NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping
Authors: Yamin Li, Ange Lou, Ziyuan Xu, Shengchao Zhang, Shiyu Wang, Dario Englot, Soheil Kolouri, Daniel Moyer, Roza Bayrak, Catie Chang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that Neuro BOLT effectively reconstructs unseen resting-state f MRI signals from primary sensory, high-level cognitive areas, and deep subcortical brain regions, achieving state-of-the-art accuracy with the potential to generalize across varying conditions and sites, which significantly advances the integration of these two modalities.3 Experiments |
| Researcher Affiliation | Academia | Yamin Li1 Ange Lou1 Ziyuan Xu1 Shengchao Zhang1 Shiyu Wang1 Dario J. Englot2 Soheil Kolouri1 Daniel Moyer1 Roza G. Bayrak1 Catie Chang1 1Vanderbilt University 2Vanderbilt University Medical Center yamin.li@vanderbilt.edu |
| Pseudocode | No | The paper describes its model architecture and process through text and diagrams (Figure 1, 2, 3), but it does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Project page: https://soupeeli.github.io/Neuro BOLT |
| Open Datasets | No | The paper states in Appendix D that the data 'can be made public upon request', indicating it is not openly and readily accessible via a specific link, DOI, or repository at the time of publication. |
| Dataset Splits | Yes | For subject-specific prediction, where training and testing occur on the same scan, we split the scan in an 8:1:1 ratio for training, validation, and testing, i.e., training on the first 80% of the data and testing on the last 10%. ... For the inter-subject analysis in resting-state data, we randomly divided the datasets into training/validation/testing sets by approximately 3:1:1 (18 scans : 5 scans : 6 scans). |
| Hardware Specification | Yes | Experiments are conducted on a single RTX A5000 GPU using Python 3.9.12, Pytorch 2.0.1, and CUDA 11.7. |
| Software Dependencies | Yes | Experiments are conducted on a single RTX A5000 GPU using Python 3.9.12, Pytorch 2.0.1, and CUDA 11.7. |
| Experiment Setup | Yes | The batch sizes are set at 16 and 64 for intra-subject and inter-subject analyses, respectively. Adam W is utilized as our optimizer, and MSE as our training objective. The initial learning rate is set at 3e-4 with a weight decay of 0.05, and a minimal learning rate of 1e-6. Table 5: Hyperparameters for Neuro BOLT |