Robust estimation of brain functional connectivity from functional magnetic resonance imaging using power, cross-correlation and cross-coherence

Abstract

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive method for investigating the structure and function of the brain. Using fMRI, brain functions and areas responsible for the particular activities are investigated. The objective of the image processing methods using fMRI is to investigate the functional connectivity. To localize mental functions of specific brain regions and to identify the brain regions, those are activated simultaneously. Correlation and cross-coherence of the time series of the pixels are used for the detection of functional connectivity in fMRI images for the different motor movements (upper and lower limb movement and finger tapping action). The methodology was applied to three groups (six subjects) consisting aged between 10 and 75 years: (1) Normal and healthy subject performing finger tapping actions, (2) brain tumour patient performing lower limb movement (LL), and (3) brain tumour patient performing upper limb movements (UL). The threshold applied for the cross-correlation is 5000. Similarly, the threshold applied for cross-coherence and power parameters is in the range of (0.6–0.9). The algorithm implemented is found to be non-destructive, and there is no loss of temporal or spatial data. The result shows that for the normal subject, functionally connected pixels are more as compared to the brain tumour patients.