The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective.

Sleep deprivation and poor sleep quality are significant societal challenges that negatively impact individuals' health. The interaction between subjective sleep quality, objective sleep measures, physical and cognitive performance, and their day-to-day variations remains poorly understood. Our year-long study of 20 healthy individuals, using subcutaneous electroencephalography, aimed to elucidate these interactions, assessing data stability and participant satisfaction, usability, well-being and adherence. In the study, 25 participants were fitted with a minimally invasive subcutaneous electroencephalography lead, with 20 completing the year of subcutaneous electroencephalography recording. Signal stability was measured using covariance of variation. Participant satisfaction, usability and well-being were measured with questionnaires: Perceived Ease of Use questionnaire, System Usability Scale, Headache questionnaire, Major Depression Inventory, World Health Organization 5-item Well-Being Index, and interviews. The subcutaneous electroencephalography signals remained stable for the entire year, with an average participant adherence rate of 91%. Participants rated their satisfaction with the subcutaneous electroencephalography device as easy to use with minimal or no discomfort. The System Usability Scale score was high at 86.3 ± 10.1, and interviews highlighted that participants understood how to use the subcutaneous electroencephalography device and described a period of acclimatization to sleeping with the device. This study provides compelling evidence for the feasibility of longitudinal sleep monitoring during everyday life utilizing subcutaneous electroencephalography in healthy subjects, showcasing excellent signal stability, adherence and user experience. The amassed subcutaneous electroencephalography data constitutes the largest dataset of its kind, and is poised to significantly advance our understanding of day-to-day variations in normal sleep and provide key insights into subjective and objective sleep quality.

Learning to generalize seizure forecasts.

OBJECTIVE: Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Nonetheless, using years-long electroencephalographic (EEG) recordings, we previously found that patient-reported seizures consistently occur when interictal epileptiform activity (IEA) cyclically builds up over days. This multidien (multiday) interictal-ictal relationship, which is shared across patients, may bear phasic information for forecasting seizures, even if individual patterns of seizure timing are unknown. To test this rigorously in a large retrospective dataset, we pretrained algorithms on data recorded from a group of patients, and forecasted seizures in other, previously unseen patients. METHODS: We used retrospective long-term data from participants (N = 159) in the RNS System clinical trials, including intracranial EEG recordings (icEEG), and from two participants in the UNEEG Medical clinical trial of a subscalp EEG system (sqEEG). Based on IEA detections, we extracted instantaneous multidien phases and trained generalized linear models (GLMs) and recurrent neural networks (RNNs) to forecast the probability of seizure occurrence at a 24-h horizon. RESULTS: With GLMs and RNNs, seizures could be forecasted above chance in 79% and 81% of previously unseen subjects with a median discrimination of area under the curve (AUC) = .70 and .69 and median Brier skill score (BSS) = .07 and .08. In direct comparison, individualized models had similar median performance (AUC = .67, BSS = .08), but for fewer subjects (60%). Moreover, calibration of pretrained models could be maintained to accommodate different seizure rates across subjects. SIGNIFICANCE: Our findings suggest that seizure forecasting based on multidien cycles of IEA can generalize across patients, and may drastically reduce the amount of data needed to issue forecasts for individuals who recently started collecting chronic EEG data. In addition, we show that this generalization is independent of the method used to record seizures (patient-reported vs. electrographic) or IEA (icEEG vs. sqEEG).

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models.

This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models.

OBJECTIVE: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. METHODS: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. RESULTS: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.

A prospective interventional study evaluating seizure activity during a radiotherapy course for high-grade gliomas (SURF-ROGG).

BACKGROUND: Gliomas are often associated with symptoms including seizures. Most patients with high-grade gliomas are treated with radiotherapy or radio-chemotherapy. Since irradiation causes inflammation, it may initially aggravate symptoms. Studies focusing on seizure activity during radiotherapy for gliomas are not available. Such knowledge may improve patient monitoring and anti-epileptic treatment. This study evaluates seizure activity during radiotherapy for high-grade gliomas. METHODS: The primary objective this prospective interventional study is the evaluation of seizure activity during a course of radiotherapy for high-grade gliomas. Progression of seizure activity is defined as increased frequency of seizures by > 50%, increased severity of seizures, or initiation/increase by ≥25% of anti-epileptic medication. Seizure frequency up to 6 weeks following radiotherapy and electroencephalography activity typical for epilepsy will also be evaluated. Patients keep a seizure diary during and up to 6 weeks following radiotherapy. Every day, they will document number (and type) of seizures and anti-epileptic medication. Once a week, the findings of the diary are checked and discussed with a neurologist to initiate or adjust anti-epileptic medication, if necessary. Patients complete a questionnaire regarding their satisfaction with the seizure diary. If the dissatisfaction rate is > 40%, the seizure diary will be considered not suitable for the investigated indication. Thirty-five patients (32 patients plus drop-outs) should be enrolled. With this sample size, a one-sample binomial test with a one-sided significance level of 2.5% has a power of 80% to yield statistical significance, if the rate of patients with progression of seizure activity is 30% (rate under the alternative hypothesis), assuming a 'natural' background progression-rate of 10% without radiotherapy (null hypothesis). DISCUSSION: If an increase in seizure activity during a course of radiotherapy for high-grade glioma occurs, the findings of this study may pave the way for a larger prospective trial and will likely lead to closer patient monitoring and better anti-epileptic treatment. TRIAL REGISTRATION: clinicaltrials.gov ( NCT04552756 ); registered on 16th of September, 2020.

A prospective interventional study investigating sleep disorders prior to and during adjuvant radiotherapy for breast cancer.

BACKGROUND: Most breast cancer patients with non-metastatic disease receive adjuvant local or loco-regional radiotherapy. To be scheduled for irradiation may cause distress and fears that can lead to sleep disorders. Few reports focused on sleep problems in patients assigned to radiotherapy. This study evaluates the course of sleep disorders during adjuvant radiotherapy for primary breast cancer and potential risk factors including the use of smartphones or tablets at bedtime. METHODS: The main goal is the evaluation of sleep disorders prior to radiotherapy and after 15 fractions of radiotherapy. A potential effect of habituation to the procedure of radiotherapy can be assumed that will likely lead to improvement (decrease) of sleep disorders. Improvement of sleep disorders (compared to baseline before radiotherapy) is defined as decrease of the severity of sleep disorders by ≥2 points on a patient self-rating scale (0 = no problems; 10 = maximum problems) or decrease of distress caused by sleep disorders by ≥2 points on a self-rating scale (0 = no distress; 10 = maximum distress) or reduction of the dose of sleeping drugs by ≥25%. Additional endpoints include sleep disorders after 5 fractions and at the end of radiotherapy. Moreover, potential risk factors including the use of smartphones or tablets at bedtime are evaluated. Fifty-one patients (48 plus potential drop-outs) are required. With this sample size, a one-sample binomial test with a one-sided significance level of 2.5% has a power of 80% to yield statistical significance, if the rate of patients with improvement of sleep disorders is 25% (rate under the alternative hypothesis) and assuming that a decrease of ≤10% has to be judged as a random, non-causal change in this uncontrolled study setting (null hypothesis). DISCUSSION: If a decrease of sleep disorders during the course of radiotherapy is shown, this aspect should be included in the pre-radiotherapy consent discussion with the patients. Moreover, identification of additional risk factors will likely lead to earlier psychological support. If the use of smartphones or tablets at bedtime is a risk factor, patients should be advised to change this behavior. TRIAL REGISTRATION: clinicaltrials.gov (NCT04879264; URL: https://clinicaltrials.gov/show/NCT04879264 ); registered on 7th of May, 2021.

Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG.

OBJECTIVE: Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study. METHODS: The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models. RESULTS: sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. SIGNIFICANCE: The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.

A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings.

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.

Ear-EEG-based sleep scoring in epilepsy: A comparison with scalp-EEG.

Ear-EEG is a wearable electroencephalogram-recording device. It relies on recording electrodes that are nested within a custom-fitted earpiece in the external ear canal. The concept has previously been tested for seizure detection in epileptic patients and for sleep recordings in a healthy population. This study is the first to examine the use of ear-EEG recordings for sleep staging in patients with epilepsy, comparing it with standard recordings from scalp-EEG. We use individuals with epilepsy because of their multiple sleep disturbances, and their complex relationship between seizures and sleep, which make this group very likely to benefit from wearable electroencephalogram devices for sleep if it were introduced in the clinic. The accuracy of the ear-EEG against that of the scalp-EEG is compared for sleep staging, and we evaluate features of sleep architecture in individuals with epilepsy. A mean kappa value of 0.74 is found for the agreement between hypnograms derived from ear-EEG and scalp-EEG. Furthermore, it was discovered that sleep stage transition frequency could be contributing to the kappa variation. These findings are related to other ear-recording systems in the literature, and the potentials and future obstacles of the device are discussed.

Event marker compliance in actigraphy.

Actigraphy is a versatile tool for evaluating sleep-wake cycles over time in the home-environment. Patients using the Phillips Actiwatch place an event marker when going to sleep and upon awakening. We investigate compliance in pressing the Actiwatch event marker button for patients referred for insomnia, hypersomnia and disorders of circadian rhythm. We retrospectively analysed event markers from 150 patients undergoing actigraphy for 2,117 nights combined. Compliance was evaluated from inspection of actigraphy records, and coded as full or partial. From patient records, a construct called the C-factor, designed to describe poor social resources and chronic unemployment, was used together with age and sex to predict compliance. We found a mean compliance between 54.0% and 76.3% for a median monitoring duration of 14 days. There was an overall insignificant effect of age (p = .081), but when analysed only for females there was a significant effect of 0.56% pr. year (p = .0038). Compliance was higher for women, Cohen's d = 0.65 (p = .01). The C-factor predicts 18.3% (confidence interval 9%-27.5%) lower compliance. Morning and evening compliance are correlated at r = .65. In conclusion, actigraphy event marker compliance is generally moderate or high, with older women exhibiting the highest compliance. C-factor predicts lower compliance, and this pattern may further translate to other circumstances. If compliance is important, clinicians may want to consider the effects of age, sex and C-factor.

Readiness among people with epilepsy to carry body-worn monitor devices in everyday life: A qualitative study.

OBJECTIVE: There have been intensive efforts to design and develop new wearable technology for epileptic seizure detection. Several studies have focused on the technical aspects, but the readiness of patients with epilepsy (PWEs) to use wearables in everyday life, which is crucial, remains relatively unexplored. METHODS: We conducted a qualitative interview study involving eight PWEs. The study was designed to provide insights into patient readiness to use wearables for home monitoring of epilepsy. RESULTS: Three themes were identified: 1) making invisible situations visible, 2) having companionship within a troubled everyday life, and 3) sharing ownership of no recognizable moments. The analysis and interpretation revealed that the expectations of the participants for wearables were rooted in aspects that had a significant impact on their lives and self-image. CONCLUSION: Patients with epilepsy disclosed that their readiness to use technology, specifically wearables, in everyday life relied on the assumption that they would provide an existential and comforting experience, in which the voids of their individual needs would be addressed in a more patient-friendly manner. Wearable design should consider the valuable insight that technology should be more than just technical tools that monitor symptoms; wearables are expected to be existential and esthetic artifacts that provide PWEs with meaningful experience.

Ultra-long-term subcutaneous home monitoring of epilepsy-490 days of EEG from nine patients.

OBJECTIVE: To explore the feasibility of home monitoring of epilepsy patients with a novel subcutaneous electroencephalography (EEG) device, including clinical implications, safety, and compliance via the first real-life test. METHODS: We implanted a beta-version of the 24/7 EEG SubQ (UNEEG Medical A/S, Denmark) subcutaneously in nine participants with temporal lobe epilepsy. Data on seizures, adverse events, compliance in using the device, and use of antiepileptic drugs (AEDs) were collected. EEG was recorded for up to 3 months, and all EEG data were reviewed visually to identify electrographic seizures. These were descriptively compared to seizure counts and AED changes reported in diaries from the same period. RESULTS: Four hundred ninety days of EEG and 338 electrographic seizures were collected. Eight participants completed at least 9 weeks of home monitoring, while one cancelled participation after 4 weeks due to postimplantation soreness. In total, 13 cases of device-related adverse events were registered, none of them serious. Recordings obtained from the device covered 73% of the time, on average (range 45%-91%). Descriptively, electrographic seizure counts were substantially different from diary seizure counts. We uncovered several cases of underreporting and revealed important information on AED response. Electrographic seizure counts revealed circadian distributions of seizures not visible from seizure diaries. SIGNIFICANCE: The study shows that home monitoring for up to 3 months with a subcutaneous EEG device is feasible and well tolerated. No serious adverse device-related events were reported. An objective seizure count can be derived, which often differs substantially from self-reported seizure counts. Larger clinical trials quantifying the benefits of objective seizure counting should be a priority for future research as well as development of algorithms for automated review of data.

Automatic sleep stage classification based on subcutaneous EEG in patients with epilepsy.

BACKGROUND: The interplay between sleep structure and seizure probability has previously been studied using electroencephalography (EEG). Combining sleep assessment and detection of epileptic activity in ultralong-term EEG could potentially optimize seizure treatment and sleep quality of patients with epilepsy. However, the current gold standard polysomnography (PSG) limits sleep recording to a few nights. A novel subcutaneous device was developed to record ultralong-term EEG, and has been shown to measure events of clinical relevance for patients with epilepsy. We investigated whether subcutaneous EEG recordings can also be used to automatically assess the sleep architecture of epilepsy patients. METHOD: Four adult inpatients with probable or definite temporal lobe epilepsy were monitored simultaneously with long-term video scalp EEG (LTV EEG) and subcutaneous EEG. In total, 11 nights with concurrent recordings were obtained. The sleep EEG in the two modalities was scored independently by a trained expert according to the American Academy of Sleep Medicine (AASM) rules. By using the sleep stage labels from the LTV EEG as ground truth, an automatic sleep stage classifier based on 30 descriptive features computed from the subcutaneous EEG was trained and tested. RESULTS: An average Cohen's kappa of [Formula: see text] was achieved using patient specific leave-one-night-out cross validation. When merging all sleep stages into a single class and thereby evaluating an awake-sleep classifier, we achieved a sensitivity of 94.8% and a specificity of 96.6%. Compared to manually labeled video-EEG, the model underestimated total sleep time and sleep efficiency by 8.6 and 1.8 min, respectively, and overestimated wakefulness after sleep onset by 13.6 min. CONCLUSION: This proof-of-concept study shows that it is possible to automatically sleep score patients with epilepsy based on two-channel subcutaneous EEG. The results are comparable with the methods currently used in clinical practice. In contrast to comparable studies with wearable EEG devices, several nights were recorded per patient, allowing for the training of patient specific algorithms that can account for the individual brain dynamics of each patient. Clinical trial registered at ClinicalTrial.gov on 19 October 2016 (ID:NCT02946151).

High similarity between EEG from subcutaneous and proximate scalp electrodes in patients with temporal lobe epilepsy.

Subcutaneous recording using electroencephalography (EEG) has the potential to enable ultra-long-term epilepsy monitoring in real-life conditions because it allows the patient increased mobility and discreteness. This study is the first to compare physiological and epileptiform EEG signals from subcutaneous and scalp EEG recordings in epilepsy patients. Four patients with probable or definite temporal lobe epilepsy were monitored with simultaneous scalp and subcutaneous EEG recordings. EEG recordings were compared by correlation and time-frequency analysis across an array of clinically relevant waveforms and patterns. We found high similarity between the subcutaneous EEG channels and nearby temporal scalp channels for most investigated electroencephalographic events. In particular, the temporal dynamics of one typical temporal lobe seizure in one patient were similar in scalp and subcutaneous recordings in regard to frequency distribution and morphology. Signal similarity is strongly related to the distance between the subcutaneous and scalp electrodes. On the basis of these limited data, we conclude that subcutaneous EEG recordings are very similar to scalp recordings in both time and time-frequency domains, if the distance between them is small. As many electroencephalographic events are local/regional, the positioning of the subcutaneous electrodes should be considered carefully to reflect the relevant clinical question. The impact of implantation depth of the subcutaneous electrode on recording quality should be investigated further. NEW & NOTEWORTHY This study is the first publication comparing the detection of clinically relevant, pathological EEG features from a subcutaneous recording system designed for out-patient ultra-long-term use to gold standard scalp EEG recordings. Our study shows that subcutaneous channels are very similar to comparable scalp channels, but also point out some issues yet to be resolved.

Modulation of task-related cortical connectivity in the acute and subacute phase after stroke.

The functional relevance of cortical reorganization post-stroke is still not well understood. In this study, we investigated task-specific modulation of cortical connectivity between neural oscillations in key motor regions during the early phase after stroke. EEG and EMG recordings were examined from 15 patients and 18 controls during a precision grip task using the affected hand. Each patient attended two sessions in the acute and subacute phase (median of 3 and 34 days) post-stroke. Dynamic causal modelling (DCM) for induced responses was used to investigate task-specific modulations of oscillatory couplings in a bilateral network comprising supplementary motor area (SMA), dorsal premotor cortex (PMd) and primary motor cortex (M1). Fourteen models were constructed for each subject, and the input induced by the experimental manipulation (task) was set to inferior parietal lobule (IPL). Bayesian model selection favoured a fully connected model. A reduced coupling from SMA and intact M1 in the γ-band (31-48 Hz) to lesioned M1 in the ß-band (15-30 Hz) was observed in patients in the acute phase compared to controls. Behavioural performance improved significantly in the subacute phase, while an increased positive coupling from intact PMd to lesioned M1 and a less negative modulation from lesioned M1 to intact M1 were observed for patients compared to controls both from the γ-band to the ß-band. We infer that the observed differences in cross-frequency cortical interactions are important for functional recovery.

Verbal learning and memory outcome in selective amygdalohippocampectomy versus temporal lobe resection in patients with hippocampal sclerosis.

PURPOSE: With the advent of new very selective techniques like thermal laser ablation to treat drug-resistant focal epilepsy, the controversy of resection size in relation to seizure outcome versus cognitive deficits has gained new relevance. The purpose of this study was to test the influence of the selective amygdalohippocampectomy (SAH) versus nonselective temporal lobe resection (TLR) on seizure outcome and cognition in patients with mesial temporal lobe epilepsy (MTLE) and histopathological verified hippocampal sclerosis (HS). METHODS: We identified 108 adults (>16years) with HS, operated between 1995 and 2009 in Denmark. Exclusion criteria are the following: Intelligence below normal range, right hemisphere dominance, other native languages than Danish, dual pathology, and missing follow-up data. Thus, 56 patients were analyzed. The patients were allocated to SAH (n=22) or TLR (n=34) based on intraoperative electrocorticography. Verbal learning and verbal memory were tested pre- and postsurgery. RESULTS: Seizure outcome did not differ between patients operated using the SAH versus the TLR at 1year (p=0.951) nor at 7years (p=0.177). Verbal learning was more affected in patients resected in the left hemisphere than in the right (p=0.002). In patients with left-sided TLR, a worsening in verbal memory performance was found (p=0.011). Altogether, 73% were seizure-free for 1year and 64% for 7years after surgery. CONCLUSION: In patients with drug-resistant focal MTLE, HS and no magnetic resonance imaging (MRI) signs of dual pathology, selective amygdalohippocampectomy results in sustained seizure freedom and better memory function compared with patients operated with nonselective temporal lobe resection.

Infrared thermographic assessment of changes in skin temperature during hypoglycaemia in patients with type 1 diabetes.

AIMS/HYPOTHESIS: Hypoglycaemia is associated with reduced skin temperature (Ts). We studied whether infrared thermography can detect Ts changes during hypoglycaemia in patients with type 1 diabetes and how the Ts response differs between patients with normal hypoglycaemia awareness and hypoglycaemia unawareness. METHODS: Twenty-four patients with type 1 diabetes (ten aware, 14 unaware) were studied during normoglycaemia (5.0-6.0 mmol/l), hypoglycaemia (2.0-2.5 mmol/l) and during recovery from hypoglycaemia (5.0-6.0 mmol/l) using hyperinsulinaemic glucose clamping. During each 1 h phase, Ts was measured twice by infrared thermography imaging in pre-defined areas (nose, glabella and the five left fingertips), symptoms of hypoglycaemia were scored and blood was sampled. RESULTS: Ts decreased during hypoglycaemia on the nose and glabella. The highest decrements were recorded on the nose (aware: -2.6 °C, unaware: -1.1 °C). In aware patients, the differences in temperature were statistically significant on both nose and glabella, whereas there was only a trend in the unaware group. There was a significant difference in hypoglycaemia-induced temperature changes between the groups. Patients in the aware group had higher hypoglycaemia symptom scores and higher adrenaline (epinephrine) levels during hypoglycaemia. CONCLUSIONS/INTERPRETATION: The hypoglycaemia-associated decrement in Ts can be assessed by infrared thermography and is larger in patients with normal hypoglycaemia awareness compared with unaware patients.

Epileptic aura and perception of self-control.

OBJECTIVE: The health locus of control is the subjective perception of control over one's health. It has been studied for years as one of several factors that determine patient health-related behaviors. The aim of this study was to investigate how the epileptic aura is associated with the health locus of control, anxiety, and depression. METHODS: Patients were included retrospectively, based on patient records from the epilepsy monitoring unit of the Rigshospitalet University Hospital. Participants were asked about the presence and nature of auras in a semistructured interview. The Multidimensional Health Locus of Control Scale, Form C was used to evaluate the health locus of control. Three domains were evaluated: internal, where health is controlled by personal action; chance, where health is controlled by fate or luck; and powerful others, where health is controlled by the actions of others (e.g., doctors and parents). The Hospital Anxiety and Depression Scale was used to evaluate levels of anxiety and depression. RESULTS: Forty-nine patients, with mean age of 38years, participated in the study. Of these, 67% reported experiencing one or more auras; i.e., subjective warning signs prior to a generalized or focal seizure with an impairment in consciousness. Patients that could react to their aura prior to a seizure scored higher on the internal subscale of the Multidimensional Health Locus of Control questionnaire compared to participants that could not react to their aura. CONCLUSIONS: The ability to react to an aura prior to a seizure correlated positively with the internal subscale of the health locus of control. However, it did not significantly correlate with the external subscales of chance and powerful others in the health locus of control. Moreover, there was no significant relation between the ability to react to an aura prior to a seizure and the levels of anxiety or depression.

Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.

Our objective was to assess the clinical reliability of a wrist-worn, wireless accelerometer sensor for detecting generalized tonic-clonic seizures (GTCS). Seventy-three consecutive patients (age 6-68 years; median 37 years) at risk of having GTCS and who were admitted to the long-term video-electroencephalography (EEG) monitoring unit (LTM) were recruited in three centers. The reference standard was considered the seizure time points identified by experienced clinical neurophysiologists, based on the video-EEG recordings and blinded to the accelerometer sensor data. Seizure time points detected real-time by the sensor were compared with the reference standard. Patients were monitored for 17-171 h (mean 66.8; total 4,878). Thirty-nine GTCS were recorded in 20 patients. The device detected 35 seizures (89.7%). In 16 patients all seizures were detected. In three patients more than two thirds of the seizures were detected. The mean of the sensitivity calculated for each patient was 91%. The mean detection latency measured from the start of the focal seizure preceding the secondarily GTCS was 55 s (95% confidence interval [CI] 38-73 s). The rate of false alarms was 0.2/day. Our results suggest that the wireless wrist accelerometer sensor detects GTCS with high sensitivity and specificity. Patients with GTCS have an increased risk for injuries related to seizures and for sudden unexpected death in epilepsy (SUDEP), and many nocturnal seizures remain undetected in unattended patients. A portable automatic seizure detection device will be an important tool for helping these patients.

No change in [¹¹C]CUMI-101 binding to 5-HT(1A) receptors after intravenous citalopram in human.

The main objective of this study was to determine the sensitivity of [¹¹C]CUMI-101 to citalopram challenge aiming at increasing extracellular 5-HT. CUMI-101 has agonistic properties in human embryonic kidney 293 cells transfected with human recombinant 5-HT(1A) receptors (Hendry et al. [2011] Nucl Med Biol 38:273-277; Kumar et al. [2006] J Med Chem 49:125-134) and has previously been demonstrated to be sensitive to bolus citalopram in monkeys (Milak et al. [2011] J Cereb Blood Flow Metab 31:243-249). We studied six healthy individuals. Two PET-scans were performed on the same day in each individual before and after constant infusion of citalopram (0.15 mg/kg). The imaging data were analyzed using two tissue compartment kinetic modeling with metabolite corrected arterial input and Simplified Reference Tissue Modeling using cerebellum as a reference region. There was no significant difference in regional distribution volume or non-displaceable binding potential values before and after citalopram infusion. The mean receptor occupancy was 0.03 (range -0.14 to 0.17). Our data imply that [¹¹C]CUMI-101 binding is not sensitive to citalopram infusion in humans.