We can carry out such a test very easily. Having determined

#selleck screening library randurls[1|1|,|CHEM1|]# the classification accuracy as described earlier, we the randomly allocate the data to the two classes of interest (thus achieving the null hypothesis of no difference between the classes) and repeat the “leave one out” testing. If we do this a very large number of times, we can establish how likely the classification process is Inhibitors,research,lifescience,medical to produce the original classification accuracy under the null hypothesis of no difference between the classes. In simple terms, we can see how far away from chance the classification lies. The further this is, the “cleaner” the separation between the groups achieved by the imaging “biomarker.” Machine learning in current image analysis Inhibitors,research,lifescience,medical – a change of emphasis? Although “brain reading” using machine learning methods (often also referred to as pattern classification methods) is currently arousing a good deal of interest, their use in the investigation of brain imaging is not new. In fact, they were used as long ago as the 1990s to investigate PET data.12,13 However, functional and structural brain imaging research has produced a host of new Inhibitors,research,lifescience,medical and interesting analysis methods over the last two decades. The reasons why some

methods become widely used whereas others do not is a topic of considerable interest. O’Toole and colleagues8 devoted considerable space to discussing this issue and raised issues of what will move researchers out of their “comfort zone” to a new and potentially useful way of using their data. Given the availability of high-quality packages such as SPM, where mass- univariate analysis is efficiently implemented, and which are well-known and respected by neuroimagers, new methods have to be Inhibitors,research,lifescience,medical easy to use and to offer considerable added value to justify the investment in using them. Why then does the author of the current article believe that machine learning Inhibitors,research,lifescience,medical methods may be widely

taken up when many other promising methods have not? In the early 2000s considerable interest in questions of face/object recognition in the AV-951 visual cortex led to some fascinating experiments. Notably, a very elegant study of face and object processing in the visual cortex by Haxby and his colleagues appeared.1“ This paper did not use machine learning methods, but introduced the idea of associating brain states (recognition of different types of object) with distributed patterns of brain activity. Shortly afterwards, in 2002, sellekchem Gotland et al wrote a highly interesting account of the use of classifiers in brain imaging,15 introducing the use of the SVM, and in 2003 Cox and Savoy10 used an SVM (see above) in the same area of research as Haxby.14 It was clear from these data that information might be available in distributed patterns of brain activity that were not accessible by considering each voxel in isolation.