Increased leptin and A-FABP levels in relapsing and progressive forms of MS

BACKGROUND: Leptin and adipocyte-fatty acid binding protein (A-FABP) are produced by white adipose tissue and may play a role in chronic inflammation in Multiple Sclerosis (MS). To assess leptin and A-FABP in relapsing and progressive forms of MS. METHODS: Adipokine levels were measured in untreated adult relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS) and Healthy control (HC). Pediatric-onset MS (POMS) and pediatric healthy controls (PHC) were also assessed. Leptin and A-FABP levels were measured in serum by ELISA. Groups were compared using linear mixed-effects model. RESULTS: Excluding two patients with Body Mass Index (BMI) > 50, a significant difference in leptin level was found between RRMS and HC controlling for age (p = 0.007), SPMS and HC controlling for age alone (p = 0.002), or age and BMI (p = 0.007). A-FABP levels were higher in SPMS than HC (p = 0.007), controlling for age and BMI. Differences in A-FABP levels between POMS and PHC was observed after controlling for age (p = 0.019), but not when BMI was added to the model (p = 0.081). CONCLUSION: Leptin and A-FABP levels are highest in SPMS compared to HC, suggesting a role in pathogenesis of this disease subtype. A-FABP levels are increased in POMS patients and may play a role in the early stages of disease

A scoping review of the fMRI-based functional connectivity of FCD-related epilepsy

Focal cortical dysplasia (FCD) is the most frequent etiology of operable pharmacoresistant epilepsy in children. There is burgeoning evidence that FCD-related epilepsy is a disorder that involves distributed brain networks. Functional magnetic resonance imaging (fMRI) is a tool that allows one to infer neuronal activity and to non-invasively map whole-brain functional networks. Despite its relative widespread availability at most epilepsy centers, the clinical application of fMRI remains mostly task-based in epilepsy. Another approach is to map and characterize cortical functional networks of individuals using resting state fMRI (rsfMRI). The focus of this scoping review is to summarize the evidence to date of investigations of the network basis of FCD-related epilepsy, and to highlight numerous potential future applications of rsfMRI in the exploration of diagnostic and therapeutic strategies for FCD-related epilepsy. There are numerous studies demonstrating a global disruption of cortical functional networks in FCD-related epilepsy. The underlying pathological subtypes of FCD influence overall functional network patterns. There is evidence that cortical functional network mapping may help to predict postsurgical seizure outcomes, highlighting the translational potential of these findings. Additionally, several studies emphasize the important effect of FCD interaction with cortical networks and the expression of epilepsy and its comorbidities

Diagnostic Yield of Electroencephalography When Seizure Is Suspected in Acute Ischemic Stroke

INTRODUCTION: Seizures are a common complication after an ischemic stroke. Electroencephalography can assist with the diagnosis of seizures however, the diagnostic yield of its use when seizure is suspected in the setting of acute ischemic stroke is unknown. We aim to evaluate the yield and cost of EEG in the acute ischemic stroke setting. METHODS: We conducted a retrospective chart review of patients admitted to a single academic tertiary care center in the United States between September 1, 2015 to November 30, 2019 with a primary diagnosis of acute ischemic stroke and who were monitored on electroencephalography (EEG) for suspected seizures (total number of 70 patients). The primary outcome was how often EEG monitoring changed clinical management defined as starting, stopping, or changing the dose of an anti-epileptic drug. Secondary analysis was estimating the cost of EEG monitoring per change in management. RESULTS: We identified 126 patients admitted with acute ischemic stroke who underwent EEG of which 70 met all inclusion and exclusion criteria. EEG monitoring resulted in a change in management in 22 patients (31%). Predictors associated with EEG monitoring resulting in a change in management were admission to the ICU, pre-existing atrial fibrillation, and symptomatic hemorrhagic transformation. Estimated cost of EEG per change in management was $1374.96 USD. CONCLUSION: EEG monitoring resulted in a changed management in nearly one-third of patients admitted with acute ischemic stroke suspected of having seizures

7T versus 3T MRI in the presurgical evaluation of patients with drug-resistant epilepsy

BACKGROUND AND PURPOSE: MRI has a crucial role in presurgical evaluation of drug-resistant focal epilepsy patients. Whether and how much 7T MRI further improves presurgical diagnosis compared to standard of care 3T MRI remains to be established. We investigate the added value 7T MRI offers in surgical candidates with remaining clinical uncertainty after 3T MRI. METHODS: 7T brain MRI was obtained on sequential patients with drug-resistant focal epilepsy undergoing presurgical evaluation at a comprehensive epilepsy center, including patients with and without suspected lesions on standard 3T MRI. Clinical information and 3T images informed the interpretation of 7T images. Detection of a new lesion on 7T or better characterization of a suspected lesion was considered to add value to the presurgical workup. RESULTS: Interpretable 7T MRI was acquired in 19 patients. 7T MRI identified a lesion relevant to the seizures in three of eight patients (38%) without a lesion on 3T MRI; no lesion in 7/11 patients (64%) with at least one suspected lesion on 3T MRI, contributing to the final classification of all seven as nonlesional; and confirmed and better characterized the lesion suspected at 3T MR in the remaining 4/11 patients. CONCLUSIONS: 7T MRI detected new lesions in over a third of 3T MRI nonlesional patients, confirmed and better characterized a 3T suspected lesion in one third of patients, and helped exclude a 3T suspected lesion in the remainder. Our initial experience suggests that 7T MRI adds value to surgical planning by improving detection and characterization of suspected brain lesions in drug-resistant focal epilepsy patients

Common functional connectivity alterations in focal epilepsies identified by machine learning

OBJECTIVE: This study was undertaken to identify shared functional network characteristics among focal epilepsies of different etiologies, to distinguish epilepsy patients from controls, and to lateralize seizure focus using functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (MRI). METHODS: Data were taken from 103 adult and 65 pediatric focal epilepsy patients (with or without lesion on MRI) and 109 controls across four epilepsy centers. We used three whole-brain FC measures: parcelwise connectivity matrix, mean FC, and degree of FC. We trained support vector machine models with fivefold cross-validation (1) to distinguish patients from controls and (2) to lateralize the hemisphere of seizure onset in patients. We reported the regions and connections with the highest importance from each model as the common FC differences between the compared groups. RESULTS: FC measures related to the default mode and limbic networks had higher importance relative to other networks for distinguishing epilepsy patients from controls. In lateralization models, regions related to somatosensory, visual, default mode, and basal ganglia showed higher importance. The epilepsy versus control classification model trained using a 400-parcel connectivity matrix achieved a median testing accuracy of 75.6% (median area under the curve [AUC] = .83) in repeated independent testing. Lateralization accuracy using the 400-parcel connectivity matrix reached a median accuracy of 64.0% (median AUC = .69). SIGNIFICANCE: Machine learning models revealed common FC alterations in a heterogeneous group of patients with focal epilepsies. The distribution of the most altered regions supports the hypothesis that shared functional alteration exists beyond the seizure onset zone and its epileptic network. We showed that FC measures can distinguish patients from controls, and further lateralize focal epilepsies. Future studies are needed to confirm these findings by using larger numbers of epilepsy patients

Resting-state functional MRI for motor cortex mapping in childhood-onset focal epilepsy

BACKGROUND AND PURPOSE: Task-based functional MRI (fMRI) mapping of the motor function prior to epilepsy surgery has limitations in children with epilepsy. We present a data-driven method to automatically delineate the motor cortex using task-free, resting-state fMRI (rsfMRI) data. METHODS: We used whole-brain rsfMRI for independent component analysis (ICA). A template matching process with Discriminability Index-based Component Identification score was used for each participant to select and combine motor ICA components in their native brain space, resulting in a whole-brain ICA Motor Map (wIMM). We validated wIMM by comparing individual results with bilateral finger-tapping motor task fMRI activation, and evaluated its reproducibility in controls. RESULTS: Data from 64 patients and 12 controls were used to generate group wIMM maps. The hit rate between wIMM and motor task activation ranged from 60% to 79% across all participants. Sensitivity of wIMM for capturing the task activation peak was 87.5% among 32 patients and 100% in 12 controls with available motor task results. We also showed high similarity in repeated runs in controls. CONCLUSIONS: Our results show the sensitivity and reproducibility of an automated motor mapping method based on ICA analysis of rsfMRI in children with epilepsy. The ICA maps may provide different, but useful, information than task fMRI. Future studies will expand our method to mapping other brain functions, and may lead to a surgical planning tool for patients who cannot perform task fMRI and help predict their postsurgical function

Using 3D-Printed Mesh-Like Brain Cortex with Deep Structures for Planning Intracranial EEG Electrode Placement

Surgical evaluation of medically refractory epilepsy frequently necessitates implantation of multiple intracranial electrodes for the identification of the seizure focus. Knowledge of the individual brain’s surface anatomy and deep structures is crucial for planning the electrode implantation. We present a novel method of 3D printing a brain that allows for the simulation of placement of all types of intracranial electrodes. We used a DICOM dataset of a T1-weighted 3D-FSPGR brain MRI from one subject. The segmentation tools of Materialise Mimics 21.0 were used to remove the osseous anatomy from brain parenchyma. Materialise 3-matic 13.0 was then utilized in order to transform the cortex of the segmented brain parenchyma into a mesh-like surface. Using 3-matic tools, the model was modified to incorporate deep brain structures and create an opening in the medial aspect. The final model was then 3D printed as a cerebral hemisphere with nylon material using selective laser sintering technology. The final model was light and durable and reflected accurate details of the surface anatomy and some deep structures. Additionally, standard surgical depth electrodes could be passed through the model to reach deep structures without damaging the model. This novel 3D-printed brain model provides a unique combination of visualizing both the surface anatomy and deep structures through the mesh-like surface while allowing repeated needle insertions. This relatively low-cost technique can be implemented for interdisciplinary preprocedural planning in patients requiring intracranial EEG monitoring and for any intervention that requires needle insertion into a solid organ with unique anatomy and internal targets