Functional Neuroimaging Methods

Neuroimaging research in our laboratory uses functional magnetic resonance imaging (fMRI) to study the mechanisms
underlying decision making. To better understand this neuroimaging technique and its limitations, we have published
studies on the basic properties of the hemodynamic response measured by fMRI. Properties investigated by our laboratory
have included refractory effects associated with repeated stimulus presentation, stimulus-specific adaptation effects,
differences in the hemodynamic response across individuals and subject groups, effects of signal-to-noise upon the
reproducibility of activation, and the relation between fMRI activation and intracranially recorded activity. Some current
studies use pattern classification algorithms derived from machine learning (e.g., support vector machines, SVM) to
identify local information carried across voxels within a brain region. We have also, in recent years, begun manipulating
the brain to alter its function, chiefly through dietary influences on neurotransmitters (e.g., rapid tryptophan depletion).
And, in collaborations with David Madden and Roberto Cabeza, we have been examining effects of normal aging upon fMRI and
diffusion tensor imaging (DTI) data. Finally, we explore major issues in neuroimaging research through review articles,
meta-analyses, and our textbook Functional Magnetic Resonance Imaging (2nd edition published in January 2009).