Convolution-GRU Based on Independent Component Analysis for fMRI Analysis with Small and Imbalanced Samples
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Functional magnetic resonance imaging (fMRI) is a commonly used method of brain research. However, due to the complexity and particularity of the fMRI task, it is difficult to find enough subjects, resulting in a small and, often, imbalanced dataset. A dataset with small samples causes overfitting of the learning model, and the imbalance will make the model insensitive to the minority class, which has been a problem in classification. It is of great significance to classify fMRI data with small and imbalanced samples. In the present study, we propose a 3-step method on a small and imbalanced fMRI dataset from a word-scene memory task. The steps of the method are as follows: (1) An independent component analysis is performed to reduce the dimension of data; (2) The synthetic minority oversampling technique is used to generate new samples of the minority class to balance data; (3) A convolution-Gated Recurrent Unit (GRU) network is used to classify the independent component signals, indicating whether the subjects are performing episodic memory tasks. The accuracy of the proposed method is 72.2%, which improves the classification performance compared with traditional classifiers such as support vector machines (SVM), logistic regression (LGR), linear discriminant analysis (LDA) and k-nearest neighbor (KNN), and this study gives a biomarker for evaluating the reactivation of episodic memory.
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SMOTE has been used successfully for this purpose in previous fMRI studies.
“Though SMOTE has been used successfully for this purpose in previous fMRI studies (Eslami and Saeed, 2019; Kawahara et al., 2017; Wang et al., 2020; Yuan et al., 2019)”
From Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing (2023) · cites this paper as Wang et al., 2020
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- Authors
- Wang, S., Duan, F., & Zhang, M.
- Affiliations
- Nankai University (CN)
- Corresponding author
- Duan, Feng
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-10-23
- Volume
- 10
- Issue
- 21
- Pages
- 7465
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3096960547
- Cited by
- 10
- References
- 39
- Citations by year
- 2026: 2 · 2024: 4 · 2023: 1 · 2021: 3
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key R&D Program of China (No. 2017YFE0129700)
- National Natural Science Foundation of China (No. 61673224)
- Research Fellowship for International Young Scientists (No. 61850410526, No. 61850410524)
- Tianjin Natural Science Foundation for Distinguished Young Scholars (No. 18JCJQJC46100)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- fMRI classification, small sample size, class imbalance, independent component analysis, SMOTE, episodic memory, dimensionality reduction
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