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Abstract Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer’s disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10–20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer’s disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer’s Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59–0.91) and Alzheimer’s disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer’s subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.
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Population-based spectral characteristics of normal interictal scalp EEG inform diagnosis and treatment planning in focal epilepsy
Normal routine electroencephalograms (EEGs) can cause delays in the diagnosis and treatment of epilepsy, especially in drug-resistant patients and those without structural abnormalities. There is a need for alternative quantitative approaches that can inform clinical decisions when traditional visual EEG review is inconclusive. We leverage a large population EEG database (N = 13,652 recordings, 12,134 unique patients) and an independent cohort of patients with focal epilepsy (N = 121) to investigate whether normal EEG segments could support the diagnosis of focal epilepsy. We decomposed expertly graded normal EEGs (N = 6,242) using unsupervised tensor decomposition to extract the dominant spatio-spectral patterns present in a clinical population. We then, using the independent cohort of patients with focal epilepsy, evaluated whether pattern loadings of normal interictal EEG segments could classify focal epilepsy, the epileptogenic lobe, presence of lesions, and drug response. We obtained six physiological patterns of EEG spectral power and connectivity with distinct spatio-spectral signatures. Both pattern types together effectively differentiated patients with focal epilepsy from non-epileptic controls (mean AUC 0.78) but failed to classify the epileptogenic lobe. Spectral power-based patterns best classified drug-resistant epilepsy (mean AUC 0.73) and lesional epilepsy (mean AUC 0.67), albeit with high variability across patients. Our findings support that visibly normal patient EEGs contain subtle quantitative differences of clinical relevance. Further development may yield normal EEG-based computational biomarkers that can augment traditional EEG review and epilepsy care.
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- Award ID(s):
- 2337909
- PAR ID:
- 10618181
- Publisher / Repository:
- Nature Scientific Reports
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41598-025-08871-w
