Distinct Resting-State EEG Biomarkers Predict Amyloid Status and Conversion to Alzheimer’s Dementia in a Memory Centre - European Medical Journal

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Distinct Resting-State EEG Biomarkers Predict Amyloid Status and Conversion to Alzheimer’s Dementia in a Memory Centre

2 Mins
Neurology
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Authors:
* Giordano Cecchetti , 1-4 Silvia Basaia , 3 Marco Cursi , 2 Edoardo G. Spinelli , 1,3,4 Elisa Canu , 3 Francesca Caso , 1 Roberto Santangelo , 1,2 Giovanna Franca Fanelli , 2 Giuseppe Magnani , 1 Federica Agosta , 1,3,4 Massimo Filippi 1-5
  • 1. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
  • 2. Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
  • 3. Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
  • 4. Vita-Salute San Raffaele University, Milan, Italy
  • 5. Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
*Correspondence to [email protected]
Disclosure:

Cecchetti has received honoraria for lectures from Neopharmed Gentili and Eli Lilly. Spinelli has received funding from the Next Generation EU programme, via the National Recovery and Resilience Plan (NRRP), under Investment PE8-Project Age-It. Agosta has received funding from the Next Generation EU programme, via the National Recovery and Resilience Plan (NRRP), under Investment PE8-Project Age-It; grants or contracts from the Italian Ministry of Health, the Italian Ministry of University and Research, Fondazione Italiana di Ricerca per la SLA (AriSLA), the European Research Council, the EU Joint Programme – Neurodegenerative Disease Research (JPND), and Foundation Research on Alzheimer Disease (France); and honoraria for lectures from Biogen Idec, Italfarmaco, Roche, Zambon, and Eli Lilly. Filippi has received funding from the Next Generation EU programme, via the National Recovery and Resilience Plan (NRRP), under Investment PE8-Project Age-It; grants or contracts from Biogen Idec, Merck-Serono, Novartis, Roche, the Italian Ministry of Health, the Italian Ministry of University and Research, and Fondazione Italiana Sclerosi Multipla; funding for consulting fees from Alexion, Almirall, Biogen, Merck, Novartis, Roche, and Sanofi; honoraria for speakers from Bayer, Biogen, Celgene, Chiesi Italia SpA, Eli Lilly, Genzyme, Janssen, Merck-Serono, Neopharmed Gentili, Novartis, Novo Nordisk, Roche, Sanofi, Takeda, and TEVA; served on Advisory Boards for Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche, Sanofi, Sanofi-Aventis, Sanofi-Genzyme, and Takeda; served as Scientific Director for educational events for Biogen, Merck, Roche, Celgene, Bristol Myers Squibb, Eli Lilly, Novartis, and Sanofi-Genzyme; is Editor-in-Chief of the Journal of Neurology; and is Associate Editor of Human Brain Mapping, Neurological Sciences, and Radiology. The other authors have declared no conflicts of interest.

Citation:
EMJ Neurol. ;13[1]:34-37. https://doi.org/10.33590/emjneurol/FDOF7047.
Keywords:
Alzheimer’s disease (AD), amyloid, clinical conversion, mild cognitive impairment (MCI), resting-state EEG (RS-EEG), source reconstruction, support vector machine.

Each article is made available under the terms of the Creative Commons Attribution-Non Commercial 4.0 License.

BACKGROUND

Resting-state EEG (RS-EEG) has emerged as a non-invasive, low-cost tool for Alzheimer’s disease (AD) diagnosis and stratification. This study evaluated whether EEG-based biomarkers can predict amyloid status and clinical progression from mild cognitive impairment (MCI) to AD dementia in patients referred to a third-level memory clinic.1

METHODS

The authors retrospectively analysed 295 patients who were cognitively impaired and underwent a standardised diagnostic work-up, including neuropsychological assessment, cerebrospinal fluid (CSF) biomarker analysis, and 19-channel RS- EEG recording (10–20 system, eyes closed; Table 1). Patients were classified as amyloid-positive (A+; n=184) or amyloid-negative (A-; n=111) based on CSF Aβ42/40 ratio. The MCI subgroup (n=106) was divided into MCI A+ (n=61) and MCI A- (n=45). A total of 39 individuals with MCI A+ were followed over 2 years, and 23 converted to AD dementia.

Table 1: Demographic and clinical features of patients stratified according to their amyloid status and conversion status from mild cognitive impairment to dementia.
Values are averages and SDs (range). p values refer to T test models or the Chi-square test. Statistical significance was defined as p<0.05.
A+: amyloid-positive; A-: amyloid-negative; CDR: Clinical Dementia Rating scale; CSF: cerebrospinal fluid; F: females; M: males; MCI: mild cognitive impairment; MCI_A+: amyloid-positive mild cognitive impairment; MCI_A-: amyloid-negative mild cognitive impairment; MCI_c: mild cognitive impairment converters; MCI_nc: mild cognitive impairment non-converters; MMSE: Mini-Mental Status Exam; N: number; pTau: phosphorylated Tau; y: years.

EEG preprocessing included bandpass filtering (1–45 Hz), independent component analysis-based artefact removal, and re-referencing to the average. Source-space analysis was performed using standardised low-resolution brain electromagnetic tomography, constrained to two networks of interest: the default mode network (DMN), typically affected in AD, and the salience network (SN), more often impaired in non-AD conditions. These networks were defined through a seed-based resting-state functional MRI analysis conducted in an independent cohort of healthy young adults, using canonical seeds placed in the posterior cingulate cortex (DMN) and anterior cingulate (SN). The resulting network maps were coregistered in Montreal Neurological Institute (MNI) space and parcellated into regions of interest. EEG source activity was then estimated voxel-wise and averaged across each region of interest to extract measures of current source density (CSD) and pairwise linear lagged connectivity (LLC), across δ (1–3.5 Hz), θ (4–7.5 Hz), α1 (8–10 Hz), α2 (10.5–12 Hz), and β (12–30 Hz) bands.

RESULTS

Patients who were A+ showed a general slowing of EEG cortical activity when compared to A- in both DMN and SN. Patients with MCI A+ showed significantly increased θ CSD and LLC in both networks, with peak differences in posterior DMN regions, consistent with early network-level hyperconnectivity. Among subjects with MCI A+, converters exhibited reduced α1 CSD in the DMN and decreased α LLC across both DMN and SN. No significant changes emerged in the β band.

Support vector machine classifiers using significant EEG features were trained to predict amyloid status (at both whole group and MCI group levels) and MCI conversion. Feature selection was guided by Shapley Additive Explanations, identifying occipital θ CSD and frontal α1 LLC as most predictive. The best-performing support vector machine models achieved around 60% balanced accuracy in all classifications, with robust results across 5-fold cross-validation.

CONCLUSION

This study underscores that AD causes early disruptions in cortical electrical activity, detectable through resting-state EEG. θ current source density and network connectivity emerged as early markers of amyloid-related pathology, while α connectivity remained relatively preserved in early stages and declined in patients progressing to dementia. These findings reflect a trajectory from early excitatory θ alterations to α disconnection, marking synaptic dysfunction and neurodegeneration.2-4 Machine learning models offer promising avenues for early detection and risk stratification.5 Notably, the DMN was more consistently impaired than the salience network, highlighting its selective vulnerability in AD.2,3

RS-EEG may support scalable, non-invasive screening strategies in memory clinics, especially where access to amyloid PET or CSF biomarkers is limited. Future directions include longitudinal validation, integration with plasma biomarkers, and multimodal frameworks for non-invasive prediction of disease progression.

References
Cecchetti G et al. Distinct resting-state EEG biomarkers predict amyloid status and conversion to Alzheimer's dementia in a memory center. Abstract OPR-068. EAN Congress, 21-24 June, 2025. Cecchetti G et al. Resting-state electroencephalographic biomarkers of Alzheimer's disease. Neuroimage Clin. 2021;31:102711. Cecchetti G et al. EEG correlates in the 3 variants of primary progressive aphasia. Neurology. 2024;102(3):e207993. Cassani R et al. Systematic review on resting-state EEG for Alzheimer's disease diagnosis and progression assessment. Dis Markers. 2018;2018:5174815. Jeong J. EEG dynamics in patients with Alzheimer's disease. Clin Neurophysiol. 2004;115(7):1490-505.

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