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Adaptive Design Optimization in Functional MRI Experiments

Bahg, Giwon
http://rave.ohiolink.edu/etdc/view?acc_num=osu1531836392551605

Abstract Details

2018, Master of Arts, Ohio State University, Psychology.
Efficient data collection is one of the most important goals to be pursued in cognitive neuroimaging studies because of the exceptionally high cost of data acquisition. Design optimization methods have been developed in cognitive science to resolve this problem, but most of them lack generalizability because their functionality tends to rely on a specific type of cognitive models (e.g., psychometric functions) or research paradigm (e.g., task-to-region mapping). In addition, traditional optimal design methods fail to exploit neural and behavioral data simultaneously, which is essential for providing an integrative explanation of human cognition. As one of the possible solutions, we propose an implementation of Adaptive Design Optimization (ADO; Cavagnaro, Myung, Pitt, & Kujala, 2010) in model-based functional MRI (fMRI) experiments using a Joint Modeling Framework (B. M. Turner, Forstmann, et al., 2013). First, we introduce a general architecture of fMRI-based ADO and discuss practical considerations in real-world applications. Second, three simulation studies show that fMRI-based ADO estimates parameters more accurately and precisely than conventional, randomized experimental designs. Third, a real-time fMRI experiment validates the performance of fMRI-based ADO in the real-world setting. The result suggests that ADO performs better than randomized designs in terms of accuracy, but the unbalanced designs proposed by ADO may inflate the variability of trial-wise estimates of neural activation and therefore model parameters. Lastly, We discuss the limitations, further developments, and applications of fMRI-based ADO.
Brandon Turner (Advisor)
Jay Myung (Committee Member)
Zhong-Lin Lu (Committee Member)
108 p.
Psychology
Bayesian optimal design; Adaptive design optimization; fMRI

Recommended Citations

Citations

  • Bahg, G. (2018). Adaptive Design Optimization in Functional MRI Experiments [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531836392551605

    APA Style (7th edition)

  • Bahg, Giwon. Adaptive Design Optimization in Functional MRI Experiments. 2018. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1531836392551605.

    MLA Style (8th edition)

  • Bahg, Giwon. "Adaptive Design Optimization in Functional MRI Experiments." Master's thesis, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531836392551605

    Chicago Manual of Style (17th edition)

osu1531836392551605
371
© 2018, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.