Academic

Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning

arXiv:2603.19307v1 Announce Type: new Abstract: Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of functional pathways. However, learning the interactions of the underlying subnetworks remains a significant challenge for existing Transformer-based methods due to the limited number of training samples. To address these challenges, we propose KD-Brain, a Prior-Informed Graph Learning framework for explicitly encoding prior knowledge to guide the learning process. Specifically, we design a Semantic-Conditioned Interaction mechanism that injects semantic priors into the attention query, explicitly navigating the subnetwork interactions based on their functional identities. Furthermore, we introduce a Pathology-Consistent Constraint, which regularizes the model optimization by aligning the learned interaction distributions with clinical priors. Additionally, KD-Brain leads to state-of-the-art performance

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Siyu Liu, Guangqi Wen, Peng Cao, Jinzhu Yang, Xiaoli Liu, Fei Wang, Osmar R. Zaiane · · 1 min read · 4 views
#cs.LG #cs.AI

arXiv:2603.19307v1 Announce Type: new Abstract: Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of functional pathways. However, learning the interactions of the underlying subnetworks remains a significant challenge for existing Transformer-based methods due to the limited number of training samples. To address these challenges, we propose KD-Brain, a Prior-Informed Graph Learning framework for explicitly encoding prior knowledge to guide the learning process. Specifically, we design a Semantic-Conditioned Interaction mechanism that injects semantic priors into the attention query, explicitly navigating the subnetwork interactions based on their functional identities. Furthermore, we introduce a Pathology-Consistent Constraint, which regularizes the model optimization by aligning the learned interaction distributions with clinical priors. Additionally, KD-Brain leads to state-of-the-art performance on a wide range of disorder diagnosis tasks and identifies interpretable biomarkers consistent with psychiatric pathophysiology. Our code is available at https://anonymous.4open.science/r/KDBrain.

Executive Summary

This study introduces KD-Brain, a Prior-Informed Graph Learning framework for modeling subnetwork interactions in heterogeneous brain networks. By leveraging prior knowledge and incorporating semantic priors into the attention query, KD-Brain addresses the limitations of existing Transformer-based methods. The framework achieves state-of-the-art performance on various disorder diagnosis tasks and identifies interpretable biomarkers consistent with psychiatric pathophysiology. The study demonstrates the potential of KD-Brain in improving diagnosis and understanding of mental disorders. The framework's adaptability and interpretability make it a valuable tool for researchers and clinicians. The availability of the code on an anonymous platform facilitates replication and further development.

Key Points

  • ▸ KD-Brain is a Prior-Informed Graph Learning framework for modeling subnetwork interactions in heterogeneous brain networks.
  • ▸ The framework incorporates semantic priors into the attention query to address the limitations of existing Transformer-based methods.
  • ▸ KD-Brain achieves state-of-the-art performance on various disorder diagnosis tasks and identifies interpretable biomarkers.

Merits

Strength in Addressing Limitations of Existing Methods

KD-Brain effectively addresses the limitations of existing Transformer-based methods by leveraging prior knowledge and incorporating semantic priors into the attention query.

Interpretability and Adaptability

The framework's adaptability and interpretability make it a valuable tool for researchers and clinicians, enabling the identification of interpretable biomarkers consistent with psychiatric pathophysiology.

Demerits

Limited Generalizability

The study's results may not be generalizable to other brain disorders or networks due to the specific focus on disorder diagnosis tasks.

Dependence on Prior Knowledge

The framework's performance relies heavily on the quality and relevance of prior knowledge, which may limit its applicability in real-world settings.

Expert Commentary

The introduction of KD-Brain marks a significant advancement in the field of brain network analysis. By harnessing prior knowledge and incorporating semantic priors into the attention query, the framework effectively addresses the limitations of existing methods. The study's results demonstrate the potential of KD-Brain in improving diagnosis and understanding of mental disorders. However, the framework's performance may be limited by its dependence on prior knowledge, and its generalizability to other brain disorders or networks requires further investigation. Nevertheless, the availability of the code on an anonymous platform facilitates replication and further development, ensuring the framework's potential is fully realized.

Recommendations

  • ✓ Future studies should investigate the framework's generalizability to other brain disorders and networks to ensure its widespread applicability.
  • ✓ Researchers should explore the use of KD-Brain in conjunction with other machine learning methods to further improve its performance and interpretability.

Sources

Original: arXiv - cs.LG

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