TY - GEN
T1 - Probabilistic source separation on resting-state fMRI and its use for early MCI identification
AU - Kang, Eunsong
AU - Suk, Heung Il
N1 - Funding Information:
Acknowledgement. This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP(2016941946), and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence).
Funding Information:
This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP(2016941946), and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - In analyzing rs-fMRI, blind source separation has been studied extensively and various machine-learning techniques have been proposed in the literature. However, to our best knowledge, most of the existing methods do not explicitly separate noise components that naturally corrupt the observed BOLD signals, thus hindering from the understanding of underlying functional mechanisms in a human brain. In this paper, we formulate the problem of latent source separation in a probabilistic manner, where we explicitly separate the observed signals into a true source signal and a noise component. As for the inference of the latent source distribution with respect to an input regional mean signal, we use a stochastic variational Bayesian inference and implement it in a neural network framework. Further, in order for identification of a subject with early mild cognitive impairment (eMCI) rs-fMRI, we also propose to use the relations of the inferred source signals as features, i.e., potential imaging-biomarkers. We presented the validity of the proposed methods by conducting experiments on the publicly available ADNI2 dataset and comparing with the existing methods.
AB - In analyzing rs-fMRI, blind source separation has been studied extensively and various machine-learning techniques have been proposed in the literature. However, to our best knowledge, most of the existing methods do not explicitly separate noise components that naturally corrupt the observed BOLD signals, thus hindering from the understanding of underlying functional mechanisms in a human brain. In this paper, we formulate the problem of latent source separation in a probabilistic manner, where we explicitly separate the observed signals into a true source signal and a noise component. As for the inference of the latent source distribution with respect to an input regional mean signal, we use a stochastic variational Bayesian inference and implement it in a neural network framework. Further, in order for identification of a subject with early mild cognitive impairment (eMCI) rs-fMRI, we also propose to use the relations of the inferred source signals as features, i.e., potential imaging-biomarkers. We presented the validity of the proposed methods by conducting experiments on the publicly available ADNI2 dataset and comparing with the existing methods.
UR - http://www.scopus.com/inward/record.url?scp=85053911387&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00931-1_32
DO - 10.1007/978-3-030-00931-1_32
M3 - Conference contribution
AN - SCOPUS:85053911387
SN - 9783030009304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 275
EP - 283
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Davatzikos, Christos
A2 - Fichtinger, Gabor
A2 - Alberola-López, Carlos
A2 - Schnabel, Julia A.
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -