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Objective: Combining population-based health registries and electronic health records offers the opportunity to create large, phenotypically detailed patient cohorts of high quality. In this study, we used text mining of clinical notes to confirm International Classification of Diseases, 10th Revision (ICD-10)-registered epilepsy diagnoses and classify patients according to focal and generalized epilepsy types. Methods: Using the Danish National Patient Registry, we identified patients who between 2006 and 2016 received an ICD-10 diagnosis of epilepsy. To validate the epilepsy diagnosis and stratify patients into focal and generalized epilepsy types, we constructed dictionaries for text mining-based extraction of clinical notes. Two physicians manually reviewed the clinical notes for a total of 527 patients and assigned epilepsy diagnoses, which were compared with the text-mined diagnoses. Results: We identified 23 632 patients with an ICD-10 diagnosis of epilepsy, of whom 50% were registered with an unspecified epilepsy diagnosis. In total, 11 211 patients were considered likely to have epilepsy by text mining, with an F1 measure ranging from 82% to 90%. Manual review of the electronic health records for 310 patients revealed a false discovery rate of 29%. This rate was decreased to 4% by the text mining algorithm. The weighted average F1 measure for text mining-assigned epilepsy types was 79% (82% for focal and 76% for generalized epilepsy). Text mining successfully assigned a focal or generalized epilepsy type to 92% of the text mining-eligible patients registered with unspecified epilepsy. Significance: Text mining of electronic health records can be used to establish a patient cohort with much higher likelihood of having a diagnosis of epilepsy and a focal or generalized epilepsy type compared to the cohort created from ICD-10 epilepsy codes alone. We believe the concept will be essential for future genome-wide and phenome-wide association studies and subseq

Objective Single-photon emission computed tomography (SPECT) with the tracer 99mTc-HMPAO is a method to visualize the cerebral hyperperfusion during an epileptic seizure and thus localize the epileptogenic zone and seizure propagation. Subtraction of interictal from Ictal SPECT Co-registered to MRI (SISCOM) visualizes areas with relative increases in cerebral blood flow. The purpose of this retrospective study is to explore the added value of visualizing areas of hypoperfusion as well as hyperperfusion, so-called reversed SISCOM. Methods Fifty-six patients operated for epilepsy who had been investigated with SISCOM were included in the analysis. The patients were divided into two groups based on seizure duration after tracer injection, above or below 30 s. The preoperative SISCOM description was compared to the area of resection and given a concordance score. The 56 SISCOM were recalculated visualizing also areas of hypoperfusion and again compared to the site of resection using the same scale of concordance. The reversed SISCOM were categorized into three subgroups: “Altered Conclusion,” “Confirmed Conclusion,” and “Adds Nothing.” If an area of hyperperfusion had an area of hypoperfusion in close proximity, it was re-interpreted as noise, thus possibly altering the conclusion. If the areas of hypoperfusion were in the opposite hemisphere it was interpreted as confirming factor. Further the concordance scores from conventional SISCOM and reversed SISCOM was compared to surgical outcome to explore the difference in sensitivity, positive predictive value (PPV), and odds ratio. Results In approximately half of the cases reversed SISCOM added additional value, meaning either altered the conclusion or confirmed the conclusion. The sensitivity, PPV, and odds ratio was also better in the subgroup of long, >30 s seizure duration after injection, and got worse in the group with short, <30 s seizure duration after injection. Signif, OBJECTIVE: Single-photon emission computed tomography (SPECT) with the tracer 99m Tc-HMPAO is a method to visualize the cerebral hyperperfusion during an epileptic seizure and thus localize the epileptogenic zone and seizure propagation. Subtraction of interictal from Ictal SPECT Co-registered to MRI (SISCOM) visualizes areas with relative increases in cerebral blood flow. The purpose of this retrospective study is to explore the added value of visualizing areas of hypoperfusion as well as hyperperfusion, so-called reversed SISCOM.METHODS: Fifty-six patients operated for epilepsy who had been investigated with SISCOM were included in the analysis. The patients were divided into two groups based on seizure duration after tracer injection, above or below 30 s. The preoperative SISCOM description was compared to the area of resection and given a concordance score. The 56 SISCOM were recalculated visualizing also areas of hypoperfusion and again compared to the site of resection using the same scale of concordance. The reversed SISCOM were categorized into three subgroups: "Altered Conclusion," "Confirmed Conclusion," and "Adds Nothing." If an area of hyperperfusion had an area of hypoperfusion in close proximity, it was re-interpreted as noise, thus possibly altering the conclusion. If the areas of hypoperfusion were in the opposite hemisphere it was interpreted as confirming factor. Further the concordance scores from conventional SISCOM and reversed SISCOM was compared to surgical outcome to explore the difference in sensitivity, positive predictive value (PPV), and odds ratio.RESULTS: In approximately half of the cases reversed SISCOM added additional value, meaning either altered the conclusion or confirmed the conclusion. The sensitivity, PPV, and odds ratio was also better in the subgroup of long, >30 s seizure duration after injection, and got worse in the group with short, <30 s seizure duration after injection.SIGNIFICANCE: Adding

OBJECTIVE: This retrospective study investigates the predictive value of ictal subtraction single-photon emission computed tomography (SPECT) co-registered to magnetic resonance imaging (MRI) (SISCOM) for successful epilepsy surgery.METHODS: 57 patients examined with SISCOM as a part of epilepsy surgery evaluation were divided into two groups based on seizure duration after tracer injection (group 1: Seizure duration above or equal to 30 s, group 2: Seizure duration under 30 s). SISCOM was compared to the surgical site and categorized as good or poor concordance. Subsequently, Odds ratios (ORs) and positive predictive values (PPVs) were calculated for each group for good surgical outcome, freedom from disabling seizures.RESULTS: The PPVs and ORs for good surgical outcome was 74.1% and 5.71 for group 1 and 40% and 0.22 for group 2. SISCOM had a similar positive predictive value regardless of whether the focus was in the same or neighboring lobe, but same hemisphere as the resection.CONCLUSION: In conclusion, the implementation of a precise definition for a well-executed ictal SPECT scan with respect to seizure duration after injection enhances the positive predictive value (PPV) and odds ratio (OR) for successful surgical outcome, surpassing previous findings, whether the focus in resected lobe or the neighboring.

Objective. Histopathological examinations will diminish as minimally invasive epilepsy surgery increasingly replaces open surgery. The objective of this study was to test if visual and computer-aided quantitative analyses of presurgical high-quality 3 Tesla MRIs complying with the International League Against Epilepsy (ILAE) Neuroimaging Task Force recommendations can inform on histopathological diagnosis. Methods. Ninety-two patients from Copenhagen and Oslo University Hospitals fulfilled patient-, imaging-, and histopathological inclusion criteria: 69 patients were diagnosed with hippocampal sclerosis (HS) ILAE type 1 or 2, and 23 patients had normal appearing hippocampi or other histopathology than HS (no-HS). MRIs from 52 healthy controls (HC) were included. Image processing was performed in FreeSurfer v.6.0 with the built-in cross-sectional hippocampal subfield segmentation tool and multimodal MRI input. Volume outputs were used to calculate volume asymmetry ratios (VARs) for whole hippocampus (WH) and subfields. Results. HS patients had significantly larger WH VARs compared to no-HS patients and HC, with a sensitivity=0.93 and specificity=1.0 for histopathological HS diagnosis. Visual MRI assessment yielded a sensitivity=0.90 and specificity=0.96 for histopathological HS diagnosis. CA1 and CA4 VARs and the number of seizure-free patients were not significantly different in HS ILAE type 1 compared to type 2 patients. Significance. FreeSurfer analyses of presurgical MRIs are excellent at separating patients histopathologically diagnosed with HS from patients with other pathology or normal appearing hippocampi. Using the FreeSurfer hippocampal subfield segmentation tool did not allow for separating HS ILAE subtypes.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted ‘gold-standard’ subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle M

Objective: EEG source imaging (ESI) is a validated tool in the multimodal workup of patients with drug resistant focal epilepsy. However, it requires special expertise and it is underutilized. To circumvent this, automated analysis pipelines have been developed and validated for the interictal discharges. In this study, we present the clinical validation of an automated ESI for ictal EEG signals. Methods: We have developed an automated analysis pipeline of ictal EEG activity, based on spectral analysis in source space, using an individual head model of six tissues. The analysis was done blinded to all other data. As reference standard, we used the concordance with the resected area and one-year postoperative outcome. Results: We analyzed 50 consecutive patients undergoing epilepsy surgery (34 temporal and 16 extra-temporal). Thirty patients (60%) became seizure-free. The accuracy of the automated ESI was 74% (95% confidence interval: 59.66–85.37%). Conclusions: Automated ictal ESI has a high accuracy for localizing the seizure onset zone. Significance: Automating the ESI of the ictal EEG signals will facilitate implementation of this tool in the presurgical evaluation.

The branched-chain amino acids (BCAAs) leucine, isoleucine, and valine are important nitrogen donors for synthesis of glutamate, the main excitatory neurotransmitter in the brain. The glutamate carbon skeleton originates from the tricarboxylic acid (TCA) cycle intermediate α-ketoglutarate, while the amino group is derived from nitrogen donors such as the BCAAs. Disturbances in neurotransmitter homeostasis, mainly of glutamate, are strongly implicated in the pathophysiology of Alzheimer's disease (AD). The divergent BCAA metabolism in different cell types of the human brain is poorly understood, and so is the involvement of astrocytic and neuronal BCAA metabolism in AD. The goal of this study is to provide the first functional characterization of BCAA metabolism in human brain tissue and to investigate BCAA metabolism in AD pathophysiology using astrocytes and neurons derived from human-induced pluripotent stem cells (hiPSCs). Mapping of BCAA metabolism was performed using mass spectrometry and enriched [15N] and [13C] isotopes of leucine, isoleucine, and valine in acutely isolated slices of surgically resected cerebral cortical tissue from human brain and in hiPSC-derived brain cells carrying mutations in either amyloid precursor protein (APP) or presenilin-1 (PSEN-1). We revealed that both human astrocytes of acutely isolated cerebral cortical slices and hiPSC-derived astrocytes were capable of oxidatively metabolizing the carbon skeleton of BCAAs, particularly to support glutamine synthesis. Interestingly, hiPSC-derived astrocytes with APP and PSEN-1 mutations exhibited decreased amino acid synthesis of glutamate, glutamine, and aspartate derived from leucine metabolism. These results clearly demonstrate that there is an active BCAA metabolism in human astrocytes, and that leucine metabolism is selectively impaired in astrocytes derived from the hiPSC models of AD. This impairment in astrocytic BCAA metabolism may contribute to neurotransm

Identifying a structural brain lesion on MRI has important implications in epilepsy and is the most important factor that correlates with seizure freedom after surgery in patients with drug-resistant focal onset epilepsy. However, at conventional magnetic field strengths (1.5 and 3T), only approximately 60%-85% of MRI examinations reveal such lesions. Over the last decade, studies have demonstrated the added value of 7T MRI in patients with and without known epileptogenic lesions from 1.5 and/or 3T. However, translation of 7T MRI to clinical practice is still challenging, particularly in centers new to 7T, and there is a need for practical recommendations on targeted use of 7T MRI in the clinical management of patients with epilepsy. The 7T Epilepsy Task Force-an international group representing 21 7T MRI centers with experience from scanning over 2,000 patients with epilepsy-would hereby like to share its experience with the neurology community regarding the appropriate clinical indications, patient selection and preparation, acquisition protocols and setup, technical challenges, and radiologic guidelines for 7T MRI in patients with epilepsy. This article mainly addresses structural imaging; in addition, it presents multiple nonstructural MRI techniques that benefit from 7T and hold promise as future directions in epilepsy. Answering to the increased availability of 7T MRI as an approved tool for diagnostic purposes, this article aims to provide guidance on clinical 7T MRI epilepsy management by giving recommendations on referral, suitable 7T MRI protocols, and image interpretation.