A Quantitative, Digital Method to Analyze Human Figure Drawings as a Tool to Assess Body Representations Distortions in Stroke Patients.

OBJECTIVE: Human figure drawings are widely used in clinical practice as a qualitative indication of Body Representations (BRs) alterations in stroke patients. The objective of this study is to present and validate the use of a new app called QDraw for the quantitative analysis of drawings and to investigate whether this analysis can reveal distortions of BRs in chronic stroke patients. RESULTS: QDraw has proven to generate reliable data as compared to manual scoring and in terms of inter-rater reliability, as shown by the high correlation coefficients. Moreover, human figure drawings from chronic stroke patients demonstrated a distortion of upper limb perception, as shown by a significantly higher arm length asymmetry compared to legs, whereas no difference was found in healthy controls. CONCLUSIONS: The present study supports the use of quantitative, digital methods (the QDraw app) to analyze human figure drawings as a tool to evaluate BRs distortions in stroke patients.

Redesigning existing transcranial magnetic stimulation coils to reduce energy: application to low field magnetic stimulation.

OBJECTIVE: To present a systematic framework and exemplar for the development of a compact and energy-efficient coil that replicates the electric field (E-field) distribution induced by an existing transcranial magnetic stimulation coil. APPROACH: The E-field generated by a conventional low field magnetic stimulation (LFMS) coil was measured for a spherical head model and simulated in both spherical and realistic head models. Then, using a spherical head model and spatial harmonic decomposition, a spherical-shaped cap coil was synthesized such that its windings conformed to a spherical surface and replicated the E-field on the cortical surface while requiring less energy. A prototype coil was built and electrically characterized. The effect of constraining the windings to the upper half of the head was also explored via an alternative coil design. MAIN RESULTS: The LFMS E-field distribution resembled that of a large double-cone coil, with a peak field strength around 350 mV m-1 in the cortex. The E-field distributions of the cap coil designs were validated against the original coil, with mean errors of 1%-3%. The cap coil required as little as 2% of the original coil energy and was significantly smaller in size. SIGNIFICANCE: The redesigned LFMS coil is substantially smaller and more energy-efficient than the original, improving cost, power consumption, and portability. These improvements could facilitate deployment of LFMS in the clinic and potentially at home. This coil redesign approach can also be applied to other magnetic stimulation paradigms. Finally, the anatomically-accurate E-field simulation of LFMS can be used to interpret clinical LFMS data.

Field Distribution of Transcranial Static Magnetic Stimulation in Realistic Human Head Model.

OBJECTIVE: The objective of this work was to characterize the magnetic field (B-field) that arises in a human brain model from the application of transcranial static magnetic field stimulation (tSMS). MATERIALS AND METHODS: The spatial distribution of the B-field magnitude and gradient of a cylindrical, 5.08 cm × 2.54 cm NdFeB magnet were simulated in air and in a human head model using the finite element method and calibrated with measurements in air. The B-field was simulated for magnet placements over prefrontal, motor, sensory, and visual cortex targets. The impact of magnetic susceptibility of head tissues on the B-field was quantified. RESULTS: Peak B-field magnitude and gradient respectively ranged from 179-245 mT and from 13.3-19.0 T/m across the cortical targets. B-field magnitude, focality, and gradient decreased with magnet-cortex distance. The variation in B-field strength and gradient across the anatomical targets largely arose from the magnet-cortex distance. Head magnetic susceptibilities had negligible impact on the B-field characteristics. The half-maximum focality of the tSMS B-field ranged from 7-12 cm3 . SIGNIFICANCE: This is the first presentation and characterization of the three-dimensional (3D) spatial distribution of the B-field generated in a human brain model by tSMS. These data can provide quantitative dosing guidance for tSMS applications across various cortical targets and subjects. The finding that the B-field gradient is high near the magnet edges should be considered in studies where neural tissue is placed close to the magnet. The observation that susceptibility has negligible effects confirms assumptions in the literature.

Simulating Clinical Trials With and Without Intracranial EEG Data.

OBJECTIVE: It is currently unknown if knowledge of clinically silent (electrographic) seizures improves the statistical efficiency of clinical trials. METHODS: Using data obtained from 10 patients with chronically implanted subdural electrodes over an average of 1 year, a Monte Carlo bootstrapping simulation study was performed to estimate the statistical power of running a clinical trial based on A) patient reported seizures with intracranial EEG (icEEG) confirmation, B) all patient reported events, or C) all icEEG confirmed seizures. A "drug" was modeled as having 10%, 20%, 30%, 40% and 50% efficacy in 1000 simulated trials each. Outcomes were represented as percentage of trials that achieved p<0.05 using Fisher Exact test for 50%-responder rates (RR50), and Wilcoxon Rank Sum test for median percentage change (MPC). RESULTS: At each simulated drug strength, the MPC method showed higher power than RR50. As drug strength increased, statistical power increased. For all cases except RR50 with drug of 10% efficacy, using patient reported events (with or without icEEG confirmation) was not as statistically powerful as using all available intracranially confirmed seizures (p<0.001). SIGNIFICANCE: This study demonstrated using simulation that additional accuracy in seizure detection using chronically implanted icEEG improves statistical power of clinical trials. Newer invasive and noninvasive seizure detection devices may have the potential to provide greater statistical efficiency, accelerate drug discovery and lower trial costs.

Monte Carlo simulations of randomized clinical trials in epilepsy.

BACKGROUND: The placebo response in epilepsy randomized clinical trials (RCTs) has recently been shown to largely reflect underlying natural variability in seizure frequency. Based on this observation, we sought to explore the parameter space of RCT design to optimize trial efficiency and cost. METHODS: We used one of the world's largest patient reported seizure diary databases, SeizureTracker.com to derive virtual patients for simulated RCTs. We ran 1000 randomly generated simulated trials using bootstrapping (sampling with replacement) for each unique combination of trial parameters, sweeping a large set of parameters in durations of the baseline and test periods, number of patients, eligibility criteria, drug effect size, and patient dropout. We studied the resulting trial efficiency and cost. RESULTS: A total of 6,732,000 trials were simulated, drawing from 5097 patients in the database. We found that the strongest regression predictors of placebo response were durations of baseline and test periods. Drug effect size had a major impact on trial efficiency and cost. Dropout did not have a major impact on trial efficiency or cost. Eligibility requirements impacted trial efficiency to a limited extent. Cost was minimized while maintaining statistical integrity with very short RCT durations. DISCUSSION: This study suggests that RCT parameters can be improved over current practice to reduce costs while maintaining statistical power. In addition, use of a large-scale population dataset in a massively parallel computing analysis allows exploration of the wider parameter space of RCT design prior to running a trial, which could help accelerate drug discovery and approval.

A big data approach to the development of mixed-effects models for seizure count data.

OBJECTIVE: Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS: Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS: For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. SIGNIFICANCE: The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.