• More than 90% of central nervous system drugs fail when they're tried in large human trials. The team at the Oxford’s FMRIB hope that combining information from many brain imaging studies with their computational methods will provide a cheaper way of filtering out drugs that are not likely to work, without the need for expensive human clinical trials.
  • The researchers then used a technique called machine learning to discriminate brain activity in participants when they had received a drug from when who had received a placebo. They 'trained' a computer algorithm with several examples of data acquired from people who had received the drug. In the same way, they trained the algorithm with data from people who had received the placebo.
  • The crucial test was whether the algorithm would then be able correctly classify an unknown dataset as coming from a placebo or a drug condition, based on the knowledge it had acquired in the training phase. They found that the algorithm was able to classify data as coming from a drug versus a placebo condition with an average accuracy of around 70%.

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