Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning
Learn to create consistent brain disorder representations using disentangled multi-atlas functional connectivity learning, improving neurological and psychiatric disorder characterization
- Apply disentangled multi-atlas functional connectivity learning to resting-state fMRI data
- Use multiple brain atlases to construct functional connectivity (FC) representations
- Evaluate the consistency of FC representations across different brain atlases
- Compare the performance of disentangled multi-atlas FC learning with traditional single-atlas approaches
- Integrate disentangled multi-atlas FC learning into neurological and psychiatric disorder diagnosis pipelines
Neuroscientists, neuroengineers, and AI researchers can benefit from this technique to improve brain disorder diagnosis and treatment, by applying disentangled multi-atlas functional connectivity learning to resting-state fMRI data
💡 Disentangled multi-atlas functional connectivity learning can create consistent brain disorder representations, alleviating the limitations of traditional single-atlas approaches
🧠 Improve brain disorder diagnosis with disentangled multi-atlas functional connectivity learning! 🤖
Key Takeaways
Learn to create consistent brain disorder representations using disentangled multi-atlas functional connectivity learning, improving neurological and psychiatric disorder characterization
Full Article
Abstract:
arXiv:2605.07026v1 Announce Type: cross Abstract: Functional connectivity (FC) derived from resting-state fMRI is widely used to characterize large-scale brain network alterations in neurological and psychiatric disorders. However, FC construction critically depends on the choice of brain atlas, and different parcellations may emphasize distinct organizational features, leading to heterogeneous and sometimes inconsistent representations. Existing multi-atlas approaches partially alleviate this i
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