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Neuroimage. 2019 Jan 1;184:669-686. doi: 10.1016/j.neuroimage.2018.09.043. Epub 2018 Sep 22.

Modeling regional dynamics in low-frequency fluctuation and its application to Autism spectrum disorder diagnosis.

Author information

1
Department of Brain and Cognitive Engineering, Korea University, Republic of Korea.
2
Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Republic of Korea.
3
Department of Brain and Cognitive Engineering, Korea University, Republic of Korea. Electronic address: hisuk@korea.ac.kr.

Abstract

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.

KEYWORDS:

Autism spectrum disorder; Hidden markov models; Representing regional BOLD fluctuations; Resting-state fMRI; Temporal dynamics

[Indexed for MEDLINE]

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