The Epidermal Growth Factor Domain of the Mutation Does Not Appear to Influence Disease Progression in CADASIL When Brain Volume and Sex Are Taken into Account

References

1. 1.↵
   1. Rutten JW,
   2. Van Eijsden BJ,
   3. Duering M, et al

   . The effect of NOTCH3 pathogenic variant position on CADASIL disease severity: NOTCH3 EGFR 1-6 pathogenic variant are associated with a more severe phenotype and lower survival compared with EGFR 7-34 pathogenic variant. Genet Med 2019;21:676–82 doi:10.1038/s41436-018-0088-3 pmid:30032161

   CrossRefPubMed
2. 2.↵
   1. Chabriat H,
   2. Hervé D,
   3. Duering M, et al

   . Predictors of clinical worsening in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy: prospective cohort study. Stroke 2016;47:4–11 doi:10.1161/STROKEAHA.115.010696 pmid:26578659

   Abstract/FREE Full Text
3. 3.↵
   1. Jouvent E,
   2. Duchesnay E,
   3. Hadj-Selem F, et al

   . Prediction of 3-year clinical course in CADASIL. Neurology 2016;87:1787–95 doi:10.1212/WNL.0000000000003252 pmid:27694265

   CrossRefPubMed
4. 4.↵
   1. Gunda B,
   2. Hervé D,
   3. Godin O, et al

   . Effects of gender on the phenotype of CADASIL. Stroke 2012;43:137–41 doi:10.1161/STROKEAHA.111.631028 pmid:22033996

   Abstract/FREE Full Text
5. 5.↵
   1. Viswanathan A,
   2. Guichard JP,
   3. Gschwendtner A, et al

   . Blood pressure and haemoglobin A1c are associated with microhaemorrhage in CADASIL: a two-centre cohort study. Brain 2006;129:2375–83 doi:10.1093/brain/awl177 pmid:16844717

   CrossRefPubMedWeb of Science
6. 6.↵
   1. Griffanti L,
   2. Zamboni G,
   3. Khan A, et al

   . BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. Neuroimage 2016;141:191–205 doi:10.1016/j.neuroimage.2016.07.018 pmid:27402600

   CrossRefPubMed
7. 7.↵
   1. Ling Y,
   2. Jouvent E,
   3. Cousyn L, et al

   . Validation and optimization of BIANCA for the segmentation of extensive white matter hyperintensities. Neuroinformatics 2018;16:269–81 doi:10.1007/s12021-018-9372-2 pmid:29594711

   CrossRefPubMed
8. 8.↵
   1. De Guio F,
   2. Duering M,
   3. Fazekas F, et al

   . Brain atrophy in cerebral small vessel diseases: Extent, consequences, technical limitations and perspectives: the HARNESS initiative. J Cereb Blood Flow Metab 2020;40:231–45 doi:10.1177/0271678X19888967 pmid:31744377

   CrossRefPubMed
9. 9.↵
   1. Smith SM,
   2. Zhang Y,
   3. Jenkinson M, et al

   . Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17:479–89 doi:10.1006/nimg.2002.1040 pmid:12482100

   CrossRefPubMedWeb of Science
10. 10.↵
    1. Smith SM,
    2. Jenkinson M,
    3. Woolrich MW, et al

    . Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23(Suppl 1):S208–19 doi:10.1016/j.neuroimage.2004.07.051 pmid:15501092

    CrossRefPubMedWeb of Science
11. 11.↵
    1. De Guio F,
    2. Jouvent E,
    3. Biessels GJ, et al

    . Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease. J Cereb Blood Flow Metab 2016;36:1319–37 doi:10.1177/0271678X16647396 pmid:27170700

    CrossRefPubMed
12. 12.↵
    1. Opfer R,
    2. Ostwaldt AC,
    3. Walker-Egger C, et al

    . Within-patient fluctuation of brain volume estimates from short-term repeated MRI measurements using SIENA/FSL. J Neurol 2018;265:1158–65 doi:10.1007/s00415-018-8825-8 pmid:29549466

    CrossRefPubMed
13. 13.↵
    1. Wardlaw JM,
    2. Smith EE,
    3. Biessels GJ, et al

    ; STandards for ReportIng Vascular changes on nEuroimaging (STRIVE v1). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822–38 doi:10.1016/S1474-4422(13)70124-8 pmid:23867200

    CrossRefPubMedWeb of Science
14. 14.↵
    1. van Rossum G,
    2. de Boer J

    . Interactively testing remote servers using the Python programming language. CWI Quarterly 1991;4:283–304
15. 15.↵
    R Core Team. R: A Language Environment for Statistical Computing. R Foundation for Statistical Computing; 2020. https://www.R-project.org.
16. 16.↵
    1. Cleveland WS

    . Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979;74:829–36 doi:10.1080/01621459.1979.10481038

    CrossRefWeb of Science
17. 17.↵
    1. Seabold S,
    2. Perktold J

    . Statsmodels: econometric and statistical modeling with Python. In: Proceedings of the Ninth Python in Science Conference, Austin, Texas; November 15, 2010
18. 18.↵
    1. Pedregosa F,
    2. Varoquaux G,
    3. Gramfort A, et al

    . Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825–30

    CrossRefPubMed
19. 19.↵
    1. Rousseeuw PJ

    . Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 1987;20:53–65 doi:10.1016/0377-0427(87)90125-7

    CrossRefPubMedWeb of Science
20. 20.↵
    1. Honaker J,
    2. King G,
    3. Blackwell M

    . Amelia II: a program for missing data. J Stat Software 2011;45:1–47 doi:10.18637/jss.v045.i07

    CrossRefPubMed
21. 21.↵
    1. Ruigrok AN,
    2. Salimi-Khorshidi G,
    3. Lai MC, et al

    . A meta-analysis of sex differences in human brain structure. Neurosci Biobehav Rev 2014;39:34–50 doi:10.1016/j.neubiorev.2013.12.004 pmid:24374381

    CrossRefPubMed
22. 22.↵
    1. Duchesnay E,
    2. Hadj Selem F,
    3. De Guio F, et al

    . Different types of white matter hyperintensities in CADASIL. Front Neurol 2018;9:526 doi:10.3389/fneur.2018.00526 pmid:30042721

    CrossRefPubMed
23. 23.↵
    1. Jouvent E,
    2. Alili N,
    3. Herve D, et al

    . Vanishing white matter hyperintensities in CADASIL: a case report with insight into disease mechanisms. J Alzheimers Dis 2020;78:907–10 doi:10.3233/JAD-201086 pmid:33074242

    CrossRefPubMed