Psychopharmacology
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Aberrant prefrontal-hippocampal (PFC-HC) connectivity is disrupted in several psychiatric and at-risk conditions. Advances in rodent functional imaging have opened the possibility that this phenotype could serve as a translational imaging marker for psychiatric research. Recent evidence from functional magnetic resonance imaging (fMRI) studies has indicated an increase in PFC-HC coupling during working-memory tasks in both schizophrenic patients and at-risk populations, in contrast to a decrease in resting-state PFC-HC connectivity. Acute ketamine challenge is widely used in both humans and rats as a pharmacological model to study the mechanisms of N-methyl-D-aspartate (NMDA) receptor hypofunction in the context of psychiatric disorders. ⋯ This translational comparison demonstrates a cross-species consistency in pharmacological effect and elucidates ketamine-induced alterations in PFC-HC coupling, a phenotype often disrupted in pathological conditions, which may give clue to understanding of psychiatric disorders and their onset, and help in the development of new treatments.
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Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. ⋯ In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study? (2) What should I measure and when should I measure it? (3) How does the pharmacological agent behave using an experimental medicine model?, and (4) How does a compound differ from and/or resemble existing compounds? Specifically, we present studies from the literature and we suggest areas for the focus of future development. Further refinement and tailoring of machine learning techniques may help realise their tremendous potential for drug discovery and drug validation.