With the advent of neuroimaging techniques, many studies in the literature have validated the use of resting-state fMRI (rs-fMRI) for understanding functional mechanisms of the brain, as well as for identifying brain disorders or diseases. One of the main streams in recent studies of modeling and analyzing rs-fMRI data is to account for the dynamic characteristics of a brain. In this study, we propose a novel method that directly models the regional temporal BOLD fluctuations in a stochastic manner and estimates the dynamic characteristics in the form of likelihoods. Specifically, we modeled temporal BOLD fluctuation of individual Regions Of Interest (ROIs) by means of Hidden Markov Models (HMMs), and then estimated the ‘goodness-of-fit’ of each ROI's BOLD signals to the corresponding trained HMM in terms of a likelihood. Using estimated likelihoods of the ROIs over the whole brain as features, we built a classifier that can discriminate subjects with Autism Spectrum Disorder (ASD) from Typically Developing (TD) controls at an individual level. In order to interpret the trained HMMs and a classifier from a neuroscience perspective, we also conducted model analysis. First, we investigated the learned weight coefficients of a classifier by transforming them into activation patterns, from which we could identify the ROIs that are highly associated with ASD and TD groups. Second, we explored the characteristics of temporal BOLD signals in terms of functional networks by clustering them based on sequences of the hidden states decoded with the trained HMMs. We validated the effectiveness of the proposed method by achieving the state-of-the-art performance on the ABIDE dataset and observed insightful patterns related to ASD.
Bibliographical noteFunding Information:
The authors would like to thank BT Thomas Yeo for helpful comments regarding Yeo atlas and Jiyeon Lee for preprocessing the dataset. This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00053 , A Technology Development of Artificial Intelligence Doctors for Cardiovascular Disease , and No. 2017-0-00451 , Development of BCI based Brain and Cognitive Computing Technology for Recognizing User's Intentions using Deep Learning).
© 2018 Elsevier Inc.
- Autism spectrum disorder
- Hidden markov models
- Representing regional BOLD fluctuations
- Resting-state fMRI
- Temporal dynamics
ASJC Scopus subject areas
- Cognitive Neuroscience