Abstract
Many studies in the literature have validated the use of resting-state fMRI (rs-fMRI) for brain disorder/disease identification. Unlike the existing methods that mostly first estimate functional connectivity and then extract features with a graph theory, in this paper, we propose a novel method that directly models the temporal stochastic patterns inherent in BOLD signals for each Region Of Interest (ROI) individually. Specifically, we model temporal BOLD signal fluctuation of an individual ROI by means of Hidden Markov Models (HMMs), and then compute a regional BOLD signal likelihood with the trained HMMs. By regarding the BOLD signal likelihood of ROIs over a whole brain as features, we build a classifier that can discriminate subjects with Autism Spectrum Disorder (ASD) from Normal healthy Controls (NC). In addition, we also devise a method to further investigate the characteristics of temporal dynamics in rs-fMRI estimated by HMMs. For group comparison, we use the metrics of state occupancy rate and lifetime of the optimal hidden states that best represent the temporal BOLD signals. In our experiments with ABIDE cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies among competing methods. We could also identify the group differences in temporal dynamics between ASD and NC in terms of state occupancy rate and lifetime of individual states.
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Notes
- 1.
ABIDE (http://fcon_1000.projects.nitrc.org/indi/abide) provides preprocessed rs-fMRI datasets for ASD and NC by performing four different preprocessing pipelines. In this work, we used datasets preprocessed by the Data Processing Assistant for Resting-State fMRI (DPARSF), a convenient plug-in software based on SPM and REST.
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Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1C1A1A01052216) and also partially supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).
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Jun, E., Suk, HI. (2017). Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis. In: Wu, G., Laurienti, P., Bonilha, L., Munsell, B. (eds) Connectomics in NeuroImaging. CNI 2017. Lecture Notes in Computer Science(), vol 10511. Springer, Cham. https://doi.org/10.1007/978-3-319-67159-8_17
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DOI: https://doi.org/10.1007/978-3-319-67159-8_17
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