Background:
Vaccination against SARS-CoV-2 has been conducted frequently to limit the pandemic but may rarely be associated with postvaccinal autoimmune reactions or disorders.
Case presentation
We ...present a 35-year-old woman who developed fever, skin rash, and headache 2 days after the second SARS-CoV-2 vaccination with BNT162b2 (Pfizer/Biontech). Eight days later, she developed behavioral changes and severe recurrent seizures that led to sedation and intubation. Cerebral magnetic resonance imaging showed swelling in the (para-) hippocampal region predominantly on the left hemisphere and bilateral subcortical subinsular FLAIR hyperintensities. Cerebrospinal fluid analysis revealed a lymphocytic pleocytosis of 7 cells/μl and normal protein and immunoglobulin parameters. Common causes of encephalitis or encephalopathy such as viral infections, autoimmune encephalitis with well-characterized autoantibodies, paraneoplastic diseases, and intoxications were ruled out. We made a diagnosis of new-onset refractory status epilepticus (NORSE) due to seronegative autoimmune encephalitis. The neurological deficits improved after combined antiepileptic therapy and immunomodulatory treatment including high-dose methylprednisolone and plasma exchange.
Conclusions
Although a causal relationship cannot be established, the onset of symptoms shortly after receiving the SARS-CoV-2 vaccine suggests a potential association between the vaccination and NORSE due to antibody-negative autoimmune encephalitis. After ruling out other etiologies, early immunomodulatory treatment may be considered in such cases.
Predicting the duration of poststroke dysphagia is important to guide therapeutic decisions. Guidelines recommend nasogastric tube (NGT) feeding if swallowing impairment persists for 7 days or longer ...and percutaneous endoscopic gastrostomy (PEG) placement if dysphagia does not recover within 30 days, but, to our knowledge, a systematic prediction method does not exist.
To develop and validate a prognostic model predicting swallowing recovery and the need for enteral tube feeding.
We enrolled participants with consecutive admissions for acute ischemic stroke and initially severe dysphagia in a prospective single-center derivation (2011-2014) and a multicenter validation (July 2015-March 2018) cohort study in 5 tertiary stroke referral centers in Switzerland.
Severely impaired oral intake at admission (Functional Oral Intake Scale score <5).
Recovery of oral intake (primary end point, Functional Oral Intake Scale ≥5) or return to prestroke diet (secondary end point) measured 7 (indication for NGT feeding) and 30 (indication for PEG feeding) days after stroke.
In total, 279 participants (131 women 47.0%; median age, 77 years interquartile range, 67-84 years) were enrolled (153 54.8% in the derivation study; 126 45.2% in the validation cohort). Overall, 64% (95% CI, 59-71) participants failed to recover functional oral intake within 7 days and 30% (95% CI, 24-37) within 30 days. Prolonged swallowing recovery was independently associated with poor outcomes after stroke. The final prognostic model, the Predictive Swallowing Score, included 5 variables: age, stroke severity on admission, lesion location, initial risk of aspiration, and initial impairment of oral intake. Predictive Swallowing Score prediction estimates ranged from 5% (score, 0) to 96% (score, 10) for a persistent impairment of oral intake on day 7 and from 2% to 62% on day 30. Model performance in the validation cohort showed a discrimination (C statistic) of 0.84 (95% CI, 0.76-0.91; P < .001) for predicting the recovery of oral intake on day 7 and 0.77 (95% CI, 0.67-0.87; P < .001) on day 30, and a discrimination for a return to prestroke diet of 0.94 (day 7; 95% CI, 0.87-1.00; P < .001) and 0.71 (day 30; 95% CI, 0.61-0.82; P < .001). Calibration plots showed high agreement between the predicted and observed outcomes.
The Predictive Swallowing Score, available as a smartphone application, is an easily applied prognostic instrument that reliably predicts swallowing recovery. It will support decision making for NGT or PEG insertion after ischemic stroke and is a step toward personalized medicine.
To assess the association of lesion location and risk of aspiration and to establish predictors of transient versus extended risk of aspiration after supratentorial ischemic stroke.
Atlas-based ...localization analysis was performed in consecutive patients with MRI-proven first-time acute supratentorial ischemic stroke. Standardized swallowing assessment was carried out within 8±18 hours and 7.8±1.2 days after admission.
In a prospective, longitudinal analysis, 34 of 94 patients (36%) were classified as having acute risk of aspiration, which was extended (≥7 days) or transient (<7 days) in 17 cases. There were no between-group differences in age, sex, cause of stroke, risk factors, prestroke disability, lesion side, or the degree of age-related white-matter changes. Correcting for stroke volume and National Institutes of Health Stroke Scale with a multiple logistic regression model, significant adjusted odds ratios in favor of acute risk of aspiration were demonstrated for the internal capsule (adjusted odds ratio, 6.2; P<0.002) and the insular cortex (adjusted odds ratio, 4.8; P<0.003). In a multivariate model of extended versus transient risk of aspiration, combined lesions of the frontal operculum and insular cortex was the only significant independent predictor of poor recovery (adjusted odds ratio, 33.8; P<0.008).
Lesions of the insular cortex and the internal capsule are significantly associated with acute risk of aspiration after stroke. Combined ischemic infarctions of the frontal operculum and the insular cortex are likely to cause extended risk of aspiration in stroke patients, whereas risk of aspiration tends to be transient in subcortical stroke.
Objective
Hippocampal sclerosis (HS) is the most common cause of drug‐resistant temporal lobe epilepsy, and its accurate detection is important to guide epilepsy surgery. Radiological features of HS ...include hippocampal volume loss and increased T2 signal, which can both be quantified to help improve detection. In this work, we extend these quantitative methods to generate cross‐sectional area and T2 profiles along the hippocampal long axis to improve the localization of hippocampal abnormalities.
Methods
T1‐weighted and T2 relaxometry data from 69 HS patients (32 left, 32 right, 5 bilateral) and 111 healthy controls were acquired on a 3‐T magnetic resonance imaging (MRI) scanner. Automated hippocampal segmentation and T2 relaxometry were performed and used to calculate whole‐hippocampal volumes and to estimate quantitative T2 (qT2) values. By generating a group template from the controls, and aligning this so that the hippocampal long axes were along the anterior‐posterior axis, we were able to calculate hippocampal cross‐sectional area and qT2 by a slicewise method to localize any volume loss or T2 hyperintensity. Individual patient profiles were compared with normative data generated from the healthy controls.
Results
Profiling of hippocampal volumetric and qT2 data could be performed automatically and reproducibly. HS patients commonly showed widespread decreases in volume and increases in T2 along the length of the affected hippocampus, and focal changes may also be identified. Patterns of atrophy and T2 increase in the left hippocampus were similar between left, right, and bilateral HS. These profiles have potential to distinguish between sclerosis affecting volume and qT2 in the whole or parts of the hippocampus, and may aid the radiological diagnosis in uncertain cases or cases with subtle or focal abnormalities where standard whole‐hippocampal measurements yield normal values.
Significance
Hippocampal profiling of volumetry and qT2 values can help spatially localize hippocampal MRI abnormalities and work toward improved sensitivity of subtle focal lesions.
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 18FGE-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 18FGE-179 VT.
Abstract Current hypotheses postulate a relationship between executive dysfunction and freezing of gait (FOG) in Parkinson's disease (PD). Hitherto, most evidence comes from entirely clinical ...approaches, while knowledge about this relationship on the morphological level is sparse. The aim of this study was therefore to assess the overlap of gray matter atrophy associated with FOG and executive dysfunction in PD. We included 18 PD patients with FOG and 20 without FOG in our analysis. A voxel-based morphometry approach was used to reveal voxel clusters in the gray matter which were associated with FOG and executive dysfunction as measured by the Frontal Assessment Battery, respectively. Conjunction analysis was applied to detect overlaps of the associated patterns. FOG correlated with different cortical clusters in the frontal and parietal lobes, whereas those associated with the FAB scores were, although widespread, widely confined to the frontal lobe. Conjunction analysis revealed a significant cluster of gray matter loss in the right dorsolateral prefrontal cortex. We could show that the patterns of neurodegeneration associated with FOG and executive dysfunction (as measured by the FAB) share atrophic changes in the same cortical areas. However, there is also a considerable number of cortical areas where neurodegenerative changes are only unique for either sign. Particularly, the involvement of parietal lobe areas seems to be more specific for FOG.
The majority of people with epilepsy achieves long-term seizure-freedom and may consider withdrawal of their anti-seizure medications (ASMs). Withdrawal of ASMs can yield substantial benefits but may ...be associated with potential risks. This review critically examines the existing literature on ASM withdrawal, emphasizing evidence-based recommendations, where available. Our focus encompasses deprescribing strategies for individuals who have attained seizure freedom through medical treatment, those who have undergone successful epilepsy surgery, and individuals initiated on ASMs following acute symptomatic seizures. We explore state-of-the-art prognostic models in these scenarios that could guide the decision-making process. The review underscores the importance of a collaborative shared-decision approach between patients, caregivers, and physicians. We describe the subjective and objective factors influencing these decisions and illustrate how trade-offs may be effectively managed in practice.
•Machine learning and artificial intelligence have gained popularity for medical applications.•We applied support vector machine (SV) and deep learning (DL) in termporal lobe epilepsy ...(TLE)•Structural and diffusion-based models showed similar classification accuracies.•Diffusion-based models to diagnose TLE performed better or similar compared to models to lateralize TLE.•Models for patients with hippocampal sclerosis were more accurate than models that stratified non-lesional patients.
Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.
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