Computational modeling of transcranial direct current stimulation (tDCS) in obesity: Impact of head fat and dose guidelines☆

Recent studies show that acute neuromodulation of the prefrontal cortex with transcranial direct current stimulation (tDCS) can decrease food craving, attentional bias to food, and actual food intake. These data suggest potential clinical applications for tDCS in the field of obesity. However, optimal stimulation parameters in obese individuals are uncertain. One fundamental concern is whether a thick, low-conductivity layer of subcutaneous fat around the head can affect current density distribution and require dose adjustments during tDCS administration. The aim of this study was to investigate the role of head fat on the distribution of current during tDCS and evaluate whether dosing standards for tDCS developed for adult individuals in general are adequate for the obese population. We used MRI-derived high-resolution computational models that delineated fat layers in five human heads from subjects with body mass index (BMI) ranging from “normal-lean” to “super-obese” (20.9 to 53.5 kg/m2). Data derived from these simulations suggest that head fat influences tDCS current density across the brain, but its relative contribution is small when other components of head anatomy are added. Current density variability between subjects does not appear to have a direct and/or simple link to BMI. These results indicate that guidelines for the use of tDCS can be extrapolated to obese subjects without sacrificing efficacy and/or treatment safety; the recommended standard parameters can lead to the delivery of adequate current flow to induce neuromodulation of brain activity in the obese population

The “Smart Dining Table”: Automatic Behavioral Tracking of a Meal with a Multi-Touch-Computer

Studying how humans eat in the context of a meal is important to understanding basic mechanisms of food intake regulation and can help develop new interventions for the promotion of healthy eating and prevention of obesity and eating disorders. While there are a number of methodologies available for behavioral evaluation of a meal, there is a need for new tools that can simplify data collection through automatic and online analysis. Also, there are currently no methods that leverage technology to add a dimension of interactivity to the meal table. In this study, we examined the feasibility of a new technology for automatic detection and classification of bites during a laboratory meal. We used a SUR40 multi-touch tabletop computer, powered by an infrared camera behind the screen. Tags were attached to three plates, allowing their positions to be tracked, and the saturation (a measure of the infrared intensity) in the surrounding region was measured. A Kinect camera was used to record the meals for manual verification and provide gesture detection for when the bites were taken. Bite detections triggered classification of the source plate by the SUR40 based on saturation flux in the preceding time window. Five healthy subjects (aged 20–40 years, one female) were tested, providing a total sample of 320 bites. Sensitivity, defined as the number of correctly detected bites out of the number of actual bites, was 67.5%. Classification accuracy, defined as the number of correctly classified bites out of those detected, was 82.4%. Due to the poor sensitivity, a second experiment was designed using a single plate and a Myo armband containing a nine-axis accelerometer as an alternative method for bite detection. The same subjects were tested (sample: 195 bites). Using a simple threshold on the pitch reading of the magnetometer, the Myo data achieved 86.1% sensitivity vs. 60.5% with the Kinect. Further, the precision of positive predictive value was 72.1% for the Myo vs. 42.8% for the Kinect. We conclude that the SUR40 + Myo combination is feasible for automatic detection and classification of bites with adequate accuracy for a range of applications