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Laboratory for Computational Neuroscience

The Laboratory for Computational Neuroscience (LCN) at McLean is defined not by any particular disease or brain area, but by an approach to neuropsychiatric inquiry: the use of highly biologically realistic simulations to provide information about the emergent behaviors of a large number of neurons working together, with the goal of revealing how lesions or interventions at a cellular level can affect the system-level behavior of a brain region, or ultimately clinical behavior. Its orientation is multi-disciplinary, drawing on the skills of neuroanatomists, neurophysiologists, psychopharmacologists, psychologists, and psychiatrists as well as researchers expert in mathematics, statistics, and computer science. A guiding principle of the LCN is that a "systems biology" approach-one that emphasizes understanding a living thing via the interaction of the functioning of its components, rather than in-depth study according to any one scientific discipline-is particularly useful, if not necessary, in psychiatric research. As this approach is highly data-intensive, the Lab owns a 72-processor Beowulf computer cluster, on which we run most of our simulations.

Two examples of substantive areas of research for the LCN are as follows:

Schizophrenia. While research on the biological basis of schizophrenia over the past 30-40 years has identified a large number of potential neuroanatomic and biochemical abnormalities, it is still not clear how these findings, alone or in combination, give rise to the clinical syndrome. Using a computational model of hippocampus that we have developed, we are conducting "virtual experiments" investigating the manner in which abnormalities in the GABA, glutamate, and dopamine systems, alone or in combination, may underlie the symptoms of the illness. This promises to shed light on the cellular etiology of the disease.

Drug development. Traditionally, researchers in the area of drug development have tended to emphasize the isolation of one or two receptor "targets" in the effort to identify effective agents. However, in as much as neuropsychiatric drugs likely exert their effects through a constellation of actions, a systems biology approach could be instrumental in identifying more effective medications. Using the power of neurocomputational modeling to reveal the non-obvious system-level and behavioral implications of cellular and sub-cellular changes and to analyze a large number of such things simultaneously, we have described a methodology to use computational models to identify more efficacious medications for neuropsychiatric illnesses; we have issued patents and pending patents on this process.


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