Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation.

OBJECTIVES: Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This cross-sectional study investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth. METHODS: Five different tractography methods were compared (three DTI-based and two CSD-based) in 22 preoperative neurosurgical patients undergoing surgery with DES mapping. The corticospinal tract (CST, N = 20) and arcuate fasciculus (AF, N = 7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes. RESULTS: Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE < .05). CONCLUSIONS: CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice.

Electric Source Imaging in Presurgical Evaluation of Epilepsy: An Inter-Analyser Agreement Study.

Electric source imaging (ESI) estimates the cortical generator of the electroencephalography (EEG) signals recorded with scalp electrodes. ESI has gained increasing interest for the presurgical evaluation of patients with drug-resistant focal epilepsy. In spite of a standardised analysis pipeline, several aspects tailored to the individual patient involve subjective decisions of the expert performing the analysis, such as the selection of the analysed signals (interictal epileptiform discharges and seizures, identification of the onset epoch and time-point of the analysis). Our goal was to investigate the inter-analyser agreement of ESI in presurgical evaluations of epilepsy, using the same software and analysis pipeline. Six experts, of whom five had no previous experience in ESI, independently performed interictal and ictal ESI of 25 consecutive patients (17 temporal, 8 extratemporal) who underwent presurgical evaluation. The overall agreement among experts for the ESI methods was substantial (AC1 = 0.65; 95% CI: 0.59-0.71), and there was no significant difference between the methods. Our results suggest that using a standardised analysis pipeline, newly trained experts reach similar ESI solutions, calling for more standardisation in this emerging clinical application in neuroimaging.

Automatic annotation correction for wearable EEG based epileptic seizure detection.

Objective. Video-electroencephalography (vEEG), which defines the ground truth for the detection of epileptic seizures, is inadequate for long-term home monitoring. Thanks to advantages in comfort and unobtrusiveness, wearable EEG devices have been suggested as a solution for home monitoring. However, one of the challenges in data-driven automated seizure detection with wearable EEG data is to have reliable seizure annotations. Seizure annotations on the gold-standard 25-channel vEEG recordings may not be optimal to delineate seizure activity on the concomitantly recorded wearable EEG, due to artifacts or absence of ictal activity on the limited set of electrodes of the wearable EEG. This paper aims to develop an automatic approach to correct for imperfect annotations of seizure activity on wearable EEG, which can be used to train seizure detection algorithms.Approach. This paper first investigates the effectiveness of correcting the seizure annotations for the training set with a visual annotation correction. Then a novel approach has been proposed to automatically remove non-seizure data from wearable EEG in epochs annotated as seizures in gold-standard video-EEG recordings. The performance of the automatic annotation correction approach was evaluated by comparing the seizure detection models trained with (a) original vEEG seizure annotations, (b) visually corrected seizure annotations, and (c) automatically corrected seizure annotations.Main results. The automated seizure detection approach trained with automatically corrected seizure annotations was more sensitive and had fewer false-positive detections compared to the approach trained with visually corrected seizure annotations, and the approach trained with the original seizure annotations from gold-standard vEEG.Significance. The wearable EEG seizure detection approach performs better when trained with automatic seizure annotation correction.

Predictive value of metabolic and perfusion changes outside the seizure onset zone for postoperative outcome in patients with refractory focal epilepsy.

The value of functional molecular changes outside the seizure onset zone as independent predictive factors of surgical outcome has been scarcely evaluated. The aim of this retrospective study was to evaluate relative metabolic and perfusion changes outside the seizure onset zone as predictors of postoperative outcome in patients with unifocal refractory focal epilepsy. Eighty-six unifocal epilepsy patients who underwent 18F-FDG PET prior to surgery were included. Ictal and interictal perfusion SPECT was available in 65 patients. Good postoperative outcome was defined as the International League against Epilepsy class 1. Using univariate statistical analysis, the predictive ability of volume-of-interest based relative metabolism/perfusion for outcome classification was quantified by AUC ROC-curve, using composite, unilateral cortical (frontal, orbitofrontal, temporal, parietal, occipital) and central volumes-of-interest. The results were cross-validated, and a false discovery rate (FDR) correction was applied. As a secondary objective, a subgroup analysis was performed on temporal lobe epilepsy patients (N = 64). Increased relative ictal perfusion in the contralateral central volume-of-interest was significantly associated with the good surgical outcome both in the total population (AUC 0.79, pFDR = 0.009) and the temporal lobe epilepsy subgroup (AUC 0.80, pFDR = 0.028). No other significant associations between functional molecular changes and postoperative outcome were found. Increased relative ictal perfusion in the contralateral central region significantly predicted outcome after epilepsy surgery in patients with refractory focal epilepsy. We postulate that these relative perfusion changes could be an expression of better preoperative neuronal network integration and centralization in the contralateral central structures, which is suggested to be associated with better postoperative outcome.

The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels.

OBJECTIVE: Wearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self-reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind-the-ear electroencephalography (EEG). However, the obtained sensitivities were too low for practical use, because not all seizures are associated with typical ictal EEG patterns. Therefore, in this paper, we aim to develop a multimodal automated seizure detection algorithm integrating behind-the-ear EEG and electrocardiography (ECG) for detecting focal seizures. In this framework, we quantified the added value of ECG to behind-the-ear EEG. METHODS: This study analyzed three multicenter databases consisting of 135 patients having focal epilepsy and a total of 896 seizures. A patient-specific multimodal automated seizure detection algorithm was developed using behind-the-ear/temporal EEG and single-lead ECG. The EEG and ECG data were processed separately using machine learning methods. A late integration approach was applied for fusing those predictions. RESULTS: The multimodal algorithm outperformed the EEG-based algorithm in two of three databases, with an increase of 11% and 8% in sensitivity for the same false alarm rate. SIGNIFICANCE: ECG can be of added value to an EEG-based seizure detection algorithm using only behind-the-ear/temporal lobe electrodes for patients with focal epilepsy.

Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning.

Objective: Automated seizure detection is a key aspect of wearable seizure warning systems. As a result, the quality of life of refractory epilepsy patients could be improved. Most state-of-the-art algorithms for heart rate-based seizure detection use a so-called patient-independent approach, which do not take into account patient-specific data during algorithm training. Although such systems are easy to use in practice, they lead to many false detections as the ictal heart rate changes are patient-dependent. In practice, only a limited amount of accurately annotated patient data is typically available, which makes it difficult to create fully patient-specific algorithms. Methods: In this context, this study proposes for the first time a new transfer learning approach that allows to personalize heart rate-based seizure detection by using only a couple of days of data per patient. The algorithm was evaluated on 2,172 h of single-lead ECG data from 24 temporal lobe epilepsy patients including 227 focal impaired awareness seizures. Results: The proposed personalized approach resulted in an overall sensitivity of 71% with 1.9 false detections per hour. This is an average decrease in false detection rate of 37% compared to the reference patient-independent algorithm using only a limited amount of personal seizure data. The proposed transfer learning approach adapts faster and more robustly to patient-specific characteristics than other alternatives for personalization in the literature. Conclusion: The proposed method allows an easy implementable solution to personalize heart rate-based seizure detection, which can improve the quality of life of refractory epilepsy patients when used as part of a multimodal seizure detection system.

Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels.

OBJECTIVE: Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind-the-ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind-the-ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind-the-ear EEG channels. METHODS: Fifty-four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video-EEG at University Hospital Leuven. In addition, extra behind-the-ear EEG channels were recorded. First, a neurologist was asked to annotate behind-the-ear EEG segments containing selected seizure and nonseizure fragments. Second, a data-driven algorithm was developed using only behind-the-ear EEG. This algorithm was trained using data from other patients (patient-independent model) or from the same patient (patient-specific model). RESULTS: The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false-positive detections (FPs)/24 hours with the patient-independent model. The patient-specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours. SIGNIFICANCE: Visual recognition of ictal EEG patterns using only behind-the-ear EEG is possible in a significant number of patients with TLE. A patient-specific seizure detection algorithm using only behind-the-ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.

Development of temporal lobe epilepsy during maintenance electroconvulsive therapy: A case of human kindling?

We describe a patient with new-onset temporal lobe epilepsy during prolonged maintenance electroconvulsive therapy. We suggest a possible causal relationship with maintenance electroconvulsive therapy through electrical kindling of the temporal lobe.

Positron emission tomography imaging of cerebral glucose metabolism and type 1 cannabinoid receptor availability during temporal lobe epileptogenesis in the amygdala kindling model in rhesus monkeys.

OBJECTIVE: We investigated changes in the endocannabinoid system and glucose metabolism during temporal lobe epileptogenesis. METHODS: Because it is rarely possible to study epileptogenesis in humans, we applied the electrical amygdala kindling model in nonhuman primates to image longitudinal changes in type 1 cannabinoid receptor (CB1R) binding and cerebral glucose metabolism. Two rhesus monkeys received [18 F]-MK-9470 and fluorodeoxyglucose-positron emission tomography ([18 F]-FDG -PET) scans in each of the 4 kindling stages to quantify relative changes over time of CB1R binding and cerebral glucose metabolism in vivo. We constructed z-score images relative to a control group (n = 8), and considered only those changes measured in both kindled animals by calculating the binary conjunction image per kindling stage. RESULTS: The seizure-onset zone exhibited an increased CB1R binding and a decreased glucose metabolism, which both aggravated gradually in extent and intensity throughout kindling. The ipsilateral thalamus and insula showed hypometabolism that coincided with an increase and a decrease in CB1R binding, respectively. These changes also became gradually more severe throughout kindling and overlapped with ictal perfusion changes during the final stage of amygdala kindling, with hyperperfusion in the ipsilateral thalamus and hypoperfusion in the ipsilateral insula. SIGNIFICANCE: The observed changes in CB1R binding may reflect a combination of a protective mechanism of neurons against seizure activity that becomes stronger over time to combat more severe seizures, and on the other hand, a process of epileptogenesis that facilitates seizure activity and generalization, depending on the cell type involved in those specific regions. This study provides unique evidence that the CB1R is dynamically and progressively involved from the start of mesial temporal lobe epileptogenesis.

On the optimal z-score threshold for SISCOM analysis to localize the ictal onset zone.

BACKGROUND: In epilepsy patients, SISCOM or subtraction ictal single photon emission computed tomography co-registered to magnetic resonance imaging has become a routinely used, non-invasive technique to localize the ictal onset zone (IOZ). Thresholding of clusters with a predefined number of standard deviations from normality (z-score) is generally accepted to localize the IOZ. In this study, we aimed to assess the robustness of this parameter in a group of patients with well-characterized drug-resistant epilepsy in whom the exact location of the IOZ was known after successful epilepsy surgery. Eighty patients underwent preoperative SISCOM and were seizure free in a postoperative period of minimum 1 year. SISCOMs with z-threshold 2 and 1.5 were analyzed by two experienced readers separately, blinded from the clinical ground truth data. Their reported location of the IOZ was compared with the operative resection zone. Furthermore, confidence scores of the SISCOM IOZ were compared for the two thresholds. RESULTS: Visual reporting with a z-score threshold of 1.5 and 2 showed no statistically significant difference in localizing correspondence with the ground truth (70 vs. 72% respectively, p = 0.17). Interrater agreement was moderate (κ = 0.65) at the threshold of 1.5, but high (κ = 0.84) at a threshold of 2, where also reviewers were significantly more confident (p < 0.01). CONCLUSIONS: SISCOM is a clinically useful, routinely used modality in the preoperative work-up in many epilepsy surgery centers. We found no significant differences in localizing value of the IOZ using a threshold of 1.5 or 2, but interrater agreement and reader confidence were higher using a z-score threshold of 2.

Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment.

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy.

A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information for the management of the disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind the ear could prove to be a solution to a wearable EEG setup. This article examines the feasibility of recording epileptic EEG from behind the ear. It is achieved by comparison with scalp EEG recordings. Traditional scalp EEG and behind-the-ear EEG were simultaneously acquired from 12 patients with temporal, parietal, or occipital lobe epilepsy. Behind-the-ear EEG consisted of cross-head channels and unilateral channels. The analysis on Electrooculography (EOG) artifacts resulting from eye blinking showed that EOG artifacts were absent on cross-head channels and had significantly small amplitudes on unilateral channels. Temporal waveform and frequency content during seizures from behind-the-ear EEG visually resembled that from scalp EEG. Further, coherence analysis confirmed that behind-the-ear EEG acquired meaningful epileptic discharges similarly to scalp EEG. Moreover, automatic seizure detection based on support vector machine (SVM) showed that comparable seizure detection performance can be achieved using these two recordings. With scalp EEG, detection had a median sensitivity of 100% and a false detection rate of 1.14 per hour, while, with behind-the-ear EEG, it had a median sensitivity of 94.5% and a false detection rate of 0.52 per hour. These findings demonstrate the feasibility of detecting seizures from EEG recordings behind the ear for patients with focal epilepsy.

The effective connectivity of the seizure onset zone and ictal perfusion changes in amygdala kindled rhesus monkeys.

Epileptic seizures are network-level phenomena. Hence, epilepsy may be regarded as a circuit-level disorder that cannot be understood outside this context. Better insight into the effective connectivity of the seizure onset zone and the manner in which seizure activity spreads could lead to specifically-tailored therapies for epilepsy. We applied the electrical amygdala kindling model in two rhesus monkeys until these animals displayed consistent stage IV seizures. At this stage, we investigated the effective connectivity of the amygdala by means of electrical microstimulation during fMRI (EM-fMRI). In addition, we imaged changes in perfusion during a seizure using ictal SPECT perfusion imaging. The spatial overlap between the connectivity network and the ictal perfusion network was assessed both at the regional level, by calculating Dice coefficients using anatomically defined regions of interest, and at the voxel level. The kindled amygdala was extensively connected to bilateral cortical and subcortical structures, which in many cases were connected multisynaptically to the amygdala. At the regional level, the spatial extents of many of these fMRI activations and deactivations corresponded to the respective increases and decreases in perfusion imaged during a stage IV seizure. At the voxel level, however, some regions showed residual seizure-specific activity (not overlapping with the EM-fMRI activations) or fMRI-specific activation (not overlapping with the ictal SPECT activations), indicating that frequently, only a part of a region anatomically connected to the seizure onset zone participated in seizure propagation. Thus, EM-fMRI in the amygdala of electrically-kindled monkeys reveals widespread areas that are often connected multisynaptically to the seizure focus. Seizure activity appears to spread, to a large extent, via these connected areas.

Ictal perfusion changes associated with seizure progression in the amygdala kindling model in the rhesus monkey.

OBJECTIVE: Amygdala kindling is a widely used animal model for studying mesial temporal lobe epileptogenesis. In the macaque monkey, electrical amygdala kindling develops slowly and provides an opportunity for investigating ictal perfusion changes during epileptogenesis. METHODS: Two rhesus monkeys were electrically kindled through chronically implanted electrodes in the right amygdala over a period of 16 and 17 months. Ictal perfusion single photon emission computed tomography (SPECT) imaging was performed during each of the four predefined clinical stages. RESULTS: Afterdischarge duration increased slowly over 477 days for monkey K and 515 days for monkey S (18 ± 8 s in stage I; 52 ± 13 s in stage IV). During this time, the animals progressed through four clinical stages ranging from interrupting ongoing behavior to bilateral convulsions. Ictal SPECT perfusion imaging showed well-localized but widely distributed regions of hyperperfusion and hypoperfusion, in both cortical and subcortical structures, at every seizure stage. A large portion of the ictal network was involved in the early stages of epileptogenesis and subsequently expanded over time as seizure severity evolved. SIGNIFICANCE: Our data indicate that the different mesial temporal lobe seizure types occur within a common network affecting several parts of the brain, and that seizure severity may be determined by seizure-induced epileptogenesis within a bihemispheric network that is implicated from the start of the process.

In vivo type 1 cannabinoid receptor availability in Alzheimer's disease.

The endocannabinoid system (ECS) is an important modulatory and potentially neuroprotective homeostatic system in the brain. In Alzheimer's disease (AD), the role of type 1 cannabinoid receptor (CB1R) is unclear, with contradictory findings in post-mortem studies showing upregulation, downregulation or unchanged CB1R status. We have investigated CB1R availability in vivo in patients with AD, in relation to amyloid deposition, cognitive functioning and apolipoprotein E (ApoE) genotype. Eleven AD patients and 7 healthy volunteers (HV) underwent combined [¹8F]MK-9470 PET and [¹¹C]PIB PET scans to assess CB1R availability and amyloid deposition, respectively, and T1 volumetric MRI for partial volume correction. We found no difference in CB1R availability between AD and HV, VOI-based fractional uptake values (FUR) were 0.043±0.01 for AD and 0.045±0.01 for controls (p=0.9). CB1R availability did not correlate with neuropsychological test scores and was not modulated by ApoE genotype. As expected, global [¹¹C]PIB SUVR (standardized uptake value ratio) was increased in AD (SUVR 1.9±0.3) compared to HV (1.2±0.1) with p<0.001, but no correlation was found between amyloid ß (Aß) deposition and CB1R availability. In conclusion, we found no in vivo evidence for a difference in CB1R availability in AD compared to age-matched controls. Taken together with recently reported in vivo CB1R changes in Parkinson's and Huntington's disease, these data suggest that the CB1R is differentially involved in neurodegenerative disorders.

Public health in the genomic era: will Public Health Genomics contribute to major changes in the prevention of common diseases?

The completion of the Human Genome Project triggered a whole new field of genomic research which is likely to lead to new opportunities for the promotion of population health. As a result, the distinction between genetic and environmental diseases has faded. Presently, genomics and knowledge deriving from systems biology, epigenomics, integrative genomics or genome-environmental interactions give a better insight on the pathophysiology of common diseases. However, it is barely used in the prevention and management of diseases. Together with the boost in the amount of genetic association studies, this demands for appropriate public health actions. The field of Public Health Genomics analyses how genome-based knowledge and technologies can responsibly and effectively be integrated into health services and public policy for the benefit of population health. Environmental exposures interact with the genome to produce health information which may help explain inter-individual differences in health, or disease risk. However today, prospects for concrete applications remain distant. In addition, this information has not been translated into health practice yet. Therefore, evidence-based recommendations are few. The lack of population-based research hampers the evaluation of the impact of genomic applications. Public Health Genomics also evaluates the benefits and risks on a larger scale, including normative, legal, economic and social issues. These new developments are likely to affect all domains of public health and require rethinking the role of genomics in every condition of public health interest. This article aims at providing an introduction to the field of and the ideas behind Public Health Genomics.