32 Chapter 2 2.7 REFERENCES 1. Jellinger KA, Attems J. Challenges of multimorbidity of the aging brain: a critical update. J Neural Transm 2015; 122: 505–521. 2. Wardlaw JM, Smith C, Dichgans M. Mechanisms of sporadic cerebral small vessel disease: Insights from neuroimaging. Lancet Neurol 2013; 12: 483–497. 3. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013; 12: 822–838. 4. Alber J, Alladi S, Bae H, Barton DA, Beckett LA, Bell JM et al. White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 2019; 5: 107–117. 5. Gao Z, Zhai Y, Zhao X, Wang W, Wu W, Wang Z et al. Deep cerebral microbleeds are associated with the severity of lacunar infarcts and hypertension. Medicine 2018; 97: e11031. 6. De Bresser J, Kuijf HJ, Zaanen K, Viergever MA, Hendrikse J, Biessels GJ et al. White matter hyperintensity shape and location feature analysis on brain MRI; Proof of principle study in patients with diabetes. Sci Rep 2018; 8: 1–10. 7. Ghaznawi R, Geerlings MI, Jaarsma-Coes MG, Zwartbol MHT, Kuijf HJ, van der Graaf Y et al. The association between lacunes and white matter hyperintensity features on MRI: The SMART-MR study. Journal of Cerebral Blood Flow and Metabolism 2019; 39: 2486–2496. 8. Fazekas F, Kleinert R, Offenbacher H, Schmidt R, Kleinert G, Payer F et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993; 43: 1683–1689. 9. Kim KW, MacFall JR, Payne ME. Classification of White Matter Lesions on Magnetic Resonance Imaging in Elderly Persons. Biol Psychiatry 2008; 64: 273–280. 10. Ghaznawi R, Geerlings MI, Jaarsma-Coes M, Hendrikse J, Bresser J de, Group on behalf of the U-SS. Association of White Matter Hyperintensity Markers on MRI and Long-term Risk of Mortality and Ischemic Stroke. Neurology 2021; 96: e2172–e2183. 11. Winterer G, Androsova G, Bender O, Boraschi D, Borchers F, Dschietzig TB et al. Personalized risk prediction of postoperative cognitive impairment – rationale for the EU-funded BioCog project. European Psychiatry 2018; 50: 34–39. 12. Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand 1983; 67: 361–370. 13. Schmidt, P. Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging. (Maximilians-Universität München, 2017). 14. Kempton MJ, Underwood TSA, Brunton S, Stylios F, Schmechtig A, Ettinger U et al. A comprehensive testing protocol for MRI neuroanatomical segmentation techniques: Evaluation of a novel lateral ventricle segmentation method. Neuroimage 2011; 58: 1051– 1059. 15. Liu EJ, Cashman K V., Rust AC. Optimising shape analysis to quantify volcanic ash morphology. GeoResJ 2015; 8: 14–30. 16. Marcus J, Gardener H, Rundek T, Elkind MSV, Sacco RL, Decarli C et al. Baseline and longitudinal increases in diastolic blood pressure are associated with greater white matter hyperintensity volume: The northern manhattan study. Stroke 2011; 42: 2639–2641. 17. Muller M, Sigurdsson S, Kjartansson O, Aspelund T, Lopez OL, Jonnson P V. et al. Joint effect of mid- and late-life blood pressure on the brain: The AGES-Reykjavik Study. Neurology 2014; 82: 2187–2195.
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