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학술지 Modeling Regional Dynamics in Low-frequency Fluctuation and Its Application to Autism Spectrum Disorder Diagnosis
Cited 19 time in scopus Download 2 time Share share facebook twitter linkedin kakaostory
저자
전은지, 강은송, 최재훈, 석흥일
발행일
201901
출처
NeuroImage, v.184, pp.669-686
ISSN
1053-8119
출판사
Elsevier
DOI
https://dx.doi.org/10.1016/j.neuroimage.2018.09.043
협약과제
18HS1500, 심혈관질환을 위한 인공지능 주치의 기술 개발, 김승환
초록
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 ?쁤oodness-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.
KSP 제안 키워드
Art performance, Brain disorders, Frequency Fluctuation, Functional networks, Model Analysis, Modeling and analyzing, Regions of interest, Resting-state fMRI, Typically developing, Weight coefficients, Whole-brain