Abstract: Schizophrenia, Alzheimer’s disease, Parkinson’s disease and other degenerative disorders and dementias impose an enormous economic and psychosocial burden on society, communities, and families. To gain a better understanding of gene-brain-behavior relationships, improve treatment and find cures for these diseases, translational research must be conducted with clinical trials of new drugs as well as other non-pharmacological interventions that are followed by genotyping and imaging biomarkers for patients with these neuropsychiatric degenerative disorders. This research, involving PharmacoGenomic Molecular Imaging (PGMI) of the brain, will be extremely costly in many ways. Therefore, knowledge engineering with effective software tools and applications built upon a semantic-enabled informatics infrastructure remains a necessary prerequisite to facilitate a reduction of those research costs by maximizing the benefit obtained from existing data and minimizing the cost of generating new data. A knowledge engineering framework that serves this goal must operate in a crossdisciplinary manner that integrates data from diverse biomedical fields while at the same time incorporating the relevant computational mathematics, statistics, and informatics analyses for productive data mining. This presentation will review the work done at the Brain Health Alliance Virtual Institute with our students in pursuit of this grand challenge from the perspective of 3 different areas of computing: 1) numeric computing with biomedical image processing and virtual reality, 2) symbolic computing with ontologies, the semantic web and distributed systems, and 3) hybrid numeric-symbolic computing with clinical telegaming applications