Implications for driving based on the risk of seizures after ischaemic stroke.

BACKGROUND: In addition to other stroke-related deficits, the risk of seizures may impact driving ability after stroke. METHODS: We analysed data from a multicentre international cohort, including 4452 adults with acute ischaemic stroke and no prior seizures. We calculated the Chance of Occurrence of Seizure in the next Year (COSY) according to the SeLECT2.0 prognostic model. We considered COSY<20% safe for private and <2% for professional driving, aligning with commonly used cut-offs. RESULTS: Seizure risks in the next year were mainly influenced by the baseline risk-stratified according to the SeLECT2.0 score and, to a lesser extent, by the poststroke seizure-free interval (SFI). Those without acute symptomatic seizures (SeLECT2.0 0-6 points) had low COSY (0.7%-11%) immediately after stroke, not requiring an SFI. In stroke survivors with acute symptomatic seizures (SeLECT2.0 3-13 points), COSY after a 3-month SFI ranged from 2% to 92%, showing substantial interindividual variability. Stroke survivors with acute symptomatic status epilepticus (SeLECT2.0 7-13 points) had the highest risk (14%-92%). CONCLUSIONS: Personalised prognostic models, such as SeLECT2.0, may offer better guidance for poststroke driving decisions than generic SFIs. Our findings provide practical tools, including a smartphone-based or web-based application, to assess seizure risks and determine appropriate SFIs for safe driving.

Multimodal prognostic features of seizure freedom in epilepsy surgery.

OBJECTIVE: Accurate preoperative predictions of seizure freedom following surgery for focal drug resistant epilepsy remain elusive. Our objective was to systematically evaluate all meta-analyses of epilepsy surgery with seizure freedom as the primary outcome, to identify clinical features that are consistently prognostic and should be included in the future models. METHODS: We searched PubMed and Cochrane using free-text and Medical Subject Heading (MeSH) terms according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses. This study was registered on PROSPERO. We classified features as prognostic, non-prognostic and uncertain and into seven subcategories: 'clinical', 'imaging', 'neurophysiology', 'multimodal concordance', 'genetic', 'surgical technique' and 'pathology'. We propose a structural causal model based on these features. RESULTS: We found 46 features from 38 meta-analyses over 22 years. The following were consistently prognostic across meta-analyses: febrile convulsions, hippocampal sclerosis, focal abnormal MRI, Single-Photon Emission Computed Tomography (SPECT) coregistered to MRI, focal ictal/interictal EEG, EEG-MRI concordance, temporal lobe resections, complete excision, histopathological lesions, tumours and focal cortical dysplasia type IIb. Severe learning disability was predictive of poor prognosis. Others, including sex and side of resection, were non-prognostic. There were limited meta-analyses investigating genetic contributions, structural connectivity or multimodal concordance and few adjusted for known confounders or performed corrections for multiple comparisons. SIGNIFICANCE: Seizure-free outcomes have not improved over decades of epilepsy surgery and despite a multitude of models, none prognosticate accurately. Our list of multimodal population-invariant prognostic features and proposed structural causal model may serve as an objective foundation for statistical adjustments of plausible confounders for use in high-dimensional models. PROSPERO REGISTRATION NUMBER: CRD42021185232.

Arterial Spin Labeling Reveals Disrupted Brain Networks and Functional Connectivity in Drug-Resistant Temporal Epilepsy.

Resting-state networks (RSNs) and functional connectivity (FC) have been increasingly exploited for mapping brain activity and identifying abnormalities in pathologies, including epilepsy. The majority of studies currently available are based on blood-oxygenation-level-dependent (BOLD) contrast in combination with either independent component analysis (ICA) or pairwise region of interest (ROI) correlations. Despite its success, this approach has several shortcomings as BOLD is only an indirect and non-quantitative measure of brain activity. Conversely, promising results have recently been achieved by arterial spin labeling (ASL) MRI, primarily developed to quantify brain perfusion. However, the wide application of ASL-based FC has been hampered by its complexity and relatively low robustness to noise, leaving several aspects of this approach still largely unexplored. In this study, we firstly aimed at evaluating the effect of noise reduction on spatio-temporal ASL analyses and quantifying the impact of two ad-hoc processing pipelines (basic and advanced) on connectivity measures. Once the optimal strategy had been defined, we investigated the applicability of ASL for connectivity mapping in patients with drug-resistant temporal epilepsy vs. controls (10 per group), aiming at revealing between-group voxel-wise differences in each RSN and ROI-wise FC changes. We first found ASL was able to identify the main network (DMN) along with all the others generally detected with BOLD but never previously reported from ASL. For all RSNs, ICA-based denoising (advanced pipeline) allowed to increase their similarity with the corresponding BOLD template. ASL-based RSNs were visibly consistent with literature findings; however, group differences could be identified in the structure of some networks. Indeed, statistics revealed areas of significant FC decrease in patients within different RSNs, such as DMN and cerebellum (CER), while significant increases were found in some cases, such as the visual networks. Finally, the ROI-based analyses identified several inter-hemispheric dysfunctional links (controls > patients) mainly between areas belonging to the DMN, right-left thalamus and right-left temporal lobe. Conversely, fewer connections, predominantly intra-hemispheric, showed the opposite pattern (controls < patients). All these elements provide novel insights into the pathological modulations characterizing a "network disease" as epilepsy, shading light on the importance of perfusion-based approaches for identifying the disrupted areas and communications between brain regions.