Multi-graph Fusion for Functional Neuroimaging Biomarker Detection
Authors: Jiangzhang Gan, Xiaofeng Zhu, Rongyao Hu, Yonghua Zhu, Junbo Ma, Ziwen Peng, Guorong Wu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two real data sets, i.e., fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Center for Future Media and School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China 2School of Natural and Computational Science, Massey University Auckland Campus, New Zealand 3School of Medicine and Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA 4College of Psychology and Sociology, Shenzhen University, Shenzhen 518060, China 5Sichuan Artificial Intelligence Research Institute, Yibin 644000, China |
| Pseudocode | Yes | Algorithm 1 The pseudo of our proposed functional connectivity analysis framework. |
| Open Source Code | No | The paper does not provide a statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | The data set fronto-temporal dementia (FTD) contains 95 FTD subjects and 86 age-matched healthy control (HC) subjects. FTD was derived from the NIFD database managed by the frontotemporal lobar degeneration neuroimaging initiative. The data set obsessive-compulsive disorder (OCD) has 20 HC subjects and 62 OCD subjects. For all imaging data, we followed the automated anatomical labeling (AAL) template [Tzourio-Mazoyer et al., 2002] to construct the functional connectivity network for each subject with 90 nodes. |
| Dataset Splits | Yes | In our experiments, we repeated the 10-fold cross-validation scheme 10 times for all methods to report the average results as the final results. |
| Hardware Specification | No | The paper mentions time and space complexity but does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions comparison methods using "Liblinear toolbox [Fan et al., 2008]" and "simplify graph convolutional networks (SGC) [Wu et al., 2019]", but does not specify version numbers for general software dependencies or the authors' own implementation libraries. |
| Experiment Setup | Yes | In the model selection, we set α, β {10 3, 10 2, ..., 103} in Eq. (2), and fixed k = 10 since the value of k is insensitive to the result of Eq. (2). We further set C {2 10, 2 9, ..., 210} for ℓ1-SVM. |