Personalized Modeling of Alzheimer's Disease Progression Estimates Neurodegeneration Severity from EEG Recordings.

Lorenzo Gaetano Amato,Alberto Arturo Vergani,Michael Lassi,Carlo Fabbiani,Salvatore Mazzeo,Rachele Burali,Benedetta Nacmias,Sandro Sorbi,Riccardo Mannella,Antonello Grippo,Valentina Bessi,Alberto Mazzoni

Alzheimer's & dementia (Amsterdam, Netherlands)（2024）

St Anna Sch Adv Studies | IRCSS Fdn Don Carlo Gnocchi | Univ Pisa | Careggi Univ Hosp

Cited 0|Views22

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.

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Key words

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

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