Shared Space Transfer Learning for analyzing multi-site fMRI data
Authors: Tony Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew Greenshaw, Russell Greiner
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effectiveness of the proposed method for transferring between various cognitive tasks. Our comprehensive experiments validate that SSTL achieves superior performance to other state-of-the-art analysis techniques. |
| Researcher Affiliation | Academia | 1University of Alberta, Canada 2Nanjing University of Aeronautics and Astronautics, China 3Alberta Machine Intelligence Institute (Amii), Canada 4Purdue University Fort Wayne, United States |
| Pseudocode | No | The paper describes the proposed method using mathematical formulations and descriptive text, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | SSTL is an open-source technique and can also be used via our GUI-based toolbox called easy f MRI. All algorithms for generating the experimental studies are shared as parts of our GUI-based toolbox called easy f MRI4. 4https://easyfmri.learningbymachine.com/ |
| Open Datasets | Yes | Table 1 lists the 8 datasets (A to H) used for our empirical studies. These datasets are provided by Open NEURO repository... 3Available at https://openneuro.org/ and [25] Xue, G. & Aron, A.R. & Poldrack, R.A. (2008) Common neural substrates for inhibition of spoken and manual responses. Cerebral Cortex. 18(8):1923 1932. |
| Dataset Splits | Yes | In the training phase, we use a one-subject-out strategy for each training site to generate the validation set i.e., all responses of a subject are considered as the validation set, and the other responses are used as the training set. |
| Hardware Specification | Yes | Main: Giga X399, CPU: AMD Ryzen Threadripper 2920X (24 3.5 GHz), RAM: 128GB, GPU: NVIDIA Ge Force RTX 2080 SUPER (8GB memory), OS: Fedora 33, Python: 3.8.5, Pip: 20.2.3, Numpy: 1.19.2, Scipy: 1.5.2, Scikit-Learn: 0.23.2, MPI4py: 3.0.3, Py Torch: 1.6.0. |
| Software Dependencies | Yes | OS: Fedora 33, Python: 3.8.5, Pip: 20.2.3, Numpy: 1.19.2, Scipy: 1.5.2, Scikit-Learn: 0.23.2, MPI4py: 3.0.3, Py Torch: 1.6.0. |
| Experiment Setup | Yes | We tune the hyper-parameters regularization ϵ {10 2, 10 4, 10 6, 10 8}, number of features k, maximum number of iterations L by using grid search based on the performance of the validation set. As mentioned before, SSTL just sets L = 1, but other TL techniques (such as SRM, MDDL, MSMD, etc.), we consider L {1, 2, ..., 50} . For selecting the number of features k, we first let k1 = min(V, Td) for d = 1 . . . e D [4]. Then, we benchmark the performance of analysis by using k = αk1, where α = {0.1, 0.5, 1, 1.1, 1.5, 2}. We use ν-support vector machine (ν-SVM) [29] for classification analysis. |