Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks.
Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks.
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DOI：
10.1007/978-3-030-00931-1_37
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发表时间：
2018-09
期刊：
影响因子：
--
通讯作者：
Fan Y
中科院分区：
文献类型：
--
作者：
Li H;Fan Y
Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies. Promising performance has been achieved using brain functional connectivity or brain activation signatures for a variety of brain decoding tasks. However, most of existing studies have built decoding models upon features extracted from imaging data at individual time points or temporal windows with a fixed interval, which might not be optimal across different cognitive processes due to varying temporal durations and dependency of different cognitive processes. In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, functional profiles extracted from task functional imaging data based on their corresponding subject-specific intrinsic functional networks are used as features to build brain decoding models, and LSTM RNNs are adopted to learn decoding mappings between functional profiles and brain states. We evaluate the proposed method using task fMRI data from the HCP dataset, and experimental results have demonstrated that the proposed method could effectively distinguish brain states under different task events and obtain higher accuracy than conventional decoding models.
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影响因子：
3
作者：
Huth AG;Lee T;Nishimoto S;Bilenko NY;Vu AT;Gallant JL
通讯作者：
Gallant JL
影响因子：
3.7
作者：
Shirer, W. R.;Ryali, S.;Greicius, M. D.
通讯作者：
Greicius, M. D.
影响因子：
5.7
作者：
Liao, CH;Worsley, KJ;Evans, AC
通讯作者：
Evans, AC
DOI：
10.1073/pnas.0905267106
发表时间：
2009-08-04
影响因子：
11.1
作者：
Smith, Stephen M.;Fox, Peter T.;Beckmann, Christian F.
通讯作者：
Beckmann, Christian F.
来自静止状态fMRI的大规模稀疏功能网络。
影响因子：
5.7
作者：
Li H;Satterthwaite TD;Fan Y
通讯作者：
Fan Y