Citations

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.

Abstract Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60–150 Hz), ripple (80–250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.

Artificial intelligence, machine learning, and deep learning are increasingly being used in all medical fields including for epilepsy research and clinical care. Already there have been resultant cutting-edge applications in both the clinical and research arenas of epileptology. Because there is a need to disseminate knowledge about these approaches, how to use them, their advantages, and their potential limitations, the goal of the 2023 Merritt-Putnam Symposium and of this synopsis review of that symposium has been to present the background and state of the art and then to draw conclusions on current and future applications of these approaches through the following: (1) Initially provide an explanation of the fundamental principles of artificial intelligence, machine learning, and deep learning. These are presented in the first section of this review by Dr Wesley Kerr. (2) Provide insights into their cutting-edge applications in screening for medications in neural organoids, in general, and for epilepsy in particular. These are presented by Dr Sandra Acosta. (3) Provide insights into how artificial intelligence approaches can predict clinical response to medication treatments. These are presented by Dr Patrick Kwan. (4) Finally, provide insights into the expanding applications to the detection and analysis of EEG signals in intensive care, epilepsy monitoring unit, and intracranial monitoring situations, as presented below by Dr Gregory Worrell. The expectation is that, in the coming decade and beyond, the increasing use of the above approaches will transform epilepsy research and care and supplement, but not replace, the diligent work of epilepsy clinicians and researchers.

ABSTRACT Aims Scalp‐EEG interictal epileptiform discharges (IEDs) may be less predictive of the outcome of frontal lobe epilepsy surgery than of temporal lobe epilepsy surgery. We identified factors associated with the location of scalp‐EEG IEDs in intractable frontal lobe epilepsy. Methods Ten factors were assessed in a retrospective review of 53 patients with either concordant (frontal lobe seizure focus) or discordant (generalized or outside frontal seizure focus) IED or both, who had excellent surgical outcomes. The Fisher exact test and the Wilcoxon rank sum test determined statistically significant associations. Results Thirty‐six patients (68%) had concordant IED, 24 (45%) discordant IED, and 17 (32%) both. Younger age at onset was significantly associated with discordant IED (mean, 7.5 years versus 17 years for patients without discordant IED; P < 0.01), whereas duration of epilepsy was not. Seizure foci at the frontal convexity were associated with concordant IED. About 72% of patients with a convexity seizure focus had concordant IED, compared with only 33% of patients with mesial frontal foci having concordant IED ( P = 0.06). Conclusions Early seizure onset in intractable frontal lobe epilepsy is associated with IEDs discordant with seizure focus. Frontal convexity seizure foci are more likely than mesial frontal seizure foci to be associated with concordant discharges.

Abstract We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone compared with other leading interictal biomarkers in a multicentre, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centres who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection [International League Against Epilepsy Class 1 outcome (ILAE 1)] and those with persistent seizures (ILAE 2–6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80–500 Hz), ripple (80–250 Hz) and fast ripple (250–500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Overall, 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P &lt; 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P &lt; 0.001 and P &lt; 0.001, respectively). Among ILAE 1 subjects, the mean spike ripple rate was higher in the resected volume (0.66/min) than in the non-removed tissue (0.08/min, P &lt; 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared with ILAE 2–6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P &lt; 0.001), spike-gamma (P &lt; 0.001), wideband HFOs (P &lt; 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01) or fast ripples (36%, P &lt; 0.001) removed. Thus, in this large, multicentre cohort, when surgical resection was successful, the majority of spike ripples were removed. Furthermore, automatically detected spike ripples localize the epileptogenic tissue better than spikes, spike-gamma, wideband HFOs, ripples and fast ripples.