Spatiotemporal dynamics of cortical somatosensory network in typically developing children.

Sense of touch is essential for our interactions with external objects and fine control of hand actions. Despite extensive research on human somatosensory processing, it is still elusive how involved brain regions interact as a dynamic network in processing tactile information. Few studies probed temporal dynamics of somatosensory information flow and reported inconsistent results. Here, we examined cortical somatosensory processing through magnetic source imaging and cortico-cortical coupling dynamics. We recorded magnetoencephalography signals from typically developing children during unilateral pneumatic stimulation. Neural activities underlying somatosensory evoked fields were mapped with dynamic statistical parametric mapping, assessed with spatiotemporal activation analysis, and modeled by Granger causality. Unilateral pneumatic stimulation evoked prominent and consistent activations in the contralateral primary and secondary somatosensory areas but weaker and less consistent activations in the ipsilateral primary and secondary somatosensory areas. Activations in the contralateral primary motor cortex and supramarginal gyrus were also consistently observed. Spatiotemporal activation and Granger causality analysis revealed initial serial information flow from contralateral primary to supramarginal gyrus, contralateral primary motor cortex, and contralateral secondary and later dynamic and parallel information flows between the consistently activated contralateral cortical areas. Our study reveals the spatiotemporal dynamics of cortical somatosensory processing in the normal developing brain.

Spatiotemporal responses to emotional conflict and its psychiatric correlates in adolescents with epilepsy using magnetoencephalography.

People with epilepsy often suffer from comorbid psychiatric disorders, which negatively affects their quality of life. Emotion regulation is an important cognitive process that is impaired in individuals with psychiatric disorders, such as depression. Adults with epilepsy also show difficulties in emotion regulation, particularly during later-stage, higher-order cognitive processing. Yet, the spatiotemporal and frequency correlates of these functional brain deficits in epilepsy remain unknown, as do the nature of these deficits in adolescent epilepsy. Here, we aim to elucidate the spatiotemporal profile of emotional conflict processing in adolescents with epilepsy, relative to controls, using magnetoencephalography (MEG) and relate these findings to anxiety and depression symptom severity assessed with self-report scales. We hypothesized to see blunted brain activity during emotional conflict in adolescents with epilepsy, relative to controls, in the posterior parietal, prefrontal and cingulate cortices due to their role in explicit and implicit regulation around participant response (500-1000 ms). We analyzed MEG recordings from 53 adolescents (28 epilepsy [14focal,14generalized], 25 controls) during an emotional conflict task. We showed that while controls exhibited behavioral interference to emotional conflict, adolescents with epilepsy failed to exhibit this normative response time pattern. Adolescents with epilepsy showed blunted brain responses to emotional conflict in brain regions related to error evaluation and learning around the average response time (500-700 ms), and in regions involved in decision making during post-response monitoring (800-1000 ms). Interestingly, behavioral patterns and psychiatric symptom severity varied between epilepsy subgroups, wherein those with focal epilepsy showed preserved response time interference. Thus, brain responses were regressed with depression and anxiety levels for each epilepsy subgroup separately. Analyses revealed that under activation in error evaluation regions (500-600 ms) predicted anxiety and depression in focal epilepsy, while regions related to learning (600-700 ms) predicted anxiety in generalized epilepsy, suggesting differential mechanisms of dysfunction in these subgroups. Despite similar rates of anxiety and depression across the groups, adolescents with epilepsy still exhibited deficits in emotional conflict processing in brain and behavioral responses. This suggests that these deficits may exist independently from psychopathology and may stem from underlying dysfunctions that predispose these individuals to develop both disorders. Findings such as these may provide potential targets for future research and therapies.

The long-term impact of cerebellar tumor resection on executive functioning, anxiety, and fear of pain: A mixed methodology pilot study.

This pilot study investigated the long-term impact of a surgery-only treatment (no exposure to other treatments, such as chemotherapy and radiation) for pediatric cerebellar low-grade gliomas on executive function, anxiety, and fear of pain (FOP) beliefs. Twelve patients who underwent surgical glioma resection during childhood (surgery age was 4-16 years, study visit age was 10-28 years), and 12 pain-free controls matched for age, sex, race, and handedness were tested. The spatial extent of resection was precisely mapped using magnetic resonance imaging (MRI). Executive function, anxiety, and FOP were assessed using validated self-report age-appropriate questionnaires for children and adults. Structured clinical interviews at a post-surgery follow-up visit were completed (average: 89 months, range: 20-99). No significant differences in FOP (FOPQ-C t[14 = 1.81, p = 0.09; FOPQ-III t[4] = 0.29, p = 0.79), executive function scores (BRIEF t[20] = 0.30, p = 0.28), or anxiety scores (MASC t[16] = 0.19, p = 0.85; MAQ t[4] = 1.80, p = 0.15) were found in pediatric or adult patients compared to pain-free controls. Clinical interviews mainly categorized pediatric patients as not anxious. One participant reported mild/subclinical anxiety, and one had moderate clinical anxiety. Neither psychologists nor patients endorsed impairments to executive functioning, anxiety, or FOP. Our pilot results suggest that pediatric cerebellar tumor survivors treated with surgery-only have favorable long-term functioning related to these themes. While these results are promising, they will need to be replicated in a larger patient sample.

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach.

OBJECTIVE: To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data. METHODS: We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization. RESULTS: FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes. SIGNIFICANCE: We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

Sleep Spindle Generation Before and After Epilepsy Surgery: A Source Imaging Study in Children with Drug-Resistant Epilepsy.

INTRODUCTION: Literature lacks studies investigating the cortical generation of sleep spindles in drug-resistant epilepsy (DRE) and how they evolve after resection of the epileptogenic zone (EZ). Here, we examined sleep EEGs of children with focal DRE who became seizure-free after focal epilepsy surgery, and aimed to investigate the changes in the spindle generation before and after the surgery using low-density scalp EEG and electrical source imaging (ESI). METHODS: We analyzed N2-sleep EEGs from 19 children with DRE before and after surgery. We identified slow (8-12 Hz) and fast spindles (13-16 Hz), computed their spectral features and cortical generators through ESI and computed their distance from the EZ and irritative zone (IZ). We performed two-way ANOVA testing the effect of spindle type (slow vs. fast) and surgical phase (pre-surgery vs. post-surgery) on each feature. RESULTS: Power, frequency and cortical activation of slow spindles increased after surgery (p < 0.005), while this was not seen for fast spindles. Before surgery, the cortical generators of slow spindles were closer to the EZ (57.3 vs. 66.2 mm, p = 0.007) and IZ (41.3 vs. 55.5 mm, p = 0.02) than fast spindle generators. CONCLUSIONS: Our data indicate alterations in the EEG slow spindles after resective epilepsy surgery. Fast spindle generation on the contrary did not change after surgery. Although the study is limited by its retrospective nature, lack of healthy controls, and reduced cortical spatial sampling, our findings suggest a spatial relationship between the slow spindles and the epileptogenic generators.

Reduced evoked cortical beta and gamma activity and neuronal synchronization in succinic semialdehyde dehydrogenase deficiency, a disorder of γ-aminobutyric acid metabolism.

Succinic semialdehyde dehydrogenase deficiency is a rare autosomal recessively inherited metabolic disorder of γ-aminobutyric acid catabolism manifested by intellectual disability, expressive aphasia, movement disorders, psychiatric ailments and epilepsy. Subjects with succinic semialdehyde dehydrogenase deficiency are characterized by elevated γ-aminobutyric acid and related metabolites, such as γ-guanidinobutyric acid, and an age-dependent downregulation of cerebral γ-aminobutyric acid receptors. These findings indicate impaired γ-aminobutyric acid and γ-aminobutyric acid sub-type A (GABAA) receptor signalling as major factors underlying the pathophysiology of this neurometabolic disorder. We studied the cortical oscillation patterns and their relationship with γ-aminobutyric acid metabolism in 18 children affected by this condition and 10 healthy controls. Using high-density EEG, we recorded somatosensory cortical responses and resting-state activity. Using electrical source imaging, we estimated the relative power changes (compared with baseline) in both stimulus-evoked and stimulus-induced responses for physiologically relevant frequency bands and resting-state power. Stimulus-evoked oscillations are phase locked to the stimulus, whereas induced oscillations are not. Power changes for both evoked and induced responses as well as resting-state power were correlated with plasma γ-aminobutyric acid and γ-guanidinobutyric acid concentrations and with cortical γ-aminobutyric acid measured by proton magnetic resonance spectroscopy. Plasma γ-aminobutyric acid, γ-guanidinobutyric acid and cortical γ-aminobutyric acid were higher in patients than in controls (P < 0.001 for both). Beta and gamma relative power were suppressed for evoked responses in patients versus controls (P < 0.01). No group differences were observed for induced activity (P > 0.05). The mean gamma frequency of evoked responses was lower in patients versus controls (P = 0.002). Resting-state activity was suppressed in patients for theta (P = 0.011) and gamma (P < 0.001) bands. Evoked power changes were inversely correlated with plasma γ-aminobutyric acid and with γ-guanidinobutyric acid for beta (P < 0.001) and gamma (P < 0.001) bands. Similar relationships were observed between the evoked power changes and cortical γ-aminobutyric acid for all tested areas in the beta band (P < 0.001) and for the posterior cingulate gyrus in the gamma band (P < 0.001). We also observed a negative correlation between resting-state activity and plasma γ-aminobutyric acid and γ-guanidinobutyric acid for theta (P < 0.001; P = 0.003), alpha (P = 0.003; P = 0.02) and gamma (P = 0.02; P = 0.01) bands. Our findings indicate that increased γ-aminobutyric acid concentration is associated with reduced sensory-evoked beta and gamma activity and impaired neuronal synchronization in patients with succinic semialdehyde dehydrogenase deficiency. This further elucidates the pathophysiology of this neurometabolic disorder and serves as a potential biomarker for therapeutic trials.

Development and characterization of transfontanelle photoacoustic imaging system for detection of intracranial hemorrhages and measurement of brain oxygenation: Ex-vivo.

We have developed and optimized an imaging system to study and improve the detection of brain hemorrhage and to quantify oxygenation. Since this system is intended to be used for brain imaging in neonates through the skull opening, i.e., fontanelle, we called it, Transfontanelle Photoacoustic Imaging (TFPAI) system. The system is optimized in terms of optical and acoustic designs, thermal safety, and mechanical stability. The lower limit of quantification of TFPAI to detect the location of hemorrhage and its size is evaluated using in-vitro and ex-vivo experiments. The capability of TFPAI in measuring the tissue oxygenation and detection of vasogenic edema due to brain blood barrier disruption are demonstrated. The results obtained from our experimental evaluations strongly suggest the potential utility of TFPAI, as a portable imaging modality in the neonatal intensive care unit. Confirmation of these findings in-vivo could facilitate the translation of this promising technology to the clinic.

Electromagnetic source imaging predicts surgical outcome in children with focal cortical dysplasia.

OBJECTIVE: To evaluate the diagnostic accuracy of electromagnetic source imaging (EMSI) in localizing spikes and predict surgical outcome in children with drug resistant epilepsy (DRE) due to focal cortical dysplasia (FCD). METHODS: We retrospectively analyzed magnetoencephalography (MEG) and high-density (HD-EEG) data from 23 children with FCD-associated DRE who underwent intracranial EEG and surgery. We localized spikes using equivalent current dipole (ECD) fitting, dipole clustering, and dynamical statistical parametric mapping (dSPM) on EMSI, electric source imaging (ESI), and magnetic source imaging (MSI). We calculated the distance from the seizure onset zone (DSOZ) and resection (DRES). We estimated receiver operating characteristic (ROC) curves with Youden's index (J) to predict outcome. RESULTS: EMSI presented shorter DSOZ (15.18 ± 9.06 mm) and DRES (8.56 ± 6.24 mm) compared to ESI (DSOZ: 25.04 ± 16.20 mm, p < 0.009; DRES: 18.88 ± 17.30 mm, p < 0.03) and MSI (DSOZ: 23.37 ± 8.98 mm, p < 0.03; DRES: 15.51 ± 10.11 mm, p < 0.02) for clustering in patients with good outcome. Clustering showed shorter DSOZ and DRES compared to ECD fitting and dSPM (p < 0.05). EMSI had higher performance as outcome predictor (J = 70.63%) compared to ESI (J = 41.27%) and MSI (J = 33.33%) for clustering. CONCLUSIONS: EMSI provides superior localization and improved predictive performance than individual modalities. SIGNIFICANCE: EMSI can help the surgical planning and facilitate the localization of epileptogenic foci.

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy.

Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.

Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy.

Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.

Non-invasive mapping of epileptogenic networks predicts surgical outcome.

Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.

An approach for reliably identifying high-frequency oscillations and reducing false-positive detections.

OBJECTIVE: Aiming to improve the feasibility and reliability of using high-frequency oscillations (HFOs) for translational studies of epilepsy, we present a pipeline with features specifically designed to reject false positives for HFOs to improve the automatic HFO detector. METHODS: We presented an integrated, multi-layered procedure capable of automatically rejecting HFOs from a variety of common false positives, such as motion, background signals, and sharp transients. This method utilizes a time-frequency contour approach that embeds three different layers including peak constraints, power thresholds, and morphological identification to discard false positives. Four experts were involved in rating detected HFO events that were randomly selected from different posttraumatic epilepsy (PTE) animals for a comprehensive evaluation. RESULTS: The algorithm was run on 768-h recordings of intracranial electrodes in 48 PTE animals. A total of 453 917 HFOs were identified by initial HFO detection, of which 450 917 were implemented for HFO refinement and 203 531 events were retained. Random sampling was used to evaluate the performance of the detector. The HFO detection yielded an overall accuracy of 0.95 ± 0.03 , with precision, recall, and F1 scores of 0.92 ± 0.05 , 0.99 ± 0.01 , and 0.94 ± 0.03 , respectively. For the HFO classification, our algorithm obtained an accuracy of 0.97 ± 0.02 . For the inter-rater reliability of algorithm evaluation, the agreement among four experts was 0.94 ± 0.03 for HFO detection and 0.85 ± 0.04 for HFO classification. SIGNIFICANCE: Our approach shows that a segregated pipeline design with a focus on false-positive rejection can improve the detection efficiency and provide reliable results. This pipeline does not require customization and uses fixed parameters, making it highly feasible and translatable for basic and clinical applications of epilepsy.

Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom.

In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80-250 Hz) and in the fast ripple (250-500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient's HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.

Virtual implantation using conventional scalp EEG delineates seizure onset and predicts surgical outcome in children with epilepsy.

OBJECTIVE: Delineation of the seizure onset zone (SOZ) is required in children with drug resistant epilepsy (DRE) undergoing neurosurgery. Intracranial EEG (icEEG) serves as gold standard but has limitations. Here, we examine the utility of virtual implantation with electrical source imaging (ESI) on ictal scalp EEG for mapping the SOZ and predict surgical outcome. METHODS: We retrospectively analyzed EEG data from 35 children with DRE who underwent surgery and dichotomized into seizure-free (SF) and non-seizure-free (NSF). We estimated virtual sensors (VSs) at brain locations that matched icEEG implantation and compared ictal patterns at VSs vs icEEG. We calculated the agreement between VSs SOZ and clinically defined SOZ and built receiver operating characteristic (ROC) curves to test whether it predicted outcome. RESULTS: Twenty-one patients were SF after surgery. Moderate agreement between virtual and icEEG patterns was observed (kappa = 0.45, p < 0.001). Virtual SOZ agreement with clinically defined SOZ was higher in SF vs NSF patients (66.6% vs 41.6%, p = 0.01). Anatomical concordance of virtual SOZ with clinically defined SOZ predicted outcome (AUC = 0.73; 95% CI: 0.57-0.89; sensitivity = 66.7%; specificity = 78.6%; accuracy = 71.4%). CONCLUSIONS: Virtual implantation on ictal scalp EEG can approximate the SOZ and predict outcome. SIGNIFICANCE: SOZ mapping with VSs may contribute to tailoring icEEG implantation and predict outcome.

Mapping the brain basis of feelings, emotions and much more: A special issue focused on 'The Human Affectome'.

The Human Affectome Project was launched by the non-profit organization Neuroqualia (www.neuroqualia.org) in 2015 with the seemingly impossible goal: To map a psychological process and form possible definitions and working models for affective states and related emotions. Twelve reviews based on emotions, feelings and motivation were written dedicated to mapping the brain basis of affect. A capstone piece 'The Human Affectome' provides a foundation for the special issue by giving detailed up-to-date definitions for key terms including feeling, affect, emotion and mood. Critically, the piece offers an overall model synthesizing three main features of affect: valence, motivation, and arousal. Affect itself is explored as the main umbrella function capturing all feeling states and related processes. Overall, the project and the special issue has been a highly successful interdisciplinary effort producing a novel approach that can be used to understand, guide and revise contemporary research on the brain basis of feeling and how diverse feeling states interact with each other in typical and atypical fashions.

Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery.

Delineation of resected brain cavities on magnetic resonance images (MRIs) of epilepsy surgery patients is essential for neuroimaging/neurophysiology studies investigating biomarkers of the epileptogenic zone. The gold standard to delineate the resection on MRI remains manual slice-by-slice tracing by experts. Here, we proposed and validated a semiautomated MRI segmentation pipeline, generating an accurate model of the resection and its anatomical labeling, and developed a graphical user interface (GUI) for user-friendly usage. We retrieved pre- and postoperative MRIs from 35 patients who had focal epilepsy surgery, implemented a region-growing algorithm to delineate the resection on postoperative MRIs and tested its performance while varying different tuning parameters. Similarity between our output and hand-drawn gold standards was evaluated via dice similarity coefficient (DSC; range: 0-1). Additionally, the best segmentation pipeline was trained to provide an automated anatomical report of the resection (based on presurgical brain atlas). We found that the best-performing set of parameters presented DSC of 0.83 (0.72-0.85), high robustness to seed-selection variability and anatomical accuracy of 90% to the clinical postoperative MRI report. We presented a novel user-friendly open-source GUI that implements a semiautomated segmentation pipeline specifically optimized to generate resection models and their anatomical reports from epilepsy surgery patients, while minimizing user interaction.

Case Report: Laser Ablation Guided by State of the Art Source Imaging Ends an Adolescent's 16-Year Quest for Seizure Freedom.

Epilepsy surgery is the most effective therapeutic approach for children with drug resistant epilepsy (DRE). Recent advances in neurosurgery, such as the Laser Interstitial Thermal Therapy (LITT), improved the safety and non-invasiveness of this method. Electric and magnetic source imaging (ESI/MSI) plays critical role in the delineation of the epileptogenic focus during the presurgical evaluation of children with DRE. Yet, they are currently underutilized even in tertiary epilepsy centers. Here, we present a case of an adolescent who suffered from DRE for 16 years and underwent surgery at Cook Children's Medical Center (CCMC). The patient was previously evaluated in a level 4 epilepsy center and treated with multiple antiseizure medications for several years. Presurgical evaluation at CCMC included long-term video electroencephalography (EEG), magnetoencephalography (MEG) with simultaneous conventional EEG (19 channels) and high-density EEG (256 channels) in two consecutive sessions, MRI, and fluorodeoxyglucose - positron emission tomography (FDG-PET). Video long-term EEG captured nine focal-onset clinical seizures with a maximal evolution over the right frontal/frontal midline areas. MRI was initially interpreted as non-lesional. FDG-PET revealed a small region of hypometabolism at the anterior right superior temporal gyrus. ESI and MSI performed with dipole clustering showed a tight cluster of dipoles in the right anterior insula. The patient underwent intracranial EEG which indicated the right anterior insular as seizure onset zone. Eventually LITT rendered the patient seizure free (Engel 1; 12 months after surgery). Retrospective analysis of ESI and MSI clustered dipoles found a mean distance of dipoles from the ablated volume ranging from 10 to 25 mm. Our findings highlight the importance of recent technological advances in the presurgical evaluation and surgical treatment of children with DRE, and the underutilization of epilepsy surgery in children with DRE.

Presurgical accuracy of dipole clustering in MRI-negative pediatric patients with epilepsy: Validation against intracranial EEG and resection.

OBJECTIVE: To assess the utility of interictal magnetic and electric source imaging (MSI and ESI) using dipole clustering in magnetic resonance imaging (MRI)-negative patients with drug resistant epilepsy (DRE). METHODS: We localized spikes in low-density (LD-EEG) and high-density (HD-EEG) electroencephalography as well as magnetoencephalography (MEG) recordings using dipoles from 11 pediatric patients. We computed each dipole's level of clustering and used it to discriminate between clustered and scattered dipoles. For each dipole, we computed the distance from seizure onset zone (SOZ) and irritative zone (IZ) defined by intracranial EEG. Finally, we assessed whether dipoles proximity to resection was predictive of outcome. RESULTS: LD-EEG had lower clusterness compared to HD-EEG and MEG (p < 0.05). For all modalities, clustered dipoles showed higher proximity to SOZ and IZ than scattered (p < 0.001). Resection percentage was higher in optimal vs. suboptimal outcome patients (p < 0.001); their proximity to resection was correlated to outcome (p < 0.001). No difference in resection percentage was seen for scattered dipoles between groups. CONCLUSION: MSI and ESI dipole clustering helps to localize the SOZ and IZ and facilitate the prognostic assessment of MRI-negative patients with DRE. SIGNIFICANCE: Assessing the MSI and ESI clustering allows recognizing epileptogenic areas whose removal is associated with optimal outcome.