Early failures of microarray, however, have not impugned its technical validity but have simply emphasized the importance of using more sophisticated experimental designs to overcome its analytic challenges. With this in mind, an approach called “imaging-guided microarray,” specifically designed to address the analytic limitations inherent to microarray when applied to disorders of the brain, was recently introduced. A detailed description is provided elsewhere,6,7but in general the approach relies on in vivo imaging to first construct a spatiotemporal model hypothesizing a priori how a pathogenic molecule should behave—anatomically and across age groups. Then, the spatiotemporal model is used as a guide in generating microarray data and in analyzing the gene expression data set. By converting a microarray experiment from one that is typically hypothesis free to one that is hypothesis driven, imaging-guided microarray naturally addresses many analytic challenges. As in any hypothesis-driven study, the results are only as good as the hypothesis and, therefore, as discussed later, any microarray finding needs to be independently confirmed and validated.