Abstract

Computational modeling of the neural networks of the human brain has numerous applications including research into neurodegeneration diseases, more specifically Alzheimer ‘s disease and mild cognitive impairment. Changes in functional connectivity as a feature of the neural networks of the human brain might precede alterations in structural connections in Alzheimer’s disease, hence the extensive research in the application of computational modeling to understand the alterations in the functional connectivity. Before functional networks and their alterations can be modeled, optimal data preprocessing steps and computational approaches need to be elected. Approaches to data preprocessing and computational modeling including seed-based, data-driven, and graph theory are discussed. The challenges associated with these approaches and their implications to research and applications are also discussed.