Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph
Zhuqing Jiao1,2, Peng Gao1, Yixin Ji1
1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
Abstract:
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Researchers tend to discuss its early state (early MCI, eMCI) due to its high conversion rate of dementia and poor treatment effect in the middle and late stages. Currently, the research on the disease evolution of the brain functional networks of patients with MCI has gradually become a research hotspot. In this study, we compare the differences in dynamic functional connectivity among eMCI, late MCI (lMCI), and normal control (NC) groups, and their graph theory indicators reveal the integration and segregation of functional connectivity states. Firstly, dynamic functional network windows were constructed based on the sliding time window method, and then these window samples were clustered by k-means to extract the functional connectivity states. The differences in the three groups were compared by analyzing the graph theory indicators, such as the participation coefficient, module degree distribution, clustering coefficient, global efficiency, and local efficiency, which distinguish the functional connectivity states. The results reveal that the NC group has the strongest integration and segregation, followed by the eMCI group, and the lMCI group has the weakest integration and segregation. We conclude that with the aggravation of MCI, the integration and segregation of dynamic functional connectivity states tend to decline. The results also reflect that the lMCI group has significantly more brain functional connections in some states, such as IPL.L-MTG.R and DCG.R-SMG.L, than the eMCI group, while the lMCI group has significantly less OLF.L-SPG.L than the NC group.
Insights
Brain functional connectivity declines with the progression of mild cognitive impairment (MCI). Early MCI (eMCI) and late MCI (lMCI) show reduced integration and segregation compared to normal controls (NC).
Area of Science:
- Neuroscience
- Cognitive Science
- Medical Imaging
Background:
- Mild cognitive impairment (MCI) is a transitional stage between normal aging and dementia.
- Early MCI (eMCI) is a critical focus due to its high dementia conversion rate and limited treatment efficacy in later stages.
- Understanding brain functional network evolution in MCI is a growing research area.
Purpose of the Study:
- To compare dynamic functional connectivity and graph theory indicators across early MCI (eMCI), late MCI (lMCI), and normal control (NC) groups.
- To investigate how brain functional network integration and segregation change with MCI progression.
- To identify specific brain network alterations associated with different MCI stages.
Main Methods:
- Dynamic functional network windows were created using a sliding time window approach.
- K-means clustering was employed to extract distinct functional connectivity states.
- Graph theory metrics (participation coefficient, module degree, clustering coefficient, global/local efficiency) were analyzed to assess network properties.
Main Results:
- Normal controls (NC) exhibited the highest integration and segregation of functional connectivity states.
- Early MCI (eMCI) showed intermediate levels, while late MCI (lMCI) displayed the weakest integration and segregation.
- Specific brain connections differed significantly between groups, with lMCI showing increased connectivity in some regions (e.g., IPL.L-MTG.R) and decreased connectivity in others (e.g., OLF.L-SPG.L) compared to NC.
Conclusions:
- The integration and segregation of dynamic functional connectivity states decrease as MCI progresses.
- These findings highlight a decline in brain network efficiency with increasing severity of cognitive impairment.
- Dynamic functional connectivity analysis offers insights into the neuropathological changes underlying MCI progression.
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