Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain

• Published in: Neural networks Vol. 183; p. 106974
• Main Authors: Yang, Lan, Qiao, Chen, Kanamori, Takafumi, Calhoun, Vince D., Stephen, Julia M., Wilson, Tony W., Wang, Yu-Ping
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2025
• Subjects: Adolescent; Brain - diagnostic imaging; Brain - physiology; Electroencephalography; Emotion processing; Emotions - physiology; Female; Gender differences; Humans; Machine Learning; Magnetic Resonance Imaging - methods; Male; Sex Characteristics; Tensor dictionary learning; Transfer learning; Transfer, Psychology - physiology; Tensor dictionary learning; Gender differences; Transfer learning; Emotion processing
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2024.106974

In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, time, and features as dimensions, brain activation and dynamic functional connectivity data can be treated as high-order heterogeneous data with heterogeneity arising from distinct feature space. To use the heterogeneous priori knowledge from the low-dimensional brain activation data to improve the classification performance of high-dimensional dynamic functional connectivity data, we propose a tensor dictionary-based heterogeneous transfer learning framework. It combines supervised tensor dictionary learning with heterogeneous transfer learning for enhance high-order heterogeneous knowledge sharing. The former can encode the underlying discriminative features in high-order data into dictionaries, while the latter can transfer heterogeneous knowledge encoded in dictionaries through feature transformation derived from mathematical relationship between domains. The primary focus of this paper is gender classification using fMRI data to identify emotion-related brain gender differences during adolescence. Additionally, experiments on simulated data and EEG data are included to demonstrate the generalizability of the proposed method. Experimental results indicate that incorporating prior knowledge significantly enhances classification performance. Further analysis of brain gender differences suggests that temporal variability in brain activity explains differences in emotion regulation strategies between genders. By adopting the heterogeneous knowledge sharing strategy, the proposed framework can capture the multifaceted characteristics of the brain, improve the generalization of the model, and reduce training costs. Understanding the gender specific neural mechanisms of emotional cognition helps to develop the gender-specific treatments for neurological diseases.