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Functional Connectivity Analysis of Brain Default Mode Networks Using Hamiltonian Path
Zhuqing Jiao, Kai Ma, Huan Wang
1School of Information Science and Engineering, Changzhou University, Changzhou, 213164,. China.
Insights
This study introduces Hamiltonian paths to analyze brain functional connectivity in default mode networks (DMNs). The method reveals significant differences in DMNs between stroke patients and healthy individuals, particularly in Hamiltonian path length.
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.
Abstract:
The aim of this study is to introduce Hamiltonian path to analyze functional connectivity of brain default mode networks (DMNs). Firstly, the brain DMNs in resting state are constructed with the employment of functional Magnetic Resonance Imaging (fMRI) data. Then, the Dijkstra algorithm is used to calculate the shortest path length of the node which represents each brain region, and the Hamiltonian path of the default network is solved through the improved adaptive ant colony algorithm. Finally, complex network analysis methods are introduced to discuss the node and network properties of brain functional connectivity in both normal subjects and stroke patients. The experimental result demonstrated that there are some significant differences in the properties of the DMNs between stroke patients and normal subjects, especially the length of Hamiltonian path. It also verifies the effectiveness on studying the functional connectivity of the brain DMNs by applying the proposed method of Hamiltonian path.
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