Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Brain connectivity analysis is crucial for understanding interregional communication and diagnosing neurodegenerative disorders.
• Identifying specific brain phenomena and quantifying their levels aids in disease explanation and diagnosis.

Purpose of the Study:

• To establish a unified framework for identifying brain connectivity-based biomarkers.
• To summarize biomarker abnormality into a single numerical value, considering connectivity attributes.
• To associate biomarkers with disease progression.

Main Methods:

• Developed a framework to identify brain connectivity biomarkers and map abnormality levels to a single score (BASIC score).
• Extracted biomarkers as connected components associated with disease progression.
• Constructed BASIC scores to maximize correlation with disease progression, accounting for spatial autocorrelation.

Main Results:

• Successfully applied the framework to construct BASIC scores for Alzheimer's disease progression across different stages.
• Demonstrated that BASIC scores are sensitive and specific to disease progression and its trajectory.
• Showcased the framework's utility for continuous disease scales and its predictive performance.

Conclusions:

• The unified framework identifies brain connectivity-based biomarkers.
• The framework generates BASIC scores that reflect disease progression continuity.
• BASIC scores are sensitive and specific, aiding in disease diagnosis and prediction.