Machine Learning for Decoding Human Brain States from
Functional MR Images
By Tom M. Mitchell,
Over the past decade, functional Magnetic Resonance Imaging
(fMRI) has emerged as a powerful new instrument to
observe activity in the human brain. A typical fMRI
experiment can produce a three-dimensional image characterizing the human
subject's brain activity every half second, at a spatial resolution of a few
millimeters. fMRI is
already causing a revolution in the fields of Psychology and Cognitive
Neuroscience. We consider the role for
Machine Learning algorithms in analyzing fMRI data. In particular, we focus on training
classifiers to decode the cognitive state of a human subject based on their
observed fRMI brain activation. We present several
case studies in which we have trained classifiers to distinguish cognitive states
such as whether the human subject is looking at a picture or a sentence, and
whether the word the subject is viewing is a word describing food, people, or
buildings. We will describe the results in these fMRI
studies, and examine the machine learning methods needed to successfully train
classifiers in the face of such extremely high dimensional ($10^5$ features),
extremely sparse (tens of training examples), noisy data sets.