Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict

Abstract

The cortico-basal ganglia circuit is needed to suppress prepotent actions and to facilitate controlled behavior. Under conditions of response conflict, the frontal cortex and subthalamic nucleus (STN) exhibit increased spiking and theta band power, which are linked to adaptive regulation of behavioral output. The electrophysiological mechanisms underlying these neural signatures of impulse control remain poorly understood. To address this lacuna, we constructed a novel large-scale, biophysically principled model of the subthalamopallidal (STN-globus pallidus externus) network and examined the mechanisms that modulate theta power and spiking in response to cortical input. Simulations confirmed that theta power does not emerge from intrinsic network dynamics but is robustly elicited in response to cortical input as burst events representing action selection dynamics. Rhythmic burst events of multiple cortical populations, representing a state of conflict where cortical motor plans vacillate in the theta range, led to prolonged STN theta and increased spiking, consistent with empirical literature. Notably, theta band signaling required NMDA, but not AMPA, currents, which were in turn related to a triphasic STN response characterized by spiking, silence, and bursting periods. Finally, theta band resonance was also strongly modulated by architectural connectivity, with maximal theta arising when multiple cortical populations project to individual STN "conflict detector" units because of an NMDA-dependent supralinear response. Our results provide insights into the biophysical principles and architectural constraints that give rise to STN dynamics during response conflict, and how their disruption can lead to impulsivity and compulsivity.SIGNIFICANCE STATEMENT The subthalamic nucleus exhibits theta band power modulation related to cognitive control over motor actions during conditions of response conflict. However, the mechanisms of such dynamics are not understood. Here we developed a novel biophysically detailed and data-constrained large-scale model of the subthalamopallidal network, and examined the impacts of cellular and network architectural properties that give rise to theta dynamics. Our investigations implicate an important role for NMDA receptors and cortico-subthalamic nucleus topographical connectivities in theta power modulation.

Keywords: NMDA; hyperdirect pathway; impulsivity; response conflict; subthalamic nucleus; theta band power.

Figure 1.

A, STN-GPe network models with stochastic connectivities. Boxed numbers indicate connection probabilities. Ctx, Cortex (spike trains). B, Tuning process to achieve asynchronous baseline (no cortical drives) network spiking. C, Asynchronous baseline network spiking with STN raster and spike counts. Colors same as in A, time-aligned to cortical drive. D, Normalized spectral power of spike trains (in C). E, Top, ISI. Bottom, Spiking frequency counts of subpopulations in baseline network. F, Top, Autonomous cellular activities after tuning, in absence of any synaptic inputs. Bottom, Typical cellular activities in baseline network. G, Top, Normalized voltage-dependent synaptic conductances after tuning for AMPA, NMDA, and GABA_STN currents on STN, and GABA_GP on GPeP and GPeA. Bottom, Corresponding I–V characteristics, normalized to some peak currents.

Figure 2.

A, Cellular activity of single unit after tuning. Top, Example is for a 4 Hz RSSD cortical drive. Other RSSD and SBED drives show similar dependence on NMDA for theta modulation. Left, Capacitative current (no synaptic components). Right, Spectrogram of capacitative current. B, Network activity by averaging capacitative currents across all STN units, under cortical inputs and in silico pharmacological blockade, aligned to cortical input onset.

Figure 3.

A, RSSD. B, SBED. Top, Cortical spike train. Middle, Synaptic currents (blue represents net; orange represents GABA; green represents NMDA; red represents AMPA) averaged across STN units in network. Bottom, Spectrogram. C, D, Resonant frequency (frequency with highest power) for (C) RSSD as in A and (D) SBED stimulations. C, D, Numbers indicate the corresponding cortical drives in A and B. Color intensity scales are different in each spectrogram.

Figure 4.

A-D, Left, Burst spiking response. Right, Triphasic (spike synchronization, silence, burst) spiking response, in STN subpopulation. A, STN raster and PSTH. Left ordinate, STN unit number. Right ordinate, spike count, bin size = 5 ms. B, Typical electrophysiological responses of stimulated units. C, Postsynaptic currents averaged across all STN units in subpopulation. D, Spectrograms (different color intensity scales). E, Published in vivo activities in rats from extracellular recordings: left, extracellular (Janssen et al., 2017); right, network (Schmidt et al., 2013) levels.

Figure 5.

A, Cortico-subthalamic network architecture. Left, Segregated information flow. Cortical subpopulations connect to specific STN subpopulations, precluding direct information sharing, showing only two subpopulations, but four were used in the simulations. Middle, Nonsegregated information flow. Cortical subpopulations connect primarily to their corresponding STN subpopulations with high probability while allowing connections to other STN subpopulations with lower probability. Right, Different STN classes based on stochastic cortical-STN connectivities. Classes: MainStim, STN units that receive only from their own cortical subpopulation; OtherStims, STN units that receive from only one cortical subpopulation that is not their own; NoStims, STN units that do not receive any cortical inputs; AllStims, STN units that receive from more than one cortical inputs, acting as conflict detectors. B, Simulating conflict condition. Top, No conflict; only one cortical subpopulation active. Bottom, Conflict; coactivation of two cortical subpopulations, with offset δ. Left, Stimulation schematics. Middle, SBED. Right, RBED.

Figure 6.

A, Average LFP theta power across all STN units that received cortical inputs. Left, No conflict. Right, Conflict. B, Average LFP theta power across different STN classes. Subpopulations first and second received from either the first or the second cortical subpopulations only. Both, STN conflict detector units received from both cortical drives; 1st + 2nd, the (linear) average power from subpopulations first and second lumped together. C, Average synaptic currents. Left, AMPA. Right, NMDA, across STN detector units. Color coding same as in B. D, Effects of cortical stimulation offset (δ) on average theta. Left, Power. Right, Energy. E, Contribution of glutamatergic currents. Left, NMDA. Right, AMPA, toward theta power. Top, δ = 5 ms. Bottom, δ = 10 ms. Broken lines indicate time points 0, 5, and 10 ms. F, Nonlinear effects of cortical stimulation overlaps and durations, and segregation on the relative charge contributions of NMDA and AMPA currents observed during theta activity.

Figure 7.

Effects of interevent intervals (Δ) in RBED cortical stimulations and segregation on STN conflict detector units. A, Top, Average LFP theta power. Bottom, Cumulative average spike counts, during and after cortically induced conflict. Overlayed tables represent the net spiking rates during (=) and after (>) stimulation, and ratios of spiking rates during/before (=/<) and during/after (=/>) stimulation periods. B, Box plots represent distributions (orange line indicates median) and trial averages (green triangles represent mean) of conflict detectors during RBED stimulation period. Top, Average theta energy. Middle, Average spiking. Bottom, Average theta spiking (theta × spiking). Within each segregation level, effects of Δ on theta energy, spiking, and theta-spiking are significant (p < 0.05) between theta-IEI 125, 250 and non-theta-IEI groups 75, 350, 450. C, Average cortically driven postsynaptic currents of conflict detectors, shown here for the lowest segregation level 25%. Colors represent IEI. NMDA, but not AMPA, responses show aliasing because of nonlinear interactions.