Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings

Abstract

Abstract INTRODUCTION Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS We developed a cortical model of AD‐related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non‐invasive EEG recordings. Highlights Personalized cortical model estimating structural alterations from EEG recordings. Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%) Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%) Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y

Keywords

• Alzheimer's disease
• Computational models
• Computational neuroscience
• EEG
• Novel Biomarkers