The goal of this review article is to redefine what the Mismatch Negativity (MMN) component of event-related potentials reflects in auditory scene analysis, and to provide an overview of how the MMN serves as a valuable tool in Cognitive Neuroscience research. In doing so, some of the old beliefs (five common ‘myths’) about MMN will be dispelled, such as the notion that MMN is a simple feature discriminator and that attention itself modulates MMN elicitation. A revised description of what MMN truly reflects will be provided, which includes a principal focus onto the highly context-dependent nature of MMN elicitation and new terminology to discuss MMN and attention. This revised framework will help clarify what has been a long line of seemingly contradictory results from studies in which behavioral ability to hear differences between sounds and passive elicitation of MMN have been inconsistent. Understanding what MMN is will also benefit clinical research efforts by providing a new picture of how to design appropriate paradigms suited to various clinical populations.

Our group has initiated experiments to epigenetically profile CpG island hypermethylation in genomic DNA from tissue specimens of head and neck squamous cell carcinoma (HNSCC) using a microarray of 12,288 CpG island clones. Our technique, known as a methylation-specific restriction enzyme (MSRE) analysis, is a variation of the differential methylation hybridization (DMH) technique, in that it is not an array comparison of two DNA samples using methylation-specific restriction enzymes. Instead, it is a comparison of a single DNA sample’s response to a methylation-sensitive restriction enzyme (HpaII) and its corresponding methylation-insensitive isoschizomer (MspI). Estimation of the reproducibility of this microarray assay by intraclass correlation (ICC) demonstrated that in four replicate experiments for three tumor specimens, the ICC observed for a given tumor specimen ranged from 0.68 to 0.85 without filtering of data. Repeated assays achieved 87% concordance or greater for all tumors after filtering of array data by fluorescence intensity. We utilized hierarchical clustering on a population of 37 HNSCC samples to cluster tumor samples with similar DNA methylation profiles. Supervised learning techniques are now being utilized to allow us to identify associations between specific epigenetic signatures and clinical parameters. Such techniques will allow us to identify select groups of CpG island loci that could be used as epigenetic markers for both diagnosis and prognosis in HNSCC.