Quantifying Functional Connectivity in Brain Imaging: The Functional Data Approach

• Activity Number: 288
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Jane-Ling Wang* and Jingjiang He and Xiaoke Zhang and Owen Carmichael and Hans-Georg G. Mueller
• Companies: UC Davis and UC Davis and UC Davis and UC Davis and UC Davis
• Keywords: fMRI Bold data ; functional correlation ; Pearson correlation ; Alzheimer disease ; dynamic correlation ; root-n consistency

Abstract

Quantifying time series similarity between pairs of voxels is central to the study of functional connectivity from blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). Temporal Pearson correlation is frequently used to measure time series similarity, but it ignores statistical dependencies between time points in a time series, and in some situations leads to a biased group-level correlation measure. We consider BOLD fMRI time series at each voxel of a subject as time-discretized observations of a random function, and employ a functional data approach to evaluate functional connectivity.

Two corrections for Pearson correlation will be discussed in this talk and compared with the Pearson correlation in simulations and in a resting state fMRI study of 231 participants, 170 were clinically diagnosed as cognitively normal and 61 were diagnosed with Alzheimer's disease (AD). We conclude that the choice of correlation measure could have important practical implications for BOLD fMRI functional connectivity studies.