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Altered Brain Morphometry in Cerebral Small Vessel Disease With Cerebral Microbleeds: An Investigation Combining
Jing Li1, Hongwei Wen2,3, Shengpei Wang4,5
1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Purpose:
The objective of this study was to evaluate whether altered gray matter volume (GMV) and white matter volume (WMV) are associated with the presence of cerebral microbleeds (CMBs) in cerebral small vessel disease (CSVD).
Materials And Methods:
In this study, we included 26 CSVD patients with CMBs (CSVD-c), 43 CSVD patients without CMBs (CSVD-n) and 39 healthy controls. All participants underwent cognitive assessment testing. Both univariate analysis and multivariate pattern analysis (MVPA) approaches were applied to investigate differences in brain morphometry among groups.
Results:
In univariate analysis, GMV and WMV differences were compared among groups using voxel-based morphometry (VBM) with diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL). Compared to healthy controls, the CSVD-c group and CSVD-n group showed significantly lower GMV than the control group in similar brain clusters, mainly including the right superior frontal gyrus (medial orbital), left anterior cingulate gyrus, right inferior frontal gyrus (triangular part) and left superior frontal gyrus (medial), while the CSVD-n group also showed significantly lower WMV in the cluster of the left superior frontal gyrus (medial). No significant GMV or WMV differences were found between the CSVD-c group and the CSVD-n group. Specifically, we applied the multiple kernel learning (MKL) technique in MVPA to combine GMV and WMV features, yielding an average of >80% accuracy for three binary classification problems, which was a considerable improvement over the individual modality approach. Consistent with the univariate analysis, the MKL weight maps revealed default mode network and subcortical region damage associated with CSVD compared to controls. On the other hand, when classifying the CSVD-c group and CSVD-n group in the MVPA analysis, we found that some WMVs were highly weighted regions (left olfactory cortex and right middle frontal gyrus), which hinted at the presence of different white matter alterations in the CSVD-c group.
Conclusion:
Our findings not only suggested that the localized alterations in GMV and WMV appeared to be associated with the pathophysiology of CSVD but also indicated that altered brain morphometry could be a potential discriminative pattern to detect CSVD at the individual level.
Insights
Altered gray matter volume (GMV) and white matter volume (WMV) are linked to cerebral small vessel disease (CSVD) and its microbleeds. Brain morphometry patterns may help detect CSVD in individuals.
Area of Science:
- Neurology
- Neuroimaging
- Brain Anatomy
Background:
- Cerebral small vessel disease (CSVD) is a common condition affecting brain vasculature.
- Cerebral microbleeds (CMBs) are a marker of small vessel disease severity.
- Understanding the relationship between brain structure and CMBs is crucial for diagnosis and management.
Purpose of the Study:
- To investigate the association between altered gray matter volume (GMV) and white matter volume (WMV) and the presence of CMBs in CSVD patients.
- To determine if brain morphometry can differentiate between CSVD patients with and without CMBs.
- To explore the potential of advanced neuroimaging analysis techniques for CSVD detection.
Main Methods:
- Inclusion of three groups: CSVD patients with CMBs (CSVD-c), CSVD patients without CMBs (CSVD-n), and healthy controls.
- Application of voxel-based morphometry (VBM) for univariate analysis of GMV and WMV differences.
- Utilizing multivariate pattern analysis (MVPA) with multiple kernel learning (MKL) to combine GMV and WMV data for classification.
Main Results:
- Both CSVD groups showed significantly reduced GMV compared to controls in frontal and cingulate regions.
- The CSVD-n group exhibited reduced WMV in the left superior frontal gyrus (medial).
- MVPA achieved >80% accuracy in classifying groups, highlighting the discriminative power of combined GMV and WMV features, with specific white matter alterations identified in the CSVD-c group.
Conclusions:
- Localized alterations in GMV and WMV are associated with the pathophysiology of CSVD.
- Altered brain morphometry patterns show potential as a discriminative biomarker for detecting CSVD at an individual level.
- The findings support the use of advanced neuroimaging techniques for a deeper understanding of CSVD and its subtypes.
