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For example, deep white matter hyperintensities are 2.5 to 3 times more likely to occur in bipolar disorder and major depressive disorder than control subjects. [ 3 ] [ 4 ] WMH volume, calculated as a potential diagnostic measure, has been shown to correlate to certain cognitive factors. [ 5 ]
Head CT showing periventricular white matter lesions. Leukoaraiosis is a particular abnormal change in appearance of white matter near the lateral ventricles. It is often seen in aged individuals, but sometimes in young adults. [1] [2] On MRI, leukoaraiosis changes appear as white matter hyperintensities (WMHs) in T2 FLAIR images.
White matter is the tissue through which messages pass between different areas of grey matter within the central nervous system. The white matter is white because of the fatty substance (myelin) that surrounds the nerve fibers (axons). This myelin is found in almost all long nerve fibers, and acts as an electrical insulation.
The gray matter remains normal in all characteristics while the white matter changes texture, becoming soft and gelatinous. Rarefaction of the white matter is seen through light microscopy and the small number of axons and U-fibers that were affected can also be seen. Numerous small cavities in the white matter are also apparent.
The simplest case of a normal distribution is known as the standard normal distribution or unit normal distribution. This is a special case when μ = 0 {\textstyle \mu =0} and σ 2 = 1 {\textstyle \sigma ^{2}=1} , and it is described by this probability density function (or density): φ ( z ) = e − z 2 2 2 π . {\displaystyle \varphi (z ...
The normal-exponential-gamma distribution; The normal-inverse Gaussian distribution; The Pearson Type IV distribution (see Pearson distributions) The Quantile-parameterized distributions, which are highly shape-flexible and can be parameterized with data using linear least squares. The skew normal distribution
Under certain assumptions, the OLS estimator has a normal asymptotic distribution when properly normalized and centered (even when the data does not come from a normal distribution). This result is used to justify using a normal distribution, or a chi square distribution (depending on how the test statistic is calculated), when conducting a ...
The fact that two random variables and both have a normal distribution does not imply that the pair (,) has a joint normal distribution. A simple example is one in which X has a normal distribution with expected value 0 and variance 1, and = if | | > and = if | | <, where >. There are similar counterexamples for more than two random variables.