FUNCTIONAL CONNECTIVITY APPROACH BASED ON RESAMPLING TECHNIQUE OF RS-FMRI FOR CLASSIFICATION OF ALZHEIMER’S DISEASE SUBTYPES

Observing brain connectivity patterns is one of the most effective approaches for analyzing brain functions. The resting-state functional magnetic resonance imaging (rs-fMRI) is a promising tool to analyze brain connectivity patterns. It is established that resting-state neuroimaging signals exhibit fractal behavior such that it can be broken down into fractal and non-fractal components. The fractal signals originate
from heart oscillations, breathing, and system noise, obscuring the neuronal activity of the brain. With the presence of fractal components, the functional dynamic of spontaneous neural activities may not be accurately represented by the conventional correlation of rs-fMRI signals. Therefore, the fractal components of the BOLD signal need to be reduced to address this issue. In this work, SBS connectivity is used to
distinguish Alzheimer’s and mild cognitive impairment patients from healthy controls, eliminating the oscillations from the rs-fMRI BOLD signal.