Classifying single trial fMRI: What can machine learning learn?

We describe three experiments combining neuroimaging and machine learning. The first experiment compares the performance of maximum likelihood and neural net classifiers for "brain reading " of fMRI data in the visual cortex. The second experiment applies the optimal classifier to measure the development of the face region in children and adolescents. While the previous experiments used block designs, the third experiment describes an event-related experiment where the classification algorithm learned something real, but not what was planned. The corroboration and validation of the classification results with brain images will be demonstrated. Object classification by imaging of the distributed representation in the visual cortex was shown first shown by Haxby [1], and demonstrated on a per block basis by Cox and Savoy [2]. In adults, regions of the ventral-occipital cortex are involved in visual processing of faces, objects, and places. The first experiment attempted to classify the visual input of faces, scrambled faces, scenes, objects, and textures on a trial by trial basis for adults. Two classifiers were studied in a "train and test " paradigm. We used