Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Authors: Kristjan Greenewald, Seyoung Park, Shuheng Zhou, Alexander Giessing
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our methodology to the discovery of time-varying spatial structures in human brain f MRI signals. and 4 Simulation study and 5 f MRI Application |
| Researcher Affiliation | Academia | Kristjan Greenewald Department of Statistics Harvard University, Seyoung Park Department of Biostatistics Yale University, Shuheng Zhou Department of Statistics University of Michigan, Alexander Giessing Department of Statistics University of Michigan |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We consider the ADHD-200 f MRI dataset (Biswal et al., 2010), and study resting state f MRIs for a variety of healthy patients in the dataset at different stages of development. |
| Dataset Splits | No | The paper uses the ADHD-200 f MRI dataset but does not specify explicit training, validation, or test dataset splits or cross-validation details. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions methods like GLasso and ADMM but does not list any specific software or library names with version numbers for reproducibility. |
| Experiment Setup | Yes | Initially we set Θ(0) = 0.25In n, where n = 100. (Simulation Study) and We use a Gaussian kernel with bandwidth h, and estimate the graphs using a variety of values of λ and h. (f MRI Application). |