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This study examined genetic associations with brain wave patterns measured by EEG in 169 Finnish individuals with mild cognitive impairment (MCI), a precursor stage to Alzheimer's disease. Researchers identified 184 candidate genes linked to EEG features that converged on pathways involving synaptic transmission, neuronal excitability, and neuroinflammation, with some genes mapping to known Alzheimer's risk regions. Genetic clustering based on EEG-associated variants successfully identified patient subgroups with significant differences in plasma p-tau217 levels and memory recall performance, suggesting EEG patterns capture distinct biological processes in early cognitive decline.
Why it matters
This research demonstrates that EEG-based brain signatures combined with genetic information could help identify biologically distinct subgroups within patients experiencing early cognitive decline. Such stratification could potentially enable more personalized approaches to predicting Alzheimer's progression and selecting appropriate interventions, while the identified genes provide new targets for understanding disease mechanisms.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
The mild cognitive impairment (MCI) continuum represents a critical stage in the Alzheimer’s disease (AD), yet much of the genetic determinants underlying early neural dysfunction in AD remain unclear. Electroencephalography (EEG)-derived features as heritable markers of neuronal network activity may provide biologically informative endophenotypes for studying cognitive decline in the MCI. We investigated associations between functional genetic variation and resting-state EEG features in 169 Finnish individuals with symptoms spanning from subjective cognitive decline to MCI. Periodic alpha- and beta-band features and aperiodic spectral parameters were derived from eyes-closed EEG recordings. Genome-wide genotypes were quality-controlled, imputed, functionally annotated (yielding 16,935 gene regions comprising 90,607 functional variants), and associated with latent EEG features using Bayesian reduced rank regression. We integrated the results with neurobiology-related literature review and known AD-associated loci and performed unsupervised clustering of participants based on EEG-associated genetic variation, testing clinical and biomarker differences between clusters. We identified 145 genes associated with periodic EEG features and 39 genes with aperiodic EEG features at P < 0.005, although no associations survived correction for multiple testing. Candidate genes converged on pathways for synaptic transmission, neuronal excitability and neurodevelopment (e.g., RASGEF1C and TREML2 mapped to loci previously associated with AD). The aperiodic-feature candidates were more enriched for neuroinflammatory processes (e.g., C5AR2 within a FinnGen AD-associated region) relative to the periodic-feature candidates which were more frequently involved in intracellular neuronal maintenance and signaling. Unsupervised clustering based on periodic EEG-associated variants delineated participant subgroups differing significantly in plasma p-tau217 concentrations and delayed verbal recall after multiple-testing correction. The top genes contributing to cluster formation included ACAN, INPP5B, CAMKK2, CABIN1 and SPATA13. These findings suggest that periodic and aperiodic EEG features capture partly distinct biological processes within the MCI continuum. The convergence of candidate genes on synaptic, neurodevelopmental and neuroinflammatory pathways, and genetically informed clustering linked to plasma p-tau217 and delayed verbal recall, supports the use of EEG-derived endophenotypes for dissecting heterogeneity in early cognitive decline and AD-related pathology.