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dc.contributor.authorKaram, Ayman M.
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.contributor.authorZayane, Chadia
dc.contributor.authorKashou, Nasser H.
dc.date.accessioned2015-08-03T12:15:39Z
dc.date.available2015-08-03T12:15:39Z
dc.date.issued2014-11
dc.identifier.issn17468094
dc.identifier.doi10.1016/j.bspc.2014.07.004
dc.identifier.urihttp://hdl.handle.net/10754/563823
dc.description.abstractOriginally inspired by biological neural networks, artificial neural networks (ANNs) are powerful mathematical tools that can solve complex nonlinear problems such as filtering, classification, prediction and more. This paper demonstrates the first successful implementation of ANN, specifically nonlinear autoregressive with exogenous input (NARX) networks, to estimate the hemodynamic states and neural activity from simulated and measured real blood oxygenation level dependent (BOLD) signals. Blocked and event-related BOLD data are used to test the algorithm on real experiments. The proposed method is accurate and robust even in the presence of signal noise and it does not depend on sampling interval. Moreover, the structure of the NARX networks is optimized to yield the best estimate with minimal network architecture. The results of the estimated neural activity are also discussed in terms of their potential use.
dc.description.sponsorshipResearch reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). The authors would like to thank anonymous reviewers for valuables comments on the manuscript.
dc.publisherElsevier BV
dc.subjectBlock design
dc.subjectBOLD
dc.subjectEvent-related
dc.subjectfMRI
dc.subjectHemodynamic model
dc.subjectNeural activity
dc.subjectNeural networks
dc.subjectNonlinear autoregressive with exogenous input (NARX)
dc.subjectState estimation
dc.titleNonlinear neural network for hemodynamic model state and input estimation using fMRI data
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalBiomedical Signal Processing and Control
dc.contributor.institutionDepartment of Biomedical, Industrial, and Human Factors Engineering, Wright State UniversityOH, United States
kaust.personLaleg-Kirati, Taous-Meriem
kaust.personZayane, Chadia
kaust.personKaram, Ayman M.


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