The Mind Research Network

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.

Facial expressions convey important social information and can initiate behavioral change through the processing and understanding of emotions. However, while this ability is known to evolve throughout development, it remains unclear whether this ability differs between girls and boys or how other variables such as level of anxiety can modulate it. Furthermore, understanding the underlying neural mechanisms of facial expression processing and how they are linked by sex and anxiety during development is essential, as alterations in this processing have been associated with psychiatric disorders. Herein, 191 typically developing youth (6- to 15-years old) completed an implicit face processing task involving three facial expressions (angry, happy, and neutral) during functional magnetic resonance imaging. We conducted linear models on the fMRI data to investigate the impact sex and anxiety on brain responses to emotional faces, accounting for age. Our findings indicated a significant anxiety-by-sex interaction in a posterior network covering bilateral visual and medial temporal cortices during the happy > neutral contrast. Specifically, girls with higher anxiety showed weaker activation while boys showed the opposite pattern. These findings suggest that the inter-subject variability reported in typically developing individuals in response to facial emotions may be related to many factors, including sex and anxiety level.

Brain dynamic effective connectivity (dEC), characterizes the information transmission patterns between brain regions that change over time, which provides insight into the biological mechanism underlying brain development. However, most existing methods predominantly capture fixed or temporally invariant EC, leaving dEC largely unexplored.

Herein we propose a deep dynamic causal learning model specifically designed to capture dEC. It includes a dynamic causal learner to detect time-varying causal relationships from spatio-temporal data, and a dynamic causal discriminator to validate these findings by comparing original and reconstructed data.

Our model outperforms established baselines in the accuracy of identifying dynamic causalities when tested on the simulated data. When applied to the Philadelphia Neurodevelopmental Cohort, the model uncovers distinct patterns in dEC networks across different age groups. Specifically, the evolution process of brain dEC networks in young adults is more stable than in children, and significant differences in information transfer patterns exist between them.

This study highlights the brain's developmental trajectory, where networks transition from undifferentiated to specialized structures with age, in accordance with the improvement of an individual's cognitive and information processing capability.

The proposed model consists of the identification and verification of dynamic causality, utilizing the spatio-temporal fusing information from fMRI. As a result, it can accurately detect dEC and characterize its evolution over age.