Computational Neuroscience: Towards Neuropharmacological Applications

1. Computational Neuroscience: Towards Neuropharmacological Applications Péter Érdi Henry R. Luce Professor Center for Complex Systems Kalamazoo College Kalamazoo, MI http://www.kzoo.edu/physics/ccss KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Science Budapest, Hungary http://www.rmki.kfki.hu/biofiz/cneuro

2. Contents • Computational neuroscience: microscopic and macroscopic methods • Modeling the pharmacological modulation of the septohippocampal system • Dynamical approach to neurology/psychiatry

3. Computational Neuroscience: Microscopic and Macroscopic Methods

4. Computational Neuroscience: Brain / Behavior / Organism Brain RegionsLayers / ModulesStructuralDecomposition SchemasFunctionalDecomposition Neural NetworksStructure meetsFunction Neurons by Micheal A. Arbib Microscopic and Macroscopic Methods The bottom-up modeling approach SubneuralComponents

5. Computational Neuroscience: Brain / Behavior / Organism Brain RegionsLayers / ModulesStructuralDecomposition Neural NetworksStructure meetsFunction SchemasFunctionalDecomposition Neurons SubneuralComponents by Micheal A. Arbib Microscopic and Macroscopic Methods The top-down modeling approach

6. Computational Neuroscience: Microscopic and Macroscopic Methods Describing morphology Reverse engineering the brain, learning how its components work... Identifying ion channels Adding synaptic connections

7. Single-cell models: the compartmental technique Cl- Na+ K+ A- Ionic movement Equivalent electrical circuit The HH equations Modelled action potential The Hodgkin-Huxley framework

8. Computational Neuroscience: Microscopic and Macroscopic Methods Incorporating knowledge on the microscopic into modeling the macroscopic MeasurementTheory Unit & intracellular recording Hodgkin-Huxley formalism EEG & brain imaging techniques Budapest Group: statistical neurodynamical approach to activity propagation in neural populations

9. Computational Neuroscience: Activity propagation in the feline cortex Adaptation of the database by Scannel et. al. Microscopic and Macroscopic Methods

10. Computational Neuroscience: Microscopic and Macroscopic Methods Activity propagation in the feline cortex Dorsomedial prefrontal cortex inhibition induced epilepsy Control high low population activity Fromhttp://www.rmki.kfki.hu/biofiz/cneuro/tutorials/duke/

11. Modeling the pharmacological modulation of the septohippocampal system

12. Modeling the pharmacological modulation of the septohippocampal system Events (Hz) 1 mV Power 3 sec Frequency (Hz) Frequency (Hz) Time (sec) Power 1 mV Events (Hz) 3 sec Frequency (Hz) Time (sec) Effects of reboxetine on theta activity Control After treatment with reboxetine Hippocampal EEG Fourier tr. Cross corr.

13. Modeling the pharmacological modulation of the septohippocampal system Events (Hz) 1 mV Power Frequency (Hz) 3 sec Time (sec) 1 mV Events (Hz) 3 sec Time (sec) Effects of desipramine on theta activity Control After treatment with reboxetine Power Frequency (Hz) Hippocampal EEG Fourier tr. Cross corr.

14. Modeling the pharmacological modulation of the septohippocampal system 1 mV Power 3 sec Frequency (Hz) Power 1 mV Events (Hz) 3 sec Frequency (Hz) Time (sec) Effects of fluvoxamine on theta activity Control Events (Hz) Time (sec) After treatment with reboxetine Hippocampal EEG Fourier tr. Cross corr.

15. Towards a computational/physiological molecular screening (and drug discovery) Septohippocampal system Temporal pattern Desired temporal pattern Nontrivial enhanced cognition e.g. Θ: anxiogenics Comp. computational & pharmaceutical modulation interface tofurther testing

16. Modeling the pharmacological modulation of the septohippocampal system The septohippocampal system Location of the hippocampus in rodents Location of the hippocampus in human

17. Modeling the pharmacological modulation of the septohippocampal system Hippocampus Septum The septohippocampal system

18. Modeling the pharmacological modulation of the septohippocampal system C: convergence, D: divergence hippocampus proper: CA3 + CA1 hippocampus: DG + CA3 + CA1 hippocampal formation: EC + DG + CA3 + CA1 + Sub The septohippocampal system Dentate Gyrus C: 50 - 100 D: 15 granule cells rat: 600 - 1000 x 103 C, D: 5 - 10 x 103 human: 9000 x 103 CA3 pyramidal cells Entorhinal Cortex rat: 160 x 103 C, D: 103 human: 2300 x 103 CA1 pyramidal cells rat: 250 x 103 human: 4600 x 103 Subiculum

19. Modeling the pharmacological modulation of the septohippocampal system Inverse benzodiazepine agonist NE re-uptake inhibition (reboxetine, desipramine) (FG-7142) Septohippocampal system NE GABA 5HT treatment induce/enhance θ NE re-uptake inhibition + 5HT re-uptake inhibition – 5HT2C antagonist + 5HT2C agonist – inverse benzodiazepine + agonist Raphe Nucleus Locus Coeruleus 5HT2C agonist 5HT2C antagonist 5HT2C re-uptake inhibition (m-cPP, Ro60-0175) (SB-206553, SB-242084) (fluvoxamine) Message from Mihaly Hajos’ works

20. Modeling the pharmacological modulation of the septohippocampal system Knowledge from • Anatomy • Pharmacology • Physiology • Behavioral neuroscience • Physics • Mathematics • Computer Science using their results understanding the phenomena Simulation versus planning Building mathematical models Conduction computer experiments Designing biological experiments

21. Modeling the pharmacological modulation of the septohippocampal system Potential (V) Potential (V) time (sec) time (sec) Firing pattern of control hippocampal CA1 pyramidal cell Firing pattern of KA current blocked hippocampal CA1 pyramidal cell Simulation versus planning Reversible and irreversible transition between modes KA blockade

22. Modeling the pharmacological modulation of the septohippocampal system Computer Experiment The experiment to be shown was done using the GENESIS simulation environment. A modified Traub’94 type pyramidal neuron was examined. The model consists of 66 compartments for dendrites, the soma and the axon. Current types implemented are: Ca2+, KDR, KAHP, KA, KC and Na currents. The model also accounts for intracellular Ca2+ concent- ration. Current injection (10 nA) Membrane potential vs. time curve measured in the axon. apical dendrites Potential (V) Recording site soma Time (sec) axon +50 mV -60 mV basal dendrites color code for membrane potential

23. Modeling the pharmacological modulation of the septohippocampal system Computer Experiment Control hippocampal CA1 pyramidal neuron

24. Modeling the pharmacological modulation of the septohippocampal system Computer Experiment Hippocampal CA1 pyramidal neuron after selective blockade of KA channels

25. Dynamical approach to neurology/psychiatry

26. Dynamical approach to neurology/psychiatry state state “E” “E” time time state state • Models: • ‘lesion models’: does not explain waving • neurotransmitter model (DOPA) • disconnection hypothesis Friston • NMDA: delayed maturation of NMDA receptors • cortical pruning (synaptic depression) Schizophrenia positive and negative symptoms hallucination uncomplicated actions and speech decreased motivation ‘waving’ ‘steady’ storage and recall of memory traces changes in attractor structure ‘pathological attractors’

27. Dynamical approach to neurology/psychiatry E. Ruppin Spontaneously occurring NMDA receptor hypofunction SCHIZOPHRENIA increase in the expression of the “immaturate” NR2D receptor subtype Reactive anomalous sprouting Excessive growth of synapses Frontal cortex, basal view The NMDA Receptor Delayed Maturation Hypothesis

28. Dynamical approach to neurology/psychiatry Pathological attractors appear recall of learned memory traces recall of never learned items “E” “E” state state “delusion” “hallucination” The NMDA Receptor Delayed Maturation Hypothesis

29. Dynamical approach to neurology/psychiatry Introduction to Attractors

30. Closing Words One of the main intention of computational neuroscience is to integrate anatomical, physiological, neurochemical/pharmacological and behavioural data by coherent concepts and models. [A basic structure for which such integration is particularly important is the hippocampal formation. Hippocampus has a crucial role in cognitive processes, such as learning, memory formation and spatial navigation. Many neurological disorders, such as epilepsy, Alzheimer diseases, depression, anxiety, partially schizophrenia are hippocampus-dependent diseases.] Computational models of normal and pathological processes may help to develop more efficient therapeutic strategies.