Functional Connectivity Analysis of Brain Default Mode Networks Using Hamiltonian Path.

Area of Science:

• Neuroscience
• Network Science
• Medical Imaging

Background:

• Default Mode Networks (DMNs) are crucial for intrinsic brain function.
• Understanding DMN functional connectivity is vital for diagnosing neurological disorders.
• Current methods may not fully capture complex network dynamics in DMNs.

Purpose of the Study:

• To introduce a novel Hamiltonian path approach for analyzing brain DMN functional connectivity.
• To investigate differences in DMN properties between normal subjects and stroke patients.

Main Methods:

• Functional Magnetic Resonance Imaging (fMRI) data acquisition for resting-state DMNs.
• Dijkstra algorithm to compute shortest path lengths for brain regions.
• Improved adaptive ant colony algorithm to solve for the Hamiltonian path.
• Complex network analysis to evaluate node and network properties.

Main Results:

• Significant differences in DMN properties were observed between stroke patients and normal subjects.
• The length of the Hamiltonian path emerged as a key distinguishing feature.
• The proposed Hamiltonian path method effectively highlights alterations in brain functional connectivity.

Conclusions:

• The Hamiltonian path analysis provides a sensitive measure of DMN functional connectivity.
• This method can effectively differentiate between healthy individuals and stroke patients based on brain network properties.
• The study validates the utility of Hamiltonian paths in neuroscience research for understanding brain disorders.