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. 2026 Jan 28;10(1):185-203.
doi: 10.1162/NETN.a.516. eCollection 2026.

Spatiotemporal profiling of functional network overlapping modules in Alzheimer's disease

Yue Gu  1 , Ying Lin  1 , Liangfang Li  1 , Junji Ma  1 , Sihan Wei  1 , Zhengjia Dai  1 ; Alzheimer’s Disease Neuroimaging Initiative
Affiliations

Spatiotemporal profiling of functional network overlapping modules in Alzheimer's disease

Yue Gu et al. Netw Neurosci. .

Abstract

Alzheimer's disease (AD) is characterized by progressive neural network degradation. In brain functional networks, overlapping module structures provide more accurate representations of brain function than nonoverlapping structures. Since the involvement of overlapping nodes in multiple modules can vary over time, investigating dynamic functional changes in the brain may provide deeper insights into the structural characteristics of these overlapping modules. However, the spatiotemporal dynamics of overlapping modular brain organization remain unclear. We employed resting-state fMRI to explore the overlapping modular organization and dynamic multilayer modules in 64 AD (Agemean = 74.04) and 61 healthy controls (HC, Agemean = 74.86) from the Alzheimer's Disease Neuroimaging Initiative. Compared with HC, AD exhibited increased overlapping modules and decreased modularity, with altered nodal overlapping probability, particularly in the superior frontal cortex and hippocampus. Higher nodal overlapping probability correlated with greater flexibility and was associated with larger amyloid deposits. Lasso regression analysis further revealed strong correlations between overlapping nodal characteristics and cognitive performance. Our findings suggest that overlapping nodes are critical components in AD, demonstrating high amyloid deposition, significant functional flexibility, and strong associations to cognitive behavior. These alterations may enhance the understanding of AD pathology and contribute to the development of biomarkers for improved diagnosis and therapeutic strategies.

Keywords: Alzheimer’s disease; Multilayer dynamic networks; Overlapping modular organization; Overlapping node; Temporal variability.

Plain language summary

Alzheimer’s disease (AD) is characterized by progressive neural network degradation, yet the spatiotemporal dynamics of overlapping modular brain organization remain poorly understood. Through investigation of overlapping modular organization and dynamic multilayer modules in AD, we demonstrated that AD is associated with increased numbers of overlapping modules and decreased modularity, accompanied by altered nodal overlapping probability, particularly in the superior frontal cortex and hippocampus. Notably, higher nodal overlapping probability was correlated with increased temporal variability, greater functional flexibility, and larger amyloid deposits. Lasso regression analysis revealed strong associations between overlapping nodal characteristics and cognitive performance. Our findings underscore the critical role of overlapping nodes in AD pathophysiology, providing insights into disease mechanisms and potential avenues for biomarker development and therapeutic interventions.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

<b>Figure 1.</b>
Figure 1.
Overview of the methodological pipeline. Abbreviations include dFC (dynamic functional connectivity), Cor (correlation), MCMOEA (maximal clique-based multiobjective evolutionary algorithm), Modov (overlapping modularity score), Modmulti (dynamic modularity), MVk (dynamic modular variability), Vk (nodal temporal variability), and SUVR (standardized uptake value ratio).
<b>Figure 2.</b>
Figure 2.
Differences in overlapping modular characteristics between AD and HC. (A) The differences in the number of overlapping modules between AD and HC. (B) Comparison of overlapping modularity between AD and HC. (C) The similarity of individual overlapping modular structures within AD and HC groups. The color bar represents the similarity values, with higher values indicating greater similarity in overlapping modular structures. (D) The between-group differences in this similarity. The asterisks indicate significant between-group differences (p < 0.05).
<b>Figure 3.</b>
Figure 3.
The nodal overlapping probability in AD and HC. (A) Spatial distribution of overlapping probabilities in AD and HC groups. The color bar represents the values of overlapping probabilities, with higher values indicating that nodes have a greater probability of overlap. (B) Between-group differences based on the t test. Red coloring indicates higher t values in AD compared with HC, whereas blue coloring indicates lower t values in AD relative to HC.
<b>Figure 4.</b>
Figure 4.
(A) The correlation between nodal overlapping probability and functional flexibility in both AD and HC groups. (B) Correlation between nodal overlapping probability and SUVR indicating amyloid deposits in the brain of AD patients. Each point represents a brain node.
<b>Figure 5.</b>
Figure 5.
The temporal variability in module affiliations. (A) Spatial distribution of the temporal variability in module affiliations across all time windows in AD and HC groups. The color bar represents the values of temporal variability in module affiliations, with higher values indicating greater variability in the structure of module. (B) Group comparison showing changes in the temporal variability of AD compared with HC.
<b>Figure 6.</b>
Figure 6.
(A) The correlation between the temporal modular variability and the nodal overlapping probability in AD and HC groups. (B) Correlation between temporal modular variability and SUVR indicating amyloid deposits in the brain of AD patients. Each point represents a brain node.
<b>Figure 7.</b>
Figure 7.
The nodal temporal variability. (A) Spatial distribution of nodal temporal variability in the AD and HC groups. The color bar represents the values of nodal temporal variability, with higher values indicating greater fluctuations in node activity over time. (B) Between-group differences based on the t test. Red coloring indicates higher t values in AD compared with HC, whereas blue coloring indicates lower t values in AD relative to HC.
<b>Figure 8.</b>
Figure 8.
All brain characteristics selected by Lasso regression for each cognitive performance measure. Features are color-coded: green labels represent dynamic nodal temporal variability, purple labels indicate characteristics in overlapping models (OMs) and overlapping nodes (ONs), and blue labels correspond to modular variability. Each color bar denotes the weight of the feature, with numerical values labeled next to the bars. The assessments included the CDR, MMSE, NPI-Q, Geriatric Depression Scale (GDS), and FAQ. The following abbreviations correspond to the brain regions depicted in the figure, with “L” denoting the left hemisphere and “R” denoting the right hemisphere: Temporal Inf (Inferior Temporal Gyrus), Parietal Sup (Superior Parietal Lobule), Fusiform (Fusiform Gyrus), Lingual (Lingual Gyrus), Frontal Inf Orb (Inferior Frontal Gyrus, Orbital part), Frontal Sup Orb (Superior Frontal Gyrus, Orbital part), Temporal Sup (Superior Temporal Gyrus), Postcentral (Postcentral Gyrus), Paracentral Lob (Paracentral Lobule), ParaHippocampal (Parahippocampal Gyrus), Frontal Inf Oper (Inferior Frontal Gyrus, Opercular part), Frontal Mid (Middle Frontal Gyrus), Precuneus (Precuneus), SupraMarginal (Supramarginal Gyrus), Occipital Inf (Inferior Occipital Gyrus), Frontal Mid Orb (Middle Frontal Gyrus, Orbital part), Precentral (Precentral Gyrus), Angular (Angular Gyrus), and Frontal Sup Orb (Superior Frontal Gyrus, Orbital part).
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