Statistical Methodologies for Neuroconnectivity Analysis Using Autistic fMRI Data
Human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease and autism. fMRI has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this talk, we will present a special type of mixed-effects model together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities for developing a neural network in whole brain studies. Results are illustrated with a large dataset known as Autism Brain Imaging Data Exchange (ABIDE) which includes 361 subjects from 8 medical centers.