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BOLD-fMRI Lag-Time Mapping: A Novel Biomarker for Early-stage Cerebral Small Vessel Disease

Poster No:

2596 

Submission Type:

Abstract Submission 

Authors:

Wan-Ting Hsieh1, Pei-Lin Lee2, Yi-Hua Huang1, Nai-Fan Chi3, Liang-Kung Chen2,4, Ching-Po Lin1,5,6, Chih-Ping Chung2,3,7, Kun-Hsien Chou1,8

Institutions:

1Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan, 2Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, 3Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, 4Taipei Municipal Gan-Dau Hospital (managed by Taipei Veterans General Hospital), Taipei, Taiwan, 5Brain Research Center and National Yang Ming Chiao Tung University, Taipei, Taiwan, 6Deptartment of Education and Research, Taipei City Hospital, Taipei, Taiwan, 7School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 8Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

First Author:

Wan-Ting Hsieh  
Institute of Neuroscience, National Yang Ming Chiao Tung University
Taipei, Taiwan

Co-Author(s):

Pei-Lin Lee  
Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University
Taipei, Taiwan
Yi-Hua Huang  
Institute of Neuroscience, National Yang Ming Chiao Tung University
Taipei, Taiwan
Nai-Fan Chi  
Department of Neurology, Neurological Institute, Taipei Veterans General Hospital
Taipei, Taiwan
Liang-Kung Chen  
Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University|Taipei Municipal Gan-Dau Hospital (managed by Taipei Veterans General Hospital)
Taipei, Taiwan|Taipei, Taiwan
Ching-Po Lin  
Institute of Neuroscience, National Yang Ming Chiao Tung University|Brain Research Center and National Yang Ming Chiao Tung University|Deptartment of Education and Research, Taipei City Hospital
Taipei, Taiwan|Taipei, Taiwan|Taipei, Taiwan
Chih-Ping Chung  
Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University|Department of Neurology, Neurological Institute, Taipei Veterans General Hospital|School of Medicine, National Yang Ming Chiao Tung University
Taipei, Taiwan|Taipei, Taiwan|Taipei, Taiwan
Kun-Hsien Chou  
Institute of Neuroscience, National Yang Ming Chiao Tung University|Brain Research Center, National Yang Ming Chiao Tung University
Taipei, Taiwan|Taipei, Taiwan

Introduction:

Cerebral Small Vessel Disease (CSVD) represents a spectrum of pathological changes in the brain's microvasculature, playing a pivotal role in dementia, disable and stroke in older people. Due to challenges in assessing the pathology within the brain's microvasculature, the diagnosis of CSVD is currently reliant on the identification of diverse ischemic and hemorrhagic lesions. These include white matter hyperintensities, lacunes, cerebral microbleeds, and dilated perivascular spaces. This diagnostic approach primarily identifies CSVD during later stages characterized by the emergence of brain parenchymal lesions. It lacks the ability to directly capture the underlying neurovascular abnormalities or dysfunctions. To address this research gap, the present study employs rs-fMRI, with a specific focus on BOLD lag-time mapping. This non-invasive technique facilitates a comprehensive examination of neurovascular blood flow dynamics, specifically the transit time, offering potential insights into microvasculature function. The study's objective is to investigate whether this approach can effectively differentiate between participants with and without CSVD, serving as a measurement of brain microvasculature at an early stage of CSVD.

Methods:

This study evaluated 101 asymptomatic CSVD patients and 539 healthy controls (HC). We processed rs-fMRI data using a standard pre-processing pipeline [1]. Various quantitative indices pertaining to vascular transit-time were calculated following the previously delineated methodology [1]. This involving extracting the global mean signal as an initial reference and further iteratively shifted by ±0.5 seconds to map temporal lag properties up to a ±7.0 sec shift. Afterwards, the arterial transit time was calculated by averaging lag-time greater than the peak location of the histogram, indicating upstream locations from this peak. Conversely, venous transit time was calculated using downstream voxels. The sum of these times provided the global transit time. We employed two separate ANCOVA models to address distinct research inquiries. The first model evaluated significant differences in transit times between CSVD and HC groups. The second model investigated the association between transit times and CSVD severity based on Fazekas scores [2]. Through the use of bidirectional linear contrasts, we delineated trends in the alterations of these indices across different stages of CSVD.

Results:

1. Transit Time Differences Between CSVD and HC Groups (Fig. 1):
A comparison between CSVD and HC revealed a notable increase in arterial transit time for CSVD group, reaching statistical significance (p=0.045). No significant differences were detected in arterial or venous transit times
2. Association Between Transit Times and CSVD Severity (Fig. 2):
Analysis spanning the spectrum of CSVD severity revealed notable trends in transit times. A discernible elevation in arterial (p-trend=0.003), venous (p-trend=0.031), and global transit times (p-trend=0.005) correlated with increasing CSVD severity.
Supporting Image: Figure_1.png
   ·Fig. 1: The difference in arterial and venous transit time between CSVD and non-CSVD groups.
Supporting Image: Figure_2.png
   ·Fig. 2: The histograms of the severity of CSVD with respect to different transit times.
 

Conclusions:

Our findings illuminate the intricate relationship between brain CSVD lesions and microvascular transit times. A significant increase in arterial transit time was observed in CSVD, establishing it as a potential non-invasive biomarker for CSVD detection. Furthermore, the ascending trends in transit times in relation to CSVD severity underscore their potential as indicators of disease progression. These results suggest that BOLD-fMRI-derived transit times could serve as valuable tools in the early detection and monitoring of CSVD, contributing to better clinical outcomes through timely interventions. Future studies could prioritize longitudinal data to better understand the changes in transit times throughout the disease progression. This could involve the inclusion of symptomatic CSVD patients, validating the tool for clinical applications and potentially unveiling new insights into the pathophysiological processes associated with CSVD.

Lifespan Development:

Aging

Novel Imaging Acquisition Methods:

BOLD fMRI 2

Physiology, Metabolism and Neurotransmission :

Cerebral Metabolism and Hemodynamics 1

Keywords:

Aging
Cerebrovascular Disease
Other - Cerebral Small Vessel Disease

1|2Indicates the priority used for review

Provide references using author date format

[1] Aso, Toshihiko, et al. "A venous mechanism of ventriculomegaly shared between traumatic brain injury and normal ageing." Brain 143.6 (2020): 1843-1856.
[2] Fazekas, Franz, et al. "MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging." American Journal of Neuroradiology 8.3 (1987): 421-426.