Functional magnetic resonance imaging (fMRI) is an important tool for scientists studying brain function. FMRI data are complex in nature: they are massive in size and a low signal-to-noise ratio makes the elimination of some noise prior to model fitting desirable for improved identification of true brain activity. We propose two methods of reducing this noise: generalized indicator functional analysis and a hidden Markov model. Brain regions showing increased fMRI signal while subjects engaged in a visual/spatial motor task are identified using concepts from social network analysis and statistical mechanics. Conditional probabilities of activation given the degree to which pairs of voxels are related are modeled for three groups: people with schizophrenia, their asymptomatic relatives, and control subjects.We compare the conditional probability maps obtained for each group to
evaluate for between-group differences in extent of task-related signal.