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bioRxiv · 10.1101/758466

In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia

Sherif, M.·Neymotin, S. A.·Lytton, W. W.
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Abstract

BackgroundTreatment of schizophrenia has had limited success in treating core cognitive symptoms. The evidence of multi-gene involvement suggests that multi-target therapy may be needed. Meanwhile, the complexity of schizophrenia pathophysiology and psychopathology, coupled with the species-specificity of much of the symptomatology, places limits on analysis via animal models, in vitro assays, and patient assessment. Multiscale computer modeling complements these traditional modes of study.\n\nMethodsUsing a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP).\n\nResultsAltering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. The strong information flow seen at the peaks were associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability.\n\nConclusionsOur results are consistent with the idea that overly low or high gamma power is associated with pathological information flow and information processing. These data suggest the need for careful titration of schizophrenia pharmacotherapy to avoid extremes that alter information flow in different ways. These results also identify gamma power as a potential biomarker for monitoring pathology and multi-target pharmacotherapy.\n\nAUTHOR SUMMARYCurrently, there are no good treatments for the cognitive symptoms of schizophrenia. We used a biophysically realistic computational model of hippocampal CA3 to investigate the effect of potential pharmacotherapeutic targets on the dynamics of CA3 activity and information processing to predict multi-target drug treatments for schizophrenia. We found an inverted-U shaped relationship between information flow and drug target manipulations, as well as between information flow and gamma power. Our study suggests that neuronal excitability and synchrony may be tuned between extremes to enhance information flow and information processing. It further predicts the need for careful titration of schizophrenia drugs, whether used individually or in drug cocktails.

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Sherif, M., Neymotin, S. A., Lytton, W. W.. 2019-09-05. In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia. https://doi.org/10.1101/758466

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