AUTHOR=Ji Junzhong , Liu Jinduo , Zou Aixiao , Zhang Aidong 

TITLE=ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.01290

DOI=10.3389/fnins.2019.01290

ISSN=1662-453X

ABSTRACT=Identifying brain effective connectivity (EC) networks from neuroimaging data plays an important role in discussing the pathogenesis of brain diseases, and has become a very hot topic in the brain neuroscience. So far, there are many methods to identify EC networks. However, most of the researches currently focus on learning EC networks from single modal imaging data such as functional magnetic resonance imaging (fMRI) data. This paper proposes a new method, called ACOEC-FD, to learn EC networks from fMRI and diffusion tensor imaging (DTI) using ant colony optimization (ACO). First, ACOEC-FD uses DTI data to acquire some positively correlated relations among regions of interest (ROI) and takes them as anatomical constraint information to effectively restrict the search space of candidate arcs in an EC network. And then, ACOEC-FD achieves multi-modal imaging data integration by incorporating anatomical constraint information into the heuristic function of probabilistic transition rules to effectively encourage ants more likely to search connectivities between structurally connected regions. Through simulation studies on generated data and real fMRI-DTI datasets, we demonstrate that the approach results in improved inference results on EC compared with some methods only used fMRI data.