Visual outcomes after anterior temporal lobectomy and transsylvian selective amygdalohippocampectomy: A quantitative comparison of clinical and diffusion data.

OBJECTIVE: Anterior temporal lobectomy (ATL) and transsylvian selective amygdalohippocampectomy (tsSAHE) are effective treatment strategies for intractable temporal lobe epilepsy but may cause visual field deficits (VFDs) by damaging the optic radiation (OpR). Due to the OpR's considerable variability and because it is indistinguishable from surrounding tissue without further technical guidance, it is highly vulnerable to iatrogenic injury. This imaging study uses a multimodal approach to assess visual outcomes after epilepsy surgery. METHODS: We studied 62 patients who underwent ATL (n = 32) or tsSAHE (n = 30). Analysis of visual outcomes was conducted in four steps, including the assessment of (1) perimetry outcome (VFD incidence/extent, n = 44/40), (2) volumetric OpR tractography damage (n = 55), and the (3) relation of volumetric OpR tractography damage and perimetry outcome (n = 35). Furthermore, (4) fixel-based analysis (FBA) was performed to assess micro- and macrostructural changes within the OpR following surgery (n = 36). RESULTS: Altogether, 56% of all patients had postoperative VFDs (78.9% after ATL, 36.36% after tsSAHE, p = .011). VFDs and OpR tractography damage tended to be more severe within the ATL group (ATL vs. tsSAHE, integrity of contralateral upper quadrant: 65% vs. 97%, p = .002; OpR tractography damage: 69.2 mm3 vs. 3.8 mm3 , p = .002). Volumetric OpR tractography damage could reliably predict VFD incidence (86% sensitivity, 78% specificity) and could significantly explain VFD extent (R2  = .47, p = .0001). FBA revealed a more widespread decline of fibre cross-section within the ATL group. SIGNIFICANCE: In the context of controversial visual outcomes following epilepsy surgery, this study provides clinical as well as neuroimaging evidence for a higher risk and greater severity of postoperative VFDs after ATL compared to tsSAHE. Volumetric OpR tractography damage is a feasible parameter to reliably predict this morbidity in both treatment groups and may ultimately support personalized planning of surgical candidates. Advanced diffusion analysis tools such as FBA offer a structural explanation of surgically induced visual pathway damage, allowing noninvasive quantification and visualization of micro- and macrostructural tract affection.

Seizure detection with deep neural networks for review of two-channel electroencephalogram.

Ultra-long-term electroencephalographic (EEG) registration using minimally invasive low-channel devices is an emerging technology to assess sporadic seizure events. Highly sensitive automatic seizure detection algorithms are needed for semiautomatic evaluation of these prolonged recordings. We describe the design and validation of a deep neural network for two-channel seizure detection. The model is trained using EEG recordings from 590 patients in a publicly available seizure database. These recordings are based on the full 10-20 electrode system and include seizure annotations created by reviews of the full set of EEG channels. Validation was performed using 48 scalp EEG recordings from an independent epilepsy center and consensus seizure annotations from three neurologists. For each patient, a three-electrode subgroup (two channels with a common reference) of the full montage was selected for validation of the two-channel model. Mean sensitivity across patients of 88.8% and false positive rate across patients of 12.9/day were achieved. The proposed training approach is of great practical relevance, because true recordings from low-channel devices are currently available only in small numbers, and the generation of gold standard seizure annotations in two EEG channels is often difficult. The study demonstrates that automatic seizure detection based on two-channel EEG data is feasible and review of ultra-long-term recordings can be made efficient and effective.

Quantitative EEG-Based Seizure Estimation in Super-Refractory Status Epilepticus.

BACKGROUND: The objective of this study was to evaluate the accuracy of seizure burden in patients with super-refractory status epilepticus (SRSE) by using quantitative electroencephalography (qEEG). METHODS: EEG recordings from 69 patients with SRSE (2009-2019) were reviewed and annotated for seizures by three groups of reviewers: two board-certified neurophysiologists using only raw EEG (gold standard), two neurocritical care providers with substantial experience in qEEG analysis (qEEG experts), and two inexperienced qEEG readers (qEEG novices) using only a qEEG trend panel. RESULTS: Raw EEG experts identified 35 (51%) patients with seizures, accounting for 2950 seizures (3,126 min). qEEG experts had a sensitivity of 93%, a specificity of 61%, a false positive rate of 6.5 per day, and good agreement (κ = 0.64) between both qEEG experts. qEEG novices had a sensitivity of 98.5%, a specificity of 13%, a false positive rate of 15 per day, and fair agreement (κ = 0.4) between both qEEG novices. Seizure burden was not different between the qEEG experts and the gold standard (3,257 vs. 3,126 min), whereas qEEG novices reported higher burden (6066 vs. 3126 min). CONCLUSIONS: Both qEEG experts and novices had a high sensitivity but a low specificity for seizure detection in patients with SRSE. qEEG could be a useful tool for qEEG experts to estimate seizure burden in patients with SRSE.

Systematic analysis and comparison of commercial seizure-detection software.

OBJECTIVE: To determine if three different commercially available seizure-detection software packages (Besa 2.0, Encevis 1.7, and Persyst 13) accurately detect seizures with high sensitivity, high specificity, and short detection delay in epilepsy patients undergoing long-term video-electroencephalography (EEG) monitoring (VEM). METHODS: Comparison of sensitivity (detection rate), specificity (false alarm rate), and detection delay of three commercially available seizure-detection software packages in 81 randomly selected patients with epilepsy undergoing long-term VEM. RESULTS: Detection rates on a per-patient basis were not significantly different between Besa (mean 67.6%, range 0-100%), Encevis (77.8%, 0-100%) and Persyst (81%, 0-100%; P = .059). False alarm rate (per hour) was significantly different between Besa (mean 0.7/h, range 0.01-6.2/h), Encevis (0.2/h, 0.01-0.5/h), and Persyst (0.9/h, 0.04-6.5/h; P < .001). Detection delay was significantly different between Besa (mean 30 s, range 0-431 s), Encevis (25 s, 2-163 s), and Persyst (20 s, 0-167 s; P = .007). Kappa statistics showed moderate to substantial agreement between the reference standard and each seizure-detection software (Besa: 0.47, 95% confidence interval [CI] 0.36-0.59; Encevis: 0.59, 95% CI 0.47-0.7; Persyst: 0.63, 95% CI 0.51-0.74). SIGNIFICANCE: Three commercially available seizure-detection software packages showed similar, reasonable sensitivities on the same data set, but differed in false alarm rates and detection delay. Persyst 13 showed the highest detection rate and false alarm rate with the shortest detection delay, whereas Encevis 1.7 had a slightly lower sensitivity, the lowest false alarm rate, and longer detection delay.

[Automatic detection of epileptiform potentials and seizures in the EEG]. / Automatische Erkennung von epilepsietypischen Potenzialen und Anfällen im EEG.

Automatic computer-based algorithms for the detection of epileptiform potentials and seizure patterns on EEG facilitate a time-saving, objective method of quantitative EEG interpretation which is available 7/24. For the automatic detection of interictal epileptiform potentials sensitivities range from 65 to 99% with false positive detections of 0,09 to 13,4 per minute. Recent studies documented equal or even better performance of automatic spike detection programs compared with experienced human EEG readers. The seizure detection problem-one of the major problems in clinical epileptology-consists of the fact that the majority of focal onset seizures with impaired awareness and of seizures arising out of sleep occur unnoticed by patients and their caregivers. Automatic seizure detection systems could facilitate objective seizure documentation and thus help to solve the seizure detection problem. Furthermore, seizure detection systems may help to prevent seizure-related injuries and sudden unexpected death in epilepsy (SUDEP), and could be an integral part of novel, seizure-triggered on-demand therapies in epilepsy. During long-term video-EEG monitoring seizure detection systems could improve patient safety, provide a time-saving objective and reproducible analysis of seizure patterns and facilitate automatic computer-based patient testing during seizures. Sensitivities of seizure detection systems range from 75 to 90% with extratemporal seizures being more difficult to detect than temporal seizures. The false positive alarm rate ranges from 0,1 to 5 per 24 hours. Finally, machine learning algorithms, especially deep learning approaches, open a new highly promising era in automatic spike and seizure detection.

Critical Care EEG.

Neuromonitoring by means of continuous EEG (cEEG) has been increasingly used in intensive care patients. Standardized EEG criteria can be used to reliably detect and distinguish nonconvulsive seizures or status epileptici as well as rhythmic and periodic ictal-interictal continuum EEG patterns. Likewise, cerebral ischemia can be detected early by means of cEEG, and sedation and therapy management can be monitored and controlled.

Epidemiology and pathophysiology of autonomic seizures: a systematic review.

PURPOSE: To review the epidemiology and pathophysiology of autonomic symptoms and signs during epileptic seizures. METHODS: We performed a systematic literature search on the following autonomic symptoms and signs during epileptic seizures: cardiovascular changes, respiratory manifestations, gastrointestinal symptoms, cutaneous manifestations, sexual and genital manifestations, and urinary symptoms. RESULTS: Autonomic symptoms and signs can represent the predominant symptom at the onset of a focal seizure, which would then lead to the seizure being classified as a focal onset autonomic seizure. Conversely, clinically relevant autonomic symptoms and signs frequently accompany seizures of focal, generalized, and/or unknown onset, but the seizure is regardless classified according to other, more relevant features. Autonomic symptoms and signs do not represent mere reactions to motor activity or other behavioral seizure manifestations, but rather they are generated by epileptic discharges affecting the central autonomic network. We have reviewed the localizing and lateralizing information currently available on the seizure onset zone and on seizure propagation pathways as provided by systematic analysis of specific autonomic seizure symptoms and signs. We present data on how autonomic seizure symptoms and signs are useful for gaining a better understanding of the anatomical and functional organization of the central autonomic network. Finally, we discuss the differential diagnosis of focal autonomic seizures with autonomic symptoms and signs representing the sole seizure manifestation versus various non-epileptic conditions. CONCLUSIONS: Autonomic seizure symptoms and signs are relevant in clinical epileptology and open a unique window on the functional organization and pathophysiology of the central autonomic network.

Seizure detection using scalp-EEG.

Scalp electroencephalography (EEG)-based seizure-detection algorithms applied in a clinical setting should detect a broad range of different seizures with high sensitivity and selectivity and should be easy to use with identical parameter settings for all patients. Available algorithms provide sensitivities between 75% and 90%. EEG seizure patterns with short duration, low amplitude, circumscribed focal activity, high frequency, and unusual morphology as well as EEG seizure patterns obscured by artifacts are generally difficult to detect. Therefore, detection algorithms generally perform worse on seizures of extratemporal origin as compared to those of temporal lobe origin. Specificity (false-positive alarms) varies between 0.1 and 5 per hour. Low false-positive alarm rates are of critical importance for acceptance of algorithms in a clinical setting. Reasons for false-positive alarms include physiological and pathological interictal EEG activities as well as various artifacts. To achieve a stable, reproducible performance (especially concerning specificity), algorithms need to be tested and validated on a large amount of EEG data comprising a complete temporal assessment of all interictal EEG. Patient-specific algorithms can further improve sensitivity and specificity but need parameter adjustments and training for individual patients. Seizure alarm systems need to provide on-line calculation with short detection delays in the order of few seconds. Scalp-EEG-based seizure detection systems can be helpful in an everyday clinical setting in the epilepsy monitoring unit, but at the current stage cannot replace continuous supervision of patients and complete visual review of the acquired data by specially trained personnel. In an outpatient setting, application of scalp-EEG-based seizure-detection systems is limited because patients won't tolerate wearing widespread EEG electrode arrays for long periods in everyday life. Recently developed subcutaneous EEG electrodes may offer a solution in this respect.

Early Epileptiform Discharges and Clinical Signs Predict Nonconvulsive Status Epilepticus on Continuous EEG.

BACKGROUND: Critical care continuous electroencephalography (CCEEG) represents the gold standard for detection of nonconvulsive status epilepticus (NCSE) in neurological critical care patients. It is unclear which findings on short-term routine EEG and which clinical parameters predict NCSE during subsequent CCEEG reliably. The aim of the present study was to assess the prognostic significance of changes within the first 30 min of EEG as well as of clinical parameters for the occurrence of NCSE during subsequent CCEEG. METHODS: Systematic analysis of the first 30 min and the remaining segments of prospective CCEEG recordings according to the ACNS Standardized Critical Care EEG Terminology and according to recently proposed NCSE criteria as well as review of clinical parameters of 85 consecutive neurological critical care patients. Logistic regression and binary classification tests were used to determine the most useful parameters within the first 30 min of EEG predicting subsequent NCSE. RESULTS: The presence of early sporadic epileptiform discharges (SED) and early rhythmic or periodic EEG patterns of "ictal-interictal uncertainty" (RPPIIIU) (OR 15.51, 95% CI 2.83-84.84, p = 0.002) and clinical signs of NCS (OR 18.43, 95% CI 2.06-164.62, p = 0.009) predicted NCSE on subsequent CCEEG. Various combinations of early SED, early RPPIIIU, and clinical signs of NCS showed sensitivities of 79-100%, specificities of 49-89%, and negative predictive values of 95-100% regarding the incidence of subsequent NCSE (p < 0.001). CONCLUSIONS: Early SED and early RPPIIIU within the first 30 min of EEG as well as clinical signs of NCS predict the occurrence of NCSE during subsequent CCEEG with high sensitivity and high negative predictive value and may be useful to select patients who should undergo CCEEG.

Feasibility of an Integrated Telemonitoring System for Home-Recorded EEG with Automated AI-Based EEG Analysis.

BACKGROUND: Self-recorded EEG by patients at home might present a viable alternative to inpatient epilepsy evaluations. OBJECTIVES AND METHODS: We developed a novel telemonitoring system comprising seamlessly integrated hard- and software with automated AI-based EEG analysis. RESULTS: The first complete study participation results demonstrate feasibility and clinical utility. CONCLUSION: Our telemonitoring solution potentially improves treatment of patients with epilepsy and moreover might help to better distribute resources in the healthcare system.

Idiopathic generalized epilepsies in the epilepsy monitoring unit: Systematic quantification of focal EEG and semiological signs.

OBJECTIVE: Focal seizure symptoms (FSS) and focal interictal epileptiform discharges (IEDs) are common in patients with idiopathic generalized epilepsies (IGEs), but dedicated studies systematically quantifying them both are lacking. We used automatic IED detection and localization algorithms and correlated these EEG findings with clinical FSS for the first time in IGE patients. METHODS: 32 patients with IGEs undergoing long-term video EEG monitoring were systematically analyzed regarding focal vs. generalized IEDs using automatic IED detection and localization algorithms. Quantitative EEG findings were correlated with FSS. RESULTS: We observed FSS in 75% of patients, without significant differences between IGE subgroups. Mostly varying/shifting lateralizations of FSS across successive recorded seizures were seen. We detected a total of 81,949 IEDs, whereof 19,513 IEDs were focal (23.8%). Focal IEDs occurred in all patients (median 13% focal IEDs per patient, range 1.1 - 51.1%). Focal IED lateralization and localization predominance had no significant effect on FSS. CONCLUSIONS: All included patients with IGE showed focal IEDs and three-quarter had focal seizure symptoms irrespective of the specific IGE subgroup. Focal IED localization had no significant effect on lateralization and localization of FSS. SIGNIFICANCE: Our findings may facilitate diagnostic and treatment decisions in patients with suspected IGE and focal signs.

Implementation of a 7T Epilepsy Task Force consensus imaging protocol for routine presurgical epilepsy work-up: effect on diagnostic yield and lesion delineation.

OBJECTIVE: Recently, the 7 Tesla (7 T) Epilepsy Task Force published recommendations for 7 T magnetic resonance imaging (MRI) in patients with pharmaco-resistant focal epilepsy in pre-surgical evaluation. The objective of this study was to implement and evaluate this consensus protocol with respect to both its practicability and its diagnostic value/potential lesion delineation surplus effect over 3 T MRI in the pre-surgical work-up of patients with pharmaco-resistant focal onset epilepsy. METHODS: The 7 T MRI protocol consisted of T1-weighted, T2-weighted, high-resolution-coronal T2-weighted, fluid-suppressed, fluid-and-white-matter-suppressed, and susceptibility-weighted imaging, with an overall duration of 50 min. Two neuroradiologists independently evaluated the ability of lesion identification, the detection confidence for these identified lesions, and the lesion border delineation at 7 T compared to 3 T MRI. RESULTS: Of 41 recruited patients > 12 years of age, 38 were successfully measured and analyzed. Mean detection confidence scores were non-significantly higher at 7 T (1.95 ± 0.84 out of 3 versus 1.64 ± 1.19 out of 3 at 3 T, p = 0.050). In 50% of epilepsy patients measured at 7 T, additional findings compared to 3 T MRI were observed. Furthermore, we found improved border delineation at 7 T in 88% of patients with 3 T-visible lesions. In 19% of 3 T MR-negative cases a new potential epileptogenic lesion was detected at 7 T. CONCLUSIONS: The diagnostic yield was beneficial, but with 19% new 7 T over 3 T findings, not major. Our evaluation revealed epilepsy outcomes worse than ILAE Class 1 in two out of the four operated cases with new 7 T findings.

DICOM® integrated EEG data: A first clinical implementation of the new DICOM standard for neurophysiology data.

OBJECTIVE: Demonstrating a pilot implementation of the Digital Imaging and Communication in Medicine (DICOM) neurophysiology standard published in 2020. METHODS: An automated workflow for converting EEG data from a proprietary vendor EEG format to standardized and interoperable DICOM format was developed and tested. RESULTS: Retrieval of proprietary EEG data, associated videos, annotations and metadata from the vendor EEG archive and their subsequent conversion to DICOM EEG was possible without changes to the departmental workflow. To transfer DICOM EEG data to the central radiology DICOM archive, only minor extensions in the parameterization of the archive's DICOM interfaces were necessary. Linkage with the electronic health record (EHR) and display in a DICOM EEG viewer could be demonstrated. A random sample of 88 DICOM EEG studies was compared to the original vendor files and EEG and video file sizes were comparable. CONCLUSIONS: Storing and reviewing EEG data in standardized DICOM format is feasible, facilitated by existing DICOM infrastructure, and therefore allows for vendor independent access to EEG data. SIGNIFICANCE: We report the first implementation of the DICOM neurophysiology standard, thus promoting standardization in the field of neurophysiology as well as data exchange and access to legacy recordings in an interoperable vendor independent format.

First Clinical Experiences with the New DICOM Neurophysiology Standard.

BACKGROUND: Exchange of EEG data among institutions is complicated due to vendor-specific proprietary EEG file formats. The DICOM standard, which has long been used for storage and exchange of imaging studies, was expanded to store neurophysiology data in 2020. OBJECTIVES: To implement DICOM as an interoperable and vendor-independent storage format for EEG recordings in the Clinic Hietzing. METHODS: A pilot implementation for automated conversion of EEG data from a proprietary to standardized DICOM format was developed. Additionally, EEG review based on a central DICOM archive in a DICOM EEG viewer (encevis by AIT) was implemented. RESULTS: More than 200 long-term video EEG recordings and over 3000 routine EEGs were archived to the central DICOM archive of the WIGEV. CONCLUSION: Using DICOM as a storage format for EEG data is feasible and leads to a substantial improvement of interoperability and facilitates data exchange between institutions.

Automatic seizure detection and seizure pattern morphology.

OBJECTIVE: We studied the influence of seizure pattern morphology on detection rate and detection delay of an automatic seizure detection system. We correlated seizure pattern morphology with seizure onset zone and assessed the influence of seizure onset zone on the performance of the seizure detection system. METHODS: We analyzed 10.000 hours of EEG in 129 patients, 193 seizures in 67 patients were included in the final analysis. Seizure pattern morphologies were classified as rhythmic activity (alpha, theta and delta), paroxysmal fast activity, suppression of activity, repetitive epileptiform and arrhythmic activity. The seizure detection system EpiScan was compared with visual analysis. RESULTS: Detection rates were significantly higher for rhythmic and repetitive epileptiform activities than for paroxysmal fast activity. Seizure patterns significantly correlated with seizure onset zone. Detection rate was significantly higher in temporal lobe (TL) seizures than in frontal lobe (FL) seizures. Detection delay tended to be shorter in seizures with rhythmic alpha or theta activity. TL seizures were significantly more often detected within 10 seconds than FL seizures. CONCLUSIONS: Seizure morphology is critical for optimization of automatic seizure detection algorithms. SIGNIFICANCE: This study is unique in exploring the influence of seizure pattern morphology on automatic seizure detection and can help future research on seizure detection in epilepsy.

Recurrent migraine aura-like symptoms in an elderly woman: symptomatic cortical spreading depression?

Cortical spreading depression (CSD) has been directly observed in humans with malignant stroke, traumatic brain injury and subarachnoid haemorrhage and is also considered to be the correlate of migraine aura. We report on a 76-year-old woman with new-onset episodes of headache, paraesthesia, hemiparesis and dysarthria, in whom a small cortical subarachnoid haemorrhage was diagnosed with MRI. Repeated diffusion-weighted MRI scans shortly after transient focal neurological episodes as well as diagnostic workup were normal, which makes recurrent transient ischaemic attacks unlikely. Ictal electroencephalogram recordings showed no epileptic activity. Long-term follow-up revealed a diagnosis of probable cerebral amyloid angiopathy. We propose that CSD could be a pathophysiological correlate of transient focal neurological deficits in patients with cortical bleeding.

Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis.

OBJECTIVE: To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns. METHODS: We analyzed long-term EEGs from 76 patients with at least one recorded epileptic seizure during monitoring. IEDs were detected with an AI-based algorithm and validated by visual inspection. We then used unsupervised clustering to characterize patient sub-cohorts with similar IED activation patterns regarding circadian rhythms, deep sleep activation, and seizure occurrence. RESULTS: Five sub-cohorts with similar IED activation patterns were found: "Sporadic" (14%, n = 10) without or few IEDs, "Continuous" (32%, n = 23) with weak circadian/deep sleep or seizure modulation, "Nighttime & seizure activation" (23%, n = 17) with high IED rates during normal sleep times and after seizures but without deep sleep modulation, "Deep sleep" (19%, n = 14) with strong IED modulation during deep sleep, and "Seizure deactivation" (12%, n = 9) with deactivation of IEDs after seizures. Patients showing "Deep sleep" IED pattern were diagnosed with temporal lobe epilepsy in 86%, while 80% of the "Sporadic" cluster were extratemporal. CONCLUSIONS: Patients with epilepsy can be characterized by using temporal relationships between rates of IEDs, circadian rhythms, deep sleep and seizures. SIGNIFICANCE: This work presents the first approach to data-driven classification of epilepsy patients based on their fully validated temporal pattern of IEDs.

Quantitative analysis of human brain microdialysate for target site pharmacokinetics of major anesthetics ketamine, midazolam and propofol.

Brain microdialysis samples of intensive care patients treated with the essential anesthetics ketamine, midazolam and propofol were investigated. Importantly, despite decades of clinical use, comprehensive human cerebral pharmacokinetic data of these drugs is still missing. To encounter this apparent lack of knowledge, we combined cerebral microdialysis with leading-edge analytical instrumentation to monitor the neurochemistry of living human patients. For the quantitative analysis, high performing analytical approaches were developed that can handle minute sample volumes and possible ultralow target analyte levels. The developed methods provided detection limits below 100 ng L-1 for all target analytes and high precision (below 4% RSD intraday). Methods were linear between LODs and 100 µg L-1 for ketamine, 75 µg L-1 for midazolam and 10 µg L-1 for propofol respectively, with coefficients of determination R2≥ 0.999. Further, being aware of the error-prone and demanding translation of microdialysis levels to interstitial concentrations, in vitro approaches for recovery testing of microdialysis probes as well as internal normalization approaches were conducted. Thus, we herein report the first cerebral pharmacokinetic data of ketamine, midazolam and propofol determined in microdialysis samples of 15 neurointensive care patients. We could prove blood-brain barrier penetration of all of the investigated anesthetics and could correlate applied dosages and actual brain exposition of ketamine. However, we emphasize the need of an expanded prospective study including individual microdialysis recovery testing as well as matched serum and/or cerebrospinal fluid collection for a more comprehensive cerebral pharmacokinetic understanding.

Endogenous arterial blood pressure increase after aneurysmal subarachnoid hemorrhage.

OBJECTIVE: Spontaneous blood pressure rise is a frequently observed phenomenon following aneurysmal subarachnoid hemorrhage (SAH). Facing the risk of aneurysmal rebleeding and the occurrence of delayed cerebral ischemia it is unclear how to react to these endogenous-driven blood pressure changes, as their predictive value for clinical course and functional outcome is still unknown. PATIENTS AND METHODS: Endogenous blood pressure characteristics within 21 days after SAH were retrospectively analyzed in 93 patients. Any use of vasopressors for active induction of hypertension marked the end of data collection. Mean arterial blood pressure (MAP) was related to the onset of cerebral vasospasm and patient characteristics (Hunt&Hess, age, pre-existing hypertension, antihypertensive therapy, sedation). Predictors for cerebral infarction and functional outcome were calculated using a logistic regression model. RESULTS: A significant MAP increase was observed in all patients from day 3 to day 7. Patients developing cerebral vasospasm had an overall steeper increase of MAP during this period (11.1 ± 11.4 mmHg vs. 6.5 ± 8.9 mmHg, p = 0.04). MAP rise started already 3 days before detection of vasospasm. Lower MAP values were recorded in patients with poor Hunt&Hess grade, under sedation and thus in patients with poor outcome. MAP had no impact on the development of cerebral infarction. In univariate analysis MAP on day 5 (OR 0.95, 95 %-CI: 0.89-0.99), MAP on day 6 (OR 0.95, 95 %-CI: 0.91-1.00), Hunt&Hess grade (OR 1.72, 95 %-CI: 1.14-2.60), sedation (OR 17.04, 95 %-CI: 2.08-139.51) and stroke (OR 5.82, 95 %-CI: 1.63-20.82) were predictors for poor outcome. In multivariable analysis, only sedation (OR 13.72, 95 %-CI: 1.62-115.94) and ischemic stroke (OR 4.48, 95 %-CI: 1.16-17.31) remained significant. CONCLUSION: Spontaneous MAP increase occured in all patients following SAH. It was highly influenced by clinical parameters, thereby limiting its prognostic value for functional outcome. However, a steep increase of MAP might be an early clinical marker to identify patients at risk for developing cerebral vasospasm.

Presurgical epilepsy evaluation and epilepsy surgery.

With a prevalence of 0.8 to 1.2%, epilepsy represents one of the most frequent chronic neurological disorders; 30 to 40% of patients suffer from drug-resistant epilepsy (that is, seizures cannot be controlled adequately with antiepileptic drugs). Epilepsy surgery represents a valuable treatment option for 10 to 50% of these patients. Epilepsy surgery aims to control seizures by resection of the epileptogenic tissue while avoiding neuropsychological and other neurological deficits by sparing essential brain areas. The most common histopathological findings in epilepsy surgery specimens are hippocampal sclerosis in adults and focal cortical dysplasia in children. Whereas presurgical evaluations and surgeries in patients with mesial temporal sclerosis and benign tumors recently decreased in most centers, non-lesional patients, patients requiring intracranial recordings, and neocortical resections increased. Recent developments in neurophysiological techniques (high-density electroencephalography [EEG], magnetoencephalography, electrical and magnetic source imaging, EEG-functional magnetic resonance imaging [EEG-fMRI], and recording of pathological high-frequency oscillations), structural magnetic resonance imaging (MRI) (ultra-high-field imaging at 7 Tesla, novel imaging acquisition protocols, and advanced image analysis [post-processing] techniques), functional imaging (positron emission tomography and single-photon emission computed tomography co-registered to MRI), and fMRI significantly improved non-invasive presurgical evaluation and have opened the option of epilepsy surgery to patients previously not considered surgical candidates. Technical improvements of resective surgery techniques facilitate successful and safe operations in highly delicate brain areas like the perisylvian area in operculoinsular epilepsy. Novel less-invasive surgical techniques include stereotactic radiosurgery, MR-guided laser interstitial thermal therapy, and stereotactic intracerebral EEG-guided radiofrequency thermocoagulation.