Biologically Plausible Brain Graph Transformer
Authors: Ciyuan Peng, Yuelong Huang, Qichao Dong, Shuo Yu, Feng Xia, Chengqi Zhang, Yaochu Jin
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three benchmark datasets demonstrate that Bio BGT outperforms state-of-the-art models, enhancing biologically plausible brain graph representations for various brain graph analytical tasks. |
| Researcher Affiliation | Academia | 1Federation University Australia, 2Dalian University of Technology, 3Zhejiang Gongshang University, 4RMIT University, 5Hong Kong Polytechnic University, 6Westlake University |
| Pseudocode | No | The paper describes its methodology using mathematical formulations and descriptive text, such as in Sections 3.1 and 3.2, but does not include a distinct pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/pcyyyy/BioBGT. |
| Open Datasets | Yes | Datasets. We conduct experiments on f MRI data collected from three benchmark datasets. (1) Autism Brain Imaging Data Exchange (ABIDE) 3 dataset. This dataset contains resting-state f MRI data of 1, 009 anonymous subjects... 3https://fcon_1000.projects.nitrc.org/indi/abide/ (2) Alzheimer s Disease Neuroimaging Initiative (ADNI) 4 dataset... 4https://adni.loni.usc.edu/ (3) Attention Deficit Hyperactivity Disorder (ADHD-200) 5 dataset... 5https://fcon_1000.projects.nitrc.org/indi/adhd200/ |
| Dataset Splits | Yes | Each dataset is randomly split, with 80% used for training, 10% for validation, and 10% for testing. |
| Hardware Specification | Yes | Model training is performed on an NVIDIA A6000 GPU with 48GB of memory. |
| Software Dependencies | Yes | Our model is implemented using PyTorch Geometric v2.0.4 and PyTorch v1.9.1. |
| Experiment Setup | Yes | The detailed hyperparameter settings for training Bio BGT on three datasets are summarized in Table 3. (Table 3 lists: #Layers 3, #Attention heads 8, Threshold of edge weight 0.3 0 0, Hidden dimensions 128, FFN hidden dimensions 256, Dropout 0.5 0.1 0.1, Readout method mean, Learning rate 3e-4, Batch size 128, #Epochs 200, Weight decay 1e-4, Warm-up Steps 10) |