Citations

Abstract Objective . Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach . Data from seven patients (age 34 ± 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1–600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results . Classification accuracy of 97.8 ± 0.3% (normal tissue) and 89.4 ± 0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8 ± 0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1 ± 1.6%). Spectral power in high frequency band features (Ripple (80–250 Hz), Fast Ripple (250–600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy ⩾90% using a single electrode contact and single spectral feature. Significance . Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

SummaryObjectiveLocal field potentials (LFPs) arise from synchronous activation of millions of neurons, producing seemingly consistent waveform shapes and relative synchrony across electrodes. Interictal spikes (IISs) are LFPs associated with epilepsy that are commonly used to guide surgical resection. Recently, changes in neuronal firing patterns observed in the minutes preceding seizure onset were found to be reactivated during postseizure sleep, a process called seizure‐related consolidation (SRC), due to similarities with learning‐related consolidation. Because IISs arise from summed neural activity, we hypothesized that changes in IIS shape and relative synchrony would be observed in the minutes preceding seizure onset and would be reactivated preferentially during postseizure slow‐wave sleep (SWS).MethodsScalp and intracranial recordings were obtained continuously across multiple days from clinical macroelectrodes implanted in patients undergoing treatment for intractable epilepsy. Data from scalp electrodes were used to stage sleep. Data from intracranial electrodes were used to detect IISs using a previously established algorithm. Partial correlations were computed for sleep and wake periods before and after seizures as a function of correlations observed in the minutes preceding seizures. Magnetic resonance imaging (MRI) and computed tomography (CT) scans were co‐registered with electroencephalography (EEG) to determine the location of the seizure‐onset zone (SOZ).ResultsChanges in IIS shape and relative synchrony were observed on a subset of macroelectrodes minutes before seizure onset, and these changes were reactivated preferentially during postseizure SWS. Changes in synchrony were greatest for pairs of electrodes where at least one electrode was located in the SOZ.SignificanceThese data suggest preseizure changes in neural activity and their subsequent reactivation occur across a broad spatiotemporal scale: from single neurons to LFPs, both within and outside the SOZ. The preferential reactivation of seizure‐related changes in IISs during postseizure SWS adds to a growing body of literature suggesting that pathologic neural processes may utilize physiologic mechanisms of synaptic plasticity.

Objectives: Approximately half of ICU admissions are comprised of patients older than 65 years old. Mild cognitive impairment is a common disorder affecting 10–20% of patients in the same age group. A need exists for exploring mild cognitive impairment and risk of critical illness. As mild cognitive impairment may be a contributor to poorer overall health or be a result of it, we sought to determine whether the presence of mild cognitive impairment independently increases the risk of critical illness admissions. Design: Data from the Mayo Clinic Study of Aging were analyzed. All study participants underwent prospective comprehensive cognitive testing and expert panel consensus diagnosis of both cognitive function and clinical state at baseline and subsequent visits. Comparisons were made between those with normal cognitive function and mild cognitive impairment regarding baseline health and frequency of critical illness. Setting: Single-center population-based cohort out of Olmsted County, MN. Participants: All individuals 70–89 years old were screened for prospective enrollment in the Mayo Clinic Study of Aging. Patients with preexisting dementia and ICU admission within 3 years of entry to the study were excluded from this analysis. Interventions: None. Measurements and Main Results: Of 2,425 patients analyzed from the Mayo Clinic Study of Aging, 1,734 patients (71%) were included in the current study. Clinical factors associated with baseline mild cognitive impairment included age, male gender, stroke, and poorer health self-rating. Using a Cox regression model adjusting for these and a priori variables of baseline health, the presence of mild cognitive impairment remained a significant predictor of ICU admission (hazard ratio, 1.50 [1.15–1.96]; p = 0.003). Conclusions and Relevance: The presence of mild cognitive impairment is independently associated with increased critical illness admission. Further prospective studies are needed to analyze the impact of critical illness on cognitive function.

Background: Numerous large-scale studies have found diverse risk factors for Parkinson’s disease (PD), including caffeine non-use, non-smoking, head injury, pesticide exposure, and family history. These studies assessed risk factors for PD overall; however, PD is a heterogeneous condition. One of the strongest identifiers of prognosis and disease subtype is the co-occurrence of rapid eye movement sleep behavior disorder (RBD). In previous studies, idiopathic RBD was associated with a different risk factor profile from PD and dementia with Lewy bodies, suggesting that the PD-RBD subtype may also have a different risk factor profile. Objective: To define risk factors for PD in patients with or without associated RBD. Methods: In a questionnaire, we assessed risk factors for PD, including demographic, medical, environmental, and lifestyle variables of 189 PD patients with or without associated polysomnography-confirmed RBD. The risk profile of patients with vs. without RBD was assessed with logistic regression, adjusting for age, sex, and disease duration. Results: PD-RBD patients were more likely to have been a welder (OR = 3.11 (1.05–9.223), and to have been regular smokers (OR = 1.96 (1.04–3.68)). There were no differences in use of caffeine or alcohol, other occupations, pesticide exposure, rural living, or well water use. Patients with RBD had a higher prevalence of the combined family history of both dementia and parkinsonism (13.3% vs. 5.5% , OR = 3.28 (1.07–10.0). Conclusion: The RBD-specific subtype of PD may also have a different risk factor profile.

The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex.