A study on the predictive strength of fractal dimension of white and grey matter on MRI images in Alzheimer's disease

Many recent studies have shown that Fractal Dimension (FD), a ratio for figuring out the complexity of a system given its measurements, can be used as an useful index to provide information about certain brain disease. Our research focuses on the Alzheimer's disease changes in white and grey brain matters detected through the FD indexes of their contours. Data used in this study were obtained from the Alzheimer's Disease (AD) Neuroimaging Initiative database (Normal Condition, N = 57, and Alzheimer's Disease, N = 60). After standard preprocessing pipeline, the white and grey matter 3D FD indexes are computed for the two groups. A statistical analysis shows that only grey matter 3D FD indexes are able to differentiate healthy and AD subjects. Although white matter 3D FD indexes do not, it is remarkable that their presence enhance the separation capability of previous ones. In order to valuate the classification capability of these indexes on healthy and AD subjects, we define several Neural Networks models. The performances of these models vary according to the statistical analysis and reach their best performances when each 3D FD input index is changed into a sequence of 2D FD indexes of (a subset of) the horizontal slices of the white and grey matter volumes.