P191 "Brain Fluidity as a Biomarker for Alzheimer's Disease: Linking Network Dynamics to Clinical Disability Prediction"
Camille Mazzara*1,2,3, Gian Marco Duma4, Giuditta Gambino5, Giuseppe Giglia5, Michele Migliore2, Pierpaolo Sorrentino3,6,7
1.Department of Promoting Health, Maternal-Infant. Excellence and Internal and Specialized Medicine (PROMISE) G. D’Alessandro, University of Palermo, Palermo, Italy.
2.Institute of Biophysics, National Research Council, Palermo, Italy.
3.Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
4.IRCCS E. Medea Scientific Institute, Conegliano, Treviso, Italy
5.Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University of Palermo, Palermo, Italy.
6.Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
7.University of Sassari, Department of Biomedical Sciences, Viale San Pietro, 07100, Sassari, Italy.
*email: camille.mazzara@ibf.cnr.it
Introduction
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and large-scale network dysfunction[1]. While amyloid-beta (Aβ) and tau pathology are well-documented [2,3], the network-level mechanisms linking neuronal degeneration to cognitive impairment remain poorly understood. Traditional functional connectivity (FC) analyses provide static representations of brain networks, failing to capture their intrinsic dynamics[4]. We proposebrain fluidity, a metric that quantify network flexibility, as a potential biomarker reflecting AD-related disruptions in brain dynamics.
Methods
After preprocessing and source reconstruction, we analyzed resting-state EEG data from 28 AD patients and 29 healthy controls. Brain fluidity was quantified measuring the variability of functional connectivity over time, reflecting how interregional synchronization evolves. We assessed its relationship with established AD biomarkers, including cerebrospinal fluid (CSF) levels of Aβ42, phosphorylated tau (p-tau), and total tau (t-tau). Additionally, we examined associations between brain fluidity and cognitive performance (Mini-Mental State Examination (MMSE)). Statistical analyses included between-group comparisons and regression models to determine the predictive value of fluidity in tracking disease severity.
Results
Fluidity analysis across frequency bands (theta, alpha, beta, gamma) revealed significant differences in AD patients (Fig.1a). In the theta band (4–8 Hz), fluidity was higher in AD compared to controls, while in the beta band (14–30 Hz), fluidity was lower. Correlation analyses showed no significant associations between theta fluidity and clinical measures. However, beta fluidity negatively correlated with tTau and pTau (Fig.1c), suggesting a link to neurodegeneration. Notably, no significant associations were found between fluidity and Aβ levels. Using a multilinear regression model we also found that adding fluidity calculated in the beta band significantly improved the predictive power for clinical disability.
Discussion
This results could imply that changes in the ability of the brain to flexibly switch between different dynamic states are associated with neurodegenerative processes, specifically tau-related damage. Reduced brain fluidity in beta may reflect underlying neurodegenerative processes, providing insights into the functional consequences of neuronal loss. Given its sensitivity to AD-related changes, brain fluidity may serve as a promising biomarker for tracking disease progression and evaluating treatment efficacy in clinical settings.
Figure 1. Fig.1 a) Fluidity for each frequency band in AD and control groups. b) dFC matrices averaged across AD (left) and control (right), computed in theta (top) and beta (bottom). c) Correlation between beta-band fluidity and tTau (left), pTau (center), Aβ (right), with significant links to tTau (p = 0.03) and pTau (p = 0.01), but not Aβ42.
Acknowledgements
References
Bibliography
1.https://doi.org/10.1016/j.lfs.2020.117996
2.https://doi.org/10.1590/S1980-57642009DN30300003
3.https://doi.org/10.7554/eLife.98920.1
