How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, we reconstructed Off-type starburst amacrine cells (SACs) and bipolar cells (BCs) in serial electron microscopic images with help from EyeWire, an online community of “citizen neuroscientists.” Based on quantitative analyses of contact area and branch depth in the retina, we found evidence that one BC type prefers to wire with a SAC dendrite near the SAC soma, while another BC type prefers to wire far from the soma. The near type is known to lag the far type in time of visual response. A mathematical model shows how such “space-time wiring specificity” could endow SAC dendrites with receptive fields that are oriented in space-time and therefore respond selectively to stimuli that move in the outward direction from the soma.

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy.Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we develop a global optimisation approach to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. Abbreviations(iEEG) intracranial electroencephalographic recordings, (EZ) epileptogenic zone, ( ) Brain Network Ictogenicity, ( ) Node Ictogenicity, ( ) Set Ictogenicity 'Declarations of interest: none'

Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short‐time resting‐state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug‐resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation‐inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non‐SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation‐inhibition balance. They found flatter 1/f power slope in non‐SOZ regions compared to the SOZ, with dominant information flow from non‐SOZ to SOZ regions. Greater differences in resting‐state information flow between SOZ and non‐SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting‐state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting‐state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long‐term ictal recordings.

IMPORTANCE Stereoelectroencephalography (SEEG) has become the criterion standard in case of inconclusive noninvasive presurgical epilepsy workup. However, up to 40% of patients are subsequently not offered surgery because the seizure-onset zone is less focal than expected or cannot be identified.OBJECTIVE To predict focality of the seizure-onset zone in SEEG, the 5-point 5-SENSE score was developed and validated. DESIGN, SETTING, AND PARTICIPANTS This was a monocentric cohort study for score development followed by multicenter validation with patient selection intervals between February 2002 to October 2018 and May 2002 to December 2019. The minimum follow-up period was 1 year. Patients with drug-resistant epilepsy undergoing SEEG at the Montreal Neurological Institute were analyzed to identify a focal seizure-onset zone. Selection criteria were 2 or more seizures in electroencephalography and availability of complete neuropsychological and neuroimaging data sets. For validation, patients from 9 epilepsy centers meeting these criteria were included. Analysis took place between May and July 2021. MAIN OUTCOMES AND MEASURES Based on SEEG, patients were grouped as focal and nonfocal seizure-onset zone. Demographic, clinical, electroencephalography, neuroimaging, and neuropsychology data were analyzed, and a multiple logistic regression model for developing a score to predict SEEG focality was created and validated in an independent sample. RESULTS A total of 128 patients (57 women [44.5%]; median [range] age, 31 [13-58] years) were analyzed for score development and 207 patients (97 women [46.9%]; median [range] age , 32 [16-70] years) were analyzed for validation. The score comprised the following 5 predictive variables: focal lesion on structural magnetic resonance imaging, absence of bilateral independent spikes in scalp electroencephalography, localizing neuropsychological deficit, strongly localizing semiology, and regional ictal scalp electroencephalography onset. The 5-SENSE score had an optimal mean (SD) probability cutoff for identifying a focal seizure-onset zone of 37.6 (3.5). Area under the curve, specificity, and sensitivity were 0.83, 76.3% (95% CI, 66.7-85.8), and 83.3% (95% CI, 72.30-94.1), respectively. Validation showed 76.0% (95% CI, 67.5-84.0) specificity and 52.3% (95% CI, 43.0-61.5) sensitivity. CONCLUSIONS AND RELEVANCEHigh specificity in score development and validation confirms that the 5-SENSE score predicts patients where SEEG is unlikely to identify a focal seizure-onset zone. It is a simple and useful tool for assisting clinicians to reduce unnecessary invasive diagnostic burden on patients and overutilization of limited health care resources.

In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long‐term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state‐of‐the‐art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty‐three full‐text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic‐detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure‐detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

word count: 399 Body word count: 6127 (limit 6000) AbstractThe epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy.Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analyzed from 1,069 non-epileptic controls and 1,249 patients: temporal lobe epilepsy with hippocampal sclerosis (N=599), temporal lobe epilepsy with normal MRI (N=275), genetic generalized epilepsy (N=182) and nonlesional extratemporal epilepsy (N=193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fiber tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at p<0.001). Across "all epilepsies" lower fractional anisotropy was observed in most fiber tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. Less robust effects were seen with mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Those with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced differences in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and in mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of microstructural abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibers in a large multicentre study of epilepsy.Overall, epilepsy patients showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding new insights into pathological substrates that may be used to guide future therapeutic and genetic studies.

Objective: Video-electroencephalography (vEEG) is an important component of epilepsy diagnosis and management. Nevertheless, inpatient vEEG monitoring fails to capture seizures in up to one third of patients during diagnostic and pre-surgical monitoring. We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG and improve diagnostic yield. Methods: We used a database of ambulatory vEEG studies to select a cohort with linked electronic seizure diaries of more than 20 reported seizures over at least 8 weeks. The total cohort included 48 participants. Diary seizure times were used to detect individuals' multi-day cycles and estimate times of high seizure risk. We then compared whether estimated seizure risk was significantly different between diagnostic and non-diagnostic vEEGs, and between vEEG with and without recorded epileptic activity. Results: Estimated seizure risk was significantly higher for diagnostic vEEGs and vEEGs with epileptic activity. Across all cycle strengths, the average time in high risk during vEEG was 29.1% compared with 14% for the diagnostic/non-diagnostic groups and 32% compared to 18% for the epileptic activity/no epileptic activity groups. On average, 62.5% of the cohort showed increased time in high risk during vEEG when epileptic activity was recorded (compared to 28% of the cohort where epileptic activity was not recorded). For diagnostic vEEGs, 50% of the cohort had increased time in high risk, compared to 21.5% for non-diagnostic vEEGs. Significance: This study provides a proof of principle that scheduling monitoring times based on personalized seizure risk forecasts can improve the yield of vEEG. Importantly, forecasts can be developed at low cost from mobile seizure diaries. A simple scheduling tool to improve diagnostic outcomes has the potential to reduce the significant cost and risks associated with delayed or missed diagnosis in epilepsy.

Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional megaanalysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1,021 adults with epilepsy compared to 1,564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy co-localized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Indices of progressive atrophy further revealed a strong influence of connectome architecture on disease progression in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients, and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided novel insights into the macroscale features that shape the pathophysiology of common epilepsies.Larivière et al.Atrophy modelling in the common epilepsies | 4