Engineering brain activity patterns by neuromodulator polytherapy for treatment of disorders

Mostafa Ghannad-Rezaie, Peter M. Eimon, Yuelong Wu and Mehmet Fatih Yanik ()
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Mostafa Ghannad-Rezaie: Massachusetts Institute of Technology
Peter M. Eimon: Massachusetts Institute of Technology
Yuelong Wu: Massachusetts Institute of Technology
Mehmet Fatih Yanik: Massachusetts Institute of Technology

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: Abstract Conventional drug screens and treatments often ignore the underlying complexity of brain network dysfunctions, resulting in suboptimal outcomes. Here we ask whether we can correct abnormal functional connectivity of the entire brain by identifying and combining multiple neuromodulators that perturb connectivity in complementary ways. Our approach avoids the combinatorial complexity of screening all drug combinations. We develop a high-speed platform capable of imaging more than 15000 neurons in 50ms to map the entire brain functional connectivity in large numbers of vertebrates under many conditions. Screening a panel of drugs in a zebrafish model of human Dravet syndrome, we show that even drugs with related mechanisms of action can modulate functional connectivity in significantly different ways. By clustering connectivity fingerprints, we algorithmically select small subsets of complementary drugs and rapidly identify combinations that are significantly more effective at correcting abnormal networks and reducing spontaneous seizures than monotherapies, while minimizing behavioral side effects. Even at low concentrations, our polytherapy performs superior to individual drugs even at highest tolerated concentrations.

Date: 2019
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DOI: 10.1038/s41467-019-10541-1

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