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Updated: Aug 30, 2025
Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
Published on: May 31, 2024
Perfusion heterogeneity of cerebral small vessel disease revealed via arterial spin labeling MRI and machine learning
Weizhao Lu1, Chunyan Yu2, Liru Wang2
1Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, China; School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China.
Insights
Researchers identified three cerebral small vessel disease (CSVD) subtypes with distinct cerebral blood flow (CBF) patterns using machine learning. These subtypes show varied clinical and imaging features, aiding precision diagnosis.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Cerebral small vessel disease (CSVD) is linked to altered cerebral perfusion, but patterns vary significantly among patients.
- Understanding these perfusion heterogeneities is crucial for diagnosing and managing CSVD.
Purpose of the Study:
- To identify distinct subtypes of CSVD based on cerebral blood flow (CBF) patterns.
- To characterize the clinical and imaging phenotypes associated with these identified CSVD subtypes.
Main Methods:
- Employed a semi-supervised machine learning algorithm on arterial spin label MRI data from 121 CSVD patients and 53 controls.
- Utilized statistical analyses to compare CBF, clinical measures, and gray/white matter volumes across subtypes and controls.
- Performed correlation analyses to link clinical measures with altered CBF within each subtype.
Main Results:
- Identified three CSVD subtypes with unique regional CBF alterations: Subtype 1 (temporal/parietal/occipital), Subtype 2 (right/left hemisphere), and Subtype 3 (posterior/anterior).
- Subtypes differed significantly in gender, lacunes, white matter hyperintensity, and overall CSVD burden.
- Subtype 3 showed a correlation between decreased CBF and higher CSVD burden.
Conclusions:
- Distinct CSVD subtypes with unique perfusion patterns and associated clinical/imaging features have been identified.
- These findings enhance the understanding of brain perfusion abnormalities in CSVD.
- The identified subtypes may facilitate more precise diagnosis and personalized treatment strategies for CSVD.
Abstract:
Cerebral small vessel disease (CSVD) is associated with altered cerebral perfusion. However, global and regional cerebral blood flow (CBF) are highly heterogeneous across CSVD patients. The aim of this study was to identify subtypes of CSVD with different CBF patterns using an advanced machine learning approach. 121 CSVD patients and 53 healthy controls received arterial spin label MRI, T1 structural MRI and clinical measurements. Regional CBF were used to identify distinct perfusion subtypes of CSVD via a semi-supervised machine learning algorithm. Statistical analyses were used to explore alterations in CBF, clinical measures, gray and white matter volume between healthy controls and different subtypes of CSVD. Correlation analysis was used to assess the association between clinical measures and altered CBF in each CSVD subtype. Three subtypes of CSVD with distinct CBF patterns were found. Subtype 1 showed decreased CBF in the temporal lobe and increased CBF in the parietal and occipital lobe. Subtype 2 exhibited decreased CBF in the right hemisphere of the brain, and increased CBF in the left cerebrum. Subtype 3 demonstrated decreased CBF in the posterior part of the brain, and increased CBF in anterior part of the brain. The three subtypes also differed significantly in gender (p = 0.005), the proportion of subjects with lacune (p = 0.002), with periventricular white matter hyperintensity (p = 0.043), and CSVD burden score (p = 0.048). In subtype 3, it was found that widespread decreased CBF was correlated with total CSVD burden score (r = -0.324, p = 0.029). Compared with healthy controls, the three CSVD subtypes also showed distinct volumetric patterns of white matter. The current results associate different subtypes with different clinical and imaging phenotypes, which can improve the understanding of brain perfusion alterations of CSVD and can facilitate precision diagnosis of CSVD.
