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Supplemental e-methods

Excluded subjects

Of all potentially eligible subjects (n=1740), 24 individuals refused to enroll, 1 could not undergo MRI because of severe claustrophobia, 9 had a history of neurological disorder (stroke, n = 6; meningitis, n = 1; encephalitis, n = 1; spinocerebellar degeneration, n = 1) and 3 had brain injury with abnormalities seen on MRI. No participants with dementia were included. Among the remaining 1703 subjects, 8 subjects with MRI motion artifacts, 1 with numerous cavernous angiomas, and 119 with incomplete data were excluded.

Brain MRI acquisition and analysis

MRIs were performed with the following parameters: axial T1-weighted imaging, repetition time (TR), 550 ms; echo time (TE), 15 ms; flip angle (FA), 80°; section thickness, 7 mm; gap width, 1.4 mm; matrix, 256×352 mm2; filed of view 220×220 mm2; axial fast spin-echo T2-weighted imaging, TR, 4000 ms; TE, 108 ms; FA, 90°; section thickness, 7 mm; gap width, 1.4 mm; matrix, 352×400 mm2; filed of view 220×220 mm2, axial fluid-attenuated inversion recovery (FLAIR) imaging, TR, 10,000 ms; inversion time, 2500 ms; TE, 96 ms; FA, 90°; section thickness, 7 mm; gap width, 1.4 mm; matrix, 224×336 mm2; filed of view 220×220 mm2, and axial gradient-echo T2*-weighted imaging TR, 735 ms; TE, 20 ms; flip angle, 30°; section thickness, 7 mm; gap width, 1.4 mm; matrix, 224×320 mm2; filed of view 220×220 mm2.

White matter changes on T2-weighted imaging and FLAIR imaging were assessed with both white matter hyperintensities (WMH) and periventricular hyperintensities (PVH) of Fazekas scale,1 because there is some differences in pathogenesis between the both (i.e. PVH is considered as non-vascular origin).2 Each grade was as follows; WMH: grade 1, punctuate; grade 2, early confluence; and grade 3, confluent; and PVH: grade 1, caps or lining; grade 2, bands; and grade 3, irregular extension into the deep white matter.1 Lacunes were defined as focal, sharply demarcated lesions >3 mm in diameter showing high intensity on T2-weighted imaging and low intensity on T1-weighted imaging.3 They were distinguished from perivascular spaces by their larger size, spheroid shape and surrounding hyperintensity on FLAIR.

Using a computer-assisted processing system (Image J version 1.46r; National Institutes of Health, Bethesda, MD),4, 5 we calculated the percentage of brain area on one axial T2-weighted image in 2 slices above the pineal body as an index of brain atrophy.

Supplemental e-discussion

A previous population-based study revealed associations between each both PVS locations and WMH or PVH.6 Meanwhile, our study showed only an association between BG-PVS and WMH. These discrepancies might be explained by some methodological or cohort differences, such as the different mean age of the samples cohorts or the rating scale used. However, the lack of association between PVS and PVH might also be explained by the hypotheses that PVH is non-vascular origin.2

The explanation for our observed association between smoking and centrum semiovale perivascular spaces severity is unknown. In vitro, nicotinic receptor stimulation protects neurons from degeneration induced by amyloid-β.7 However, among population-based studies, such effects are controversial,8, 9, suggesting that a complex relationship between tobacco use and Alzheimer’s disease pathology, which may include the established adverse effects of smoking on cerebrovascular system and interactions between amyloid and microvascular pathology in Alzheimer’s disease.7

Supplemental References

1. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol 1987;149:351-356.

2. Schmidt R, Schmidt H, Haybaeck J, et al. Heterogeneity in age-related white matter changes. Acta Neuropathol 2011;122:171-185.

3. Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822-838.

4. Yakushiji Y, Noguchi T, Hara M, et al. Distributional impact of brain microbleeds on global cognitive function in adults without neurological disorder. Stroke 2012;43:1800-1805.

5. Koga H, Yuzuriha T, Yao H, et al. Quantitative MRI findings and cognitive impairment among community dwelling elderly subjects. J Neurol Neurosurg Psychiatry 2002;72:737-741.

6. Zhu YC, Tzourio C, Soumare A, Mazoyer B, Dufouil C, Chabriat H. Severity of dilated Virchow-Robin spaces is associated with age, blood pressure, and MRI markers of small vessel disease: a population-based study. Stroke 2010;41:2483-2490.

7. Court JA, Johnson M, Religa D, et al. Attenuation of Abeta deposition in the entorhinal cortex of normal elderly individuals associated with tobacco smoking. Neuropathol Appl Neurobiol 2005;31:522-535.

8. Ott A, Slooter AJ, Hofman A, et al. Smoking and risk of dementia and Alzheimer's disease in a population-based cohort study: the Rotterdam Study. Lancet 1998;351:1840-1843.

9. van Duijn CM, Havekes LM, Van Broeckhoven C, de Knijff P, Hofman A. Apolipoprotein E genotype and association between smoking and early onset Alzheimer's disease. BMJ 1995;310:627-631.

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Supplemental Tables

Table e-1. Sensitively Analysis (univariable analysis): Differences in clinical characteristics and MRI findings between the groups dichotomized by the different BG-PVS classification.

| |BG-PVS | | |

| |Low degree (0-2) |High degree (3-4) |p-value |

|Variables |n= 1535 |n= 40 | |

|Age, mean (SD), years |56.8 (9.7) |66.1 (6.9) | ................
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