Report
Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks
| العنوان: | Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks |
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| المؤلفون: | Hjelm, R Devon, Damaraju, Eswar, Cho, Kyunghyun, Laufs, Helmut, Plis, Sergey M., Calhoun, Vince |
| سنة النشر: | 2016 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition |
| الوصف: | We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely studied in a static sense, but not well-characterized dynamically. RNN-ICA predicts dynamics directly from the recurrent states of the RNN in both task and resting state fMRI. Our results show both task-related and group-differentiating directed connectivity. Comment: Accepted to Frontiers of Neuroscience |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1611.00864 |
| رقم الانضمام: | edsarx.1611.00864 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |