Multi metric functional connectivity analysis based on continuous hidden Markov model with application in early diagnosis of Alzheimer’s disease

Abstract

Functional connectivity (FC) is referred to as statistical dependencies between regions of interest. To investigate brain functional connectivity, there are many different connectivity metrics and researches show that the choice of the connectivity metric influences the results of the study and there is no golden rule of choosing the best connectivity metric.It is assumed that functional connectivity has a neural basis, and therefor is related to a variety of different neurological disorders like Alzheimer’s disease (AD) and Parkinson. AD is the most common neurodegenerative disorder. Cerebral cortex damage and synaptic plasticity disturbance in AD cause a decrease in functional connectivity. Mild Cognitive Impairment (MCI) is the first stage in AD progression and causes measurable decline in memory and cognitive abilities.In this study, a novel methodology is presented to combine several connectivity metrics with the goal of improving between-class discrimination. In the proposed method, temporal changes of multiple functional connectivity metrics are investigated along sliding windows by modeling it as the observation vector of a continuous observation hidden Markov model (HMM). The performance of the proposed method is evaluated using resting state eyes-closed EEG data from 7 MCI patients and 7 age-matched normal controls (NC).Group differences were investigated in five different frequency bands: delta, theta, alpha, beta, and gamma. Method analysis revealed that NC subjects and MCI patients are discriminated with accuracy of %95.9 ± 0.4 and %97.2 ± 0.5 over the alpha and gamma frequency bands respectively, using leave one subject out cross validation. These results indicate the proficiency of the connectivity metrics combination in distinguishing MCI from NC.