BRAPH: A graph theory software for the analysis of brain connectivity.

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• The brain functions as a complex network, with its organization extensively studied using graph theory and connectomics.
• Analyzing brain network topology is crucial for understanding neurological conditions.
• Existing tools may lack comprehensive features or longitudinal analysis capabilities.

Purpose of the Study:

• To introduce BRAPH (BRain Analysis using graPH theory), a novel freeware software for brain network connectivity analysis.
• To demonstrate BRAPH's utility in analyzing brain network topology using structural and functional neuroimaging data.
• To enable longitudinal comparisons of brain networks in patient cohorts.

Main Methods:

• Developed BRAPH, a MatLab-based software package for brain network analysis.
• Utilized graph theory concepts to represent brain networks as interconnected nodes and edges.
• Applied BRAPH to structural MRI and resting-state fMRI data for network topology analysis in patient groups.

Main Results:

• BRAPH facilitates building connectivity matrices, calculating network measures, and performing group comparisons.
• The software supports the assessment of network modules and comparison with random networks.
• Demonstrated BRAPH's capability in identifying topological differences in Alzheimer's disease and Parkinson's disease mild cognitive impairment cohorts.

Conclusions:

• BRAPH provides a versatile and extensible platform for advanced brain network analysis.
• The software enables detailed investigation of brain network alterations in various neurological disorders.
• BRAPH supports longitudinal studies, offering unique insights into dynamic brain changes over time.