Frequency-Specific and Across-Frequency Network Disruptions in the Alzheimer’s Continuum
Poster No:
1395
Submission Type:
Abstract Submission
Authors:
Hüden Neşe1, Emre Harı2, Elif Kurt2, Hakan Gürvit2, Ahmet Ademoğlu1, Tamer Demiralp2
Institutions:
1Boğaziçi University, Istanbul, Türkiye, 2Istanbul University, Istanbul, Türkiye
First Author:
Co-Author(s):
Introduction:
Functional connectivity changes in the Alzheimer's continuum, including subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and Alzheimer's disease dementia (ADD), have been studied using resting-state fMRI, highlighting disruptions in large-scale brain networks (Greicius et al., 2004). However, frequency-specific connectivity and across-frequency interactions remain underexplored. A multilayer network approach integrating within- and across-frequency connectivity could provide deeper insights into network disruptions and cognitive decline across disease progression.
Methods:
Resting-state fMRI data from 88 participants (21 ADD, 34 MCI, and 33 SCI) were acquired using a 3T MRI scanner. Data preprocessing was performed using the CONN Toolbox. For network analysis, we utilized the 400-parcel, 7-network parcellation schema (Schaefer et al., 2018). Empirical Mode Decomposition (EMD) was applied to extract frequency-specific modes (IMF1:0.04-0.1Hz, IMF2:0.02-0.04Hz and IMF3:0.01-0.02Hz). Functional connectivity matrices for each frequency band were computed using Pearson's correlation coefficient. A three-layer network was generated for each participant, with each layer corresponding to a distinct frequency band. We computed multilayer betweenness centrality (MBC), identifying brain regions critical for mediating information flow across frequency bands. Modularity analysis was performed, followed by calculating flexibility for each parcel, which reflects a region's ability to engage in different modules at varying temporal scales. Global and local efficiency metrics were calculated to assess global and local information transfer efficiency at each layer (Rubinov, M., & Sporns, 2010). Statistical differences in these metrics across groups were analyzed using ANOVA. Additionally, correlations between these metrics and participants' cognitive scores were examined.
·Overview of the Multilayer Functional Connectivity Analysis Pipeline.
Results:
We investigated patient group (ADD, MCI, and SCI) differences in flexibility, MBC and local efficiency across ICNs using a two-way ANOVA with independent factors of patient group and ICN. For flexibility, significant main effects were observed for both patient group (p = 0.009) and ICN (p < 0.001). Post-hoc analysis revealed that the ADD group exhibited significantly higher flexibility compared to SCI (p= 0.006). For MBC, significant main effects were found for patient groups (p < 0.001) and ICN (p < 0.001). Post-hoc comparisons demonstrated that the ADD group had the highest MBC, significantly differing from both MCI (p < 0.001) and SCI (p < 0.001). For local efficiency, significant main effects were detected for patient group (p< 0.001) and ICN (p < 0.001). Post-hoc analysis showed that the ADD group displayed significantly higher local efficiency compared to both MCI (p < 0.001) and SCI (p < 0.001). Correlation analyses revealed that frequency domain flexibility negatively correlated with cognitive tasks (Fluency, Free Recall), while MBC demonstrated consistent negative associations with cognitive performance. In contrast, global and local efficiency were positively associated with cognitive performance across all domains, highlighting that higher network efficiency supports better cognitive outcomes.
·Correlation Between Network Metrics and Cognitive Performance.
Conclusions:
On healthy subjects, we found that specific parcels, primarily in attention networks, exhibit frequency domain flexibility at the group level, supporting cognitive functions (Neşe et al., 2024). In ADD, this flexibility is disrupted, shifting toward randomness and reducing network efficiency. The observed disruptions in flexibility and MBC indicate a breakdown of inhibitory mechanisms and a shift toward excitation, aligning with growing evidence that excitatory-inhibitory dysregulation in ADD progression. These disruptions were not detectable in individual frequency-specific layers, underscoring the value of a multiplex network approach. This framework reveals deeper pathological mechanisms driving ADD and highlights potential biomarkers for cognitive decline.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Higher Cognitive Functions:
Higher Cognitive Functions Other
Lifespan Development:
Aging
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Keywords:
Cognition
FUNCTIONAL MRI
Other - Alzheimer; Frequency-specific; Across-frequency; Multilayer Network Analysis; Modularity; Flexibility; Multilayer Betweenness Centrality; Network Efficiency
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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For human MRI, what field strength scanner do you use?
Provide references using APA citation style.
Neşe, H., Harı, E., Ay, U., Demiralp, T., & Ademoğlu, A. (2024). Integrative role of attention networks in frequency-dependent modular organization of human brain. Brain Structure and Function, 1-13.
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059-1069.
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., ... & Yeo, B. T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.