Intracranial EEG fluctuates over months after implanting electrodes in human brain.

OBJECTIVE: Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. APPROACH: Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient\u27s recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. MAIN RESULTS: A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. SIGNIFICANCE: These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring

Prediction of complicated disease course for children newly diagnosed with Crohn's disease: a multicentre inception cohort study

Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohn’s disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown

Model-based design for seizure control by stimulation

ObjectiveCurrent brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs.ApproachInspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy.Main resultsAlthough each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes.SignificanceOur integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices

Measuring Quality of Life in Pediatric Patients With Inflammatory Bowel Disease: Psychometric and Clinical Characteristics

Objective: To extend development of a pediatric inflammatory bowel disease (IBD) health-related quality of life (HRQoL) measure by determining its factor structure and associations of factors with generic HRQoL measures and clinical variables. Patients and Methods: Cross-sectional survey of children and adolescents ages 8 years to 18 years and their parents attending any of 6 US IBD centers, recruited from either existing registry of age-eligible subjects or visits to participating centers. The survey included generic (Pediatric Quality of Life Inventory) and IBD-specific (Impact Questionnaire) quality of life measures, disease activity, and other clinical indicators. We carried out factor analysis of Impact responses, comparing resulting factors with results on the generic HRQoL and the clinical measures. Results: We included 220 subjects (161 with Crohn disease and 59 with ulcerative colitis). Initial confirmatory factor analysis did not support the 6 proposed Impact domains. Exploratory factor analysis indicated 4 factors with good to excellent reliability for IBD responses: general well-being and symptoms, emotional functioning, social interactions, and body image. Two items did not load well on any factor. The 4 factors correlated well with the Pediatric Quality of Life Inventory and subscales. Children with higher disease activity scores and other indicators of clinical activity reported lower HRQoL. Conclusions: This study provides further characteristics of a HRQoL measure specific to pediatric IBD and indicates ways to score the measure based on the resulting factor structure. The measure correlates appropriately with generic HRQoL measures and clinical severity indicators

Diffusion-weighted imaging fluid-attenuated inversion recovery mismatch on portable, low-field MRI among acute stroke patients

Objective: For stroke patients with unknown time of onset, mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can guide thrombolytic intervention. However, access to MRI for hyperacute stroke is limited. Here, we sought to evaluate whether a portable, low-field (LF)-MRI scanner can identify DWI-FLAIR mismatch in acute ischemic stroke. Methods: Eligible patients with a diagnosis of acute ischemic stroke underwent LF-MRI acquisition on a 0.064-T scanner within 24 h of last known well. Qualitative and quantitative metrics were evaluated. Two trained assessors determined the visibility of stroke lesions on LF-FLAIR. An image coregistration pipeline was developed, and the LF-FLAIR signal intensity ratio (SIR) was derived. Results: The study included 71 patients aged 71 ± 14 years and a National Institutes of Health Stroke Scale of 6 (interquartile range 3–14). The interobserver agreement for identifying visible FLAIR hyperintensities was high (κ = 0.85, 95% CI 0.70–0.99). Visual DWI-FLAIR mismatch had a 60% sensitivity and 82% specificity for stroke patients <4.5 h, with a negative predictive value of 93%. LF-FLAIR SIR had a mean value of 1.18 ± 0.18 <4.5 h, 1.24 ± 0.39 4.5–6 h, and 1.40 ± 0.23 >6 h of stroke onset. The optimal cut-point for LF-FLAIR SIR was 1.15, with 85% sensitivity and 70% specificity. A cut-point of 6.6 h was established for a FLAIR SIR <1.15, with an 89% sensitivity and 62% specificity. Interpretation: A 0.064-T portable LF-MRI can identify DWI-FLAIR mismatch among patients with acute ischemic stroke. Future research is needed to prospectively validate thresholds and evaluate a role of LF-MRI in guiding thrombolysis among stroke patients with uncertain time of onset

Detecting Microbial Dysbiosis Associated with Pediatric Crohn Disease Despite the High Variability of the Gut Microbiota

The relationship between the host and its microbiota is challenging to understand because both microbial communities and their environments are highly variable. We have developed a set of techniques based on population dynamics and information theory to address this challenge. These methods identify additional bacterial taxa associated with pediatric Crohn disease and can detect significant changes in microbial communities with fewer samples than previous statistical approaches required. We have also substantially improved the accuracy of the diagnosis based on the microbiota from stool samples, and we found that the ecological niche of a microbe predicts its role in Crohn disease. Bacteria typically residing in the lumen of healthy individuals decrease in disease, whereas bacteria typically residing on the mucosa of healthy individuals increase in disease. Our results also show that the associations with Crohn disease are evolutionarily conserved and provide a mutual information-based method to depict dysbiosis