Giordano Cecchetti1, Federica Agosta2, Elisa Canu5, Silvia Basaia5, Giulia Rugarli2, Davide G. Curti1, Federico Coraglia3, Marco Cursi6, Edoardo Spinelli1, Roberto Santangelo7, Francesca Caso8, Giovanna Fanelli6, Giuseppe Magnani8, Massimo Filippi4
1Neuroimaging Research Unit, Division of Neuroscience; Neurology Unit; and Neurophysiology Service, 2Neuroimaging Research Unit, Division of Neuroscience; and Neurology Unit, 3Neuroimaging Research Unit, Division of Neuroscience, 4Neuroimaging Research Unit, Division of Neuroscience; Neurology Unit; Neurorehabilitation Unit; and Neurophysiology Service, IRCCS San Raffaele Scientific Institute; and Vita-Salute San Raffaele University, 5Neuroimaging Research Unit, Division of Neuroscience, 6Neurophysiology Service, 7Neurology Unit; and Neurophysiology Service, 8Neurology Unit, IRCCS San Raffaele Scientific Institute
Objective:
This study aimed to investigate the potential of the Individual Alpha Frequency (IAF) derived from electroencephalography (EEG) in stratifying patients with cognitive impairment based on the AT(N) classification system, differentiating clinical dementia subtypes, and predicting the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia.
Background:
Dementia poses a global public health challenge, especially as populations age. Novel AD treatments necessitate precise diagnostics, but advanced techniques can be invasive and costly. EEG-derived IAF offers a non-invasive, cost-effective aid for diagnosis and prognosis in dementia.
Design/Methods:
The study included 907 patients with cognitive impairment from a Memory Clinic. They underwent CSF lumbar puncture, clinical evaluation, neuropsychological assessment, imaging, and EEG. Patients were categorized by AT(N) criteria and clinical diagnoses. IAF was calculated from EEG data. Statistical analyses were conducted to assess correlations, compare IAF values among groups, and predict MCI conversion to AD dementia.
Results:
After stringent criteria, 375 patients remained. Correlation analysis found negligible associations between IAF and conventional clinical features. Within AT(N) groups, IAF was lower in A+ individuals and those with neurodegeneration markers. Combining A status with FDG-PET, A+/PET+ patients had lower IAF values compared to other combinations. IAF effectively distinguished AD and Lewy body dementia from other subtypes, including MCI. In the context of MCI conversion, IAF was significantly lower in converters (A+ MCIc) than stable MCI patients (A+ MCIs). A linear regression model showed IAF predicting time to conversion in A+ MCI patients (R2 = 0.38).
Conclusions:
This study underscores IAF's potential as a valuable asset in dementia diagnosis and prognosis. Its independence from traditional clinical markers, associations with amyloid status and neurodegeneration, and its ability to predict MCI conversion to AD dementia highlight its role in guiding timely and targeted interventions, particularly in the context of emerging disease-modifying therapies for AD.