Neurochemical Correlates of White Matter Hyperintensities in Cognitively Normal Older Adults
Poster No:
1210
Submission Type:
Abstract Submission
Authors:
Shaolin Yang1, Minjie Wu1, Olusola Ajilore2, Howard Aizenstein1, Anand Kumar2
Institutions:
1University of Pittsburgh, Pittsburgh, PA, 2University of Illinois at Chicago, Chicago, IL
First Author:
Co-Author(s):
Introduction:
White matter hyperintensities (WMH) on T2-FLAIR MR brain images is the traditional radiologic marker of cerebral small vessel disease (SVD). Pathological changes associated with WMH are heterogeneous including components of edema, gliosis, ischemia, and inflammation. However, conventional T2-FLAIR imaging is non-specific and does not differentiate these types of lesions. In this study, we evaluated the association between neurochemical measures and WMH in cognitively normal older adults. Findings from this study will provide insight into the heterogeneity of WMH.
Methods:
Our study consisted of 42 cognitively normal older adults (mean age=68.8 years, age range: 60-84 years, 26 female). Brain MRI and MRS were acquired on a Philips Achieva 3T scanner, including T1w MPRAGE, T2-FLAIR, single-voxel 1H-MRS with voxels placed at bilateral frontal white matter. Specifically, the 1H-MRS spectra were acquired from the frontal white matter left (FWM-L) and right (FWM-R) (2x1x2 cm3). Spectral quantification was carried out in LCModel (Provencher et al., 1993) using unsuppressed water signal for scaling. Only the metabolite concentrations with a Cramer-Rao Lower Bound (CRLB) less than 20% were included in the data analysis. WMH volume was quantified on T2-FLAIR images using a 2D U-Net machine learning method implemented and validated by our lab (Li et al., 2023). WMH was parcellated into deep WMH, periventricular WMH, and frontal WMH. These global and regional WMH volumes were normalized by intracranial volume and log-transformed for second level analyses. Regression analyses were performed to evaluate the association between neurochemical measures in frontal white matter and normalized white matter hyperintensities, controlling for age.
Results:
N-acetylaspartate (NAA) plus N-acetylaspartyl-glutamate (NAAG) in frontal WM was negatively associated with whole-brain normalized WMH (r = -0.403, p = 0.009), controlling for age (Fig. 1 top left). Myo-inositol (Ins) in the frontal WM is marginally positively associated with deep WMH (r = 0.282, p = 0.074) (Fig.1 top right) and frontal WMH (r = 0.294, p = 0.062) (data not shown). In addition, glutamate level in frontal WM is negatively associated with whole-brain WMH (r = -0.415, p = 0.016) (Fig. 1 bottom left) and periventricular WMH (r = -0.452, p = 0.008) (data not shown), controlling for age.
·Fig. 1. Significant associations between frontal WM neurochemical measures and WMH burden, controlling for age: NAA+NAAG (top left), Ins (top right), and Glutamate (bottom left).
Conclusions:
Our data reveal greater burden of WMH is associated with lower NAA level and greater Ins level. NAA is found almost exclusively in neurons. Reduced NAA + NAAG level in white matter reflects deficits in maintaining axonal-glial system (Moffett et al., 2007) and myelin synthesis (Chakraborty et al., 2001). Ins is primarily located in glial cells. Elevated Ins is believed to be a marker for gliosis and ongoing neuroinflammation (Heckova et al., 2022). Our findings provide insight into heterogeneity of WMH, suggesting SVD-related astrogliosis and axonal impairment (Llufriu et al., 2014).
Lifespan Development:
Aging 1
Novel Imaging Acquisition Methods:
MR Spectroscopy 2
Keywords:
Aging
Cerebrovascular Disease
Magnetic Resonance Spectroscopy (MRS)
1|2Indicates the priority used for review
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