	95.0% Confidence Interval for B
	B value	P value	Lower Bound	Upper Bound
Male	-0.026	0.790	-0.219	0.167
Age	0.027	0.778	-0.163	0.218
Hypertension	-0.102	0.298	-0.297	0.092
Diabetes mellitus	0.053	0.558	-0.128	0.234
Current smoking	-0.100	0.326	-0.300	0.101
Hyperlipidemia	0.009	0.923	-0.175	0.193
Coronary heart Disease	0.027	0.760	-0.150	0.205
CBF (PLD = 2.5s) in NAWM	0.258	0.010	0.063	0.453
ΔCBF in NAWM	0.327	0.004	0.109	0.545
WMH, white matter hyperintensities; CBF, cerebral blood flow; NAWM, normal-appearing white matter; PLD, post label delays.

This study utilized dual-PLD ASL technology to investigate the relationship between WMH volume and CBF changes, showing that larger WMH volumes were associated with increased CBF (PLD = 2.5s) and ΔCBF in NAWM. However, no significant correlation was observed between CBF and WMH volume at the shorter PLD (PLD = 1.6s). This finding diverges from previous studies, which commonly link larger WMH volumes to reduced CBF, with decreased blood flow often considered a key contributor to WMH formation. One plausible explanation for this discrepancy is that WMH development is not a static process but evolves dynamically over time. In the early stages, prolonged arterial transit time (ATT) may play a more crucial role than absolute reductions in CBF. Consequently, although CBF did not show significant changes at PLD = 1.6s, the increase in CBF at PLD = 2.5s might reflect early hemodynamic adjustments related to microvascular changes, particularly those involving prolonged ATT.

While earlier studies have often emphasized low CBF as a driver of WMH progression, this remains a topic of debate. For example, Kang P. et al. (8) found no significant association between CBF and WMH volume but did report a correlation between WMHs and increased oxygen extraction fraction. Another study (16) investigating different severities of WMHs found that mild WMHs (3.59–6.40 mL) were associated with increased CBF. Similarly, Lu W. et al. (17), using machine learning to classify CSVD subtypes, observed elevated CBF in type I, supporting the notion that early WMH formation may involve compensatory mechanisms such as ATT prolongation and increased oxygen extraction fraction. These findings suggest that increased CBF in the NAWM might represent a compensatory response in the early phases of WMH development.

Furthermore, the significant association between ΔCBF and WMH volume observed in this study further supports the role of ATT prolongation in WMH formation. ΔCBF, reflecting blood flow differences between PLD = 1.6s and PLD = 2.5s, can be interpreted as an indirect measure of ATT. The correlation between ΔCBF and WMH volume suggests that prolonged ATT may lead to microvascular changes in white matter, which in turn contribute to WMH progression. Therefore, ΔCBF may serve as a sensitive biomarker of early WMH pathology, potentially offering more detailed pathophysiological insights than CBF measured at a single PLD.

Nevertheless, this study has several limitations. First, the small sample size and single-center design may restrict the generalizability of the findings. Additionally, the cross-sectional nature of the study limits the ability to establish a causal relationship between CBF changes and WMH development. Longitudinal studies are needed to track the evolution of CBF alterations over time and their role in WMH progression. Finally, while this study focused on the association between CBF and WMH volume, other important factors such as vascular structural abnormalities, cerebrospinal fluid dynamics, and metabolic changes were not extensively explored. Future research should incorporate these factors to provide a more comprehensive understanding of WMH pathophysiology.

In conclusion, our findings suggest that increased WMH volume is associated with higher CBF (PLD = 2.5s) in NAWM, indicating that ATT prolongation may play a critical role in WMH development. This study highlighted the potential importance of ATT as a marker of early WMH-related microvascular dysfunction. ΔCBF, as a proxy for ATT, holds promise as a valuable tool for the early detection of WMH-related changes, providing new perspectives on the pathophysiology of WMHs and offering insights into the potential use of dual-PLD ASL in the clinical management of CSVD.

WMH white matter hyperintensities

CBF cerebral blood flow

NAWM normal-appearing white matter

PLD post label delays

ASL arterial spin labeling

CSVD cerebral small vessel disease

ATT arterial arrival time

ROI regions of interest

WM white matter

IQR inter quartile range

Acknowledgements

None

Funding

This study was supported by grant 2022KY707 and 2024KY866 from the Medical and Health Science and Technology Program of Zhejiang Provincial Health Commission of China; by grant 2024ZR048 from Zhejiang Province Traditional Chinese Medicine Science and Technology Project.

Data availability

No datasets were generated or analysed during the current study.

Ethical approval/Informed consent

The study was approved by the institutional Review Board of Tongde Hospital, Zhejiang Province, with a waiver of the requirement for informed consent (2024-097-JY). This study was performed in accordance with the principles of the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Disclosures

None

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You are reading this latest preprint version

Exploring the link between white matter hyperintensities volume and cerebral blood flow in normal-appearing white matter using dual post label delays of arterial spin labeling

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

Participants

Protocol

Image Analysis

White Matter Hyperintensity Volume

Cerebral Blood Flow (CBF) and ΔCBF

Statistical analyses

Results

Univariate Analysis of Factors Associated with WMH Volume

Multivariate Analysis of Factors Associated with WMH Volume

Discussion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1