Paper Info
Title
Group-wise connection activation detection based on DICCCOL
Abstract
Task-based fMRI is widely used to locate activated cortical regions during task performance. In the community of fMRI analysis, the general linear model (GLM) is the most popular method to detect activated brain regions, based on the assumption that fMRI BOLD signals follow well the shape of external stimulus. In this paper, instead of analyzing the voxel-based BOLD signal, we examine the functional connection curves between pairs of brain regions. Specifically, we calculate the dynamic functional connection (DFC) between a pair of our recently developed and validated Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL), and use the GLM to estimate if DFC time series follow the shape of external stimulus. Since the DICCCOL landmarks possess structural and functional correspondence across subjects and these correspondences also apply to their connections, the mixed-effects model is thus performed to effect sizes estimated from GLM of each corresponding connection across subjects to detect group-wise activation. In other words, we assess the activation of cortical landmarks' dynamic interactions at the group-level. Our experimental results demonstrate that the proposed approach is able to detect reasonable activated connection patterns.
Year
DOI
Venue
2014
10.1109/ISBI.2014.6867962
ISBI
Keywords
DocType
ISSN
functional connection curves,fMRI analysis,activated brain regions,activated cortical regions,general linear model,DICCCOL,voxel-based BOLD signal,biomedical MRI,dense individualized common connectivity-based cortical landmarks,DTI,brain,DFC time series,activation detection,mixed-effects model,task performance,GLM,connection,group-wise connection activation detection,group-wise,time series,dynamic functional connection,task-based fMRI,medical image processing,fMRI BOLD signals,fMRI
Conference
1945-7928
Citations 
PageRank 
References 
0
0.34
2
Authors
7
Name
Order
Citations
PageRank
Jinglei Lv120526.70
Tuo Zhang223332.92
Xintao Hu300.34
Dajiang Zhu432036.72
Kaiming Li538530.92
Lei Guo61661142.63
Tianming Liu71033112.95