Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies
Authors: Dushyant Sahoo, Christos Davatzikos
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on simulated datasets display that the proposed method can estimate components with higher accuracy and reproducibility, while preserving age-related variation on a multi-center clinical data set. |
| Researcher Affiliation | Academia | Dushyant Sahoo Department of Electrical Engineering University of Pennsylvania sadu@seas.upenn.edu Christos Davatzikos Department of Radiology University of Pennsylvania christos.davatzikos@uphs.upenn.edu |
| Pseudocode | No | The paper states 'Complete algorithm and the details about the optimization are described in Appendix B.', but the appendix content is not provided in the given text. |
| Open Source Code | No | All the code is implemented in MATLAB and will be released upon publication. |
| Open Datasets | Yes | We collected functional MRI data from 5 different multi-center imaging studies1) Baltimore Longitudinal Study of Aging (BLSA) [32, 33], the Coronary Artery Risk Development in Young Adults study (CARDIA) [34], UK Bio Bank (UKBB) [35], Open access series of imaging studies (OASIS) [36] and Aging Brain Cohort Study (ABC) from Penn Memory Center [37]. |
| Dataset Splits | Yes | Optimal value of hyperparameters α, β, µ and τ1 are selected from [0.1, 1], [1, 5], [0.1, 0.5, 1] and 10[ 2:2]. The criterion for choosing the best hyperparameter is maximum split-sample reproducibility. We performed a 5 fold cross-validation using SVM with RBF kernel. |
| Hardware Specification | Yes | All the experiments were run on a four i7-6700HQ CPU cores single ubuntu machine. |
| Software Dependencies | No | The paper states 'All the code is implemented in MATLAB' but does not provide specific version numbers for MATLAB or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | Optimal value of hyperparameters α, β, µ and τ1 are selected from [0.1, 1], [1, 5], [0.1, 0.5, 1] and 10[ 2:2]. We used a feed-forward neural network for the classification model with two hidden layers. The networks contain the following layers: a fully connected layer with 50 hidden unites, dropout layer with rate 0.2, Re LU, a fully-connected layer with 4 hidden units and a softmax layer. |