Real-time Adaptive Design Optimization Within Functional MRI Experiments
Real-time Adaptive Design Optimization Within Functional MRI Experiments
复制标题
功能 MRI 实验中的实时自适应设计优化
DOI：
10.1007/s42113-020-00079-7
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Turner, Brandon M.
中科院分区：
文献类型：
--
作者：
Bahg, Giwon;Sederberg, Per B.;Myung, Jay I.;Li, Xiangrui;Pitt, Mark A.;Lu, Zhong-Lin;Turner, Brandon M.
Efficient data collection is an important goal in cognitive neuroimaging studies because of the high cost of data acquisition. One method of improving efficiency is to maximize the informativeness of the data collected on each trial. We propose an Adaptive Design Optimization (Cavagnaro et al.Neural Computation22, 887–905 2010; Myung et al.Journal of Mathematical Psychology57, 53–67 2013) procedure to optimize the sequencing of stimuli for model-based functional neuroimaging studies. Our method uses the Joint Modeling Framework (Turner et al.NeuroImage72, 193–206 2013, 2019) to maximize the information learned about how the brain produces a behavior by integrating over neural and behavioral data simultaneously. We validate our method in simulation and real-world experiments by showing how Adaptive Design Optimization proposes the optimal stimulus sequence to reduce uncertainty and improve accuracy from a Bayesian perspective.
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