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Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.

This report contains the final set of topic guides identified by the NGI Forward project. The results of three consortium partners was synthesized and combined into a selection of eight topics, which are described and elaborated. This finalfinal selection of topicsreflects the common research findings and the subjects receiving the most attention from researchers, tech journalists and other stakeholder communities. The purpose of this report isto pinpoint the key issues that are significant in the development of a future internet by providing insight into internet-related subjects.

This report contains several analyses on social media datasets with a focus on internet technology and human rights. The main part of the report is about identifying trends in discussions on Reddit with a basis onhuman rights, and additionally it contains several deep dives into different focus areas, which constitute stand-alone smaller parts. The deep dives were chosen to highlight several of the ten key rights and principles of human rights online. They each study a specific case at the interface of human rights and internet technology. The main aim of this report was to analyze discussions on several social media platforms to identify trends and topics relevant for the next generation internet. A secondary goal was to examine social issues that accompany internet technology. We have analyzed various data sources from the platforms Reddit, Twitter, and Facebook. They have a large number of users and encompass discussions on internet technology, societal issues, news, and everyday life. In this report, we present our main analysis about detecting upcoming trends of internet technology. The analysis centers on discussions based on human values to incorporate societal aspects instead of a purely technological aspect. Additionally, we describe several deep dives into internet technology related topics and societal issues. The first deep dive is into NGI-related topics, where we identify discussed topics, investigate the emotions of those discussions, and map out the related topics. The second deep dive investigates the societal issue of gender inequality in relation to the discriminatory bias of algorithms. For the third deep dive, we look into the issue of disinformation on social media and the attached sentiments. The fourth deep dive is into privacy and whether topics determine privacy settings. Those deep dives give insight into both current discussions as well as societal issues accompanying internet technology.

The spreading of COVID-19 misinformation on social media could have severe consequences on people's behavior. In this paper, we investigated the emotional expression of misinformation related to the COVID-19 crisis on Twitter and whether emotional valence differed depending on the type of misinformation. We collected 17,463,220 English tweets with 76 COVID-19-related hashtags for March 2020. Using Google Fact Check Explorer API we identified 226 unique COVID-19 false stories for March 2020. These were clustered into six types of misinformation (cures, virus, vaccine, politics, conspiracy theories, and other). Applying the 226 classifiers to the Twitter sample we identified 690,004 tweets. Instead of running the sentiment on all tweets we manually coded a random subset of 100 tweets for each classifier to increase the validity, reducing the dataset to 2,097 tweets. We found that only a minor part of the entire dataset was related to misinformation. Also, misinformation in general does not lean towards a certain emotional valence. However, looking at comparisons of emotional valence for different types of misinformation uncovered that misinformation related to “virus” and “conspiracy” had a more negative valence than “cures,” “vaccine,” “politics,” and “other.” Knowing from existing studies that negative misinformation spreads faster, this demonstrates that filtering for misinformation type is fruitful and indicates that a focus on “virus” and “conspiracy” could be one strategy in combating misinformation. As emotional contexts affect misinformation spreading, the knowledge about emotional valence for different types of misinformation will help to better understand the spreading and consequences of misinformation.

BackgroundThe success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures.MethodsThe propagation of seizures was modelled as an epidemic process (susceptible-infected-recovered (SIR) model) on individual structural networks derived from presurgical diffusion tensor imaging (DTI) in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the Eigenvector Centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network.ResultsWe found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was more effective than random removal of the same number of connections, and equally or more effective than removal based on structural network characteristics.ConclusionThe approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.

In some patients with medically refractory epilepsy, EEG with intracerebrally placed electrodes (stereo-electroencephalography, SEEG) is needed to locate the seizure onset zone (SOZ) for successful epilepsy surgery. SEEG has limitations and entails risk of complications because of its invasive character. Non-invasive magnetoencephalography virtual electrodes (MEG-VEs) may overcome SEEG limitations and optimize electrode placement making SOZ localization safer. Our purpose was to assess whether interictal activity measured by MEG-VEs and SEEG at identical anatomical locations were comparable, and whether MEG-VEs activity properties could determine the location of a later resected brain area (RA) as an approximation of the SOZ. We analyzed data from nine patients who underwent MEG and SEEG evaluation, and surgery for medically refractory epilepsy. MEG activity was retrospectively reconstructed using beamforming to obtain VEs at the anatomical locations corresponding to those of SEEG electrodes. Spectral, functional connectivity and functional network properties were obtained for both, MEG-VEs and SEEG time series, and their correlation and reliability were established. Based on these properties, the approximation of the SOZ was characterized by the differences between RA and non-RA (NRA). We found significant positive correlation and reliability between MEG-VEs and SEEG spectral measures (particularly in delta [0.5–4 Hz], alpha2 [10–13 Hz], and beta [13–30 Hz] bands) and broadband functional connectivity. Both modalities showed significantly slower activity and a tendency towards increased broadband functional connectivity in the RA compared to the NRA. Our findings show that spectral and functional connectivity properties of non-invasively obtained MEG-VEs match those of invasive SEEG recordings, and can characterize the SOZ. This suggests that MEG-VEs might be used for optimal SEEG planning and fewer depth electrode implantations, making the localization of the SOZ safer and more successful., Highlights • Reconstruction of resting state brain activity at specific brain locations is feasible using MEG virtual electrodes. • MEG-VE interictal activity at the stereo-EEG (SEEG) locations correlates well with SEEG activity. • MEG-VE and SEEG activity in the resected area was slower than in the non-resected area in epilepsy surgery patients. • MEG-VE may be used in optimization of the SEEG electrode planning. • MEG-VEs evaluation could make the localization of the seizure onset zone safer.

Highlights • Objective localizations of interictal spikes using a kurtosis beamformer. • Kurtosis Beamforming can provide confidence to scattered dipoles. • Kurtosis beamforming can assist in localizing the epileptogenic zone., Objective Kurtosis beamforming is a useful technique for analysing magnetoencephalograpy (MEG) data containing epileptic spikes. However, the implementation varies and few studies measure concordance with subsequently resected areas. We evaluated kurtosis beamforming as a means of localizing spikes in drug-resistant epilepsy patients. Methods We retrospectively applied kurtosis beamforming to MEG recordings of 22 epilepsy patients that had previously been analysed using equivalent current dipole (ECD) fitting. Virtual electrodes were placed in the kurtosis volumetric peaks and visually inspected to select a candidate source. The candidate sources were compared to the ECD localizations and resection areas. Results The kurtosis beamformer produced interpretable localizations in 18/22 patients, of which the candidate source coincided with the resection lobe in 9/13 seizure-free patients and in 3/5 patients with persistent seizures. The sublobar accuracy of the kurtosis beamformer with respect to the resection zone was higher than ECD (56% and 50%, respectively), however, ECD resulted in a higher lobar accuracy (75%, 67%). Conclusions Kurtosis beamforming may provide additional value when spikes are not clearly discernible on the sensors and support ECD localizations when dipoles are scattered. Significance Kurtosis beamforming should be integrated with existing clinical protocols to assist in localizing the epileptogenic zone.

OBJECTIVES: Reusing baseline volumes of interest (VOI) by applying non-rigid and to some extent (local) rigid image registration showed good test-retest variability similar to delineating VOI on both scans individually. The aim of the present study was to compare response assessments and classifications based on various types of image registration with those based on (semi)-automatic tumour delineation. METHODS: Baseline (n = 13), early (n = 12) and late (n = 9) response (after one and three cycles of treatment, respectively) whole body [(18)F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) scans were acquired in subjects with advanced gastrointestinal malignancies. Lesions were identified for early and late response scans. VOI were drawn independently on all scans using an adaptive 50% threshold method (A50). In addition, various types of (non-)rigid image registration were applied to PET and/or CT images, after which baseline VOI were projected onto response scans. Response was classified using PET Response Criteria in Solid Tumors for maximum standardized uptake value (SUV(max)), average SUV (SUV(mean)), peak SUV (SUV(peak)), metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and the area under a cumulative SUV-volume histogram curve (AUC). RESULTS: Non-rigid PET-based registration and non-rigid CT-based registration followed by non-rigid PET-based registration (CTPET) did not show differences in response classifications compared to A50 for SUV(max) and SUV(peak), however, differences were observed for MATV, SUV(mean), TLG and AUC. For the latter, these registrations demonstrated a poorer performance for small lung lesions (

OBJECTIVE: Previous studies have associated network hubs and epileptiform activity, such as spikes and high frequency oscillations (HFOs), with the epileptogenic zone. The epileptogenic zone is approximated by the area that generates interictal epileptiform activity: the irritative zone. Our aim was to determine the relation between network hubs and the irritative zone.METHODS: Interictal resting-state MEG recordings of 12 patients with refractory epilepsy were analysed. Beamformer-based virtual electrodes were calculated at 70 locations around the epileptic spikes (irritative zone) and in the contralateral hemisphere. Spikes and HFOs were marked in all virtual electrodes. A minimum spanning tree network was generated based on functional connectivity (phase lag index; PLI) between all virtual electrodes to calculate the betweenness centrality, an indicator of hub status of network nodes.RESULTS: Betweenness centrality was low, and PLI was high, in virtual electrodes close to the centre of the irritative zone, and in virtual electrodes with many spikes and HFOs.CONCLUSION: Node centrality increases with distance from brain areas with spikes and HFOs, consistent with the idea that the irritative zone is a functionally isolated part of the epileptic network during the interictal state.SIGNIFICANCE: A new hypothesis about a pathological hub located remotely from the irritative zone and seizure onset zone opens new ways for surgery when epileptogenic areas and eloquent cortex coincide.