Computational Convergence of Functional and Neurochemical Fingerprints of Psychiatric Drugs


Psychiatric conditions such as depression, schizophrenia, addiction and others produce the largest global burden of disease. These disorders are difficult to treat and despite the fact that more than 100 psychiatric medications are in clinical use the treatment success is modest. For optimal drug development it is critical to understand the effects of drugs on brain network activity and neurochemistry. Preclinical functional magnetic resonance imaging (fMRI) is an emerging approach to study brain network function in relation to pharmacological manipulations and disease models with a high translational value. The neural basis of neuroimaging signals is probably neuronal activity but the role of the underlying neurochemistry, defined here by the interaction of synaptic inhibition, excitation, and the neurochemical responses of the targeted neurons, is not well understood. While both functional and neurochemical investigations of drug effects suggest the presence of substance-specific activity patterns, it remains unclear how the functional observations reflect the underlying neurochemical processes.
The aim of this project is to develop a novel in silico framework to study convergent mechanisms of drug-induced MRI driven functional activity patterns and neurochemical fingerprints in the rat brain based on already existing databases for a variety of psychiatric medications.
This approach will

  • (i) integrate the different system levels and multi-dimensional network activity patterns into a unified framework,
  • (ii) will link global and local network activity with underlying neurochemical events and changes in connectivity,
  • (iii) will provide better predictions of the effects of a new potential drug on the circuitry,
  • (iv) will indicate the best functional biomarker to monitor treatment efficacy, and
  • (v) will provide a better understanding of preclinical functional neuroimaging data which can then be translated into human studies.


Foo, J.C., Noori, H.R., Yamaguchi, I., Vengeliene, V., Cosa-Linan, A., Nakamura, T., Morita, K., Spanagel, R., Yamamoto, Y., 2017. Dynamical state transitions into addictive behaviour and their early-warning signals. Proc. Biol. Sci. 284.

Fritze, S., Spanagel, R., Noori, H.R., 2017. Adaptive dynamics of the 5-HT systems following chronic administration of selective serotonin reuptake inhibitors: a meta-analysis. J. Neurochem. 142, 747–755.

Noori, H.R., Cosa Linan, A., Spanagel, R., 2016. Largely overlapping neuronal substrates of reactivity to drug, gambling, food and sexual cues: A comprehensive meta-analysis. Eur Neuropsychopharmacol 26, 1419–1430.

Noori, H.R., Schöttler, J., Ercsey-Ravasz, M., Cosa-Linan, A., Varga, M., Toroczkai, Z., Spanagel, R., 2017. A multiscale cerebral neurochemical connectome of the rat brain. PLoS Biol. 15, e2002612.

Oberhofer, J., Noori, H.R., 2017. Quantitative evaluation of cue-induced reinstatement model for evidence-based experimental optimization. Addict Biol.

Vengeliene, V., Bespalov, A., Roßmanith, M., Horschitz, S., Berger, S., Relo, A.L., Noori, H.R., Schneider, P., Enkel, T., Bartsch, D., Schneider, M., Behl, B., Hansson, A.C., Schloss, P., Spanagel, R., 2017. Towards trans-diagnostic mechanisms in psychiatry: neurobehavioral profile of rats with a loss-of-function point mutation in the dopamine transporter gene. Dis Model Mech 10, 451–461.