Introduction

Perivascular space (PVS, also known as Virchow-Robins space) is an immunological space that are normally microscopic but can be seen on MRI images only when dilated.[1] These spaces that act as a clearance of metabolic waste and fluid from the brain parenchyma may get enlarged when there is Amyloid - Beta deposition due to the BBB dysfunction. [2,3] Alzheimer’s disease (AD) is a progressive neurodegenerative disease that progress from incipient cognitive impairment to severe cognitive damage and is the most common cause of dementia in elderly people predominant in females. [4,5,6] There is no cure and no effective treatment so far. Various neuropathological studies have demonstrated that the frequency and severity of white matter PVS is greater in AD than that in controls. This was associated with access of amyloid-beta protein in the brain. [7] Recently, enlarged PVS (EPVS) density has been found to be related to the early diagnosis of mild cognitive impairment. Although EPVS with advancing age and disease pathologies have already been reported previously, characteristics of the EPVS density in AD are not well understood yet. This study also reflect if the quantitative method is a reliable tool in assessment of EPVS that is less time consuming and doesn’t require the intervention of an expert during the classification /retrival phase. It also investigated the risk factors associated with the severity of EPVS in AD patients.

Materials and methods:

Structural 3.0T MR imaging data were acquired in this retrospective study from 84 participants including 44 patients with Alzheimer’s disease (male/female = 22/22) and 40 healthy controls (male/female = 22/18). All the patient’s clinical and cranial MRI information were obtained with vascular risk factors including their age and sex. MRI images were acquired using 3.0T scanner (Signa, HDxt, GE, USA) with an 8-channel array head coil. The imaging protocols included T2WI along with its corresponding T2FLAIR. We acquired the T2WI with total of 23 axial slices with the thickness of 5mm, and 0 mm gap. The other parameters included TR/TE= 4680/105.34 ms, echo-train length = 32, FOV = 240mm,image size = 512 X 512, matrix size= 384 X 384 . The total acquisition time for T2WI is about 2-3 minutes. We manually counted and rated the frequency of EPVS in deep white matter and Basal ganglia level according to the method of rating scale [8]. T2FLAIR was more important to distinguish PVS with any other white matter lesions. The severity of EPVS was evaluated by using semi-quantitative and quantitative methods in the white matter and basal ganglia regions. Two neuroradiologists blinded to each other’s rating and not involving in initial testing scale and tested the PVS rating scale by T2-weighted MR scans (T1 weighted and FLAIR was also available). We, then , automatically identified EPVS in brain MRI for white matter and basal ganglia using Matlab and also quantified its density in AD patient and controls. All the patients' original images were processed in FSL that performed the skull stripping followed by tissue segmentation. Then, we generated regions of interest (ROI) masks. Finally, VRS counts and volumes, brain tissue volumes, and head circumference were calculated. All the statistical analysis were performed using SPSS (version 23.0). T-test was used to compare the age. We performed a 2-tailed unpaired student t-tests to determine whether there was a statistically significant difference between the EPVS in the healthy controls and AD patients according to their densities. We also evaluated EPVS according to the lobes that had EPVS. Kappa statistics was performed to calculate the intra and inter-rater agreement in white matter and basal ganglia in the brain and pearsons’ chi square test was used for age, sex and other risk factors. And P<0.05 were considered as statistically significant.

Result:

Hypertension and dyslipidemia were the risk factors for the development of the disease which showed the P<0.05.The frequency of EPVS was more on the frontal lobe than any other lobes. Overall kappa statistic measurement of agreement for intra rater agreement was strong but the level of agreement for inter- rater kappa values were moderate to weak which is shown below in the table. And we found that the EPVS was more frequent in the frontal lobe than compared to any other lobes. The density was higher in case group than in the controls.

Conclusion:

From our study, we concluded that the EPVS density on 3.0T MR imaging was found to be significantly higher in AD patient than in the healthy controls. So, EPVS density could be used as an indicator in the assessment of dilated PVS in Alzheimer’s disease. We also concluded that hypertension and dyslipidemia are the risk factors for the disease. These finding may suggest us to screen the patients for the early diagnosis or prevention of the disease.

Acknowledgements

This study was supported by the National Key Research and Development Program of China (2016YFC0100300), National Natural Science Foundation of China (81471631, 81771810 and 81171317), the 2011 New Century Excellent Talent Support Plan of the Ministry of Education, China (NCET-11-0438), the Fundamental Research Funds for the Central Universities (xjj2018265), the Fundamental Research Funds of the First Affiliated Hospital of Xi'an Jiaotong University (2017QN-09).

References

1. Hirabuki N, Fujita N, Fujii K, Hashimoto T, Kozuka T. MR ap- pearance of Virchow-Robin spaces along lenticulostriate arteries: spin-echo and two-dimensional fast low-angle shot imaging. AJNR Am J Neuroradiol 1994;15:277–281 2. Zhang GS, Tian Y, Huang JY, Tao RR, Liao MH, Lu YM, Ye WF, Wang R, Fukunaga K, Lou YJ, Han F (2013) The gamma-secretase blocker DAPT reduces the permeability of the blood-brain barrier by decreasing the ubiquitina- tion and degradation of occludin during permanent brain ischemia. CNS Neurosci Ther 19, 53-60. 3. Zhiyou Cai∗, Pei-Feng Qiao, Cheng-Qun Wan, Min Cai, Nan-Kai Zhou and Qin Li Role of Blood-Brain Barrier in Alzheimer’s Disease Department of Neurology, Chongqing General Hospital, Chongqing, Chongqing, China Journal of Alzheimer’s Disease 63 (2018) 1223–1234 4. Li R, Singh M. Sex differences in cognitive impairment and Alzheimer’s disease. Front Neuroendocrinol 2014;35:385–403 5. Barnes LL, Wilson RS, Schneider JA, et al. Gender, cognitive decline, and risk of AD in older persons. Neurology 2003;60:1777–81 6. Jessica R. Filon, BS, Anthony J. Intorcia, BS, Lucia I. Sue, BS, Elsa Vazquez Arreola, BS, Jeffrey Wilson, PhD, Kathryn J. Davis, BS, Marwan N. Sabbagh, MD, Christine M. Belden, PsyD, Richard J. Caselli, MD, Charles H. Adler, MD, PhD, Bryan K. Woodruff, MD, Steven Z. Rapscak, MD, Geoffrey L. Ahern, MD, Anna D. Burke, MD, Sandra Jacobson, MD, Holly A. Shill, MD, Erika Driver-Dunckley, MD, Kewei Chen, PhD, Eric M. Reiman, MD, Thomas G. Beach, MD, PhD, and Geidy E. Serrano, PhD Gender Differences in Alzheimer Disease: Brain Atrophy, Histopathology Burden, and Cognition J Neuropathol Exp Neurol Vol. 75, No. 8, August 2016, pp. 748–754 7. Roher AE, Kuo YM, Esh C, Knebel C, Weiss N, Kalback W, et al. Cortical and leptomeningeal cerebrovascular amyloid and white matter pathology in Alzheimer’s disease. Mol Med 2003; 9: 112–22. 8. Doubal FN, MacLullich AMJ, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke 2010; 41:450–4.