24 Chapter 2 2.3.6 Statistical analysis Normality of data was assessed by visual inspection of histograms and Q-Q plots. WMH volumes, solidity and concavity index were multiplied by 100 and natural log transformed to correct for non-normal distribution. The statistical analyses performed in this study are exploratory. Associations between cardiovascular risk factors (diabetes, hypertension, BMI, hyperlipidemia) and WMH markers (volume, type, shape) were assessed by linear regression analyses adjusted for age and sex; while WMH volume was additionally adjusted for intracranial volume. In secondary analyses, we performed stepwise linear regression analyses to investigate which of the cardiovascular risk factors accounts for most of the variation of the significantly associated WMH markers. In further secondary analyses, associations between age and sex with the WMH markers were assessed by linear regression analyses; while WMH volumes were adjusted for intracranial volume. Statistical analysis was performed in SPSS version 25. A p value of < 0.05 was considered as statistically significant. Figure 2.1 Schematic illustration of the image processing pipeline. The lateral ventricles were segmented from the T1-weighted MRI images. WMH segmentation was performed using the registered FLAIR images. WMH were subsequently classified into three types (deep, periventricular, and confluent) using two different masks based on the distance from the ventricles. Periventricular WMH were defined as WMH contiguous with the lateral ventricles and extending ≤ 10 mm into the deep white matter. Confluent WMH were defined as WMH contiguous with the lateral ventricles and extending > 10 mm into the deep white matter. Deep WMH were defined as being located > 3 mm from the lateral ventricles and > 5 voxels (3.45 × 10−3 ml). Based on the WMH type, different WMH shape markers were calculated.
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