P003 Digital Twins Enable Early Alzheimer’s Disease Diagnosis by Reconstructing Neurodegeneration Levels from Non-Invasive Recordings
Lorenzo Gaetano Amato1,2*, Michael Lassi1,2, Alberto Arturo Vergani1,2, Jacopo Carpaneto1,2, Valentina Moschini3, Giulia Giacomucci4, Benedetta Nacmias4,5, Sandro Sorbi4,5, Antonello Grippo4, Valentina Bessi4, Alberto Mazzoni1,2
1The BioRobotics Institute, Sant’Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
2Department of Excellence in Robotics and AI, Sant’Anna School of Advanced Studies, Pisa, Italy, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
3Skeletal Muscles and Sensory Organs Department, Careggi University Hospital,Largo Brambilla 3, 50134,Florence, Italy
4Department of Neuroscience, Psychology, Drug Research and Child Health, Careggi University Hospital,Largo Brambilla 3, 50134,Florence, Italy
5IRCSS Fondazione Don Carlo Gnocchi,Via di Scandicci 269, 50143,Florence, Italy
*Presenting author: lorenzogaetano.amato@santannapisa.it
Introduction
Early detection of Alzheimer’s disease (AD) is essential for timely intervention and improved patient outcomes. However, current diagnostic methods, including cerebrospinal fluid (CSF) analysis and neuroimaging techniques, are often invasive, costly, and unsuitable for large-scale population screenings. Non-invasive neural recordings like electroencephalography (EEG) provide a non-invasive alternative[1], yet conventional EEG analysis struggles to identify cortical alterations associated with AD at preclinical stages. To address these limitations, we propose a novel approach based on digital twin models that extract personalized digital biomarkers from non-invasive neural recordings.
Methods
We developed the DADD (Digital Alzheimer’s Disease Diagnosis) digital twin model to estimate individual neurodegeneration levels from non-invasive neural recordings[2]. EEG recordings were collected in resting-state and in task condition from 145 participants across various stages of cognitive decline, including healthy controls (HC), SCD, and mild cognitive impairment (MCI). Through model inversion, DADD reconstructed personalized neurodegeneration parameters from experimental recordings (Fig. 1). Personalized parameters were employed as digital biomarkers to predict CSF biomarker positivity and conversion to clinical cognitive decline, comparing their diagnostic power relative to traditional EEG analysis.
Results
The DADD model significantly outperformed standard EEG analysis in identifying AD-related neurodegeneration. It increased the classification accuracy between HC and MCI by 20% and between HC and SCD by 8% compared to conventional EEG measures. Digital biomarkers also improved by 30% the identification of individuals positive for CSF biomarkers of AD and by 33% the prediction of future clinical conversions with respect to EEG features, highlighting their potential as prognostic markers. Notably, the model also shed light on the structural underpinnings of disease progression, revealing a neurodegeneration-driven transition between distinct regimes of network efficiency and functional connectivity that was backed by experimental EEG data.
Discussion
These findings establish digital twin models as powerful tools for non-invasive AD diagnosis and prognosis. By leveraging EEG-derived digital biomarkers, our approach supports classification of MCI, assessment of AD pathology, and estimation of cognitive decline risk with unprecedented accuracy. The ability of digital twins to replicate individual brain dynamics provides deeper insights into disease progression, bridging the gap between network structure and cognitive outcomes. This method represents a scalable and cost-effective solution for early AD detection, potentially facilitating widespread clinical implementation and improving patient management strategies.
Figure 1. Experimental EEGs are compared with simulated signals through model inversion, enabling the identification of a personalized set of model parameters for each patient. These parameters are then utilized as digital biomarkers to aid in patient classification and diagnosis.
Acknowledgements
This project is funded by Tuscany Region - PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer’s Disease With machine learning – PREVIEW CUP.D18D20001300002.
References
References
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2. L. G. Amato, A. A. Vergani, M. Lassi, C. Fabbiani, S. Mazzeo, R. Burali, B. Nacmias, S. Sorbi, R. Mannella, A. Grippo, V. Bessi, A. Mazzoni, Personalized modeling of Alzheimer’s disease progression estimates neurodegeneration severity from EEG recordings.Alzheimers Dement. Diagn. Assess. Dis. Monit.16, e12526 (2024).
