NEUROPHYSIOLOGICAL CORRELATES OF COGNITIVE DECLINE IN A COHORT OF ALZHEIMER’S DISEASE PATIENTS: A MULTIMODAL LONGITUDINAL STUDY

Introduction: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, with a dramatic socio-economic impact. Despite advancements in the diagnostic process thanks to the introduction of in vivo biomarkers of amyloid and tau pathology, the need for robust predictors of disease progression remains critical. This study aims to identify neurophysiological markers that best predict disease progression in AD patients. Methods: This was an observational study conducted on patients with a biological diagnosis of AD. All participants underwent a comprehensive diagnostic work-up – including lumbar puncture and amyloid PET imaging - to confirm AD. At enrollment (T0), a neurophysiological evaluation was performed. Several measures were considered, including the EEG-derived individual alpha frequency (IAF) – a measure of global cognition – Somatosensory Evoked Potentials (SEP) and the TMS protocols Short Latency Intracortical Inhibition (SICI), Intracortical facilitation (ICF) and Short-Latency Afferent Inhibition (SAI) – three paired-pulse protocols measuring cholinergic, glutamatergic, and GABAergic neurotransmission respectively. The cognitive status was evaluated through the Mini-Mental State Examination (MMSE) at T0 and after one year (T1). A subgroup of patients underwent TMS and SEP recordings also at T1. Results: Twenty-four patients with a biological diagnosis of AD were recruited. The mean age at baseline was 70.7 (± 7.6) years, with nine females. The mean Mini-Mental State Evaluation (MMSE) score at baseline was 23.7 (± 3.9) and of 20.6 (± 4.5) at T1. The results of a multiple linear regression model indicated that putting together six predictors – i.e., EEG-derived IAF, TMS measures (SICI_ratio, SAI_ratio, ICF_ratio), lab tests (Abeta42/40, p-tau/Abeta42) – explained 94.8% of the variation of the monthly MMSE score change. Discussion: In the present study we found that a set of neurophysiological parameters – i.e., EEG-derived IAF and the TMS protocols SICI, ICF and SAI – together with Abeta42/40 and p-tau/Abeta42 ratios could effectively predict cognitive decline. This proof-of-principle study demonstrates that a multimodal neurophysiological assessment can significantly enhance our ability to predict and monitor the progression of Alzheimer's disease. The study also highlights the potential of using non-invasive, cost-effective tools to monitor disease progression, which is particularly relevant in the context of emerging disease-modifying therapies. Implementing these findings in clinical settings could optimize patient stratification and treatment outcomes, addressing the growing AD burden effectively.