MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity
Authors: Zuozhen Zhang, Junzhong Ji, Jinduo Liu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on both simulated and real-world data to demonstrate the efficacy of our proposed method.Systematic experiments conducted on both simulated and real f MRI datasets demonstrate that the proposed method surpasses several state-of-the-art approaches in its performance on small-sample f MRI data. |
| Researcher Affiliation | Academia | Zuozhen Zhang, Junzhong Ji, Jinduo Liu * Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China zzz3582@emails.bjut.edu.cn, jjz01@bjut.edu.cn, jinduo@bjut.edu.cn |
| Pseudocode | Yes | Algorithm 1: Meta RLEC Input: Original f MRI time-series data. Output: Brain EC network. |
| Open Source Code | Yes | The code is available at https://github.com/layzoom/Meta RLEC. |
| Open Datasets | Yes | The benchmark simulation datasets 1 we used are supported by Smith et al. (Smith et al. 2011), which are generated by dynamic causal models (DCM).1https://www.fmrib.ox.ac.uk/datasets/netsim/index.html 2https://github.com/shahpreya/MTlnet |
| Dataset Splits | Yes | dtrn is for training and dval is for validation of online learning.Algorithm 1: ...7: Sample training batch dtrn from X; ... 14: Sample testing batch dval from X; |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper describes model components and mathematical functions but does not provide specific software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | where λ ≥ 0 is a parameter that controls the sparsity of brain EC networks and A(G) is the sparse penalty function as A(G) = G 1.where η denotes the learning rate.The parameters of the algorithms under comparison are selected according to the existing literature and we fine-tune 10 subjects to select the optimal parameters. |