Citations

AbstractObjectiveMost seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.MethodsThis study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective.ResultsFor the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature.SignificanceEnvironmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

AbstractBackgroundPast research has demonstrated that clinical EEG is sensitive to dementia associated with Alzheimer's (AD) and Parkinson’s diseases (PD), and nonfluent/agrammatic Primary Progressive Aphasia (agPPA), but not Primary Progressive Apraxia of Speech (PPAOS). However, clinical EEG studies do not quantify interactions of multiple brain areas, or network activity. Graph theory (GT) simplifies analyzing and comparing complex brain networks. Studies have shown changes in EEG GT measures in AD and PD, but have not been studied in agPPA or PPAOS. The goal of this study was to use GT to characterize network‐level changes and associations with speech and language functioning in patients with agPPA and PPAOS.MethodsScalp EEGs of 14 agPPA and 7 PPAOS patients were collected during relaxed wakefulness (10‐20 positions, 256Hz sampling, 1–200Hz bandpass filter). Eight artifact‐free epochs were selected from non‐overlapping 1024‐point epochs. Via Brainwave software, GT weighted connectivity and minimum spanning tree (MST) measures were calculated for theta and alpha frequency bands. Differences in theta MST measures between agPPA and PPAOS were assessed with Wilcoxon rank‐sum tests. Spearman correlations were computed between behavioral and all network measures across all patients.ResultsTheta frequency MST diameter and eccentricity were significantly lower and leaf was significantly higher in agPPA compared to PPAOS patients; betweenness centrality did not differ. This indicates agPPA networks are more integrated (star‐like) than PPAOS. There were significant correlations between several network measures (e.g. kappa, lambda, PLI) and indices of speech‐language functioning.ConclusionsThe results provide EEG evidence of network alteration in agPPA and PPAOS patients. The GT measures and associations with behavioral measures provide support for reduced global efficiency, or network specificity, in these patients. The correlation of language measures with lambda (path length) in the theta frequency band suggests agPPA is associated with an inefficient path length and/or increased integration. The correlation between apraxia of speech (AOS) measures and MST eccentricity in the alpha frequency band suggests that more severe AOS is associated with reduced distance between two nodes. This study demonstrates potential for EEG GT measures to quantify network changes and distinguish network changes between related but different clinical syndromes.

Abstract Scientists debate where, when, and how different visual, orthographic, lexical, and semantic features are involved in visual word recognition. In this study, we investigate intracranial neurophysiology data from 151 patients engaged in reading single words. Using representational similarity analysis, we characterize the neural representation of a hierarchy of word features across the entire cerebral cortex. We find evidence of both feed-forward and feedback processing, with early representation of visual and lexical information in lingual gyrus followed by lexical representation in fusiform gyrus and semantic sensitivity in inferior frontal gyrus, with letter representation emerging later in fusiform gyrus. Furthermore, we observed a variety of anatomically heterogeneous temporal response shapes, and these functional populations had significant feature sensitivity. Taken together, our results demonstrate the early influence of lexical, phonological, and semantic features in visual word recognition and reveal feed-forward, feed-back, and anatomically distributed processing mechanisms that contribute to visual word recognition.

AbstractEpilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Microglial calcium signaling underlies a number of key physiological and pathological processes in situ, but has not been studied in vivo in awake mice. Using multiple GCaMP6 variants targeted to microglia, we assessed how microglial calcium signaling responds to alterations in neuronal activity across a wide range. We find that only a small subset of microglial somata and processes exhibited spontaneous calcium transients in a chronic window preparation. However, hyperactive shifts in neuronal activity (kainate status epilepticus and CaMKIIa Gq DREADD activation) triggered increased microglial process calcium signaling, often concomitant with process extension. Additionally, hypoactive shifts in neuronal activity (isoflurane anesthesia and CaMKIIa Gi DREADD activation) also increased microglial process calcium signaling. Under hypoactive neuronal conditions, microglia also exhibited process extension and outgrowth with greater calcium signaling. Our work reveals that microglia have highly distinct microdomain signaling, and that processes specifically respond to bi-directional shifts in neuronal activity through increased calcium signaling.

Intracranial electroencephalography (iEEG) has been the mainstay of identifying the seizure onset zone (SOZ), a key diagnostic procedure in addition to neuroimaging when considering epilepsy surgery. In many patients, iEEG has been the basis for resective epilepsy surgery, to date still the most successful treatment for drug-resistant epilepsy. Intracranial EEG determines the location and resectability of the SOZ. Advances in recording and implantation of iEEG provide multiple options in the 21st century. This not only includes the choice between subdural electrodes (SDE) and stereoelectroencephalography (SEEG) but also includes the implantation and recordings from microelectrodes. Before iEEG implantation, especially in magnetic resonance imaging -negative epilepsy, a clear hypothesis for seizure generation and propagation should be based on noninvasive methods. Intracranial EEG implantation should be planned by a multidisciplinary team considering epileptic networks. Recordings from SDE and SEEG have both their advantages and disadvantages. Stereo-EEG seems to have a lower rate of complications that are clinically significant, but has limitations in spatial sampling of the cortical surface. Stereo-EEG can sample deeper areas of the brain including deep sulci and hard to reach areas such as the insula. To determine the epileptogenic zone, interictal and ictal information should be taken into consideration. Interictal spiking, low frequency slowing, as well as high frequency oscillations may inform about the epileptogenic zone. Ictally, high frequency onsets in the beta/gamma range are usually associated with the SOZ, but specialized recordings with combined macro and microelectrodes may in the future educate us about onset in higher frequency bands. Stimulation of intracranial electrodes triggering habitual seizures can assist in identifying the SOZ. Advanced computational methods such as determining the epileptogenicity index and similar measures may enhance standard clinical interpretation. Improved techniques to record and interpret iEEG may in the future lead to a greater proportion of patients being seizure free after epilepsy surgery.

Abstract Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in‐hospital or in‐home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high‐quality, marginal‐quality, or poor‐quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good‐quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good‐quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good‐, marginal‐, and poor‐quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist‐worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.