Brain stimulation treatments can alter neural circuits electrically instead of chemically. However, understanding what brain regions should be targeted, by condition, remains a challenge, particularly in non-invasive rTMS. A Beth Israel Deaconess study suggests that brain networks – the interconnected pathways that link brain circuits to one another– can help guide site selection for brain stimulation therapies.
According to author Michael Fox, “Although different types of brain stimulation are currently applied in different locations, we found that the targets used to treat the same disease are nodes in the same connected brain network.”
Brain stimulation treatment data for 14 conditions, including addiction, Alzheimer’s, depression, dystonia, epilepsy, essential tremor, Huntington’s, and Parkinson’s were studied. The researchers listed the stimulation sites, deep in the brain and near the surface, thought to be effective for the treatment of each disease.
Through a data set of fMRI images of people’s brains at rest, the team found correlated fluctuations in spontaneous brain activity, illustrating which sites were functionally connected. A map of connections from deep brain stimulation sites to the surface of the brain was created. When the research team compared the map to sites on the brain surface that work for noninvasive brain stimulation, the two matched.
Albert Einstein College of Medicine professor Sophie Molholm has published a paper describing the way that autistic children process sensory information, as determined by EEG. She believes that this could lead to earlier diagnosis (before symptoms of social and developmental delays emerge), hence earlier treatment, which might reduce the condition’s symptoms.
EEG readings were taken from 40 children, ages 6-17, who were diagnosed with autism, and compared to those of unaffected children of similar age. All were given either a flash cue, a beep cue or a combination, and asked to press a button when these stimuli occurred. A 70 electrode cap measured brain responses every two milliseconds, including those that recorded how the brain first processed the information.
The children with autism showed a distinctly different brain wave signature from those without the condition. There were differences in the speed in which the sights or sounds were processed, and in how the sensory neurons recruited neurons in other areas of the brain to register and understand the information. The more different this multi-processing was, the more severe the child’s autistic symptoms.
Professor Molholm acknowledges that the sample was too small to use the profile for diagnosing autism, but it could lead to such a test if the results are confirmed and repeated.
Using a single set of APIs, Google Fit collects and aggregates data from fitness apps and sensors to manage a user’s fitness stream.
The platform will work with wearables and other peripherals. To protect privacy, permission is required and data can be deleted. Initally, Adidas, Nike , Intel, LG and Motorola will participate. Nike will add its Fuel number to the Fit stream for other apps to utilize.
Apple announced a similar fitness data aggregation platform, Healthkit, earlier this month. (See ApplySci, June 5 2014.) Both platforms are expected to go live this fall.
DEKA is a robotic, prosthetic arm that will allow amputees to perform complex movements and tasks. It has just received FDA approval.
Electrodes attached to the arm detect muscle contractions close to the prosthesis, and a computer translates them into movement. Six “grip patterns” allow wearers to drink a cup of water, hold a cordless drill or pick up a credit card or a grape, among other functions.
DARPA‘s Justin Sanchez believes that DEKA “provides almost natural control of upper extremities for people who have required amputations.” He claims that “this arm system has the same size, weight, shape and grip strength as an adult’s arm would be able to produce.”