Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales-Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2

Abstract

The basal ganglia are involved in the motivational and habitual control of motor and cognitive behaviors. Striatum, the largest basal ganglia input stage, integrates cortical and thalamic inputs in functionally segregated cortico-basal ganglia-thalamic loops, and in addition the basal ganglia output nuclei control targets in the brainstem. Striatal function depends on the balance between the direct pathway medium spiny neurons (D1-MSNs) that express D1 dopamine receptors and the indirect pathway MSNs that express D2 dopamine receptors. The striatal microstructure is also divided into striosomes and matrix compartments, based on the differential expression of several proteins. Dopaminergic afferents from the midbrain and local cholinergic interneurons play crucial roles for basal ganglia function, and striatal signaling via the striosomes in turn regulates the midbrain dopaminergic system directly and via the lateral habenula. Consequently, abnormal functions of the basal ganglia neuromodulatory system underlie many neurological and psychiatric disorders. Neuromodulation acts on multiple structural levels, ranging from the subcellular level to behavior, both in health and disease. For example, neuromodulation affects membrane excitability and controls synaptic plasticity and thus learning in the basal ganglia. However, it is not clear on what time scales these different effects are implemented. Phosphorylation of ion channels and the resulting membrane effects are typically studied over minutes while it has been shown that neuromodulation can affect behavior within a few hundred milliseconds. So how do these seemingly contradictory effects fit together? Here we first briefly review neuromodulation of the basal ganglia, with a focus on dopamine. We furthermore use biophysically detailed multi-compartmental models to integrate experimental data regarding dopaminergic effects on individual membrane conductances with the aim to explain the resulting cellular level dopaminergic effects. In particular we predict dopaminergic effects on Kv4.2 in D1-MSNs. Finally, we also explore dynamical aspects of the onset of neuromodulation effects in multi-scale computational models combining biochemical signaling cascades and multi-compartmental neuron models.

Keywords: Kv4.2; dopamine; kinetic modeling; medium spiny projection neurons; simulations; striatum; subcellular signaling.

Figure 1

A multi-scale view of basal ganglia. (Left) an illustration of the basal ganglia macro-circuitry, showing the glutamatergic and dopaminergic afferent inputs to striatum, as well as the projections via the direct and indirect pathways. Ca 50% of MSNs belong to the direct pathways, they carry dopamine type 1 receptors and project to GPi/SNr (D1-MSN). The other MSNs express dopamine type 2 receptors and project to GPe (globus pallidus externa) before reaching basal ganglia output structures (D2-MSN). At rest these output structure neurons keep motor programs in the brain stem and thalamus under tonic inhibition. When e.g., direct pathway GABAergic MSNs in the striatum become active they inhibit these tonically active neurons, and thereby the inhibition from basal ganglia onto the action/motor centers is relieved. This is the basis for the classical model of basal ganglia function in selection of (motor) actions. (Middle) a schematic representation of the striatal micro-circuitry. The main neuron type in the striatum is the medium spiny neuron (MSN), constituting ~95% of striatal neurons. MSNs are the projection neurons from the striatum and receive convergent excitatory glutamatergic input from cortex and thalamus, inhibitory GABAergic input from neighboring MSNs and striatal fast-spiking (FS) and low-threshold spiking (LTS) neurons, cholinergic (ACh) input from cholinergic interneurons (ChINs) and pedunculopontine nucleus (PPN), and dopaminergic input from substantia nigra compacta (SNc). (Right) Examples of receptor induces cascades involved in controlling LTP in the cortico-striatal synapse.

Figure 2

Validation. The model is validated against experimental data from striatonigral medium spiny neurons of the direct basal ganglia pathway. Both somatic (B and C) as well as dendritic excitability (D) is validated. In (A), the dendritic arborisation of the morphology is shown. (B) is showing the voltage response of the cell, following current injections ranging from −100 to 340 pA in steps of 40 pA. In (C) the current-frequency curve of the model is plotted together with experimental curves from Planert et al. (2013). In (D) the dendritic excitability is validated as the local change in calcium concentration as a function of somatic distance following a backpropagating action potential (Day et al., 2008).

Figure 3

Distribution of modulation factors and overall model excitability. The overall excitability of the uniformly distributed sample set is compared to the unmodulated control case and the contribution of different ion channels to the change in excitability is assessed. In (A), the general setup of the simulation is illustrated. First a random modulation factor (MF) is drawn for each modulated channel and the conductances are scaled accordingly; then the excitability (in the form or rheobase current) of the new setup is compared to the unmodulated control case and is sorted into groups (More, Equal, and Less). A trial that needs at least 10 pA more or less current to spike is sorted into the Less (blue) and More (red) groups, respectively, the rest are categorized as Equal excitable (gray). In (B), the proportions of simulations in each group is shown. In (C), the MF distribution over groups is shown. In (D), the correlation of each channel with excitability is shown as a Hilton diagram. The size of the square is here indicating the magnitude of the correlation while the color of the square shows if the correlation is positive (orange) or negative (black). In (E), the channel-channel correlations as well as pair vice scatter plots are shown for the potassium and sodium channels. In (F), information of all modulated potassium and sodium channels are shown as a function of excitability group. The x and y axes, the color and the size of the dots here shows the modulation factors of the Kaf, Naf, Kir, and Kas channels, respectively.

Figure 4

Static modulation. In (A), the modulation factors (MF) are restricted to the experimentally reported range (see Table 3) except for the Ca channels that are kept as free parameters since they did not contribute much to the excitability (see Figures 3C and D). In the top panel the distribution of each channel is shown split over subgroup (Less, Equal, or More excitable). The middle panel shows spike rasters. All black dots here represent spikes from modulated traces while the large gray stripes show the timing of spikes from the unmodulated control case. The bottom panel shows the proportion of the samples of each category. The same type of plots is shown in (B and C) but here the underlying sample sets have been restricted in different ways. In both plots the Kaf channel is also modulated 80 ± 5% and in (C) the axon initial segment is additionally not modulated. In (D), the Kir channel is allowed to reduce more than reported in the literature (down to zero conductance). In (E), the reduction of the fast sodium channel is allowed to reduce less than experimentally reported (up to no modulation). In (F), the slow potassium channel (Kas) is allowed to reduce down to zero conductance. In (G), the same modulation is used as in (A), with the difference that the axon initial segment is not modulated.

Figure 5

Kaf channel control of calcium influx following a backpropagating action potential. In (A), an illustration of the simulation setup is shown. An action potential is triggered by giving a strong depolarizing current pulse (2 nA, 2 ms) to the soma, and the calcium (Ca) concentration is monitored as a function of somatic distance. Similar to florescent imaging. This setup is repeated for different proportions of Kaf channel blocking; 100, 50, 20, and 0% (control). In (B), example traces of concentration are shown for either proximal (30–50 μm), or distal (170–200 μm) dendritic compartments. The upper panel shows all groups of blocking proportions and the lower panel only holds control and the assumed proportion blocked by dopamine (20%). In (C), the mean change in amplitude of the Ca concentration, is plotted, as a function of somatic distance. The curves are normalized to the proximal location, mimicking experimental data (brown trace, Day et al., 2008).

Figure 6

Dynamic modulation. The timing aspects of the DA modulation is assessed by connecting the channel conductances to a DA transient. The transient follows an alpha function with 0.5 μM amplitude and a time constant of 500 ms. In (A), the simulation setup is outlined, showing a simplified illustration of the intracellular cascade. The model is here driven using synaptic activation as in an in vivo like situation. In (B), the time to first spike is quantified as a function of modulating substrate (bottom panel). The median value for each substrate is given above respective box. In the top panel the underlying kinetics of the modulating substrates are shown. In the middle panel a few examples of voltage traces are shown. Both modulated (black) and unmodulated control traces (gray) are shown. In (C), different modulation paradigms are used; Intrinsic means that only intrinsic ion channels are modulated, Excitatory means that only AMPA and NMDA channels are modulated (for range see Table 4), Synaptic means that the GABA channels as well as the AMPA and NMDA channels are modulated, No GABA means that all channels but the GABA channels are modulated, and in the final group (PKA) all channels are modulated. In (D) the proportion of traces that spikes are shown as a function of modulating substrate and modulation paradigm (all connected to PKA). Additionally to the ones shown in (C), there are also the groups No Naf, No Kaf, “No Kaf, Naf,” and PKA 1.5 s. In the first three groups the simulation is run without modulation of the respective channel(s) mentioned in the title. In the last group the simulation time is extended from 1 to 1.5 s.