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Dynamic Network Synchrony and Complexity in Mild Cognitive Impairment and Alzheimer's Disease
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1
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| Subject:
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Clinical Applications of Brain Mappin
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Presenter Author:
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| Name:
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Hamidreza Shahi
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Author:
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| Name 1:
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Hamidreza Shahi - Corresponding - Presenter
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| Affiliation 1:
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Iranian Neurowave Lab
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| Name 2:
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Amir Hossein Ghaderi
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| Affiliation 2:
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Iranian Neurowave Lab
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Abstract
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Original Research
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introduction :
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Mild cognitive impairment (MCI) and Alzheimer's disease (AD) are among the most prevalent age-related conditions, with AD leading to progressive cognitive decline and substantial impairment in daily functioning. Numerous studies have investigated these populations, particularly within the field of network neuroscience; however, most have focused primarily on the topological organization of brain networks. In the present study, we instead employ dynamic network measures—namely, network synchrony and complexity—to characterize alterations in brain function.
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Methods:
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Resting-state fMRI data were obtained from the ADNI database and included 48 normal controls (NC; 26/22 males/females, age: 73.4 ± 6.5), 48 individuals with MCI (31/17 males/females, age: 73.9 ± 10), and 48 individuals with AD (27/21 males/females, age: 73.5 ± 8.5). Using the Desikan–Killiany atlas, and to reduce computational burden, limit overfitting, and focus on networks commonly implicated in AD, we selected 46 regions of interest (ROIs) from the default mode, salience, executive control, and limbic networks. Functional connectivity was computed using Pearson correlation, and four graph-theoretical measures—clustering coefficient (CC), characteristic path length (CPL), graph energy (H), and graph entropy (S)—were computed and normalized against null models (except for S). These measures were compared across the three groups using permutation-based t-tests (n = 5000), with FDR correction applied to account for multiple comparisons.
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Results:
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The results revealed pronounced group differences. Specifically, significant differences were observed between AD and NC in CC (q = 0.042, t = −2.342), H (q = 0.005, t = 3.300), and S (q < 0.001, t = −5.127). Similarly, significant differences emerged between MCI and NC in H (q = 0.013, t = 2.844) and S (q < 0.001, t = −5.464). No significant differences were detected between AD and MCI across any of the investigated measures.
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Conclusion:
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The limited differences observed in conventional topological measures are noteworthy. CC was significantly lower in AD than in NC, suggesting reduced local clustering, whereas CPL showed no significant group differences, indicating relatively preserved overall topological organization. In contrast, H was significantly higher in both MCI and AD compared with NC, indicating alterations in network-level synchrony, while S was significantly lower in both clinical groups compared to NC, suggesting reduced complexity and diversity of functional connectivity. Notably, these dynamic measures—particularly S—demonstrated stronger and more consistent group differences than the conventional topological measures, indicating their greater sensitivity and discriminative value in characterizing MCI and AD.
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| Attachment:
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| Keywords:
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mild cognitive impairment, Alzheimer's disease, network neuroscience, fMRI, synchrony, complexity
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| Key message:
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