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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.
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 ]
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 presence of incidental MRI findings in the CNS white matter: Ovoid and well-circumscribed homogeneous foci, with or without involvement of the corpus callosum; T2 hyperintensities larger than 3 mm in diameter, which fulfill at least 3 of the 4 Barkhof MRI criteria [7] for DIS; The CNS abnormalities are not consistent with a vascular condition
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
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 ...
In this context, the log-normal distribution has shown a good performance in two main use cases: (1) predicting the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) i.e. link dimensioning based on bandwidth provisioning and (2) predicting 95th percentile pricing. [94]