Mapping hippocampal glutamate in mesial temporal lobe epilepsy with glutamate weighted CEST (GluCEST) imaging.

Temporal lobe epilepsy (TLE) is one of the most common subtypes of focal epilepsy, with mesial temporal sclerosis (MTS) being a common radiological and histopathological finding. Accurate identification of MTS during presurgical evaluation confers an increased chance of good surgical outcome. Here we propose the use of glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) at 7 Tesla for mapping hippocampal glutamate distribution in epilepsy, allowing to differentiate lesional from non-lesional mesial TLE. We demonstrate that a directional asymmetry index, which quantifies the relative difference between GluCEST contrast in hippocampi ipsilateral and contralateral to the seizure onset zone, can differentiate between sclerotic and non-sclerotic hippocampi, even in instances where traditional presurgical MRI assessments did not provide evidence of sclerosis. Overall, our results suggest that hippocampal glutamate mapping through GluCEST imaging is a valuable addition to the presurgical epilepsy evaluation toolbox.

Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy.

OBJECTIVE: Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE). METHODS: In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration-segregation axis," by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction. RESULTS: Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE. SIGNIFICANCE: Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation.

Quantifying trial-by-trial variability during cortico-cortical evoked potential mapping of epileptogenic tissue.

OBJECTIVE: Measuring cortico-cortical evoked potentials (CCEPs) is a promising tool for mapping epileptic networks, but it is not known how variability in brain state and stimulation technique might impact the use of CCEPs for epilepsy localization. We test the hypotheses that (1) CCEPs demonstrate systematic variability across trials and (2) CCEP amplitudes depend on the timing of stimulation with respect to endogenous, low-frequency oscillations. METHODS: We studied 11 patients who underwent CCEP mapping after stereo-electroencephalography electrode implantation for surgical evaluation of drug-resistant epilepsy. Evoked potentials were measured from all electrodes after each pulse of a 30 s, 1 Hz bipolar stimulation train. We quantified monotonic trends, phase dependence, and standard deviation (SD) of N1 (15-50 ms post-stimulation) and N2 (50-300 ms post-stimulation) amplitudes across the 30 stimulation trials for each patient. We used linear regression to quantify the relationship between measures of CCEP variability and the clinical seizure-onset zone (SOZ) or interictal spike rates. RESULTS: We found that N1 and N2 waveforms exhibited both positive and negative monotonic trends in amplitude across trials. SOZ electrodes and electrodes with high interictal spike rates had lower N1 and N2 amplitudes with higher SD across trials. Monotonic trends of N1 and N2 amplitude were more positive when stimulating from an area with higher interictal spike rate. We also found intermittent synchronization of trial-level N1 amplitude with low-frequency phase in the hippocampus, which did not localize the SOZ. SIGNIFICANCE: These findings suggest that standard approaches for CCEP mapping, which involve computing a trial-averaged response over a .2-1 Hz stimulation train, may be masking inter-trial variability that localizes to epileptogenic tissue. We also found that CCEP N1 amplitudes synchronize with ongoing low-frequency oscillations in the hippocampus. Further targeted experiments are needed to determine whether phase-locked stimulation could have a role in localizing epileptogenic tissue.

Quantitative [18]FDG PET asymmetry features predict long-term seizure recurrence in refractory epilepsy.

OBJECTIVE: Fluorodeoxyglucose-positron emission tomography (FDG-PET) is an established, independent, strong predictor of surgical outcome in refractory epilepsy. In this study, we explored the added value of quantitative [18F]FDG-PET features combined with clinical variables, including electroencephalography (EEG), [18F]FDG-PET, and magnetic resonance imaging (MRI) qualitative interpretations, to predict long-term seizure recurrence (mean post-op follow-up of 5.85 ±â€¯3.77 years). METHODS: Machine learning predictive models of surgical outcome were created using a random forest classifier trained on quantitative features in 89 patients with drug-refractory temporal lobe epilepsy evaluated at the Hospital of the University of Pennsylvania epilepsy surgery program (2003-2016). Quantitative features were calculated from asymmetry features derived from image processing using Advanced Normalization Tools (ANTs). RESULTS: The best-performing model used quantification and had an out-of-bag accuracy of 0.71 in identifying patients with seizure recurrence (Engel IB or worse) which outperformed that using qualitative clinical data by 10%. This model is shared through open-source software for research use. In addition, several asymmetry features in temporal and extratemporal regions that were significantly associated with seizure freedom are identified for future study. SIGNIFICANCE: Complex quantitative [18F]FDG-PET imaging features can predict seizure recurrence in patients with refractory temporal lobe epilepsy. These initial retrospective results in a cohort with long-term follow-up suggest that using quantitative imaging features from regions in the epileptogenic network can inform the clinical decision-making process.

Virtual resection predicts surgical outcome for drug-resistant epilepsy.

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.

Variation in the Presentation of Intussusception by Age.

OBJECTIVE: To compare the clinical presentation of intussusception among children younger and older than 24 months of age. DESIGN/METHODS: We performed a retrospective cross-sectional cohort study of children treated in the emergency department, aged 1 month to 6 years, who had an abdominal ultrasound to evaluate for intussusception over a 5-year period. After stratifying by an age cut-point of 24 months, univariate and multivariate analyses were performed. RESULTS: One thousand two hundred fifty-eight cases of suspected intussusception were studied; median age was 1.7 years (interquartile range, 0.8, 2.9 years), and 37% were female. Intussusception was identified in 176 children (14%); 153 (87%) were ileocolic, and 23 were ileoileal. Abdominal pain (odds ratio, 4.0; 95% confidence interval [CI], 1.5-10.5), emesis (OR, 3.5; 95% CI, 1.8-6.7), bilious emesis (OR, 2.9; 95% CI, 1.5-5.7), lethargy (OR, 2.3; 95% CI, 1.3-5.7), rectal bleeding (OR, 2.8; 95% CI, 1.4-5.7), and irritability (OR, 0.4; 95% CI, 0.2-0.8) were found to be predictors in those younger than 24 months. In children older than 24 months, male sex was the only predictor identified (OR, 2.0; 95% CI, 1.1-3.7). In cases where abdominal radiographs were obtained (n = 1212), any abnormality on abdominal radiograph was found to be predictive in both age groups (OR, 7.8; 95% CI, 3.8-25.7; and OR, 3.1; 95% CI, 1.8-5.2, respectively). CONCLUSIONS: Intussusception presents differently in children younger than 24 months compared with older children. "Traditional" clinical predictors of intussusception should be interpreted with caution when assessing children older than 2 years.

Positive guaiac and bloody stool are poor predictors of intussusception.

BACKGROUND: Currant jelly stool is a late manifestation of intussusception and is rarely seen in clinical practice. Other forms of GI bleeding have not been thoroughly studied and little is known about their respective diagnostic values. OBJECTIVE: To assess the predictive value of GI bleeding (positive guaiac test, bloody stool and rectal bleeding in evaluation of intussusception. METHODS: We performed a retrospective cross-sectional study cohort of all children, ages 1month-6years of age, who had an abdominal ultrasound obtained evaluating for intussusception over 5year period. We identified intussusception if diagnosed by ultrasound, air-contrast enema or surgery. Univariate and a multivariate logistic regression analysis were performed. RESULTS: During the study period 1258 cases met the study criteria; median age was 1.7years (IQR 0.8, 2.9) and 37% were females. Overall 176 children had intussusception; 153 (87%) were ileo-colic and 23 were ileo-ileal. Univariate risk ratio and adjusted Odds ratio were 1.3 (95% CI, 0.8, 2.0) and 1.3 (0.7, 2.4) for positive guaiac test, 1.1 (0.6, 2.1) and 0.9 (0.3, 3.0) for bloody stool, and 1.7 (1.02, 2.8) and 1.3 (0.5, 3.1) for rectal bleeding . CONCLUSION: Blood in stool, whether visible or tested by guaiac test has poor diagnostic performance in the evaluation of intussusception and is not independently predictive of intussusception. If the sole purpose of a rectal exam in these patients is for guaiac testing it should be reconsidered.

Enhancing Value and Well-Being: The Basket of Motivators Framework for Aligning Neurology Clinical Practices With Performance Outcomes.

Purpose of Review: Physician burnout, which is prevalent in neurology, has accelerated in recent years. While multifactorial, a major contributing factor to burnout is a payment model that rewards volume over quality, leaving physicians overburdened and unfulfilled. The aim of this review was to investigate ways of reducing burnout while improving quality-based outcomes in a value-based health care model. Recent Findings: Burnout affects researchers, educators, clinicians, and administrators in all fields and tracks, but neurologists experience some of the worst burnout rates among specialties. Transitioning to a value-based health care model, which rewards quality and outcomes over volume, may contribute to reversing the burnout trend. However, this requires that physicians feel valued in the workplace in ways corresponding to their preferences. We propose to stratify neurologists using the "basket of motivators" framework, which operates multiple individual-based and team-based motivators including balance among work responsibilities, work-life balance, institutional pride, self-actualization at work, work environment, and finances. By tailoring individual-based and team-based financial and nonfinancial incentives, neurologists are empowered to work at the top of their license to provide high-impact clinical care while combating the most prominent causes of burnout. Summary: To address the neurologist burnout epidemic, a transition to value-based health care is needed that rewards quality-based performance outcomes through both individual-based and team-based approaches that apply financial and nonfinancial incentives. Understanding the underlying motivations behind neurologists' drives to work can inform tailored incentives that allow neurologists to provide value to their patients and feel valued by their organizations.

The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond.

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.

Evaluating the concordance between International Classification of Diseases, Tenth Revision Code and stroke severity as measured by the National Institutes of Health Stroke Scale.

Background: The National Institutes of Health Stroke Scale (NIHSS) scores have been used to evaluate acute ischaemic stroke (AIS) severity in clinical settings. Through the International Classification of Diseases, Tenth Revision Code (ICD-10), documentation of NIHSS scores has been made possible for administrative purposes and has since been increasingly adopted in insurance claims. Per Centres for Medicare & Medicaid Services guidelines, the stroke ICD-10 diagnosis code must be documented by the treating physician. Accuracy of the administratively collected NIHSS compared with expert clinical evaluation as documented in the Paul Coverdell registry is however still uncertain. Methods: Leveraging a linked dataset comprised of the Paul Coverdell National Acute Stroke Program (PCNASP) clinical registry and matched individuals on Medicare Claims data, we sampled patients aged 65 and above admitted for AIS across nine states, from January 2017 to December 2020. We excluded those lacking documentation for either clinical or ICD-10-based NIHSS scores. We then examined score concordance from both databases and measured discordance as the absolute difference between the PCNASP and ICD-10-based NIHSS scores. Results: Among 87 996 matched patients, mean NIHSS scores for PCNASP and Medicare ICD-10 were 7.19 (95% CI 7.14 to 7.24) and 7.32 (95% CI 7.27 to 7.37), respectively. Concordance between the two scores was high as indicated by an intraclass correlation coefficient of 0.93. Conclusion: The high concordance between clinical and ICD-10 NIHSS scores highlights the latter's potential as measure of stroke severity derived from structured claims data.