Characterisation of inputs and outputs of striatal medium spiny neurons in health and disease

Time: Wed 2019-12-18 09.00

Location: Kollegiesalen, Brinellvägen 8, Stockholm (English)

Subject area: Numerical Analysis Computer Science Biological and Biomedical Physics

Doctoral student: Marko Filipović , Beräkningsvetenskap och beräkningsteknik (CST), Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Germany, Kumar

Opponent: Andrew Sharott, Oxford University, UK

Supervisor: Jeanette Hellgren Kotaleski, Numerisk analys och datalogi, NADA; Arvind Kumar, Beräkningsvetenskap och beräkningsteknik (CST); Gilad Silberberg, Beräkningsvetenskap och beräkningsteknik (CST), Karolinska Institutet; Ad Aertsen, Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Germany

Abstract

Striatal medium spiny neurons (MSNs) play a crucial role in various motor and cognitive functions. They are separated into those belonging to the direct pathway (dMSNs) and the indirect pathway (iMSNs) of the basal ganglia, depending on whether they express D1 or D2 type dopamine receptors, respectively. In this thesis I investigated the input processing of both MSN types, the characteristics of dMSN outputs, and the effect that aberrant iMSN activity has on the subthalamic nucleus-globus pallidus externa (STN-GPe) network.In order to verify a previous result from a computational study claiming that dMSNs should receive either more or stronger total input than iMSNs, I performed an analysis of in vivo whole-cell MSN recordings in healthy and dopamine (DA) depleted (6OHDA) anesthetized mice. To test this prediction, I compared subthreshold membrane potential fluctuations and spike-triggered average membrane potentials of the two MSN types. I found that dMSNs in healthy mice exhibited considerably larger fluctuations over a wide frequency range, as well as significantly faster  depolarization towards the spiking threshold than iMSNs. However, these effects were not present in recordings from 6OHDA animals. Together, these findings strongly suggest that dMSNs do  receive stronger total input than iMSNs in healthy condition.I also examined how different concentrations of dopamine affect neural trial-by-trial (or response) variability in a biophysically detailed compartmental model of a direct-pathway MSN.  Some of the sources of trial-by-trial variability include synaptic noise, neural refractory period, and ongoing neural activity. The focus of this study was on the effects of two particular  properties of the synaptic input: correlations of synaptic input rates, and the balance between excitatory and inhibitory inputs (E-I balance). The model demonstrates that dopamine is in  general a significant diminisher of trial-by-trial variability, but that its efficacy depends on the properties of synaptic input. Moreover, input rate correlations and changes in the E-I balance by themselves also proved to have a marked impact on the response variability.Finally, I investigated the beta-band phase properties of the STN-GPe network, known for its exaggerated beta-band oscillations during Parkinson’s disease (PD). The current state-of-the-art  computational model of the network can replicate both transient and persistent beta oscillations, but fails to capture the beta-band phase alignment between the two nuclei as seen in human  recordings. This was particularly evident during simulations of the PD condition, where STN or GPe were receiving additional stimulation in order to induce pathological levels of beta-band  activity. Here I show that by manipulating the percentage of the neurons in either population that receives stimulation it is possible to increase STN-GPe phase difference heterogeneity.  Furthermore, a similar effect can be achieved by adjusting synaptic transmission delays between the two populations. Quantifying the difference between human recordings and network  simulations, I provide the set of parameters for which the model produces the greatest correspondence with experimental results.

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264255

Belongs to: School of Electrical Engineering and Computer Science
Last changed: Nov 26, 2019