Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease

Abstract 

Functional magnetic resonance imaging (fMRI) of default mode network (DMN) brain activity during resting is recently gaining attention as a potential noninvasive biomarker to diagnose incipient Alzheimer's disease. The aim of this study was to determine which method of data processing provides highest diagnostic power and to define metrics to further optimize the diagnostic value. fMRI was acquired in 21 healthy subjects, 17 subjects with mild cognitive impairment and 15 patients with Alzheimer's disease (AD) and data evaluated both with volumes of interest (VOI)-based signal time course evaluations and independent component analyses (ICA). The first approach determines the amount of DMN region interconnectivity (as expressed with correlation coefficients); the second method determines the magnitude of DMN coactivation. Apolipoprotein E (ApoE) genotyping was available in 41 of the subjects examined. Diagnostic power (expressed as accuracy) of data of a single DMN region in independent component analyses was 64%, that of a single correlation of time courses between 2 DMN regions was 71%, respectively. With multivariate analyses combining both methods of analysis and data from various regions, accuracy could be increased to 97% (sensitivity 100%, specificity 95%). In nondemented subjects, no significant differences in activity within DMN could be detected comparing ApoE ε4 allele carriers and ApoE ε4 allele noncarriers. However, there were some indications that fMRI might yield useful information given a larger sample. Time course correlation analyses seem to outperform independent component analyses in the identification of patients with Alzheimer's disease. However, multivariate analyses combining both methods of analysis by considering the activity of various parts of the DMN as well as the interconnectivity between these regions are required to achieve optimal and clinically acceptable diagnostic power.

Keywords:  fMRI , Default mode network , Independent component analysis , Alzheimer's disease