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Brain imaging has a crucial role in the presurgical assessment of patients with epilepsy. Structural imaging reveals most cerebral lesions underlying focal epilepsy. Advances in MRI acquisitions including diffusion-weighted imaging, post-acquisition image processing techniques, and quantification of imaging data are increasing the accuracy of lesion detection. Functional MRI can be used to identify areas of the cortex that are essential for language, motor function, and memory, and tractography can reveal white matter tracts that are vital for these functions, thus reducing the risk of epilepsy surgery causing new morbidities. PET, SPECT, simultaneous EEG and functional MRI, and electrical and magnetic source imaging can be used to infer the localisation of epileptic foci and assist in the design of intracranial EEG recording strategies. Progress in semi-automated methods to register imaging data into a common space is enabling the creation of multimodal three-dimensional patient-specific datasets. These techniques show promise for the demonstration of the complex relations between normal and abnormal structural and functional data and could be used to direct precise intracranial navigation and surgery for individual patients.

Stroke is the leading cause of seizures and epilepsy in older adults. Patients who have larger and more severe strokes involving the cortex, are younger, and have acute symptomatic seizures and intracerebral haemorrhage are at highest risk of developing post-stroke epilepsy. Prognostic models, including the SeLECT and CAVE scores, help gauge the risk of epileptogenesis. Early electroencephalogram and blood-based biomarkers can provide information additional to the clinical risk factors of post-stroke epilepsy. The management of acute versus remote symptomatic seizures after stroke is markedly different. The choice of an ideal antiseizure medication should not only rely on efficacy but also consider adverse effects, altered pharmacodynamics in older adults, and the influence on the underlying vascular co-morbidity. Drug–drug interactions, particularly those between antiseizure medications and anticoagulants or antiplatelets, also influence treatment decisions. In this review, we describe the epidemiology, risk factors, biomarkers, and management of seizures after an ischaemic or haemorrhagic stroke. We discuss the special considerations required for the treatment of post-stroke epilepsy due to the age, co-morbidities, co-medication, and vulnerability of stroke survivors.

Epilepsy is a diverse brain disorder, and the pathophysiology of its various forms and comorbidities is largely unknown. A recent machine learning method enables us to estimate an individual’s “brain-age” from MRI; this brain-age prediction is expected as a novel individual biomarker of neuropsychiatric disorders. The aims of this study were to estimate the brain-age for various categories of epilepsy and to evaluate clinical discrimination by brain-age for (1) the effect of psychosis on temporal lobe epilepsy (TLE), (2) psychogenic nonepileptic seizures (PNESs) from MRI-negative epilepsies, and (3) progressive myoclonic epilepsy (PME) from juvenile myoclonic epilepsy (JME). In total, 1196 T1-weighted MRI scans from healthy controls (HCs) were used to build a brain-age prediction model with support vector regression. Using the model, we calculated the brain-predicted age difference (brain-PAD: predicted age—chronological age) of the HCs and 318 patients with epilepsy. We compared the brain-PAD values based on the research questions. As a result, all categories of patients except for extra-temporal lobe focal epilepsy showed a significant increase in brain-PAD. TLE with hippocampal sclerosis presented a significantly higher brain-PAD than several other categories. The mean brain-PAD in TLE with inter-ictal psychosis was 10.9 years, which was significantly higher than TLE without psychosis (5.3 years). PNES showed a comparable mean brain-PAD (10.6 years) to that of epilepsy patients. PME had a higher brain-PAD than JME (22.0 vs. 9.3 years). In conclusion, neuroimaging-based brain-age prediction can provide novel insight into or clinical usefulness for the diverse symptoms of epilepsy.

Epileptic seizures are generally unpredictable and arise spontaneously. Patients often report non-specific triggers such as stress or sleep deprivation, but only rarely do seizures occur as a reflex event, in which they are objectively and consistently modulated, precipitated, or inhibited by external sensory stimuli or specific cognitive processes. The seizures triggered by such stimuli and processes in susceptible individuals can have different latencies. Once seizure-suppressing mechanisms fail and a critical mass (the so-called tipping point) of cortical activation is reached, reflex seizures stereotypically manifest with common motor features independent of the physiological network involved. The complexity of stimuli increases from simple sensory to complex cognitive-emotional with increasing age of onset. The topography of physiological networks involved follows the posterior-to-anterior trajectory of brain development, reflecting age-related changes in brain excitability. Reflex seizures and traits probably represent the extremes of a continuum, and understanding of their underlying mechanisms might help to elucidate the transition of normal physiological function to paroxysmal epileptic activity.

Stroke is one of the leading causes of acquired epilepsy in adults. An instrument to predict whether people are at high risk of developing post-stroke seizures is not available. We aimed to develop and validate a prognostic model of late (>7 days) seizures after ischaemic stroke. In this multivariable prediction model development and validation study, we developed the SeLECT score based on five clinical predictors in 1200 participants who had an ischaemic stroke in Switzerland using backward elimination of a multivariable Cox proportional hazards model. We externally validated this score in 1169 participants from three independent international cohorts in Austria, Germany, and Italy, and assessed its performance with the concordance statistic and calibration plots. Data were complete for 99·2% of the predictors (99·2% for Switzerland, 100% for Austria, 97% for Germany, and 99·7% for Italy) and 100% of the outcome parameters. Overall, the risk of late seizures was 4% (95% CI 4–5) 1 year after stroke and 8% (6–9) 5 years after stroke. The final model included five variables and was named SeLECT on the basis of the first letters of the included parameters (severity of stroke, large-artery atherosclerotic aetiology, early seizures, cortical involvement, and territory of middle cerebral artery involvement). The lowest SeLECT value (0 points) was associated with a 0·7% (95% CI 0·4–1·0) risk of late seizures within 1 year after stroke (1·3% [95% CI 0·7–1·8] within 5 years), whereas the highest value (9 points) predicted a 63% (42–77) risk of late seizures within 1 year (83% [62–93] within 5 years). The model had an overall concordance statistic of 0·77 (95% CI 0·71–0·82) in the validation cohorts. Calibration plots indicated high agreement of predicted and observed outcomes. This easily applied instrument was shown to be a good predictor of the risk of late seizures after stroke in three external validation cohorts and is freely available as a smartphone app. The SeLECT score has the potential to identify individuals at high risk of seizures and is a step towards more personalised medicine. It can inform the selection of an enriched population for antiepileptogenic treatment trials and will guide the recruitment for biomarker studies of epileptogenesis. None.

Blood-based kinetic analysis of PET data relies on an accurate estimate of the arterial plasma input function (PIF). An alternative to invasive measurements from arterial sampling is an image-derived input function (IDIF). However, an IDIF provides the whole blood radioactivity concentration, rather than the required free tracer radioactivity concentration in plasma. To estimate the tracer PIF, we corrected an IDIF from the carotid artery with estimates of plasma parent fraction (PF) and plasma-to-whole blood (PWB) ratio obtained from five venous samples. We compared the combined IDIF+venous approach to gold standard data from arterial sampling in 10 healthy volunteers undergoing [18F]GE-179 brain PET imaging of the NMDA receptor. Arterial and venous PF and PWB ratio estimates determined from 7 patients with traumatic brain injury (TBI) were also compared to assess the potential effect of medication. There was high agreement between areas under the curves of the estimates of PF (r = 0.99, p<0.001), PWB ratio (r = 0.93, p<0.001), and the PIF (r = 0.92, p<0.001) as well as total distribution volume (VT) in 11 regions across the brain (r = 0.95, p<0.001). IDIF+venous VT had a mean bias of −1.7% and a comparable regional coefficient of variation (arterial: 21.3 ± 2.5%, IDIF+venous: 21.5 ± 2.0%). Simplification of the IDIF+venous method to use only one venous sample provided less accurate VT estimates (mean bias 9.9%; r = 0.71, p<0.001). A version of the method that avoids the need for blood sampling by combining the IDIF with population-based PF and PWB ratio estimates systematically underestimated VT (mean bias −20.9%), and produced VT estimates with a poor correlation to those obtained using arterial data (r = 0.45, p<0.001). Arterial and venous blood data from 7 TBI patients showed high correlations for PF (r = 0.92, p = 0.003) and PWB ratio (r = 0.93, p = 0.003). In conclusion, the IDIF+venous method with five venous samples provides a viable alternative to arterial sampling for quantification of [18F]GE-179 VT.

[...]a drug cocktail was able to irreversibly terminate pilocarpine-induced status epilepticus and modify disease outcome in such a way that only a subpopulation of animals with status epilepticus developed epilepsy.8 Alternatively, prospective trials could be done using change in the putative biomarker itself as an outcome measure, rather than waiting for the seizure to occur.

Studies in rodent models of epilepsy suggest that multidrug efflux transporters at the blood–brain barrier, such as P-glycoprotein, might contribute to pharmacoresistance by reducing target-site concentrations of antiepileptic drugs. We assessed P-glycoprotein activity in vivo in patients with temporal lobe epilepsy. We selected 16 patients with pharmacoresistant temporal lobe epilepsy who had seizures despite treatment with at least two antiepileptic drugs, eight patients who had been seizure-free on antiepileptic drugs for at least a year after 3 or more years of active temporal lobe epilepsy, and 17 healthy controls. All participants had a baseline PET scan with the P-glycoprotein substrate (R)-[11C]verapamil. Pharmacoresistant patients and healthy controls then received a 30-min infusion of the P-glycoprotein-inhibitor tariquidar followed by another (R)-[11C]verapamil PET scan 60 min later. Seizure-free patients had a second scan on the same day, but without tariquidar infusion. Voxel-by-voxel, we calculated the (R)-[11C]verapamil plasma-to-brain transport rate constant, K1 (mL/min/cm3). Low baseline K1 and attenuated K1 increases after tariquidar correspond to high P-glycoprotein activity. Between October, 2008, and November, 2011, we completed (R)-[11C]verapamil PET studies in 14 pharmacoresistant patients, eight seizure-free patients, and 13 healthy controls. Voxel-based analysis revealed that pharmacoresistant patients had lower baseline K1, corresponding to higher baseline P-glycoprotein activity, than seizure-free patients in ipsilateral amygdala (0·031 vs 0·036 mL/min/cm3; p=0·014), bilateral parahippocampus (0·032 vs 0·037; p<0·0001), fusiform gyrus (0·036 vs 0·041; p<0·0001), inferior temporal gyrus (0·035 vs 0·041; p<0·0001), and middle temporal gyrus (0·038 vs 0·044; p<0·0001). Higher P-glycoprotein activity was associated with higher seizure frequency in whole-brain grey matter (p=0·016) and the hippocampus (p=0·029). In healthy controls, we noted a 56·8% increase of whole-brain K1 after 2 mg/kg tariquidar, and 57·9% for 3 mg/kg; in patients with pharmacoresistant temporal lobe epilepsy, whole-brain K1 increased by only 21·9% for 2 mg/kg and 42·6% after 3 mg/kg. This difference in tariquidar response was most pronounced in the sclerotic hippocampus (mean 24·5% increase in patients vs mean 65% increase in healthy controls, p<0·0001). Our results support the hypothesis that there is an association between P-glycoprotein overactivity in some regions of the brain and pharmacoresistance in temporal lobe epilepsy. If this relation is confirmed, and P-glycoprotein can be identified as a contributor to pharmacoresistance, overcoming P-glycoprotein overactivity could be investigated as a potential treatment strategy. EU-FP7 programme (EURIPIDES number 201380).

Approximately 40% of individuals undergoing anterior temporal lobe resection for temporal lobe epilepsy experience episodic memory decline. There has been a focus on early memory network changes; longer-term plasticity and its impact on memory function are unclear. Our study investigates neural mechanisms of memory recovery and network plasticity over nearly a decade post-surgery. We assess memory network changes, from 3–12 months to 10 years postoperatively, in 25 patients (12 left-sided resections) relative to 10 healthy matched controls, using longitudinal task-based functional MRI and standard neuropsychology assessments. We observe key adaptive changes in memory networks of a predominantly seizure-free cohort. Ongoing neuroplasticity in posterior medial temporal regions and contralesional cingulum or pallidum contribute to long-term verbal and visual memory recovery. Here, we show the potential for sustained cognitive improvement and importance of strategic approaches in epilepsy treatment, advocating for conservative surgeries and long-term use of cognitive rehabilitation for ongoing recovery. Using functional MRI, the authors tracked memory recovery over a decade after epilepsy surgery, revealing key brain changes in spared medial temporal structures and contralateral regions, highlighting neural mechanisms behind sustained improvement.

Diffusion Tensor Imaging (DTI) is being increasingly used to assess white matter integrity and it is therefore paramount to address the test-retest reliability of DTI measures. In this study we assessed inter- and intra-site reproducibility of two nominally identical 3 T scanners at different sites in nine healthy controls using a DTI protocol representative of typical current \"best practice\" including cardiac gating, a multichannel head coil, parallel imaging and optimized diffusion gradient parameters. We calculated coefficients of variation (CV) and intraclass correlation coefficients (ICC) of fractional anisotropy (FA) measures for the whole brain, for three regions of interest (ROI) and for three tracts derived from these ROI by probabilistic tracking. We assessed the impact of affine, nonlinear and template based methods for spatially aligning FA maps on the reproducibility. The intra-site CV for FA ranged from 0.8% to 3.0% with ICC from 0.90 to 0.99, while the inter-site CV ranged from 1.0% to 4.1% with ICC of 0.82 to 0.99. Nonlinear image coregistration improved reproducibility compared to affine coregistration. Normalization to template space reduced the between-subject variation, resulting in lower ICC values and indicating a possibly reduced sensitivity. CV from probabilistic tractography were about 50% higher than for the corresponding seed ROI. Reproducibility maps of the whole scan volume showed a low variation of less than 5% in the major white matter tracts but higher variations of 10-15% in gray matter regions. One of the two scanners showed better intra-site reproducibility, while the intra-site CV for both scanners was significantly better than inter-site CV. However, when using nonlinear coregistration of FA maps, the average inter-site CV was below 2%. There was a consistent inter-site bias, FA values on site 2 were 1.0-1.5% lower than on site 1. Correction for this bias with a global scaling factor reduced the inter-site CV to the range of intra-site CV. Our results are encouraging for multi-centre DTI studies in larger populations, but also illustrate the importance of the image processing pipeline for reproducibility.