Yargholi Elahe', Hossein-Zadeh Gholam-Ali
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Iran; School of Cognitive Science, Institute for Research in fundamental Sciences (IPM), Tehran, Iran.
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Iran; School of Cognitive Science, Institute for Research in fundamental Sciences (IPM), Tehran, Iran.
J Neurosci Methods. 2016 Jan 15;257:159-67. doi: 10.1016/j.jneumeth.2015.09.032. Epub 2015 Oct 17.
Newly emerged developments in decoding of stimulus images from fMRI measurements have shown promising results. Decoding-classification has been the main concern of decoding studies, whereas the matter of reconstruction (decoding) of stimulus images from fMRI data, especially natural images, lacks adequate examination and it requires plenty of efforts to improve.
The present study employs Bayesian networks for decoding-reconstruction which is a novel application of this tool. Moreover, as a novel approach, we exploit the brain connectivity information in decoding-reconstruction procedure through Bayesian networks.
The proposed method was applied to reconstruct 100 images of digits 6 and 9 from the fMRI measurements obtained when showing some handwritten images of 6 and 9 to the subject. The information of only 10 brain voxels were exploited and an average (standard deviation) city-block distance error of 0.1071(0.0134) was obtained for all stimuli's reconstruction. In comparison with current common methods: The results reveal that Bayesian networks are successful in decoding-reconstruction of handwritten digits and inclusion of brain connectivity information makes them perform even more efficiently and improves decoding-reconstruction as well (reducing average error by almost 5%).
In the task of decoding-reconstruction, the models including brain connectivity appear significantly superior to other existing models.
功能磁共振成像（fMRI）测量中刺激图像解码方面的新进展已显示出有前景的结果。解码分类一直是解码研究的主要关注点，而从fMRI数据，特别是自然图像中重建（解码）刺激图像的问题缺乏充分研究，且需要大量努力来改进。
本研究采用贝叶斯网络进行解码重建，这是该工具的一种新应用。此外，作为一种新方法，我们在解码重建过程中通过贝叶斯网络利用大脑连接信息。
将所提出的方法应用于从向受试者展示一些手写数字6和9的图像时获得的fMRI测量中重建100个数字6和9的图像。仅利用了10个脑体素的信息，并且对于所有刺激的重建获得了平均（标准差）街区距离误差为0.1071（0.0134）。与当前常用方法相比：结果表明贝叶斯网络在手写数字的解码重建中是成功的，并且包含大脑连接信息使它们执行得更高效，也改善了解码重建（平均误差降低近5%）。
在解码重建任务中，包含大脑连接的模型明显优于其他现有模型。
