Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
Authors: Weida Li, Mingxia Liu, Fang Chen, Daoqiang Zhang2653-2660
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on five datasets suggest that the proposed method is not only superior to several state-of-the-art methods on temporally-aligned f MRI data, but also suitable for dealing with temporally-unaligned f MRI data. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology & MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China 2School of Information Science and Technology, Taishan University, Taian, China |
| Pseudocode | Yes | Algorithm 1 Graph-Based Decoding Model (GDM) Input: Aligning data {Xi RVi Ti}M i=1, the number of the shared features K, the energy {pi%}M i=1 to be kept, a specific Laplacian matrix L and kernel functions for each subject. 1: For each i, standardize Xi such that it has zero mean along the second dimension and the variance of each feature, i.e., voxel, is 1. 2: Generate {Ki}M i=1 via specified kernel functions. 3: Centralize Gram matrices: Ki Ki + T 2 i JTi Ti Ki JTi Ti T 1 i JTi Ti Ki T 1 i Ki JTi Ti 4: for i 1 to M do 5: Ki = Vi Di VT i by spectral decomposition. The eigenvalues in Di is in descending order. 6: Find Li such that the first Li diagonal elements of D 1 2 i contains approximately pi% energy. 7: Let ˆVi be the first Li columns of Vi . 8: Let ˆDi be the top left Li Li submatrix of Di . 9: end for 10: By spectral decomposition, ˆVT L ˆV = EΣET where the diagonal elements of Σ is ascending. 11: Let ˆE be the first K columns of E and then cut ˆE along the first dimension such that ˆEi RLi K. 12: For 1 i M, W i Φi ˆVi ˆD 1 i ˆEi . |
| Open Source Code | No | The paper does not provide any link or explicit statement about releasing source code. |
| Open Datasets | Yes | We utilize five datasets shared by openfmri.org and Chen et al. (Chen et al. 2015). The relevant information about each dataset is outlined in Table 1. |
| Dataset Splits | Yes | We follow the experiment setup with a cross-validation strategy in previous studies (Chen et al. 2014; Haxby et al. 2011), as illustrated by Fig. S1 and Fig. S2 in the Supplementary File. Specifically, except for the Raider, each subject s data is equally divided into two parts with each category being equally split, one is for alignment whereas the other is for training or testing a classifier. Switching the roles of the two parts and leave-k-subject-out strategy are adopted for cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | Raw datasets are preprocessed by using FSL (fsl.fmrib.ox.ac.uk), following a standard process (i.e., slice timing, anatomical alignment, normalization, and smoothing). The default parameters in FSL were taken when the dataset does not provide. ... Like previous studies, ν-SVM is used for classification (Chang and Lin 2011). All methods are implemented by ourselves in Python. |
| Experiment Setup | Yes | The parameter settings for each dataset are briefly listed in Table 1. For a fair comparison, the parameter ν in ν-SVM (with a linear kernel) is fixed for all methods on each dataset. For six competing methods, we choose the optimal hyperparameters according to their original papers. For our GDM model, a linear kernel is fixed, while the influence of different kernels are shown in Figs. S3-S8 in the Supplementary File. |