Digital twins and non-invasive recordings enable early diagnosis of Alzheimer's disease

The diagnosis of Alzheimer's disease (AD) in its preclinical stages, such as subjective cognitive decline (SCD), is crucial for a timely management of the condition. However, current early diagnostic methods are unsuitable for preclinical screenings due to limited availability and diagnostic reliability. Additionally, reliance on invasive and scarcely available methods exacerbates the underdiagnosis of AD in its preclinical forms.

We introduce an early diagnostic pipeline based on the Digital Alzheimer's Disease Diagnosis (DADD) digital twin model, which derives personalized AD biomarkers from non-invasive electroencephalographic (EEG) recordings. These biomarkers reconstruct patient-specific neurodegeneration, capturing synaptic and connectivity degeneration mechanisms. Digital biomarkers were used to predict cerebrospinal fluid (CSF) biomarker positivity for AD and clinical conversions at follow-up in 124 participants with varying degrees of cognitive decline, including a control group of 19 healthy subjects.

Digital biomarkers derived from the DADD model: i) Robustly distinguished SCD from healthy participants, improving classification accuracy by 7% compared to standard EEG biomarkers; ii) Identified patients positive for CSF biomarkers of AD with 88% accuracy (significantly outperforming standard EEG biomarkers, which achieved 58% accuracy); iii) Predicted follow-up conversions to clinical cognitive decline with 87% accuracy (compared to 54% accuracy for standard EEG biomarkers).

The DADD model provided robust digital AD biomarkers with strong diagnostic and prognostic value for preclinical AD, enabling the prediction of CSF biomarkers and clinical conversions using only non-invasive EEG recordings. This is particularly important as preclinical patients, such as those with SCD, are often excluded from diagnostic procedures like lumbar puncture. Predicting CSF biomarkers by combining digital twins with non-invasive recordings could revolutionize AD diagnosis in its early stages, paving the way for the clinical application of digital twins in AD diagnostics.

Clinical Trial identifier: NCT05569083 (submitted 2022-08-24).