Li, Wentao

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.

Summary: Reading epilepsy is a relatively rare reflex epilepsy syndrome that typically presents as orofacial reflex myoclonus triggered by reading. Seizures are thought to be because of activation of hyperexcitable language-related pathways in the dominant hemisphere that subsequently spreads to adjacent motor cortices. More difficult reading tasks are thought to be more provocative of seizures regardless of semantic understanding of the text. The authors sought to better characterize the role of text difficulty and comprehension in triggering seizures in a 27-year-old patient with reading epilepsy. As a part of his epilepsy monitoring unit admission, the patient underwent a series of reading trials with increasing semantic salience and/or difficulty, including pseudo-reading of colors and foreign-language texts, which demonstrated a positive correlation between spike count and clinical events and increasing semantic salience. This suggests that our novel reading task may be able to differentiate between different processes in the reading pathway and that increased semantic relevance of the text, rather than increased difficulty per se, can be associated with increased seizures in reading epilepsy. The authors theorize that this may be associated with his atypical (nondominant) right hemispheric seizure focus and propose that further study of patients with reading epilepsy syndrome may help elucidate the neurobiological networks involved in reading and language processing.