The Subthalamic Nucleus: Unravelling New Roles and Mechanisms in the Control of Action

Abstract

How do we decide what we do? This is the essence of action control, the process of selecting the most appropriate response among multiple possible choices. Suboptimal action control can involve a failure to initiate or adapt actions, or conversely it can involve making actions impulsively. There has been an increasing focus on the specific role of the subthalamic nucleus (STN) in action control. This has been fueled by the clinical relevance of this basal ganglia nucleus as a target for deep brain stimulation (DBS), primarily in Parkinson's disease but also in obsessive-compulsive disorder. The context of DBS has opened windows to study STN function in ways that link neuroscientific and clinical fields closely together, contributing to an exceptionally high level of two-way translation. In this review, we first outline the role of the STN in both motor and nonmotor action control, and then discuss how these functions might be implemented by neuronal activity in the STN. Gaining a better understanding of these topics will not only provide important insights into the neurophysiology of action control but also the pathophysiological mechanisms relevant for several brain disorders and their therapies.

Keywords: action control; decision making; deep brain stimulation; neuronal oscillations; subthalamic nucleus.

Figure 1.

Functional anatomy of the basal ganglia and the STN. (A) (i) The basal ganglia pathways, highlighting both the STN and the striatum as input structures of the basal ganglia: the hyperdirect pathway (Cx-STN-GPI/SNR), the direct pathway (Cx-Str-GPI/SNR), and the indirect pathway (Cx-Str-GPe-GPI/SNR). Filled and empty arrows indicate excitatory glutamatergic (glu) and inhibitory GABAergic (GABA) projections, respectively. Abbreviations: Cerebral cortex (Cx); striatum (Str); subthalamic nucleus (STN); external segment of the globus pallidus (GPe); internal segment of the globus pallidus (GPI); substantia nigra pars reticulata (SNR); thalamus (Th). (ii) Schematic diagram of the “center-surround model” of basal ganglia activity; early activation of the hyperdirect pathway causes a broad inhibition, while subsequent activation of the direct pathway causes a specific activation of motor programs, antagonized by the indirect pathway. Republished with permission from (Nambu and others 2002) and original figure. (B) Topographical organization of the projections from different regions of the frontal cortex to the STN, which partially overlap. Colored meshes represent dense projections from the cortical areas ventromedial prefrontal cortex/orbitofrontal cortex (vmPFC/OFC), anterior cingulate cortex (ACC), dorsal prefrontal cortex (DPFC), supplementary motor areas (SMA), and motor cortex (M1). Coronal view at (i) anterior, (ii) central, and (iii) posterior STN. Republished with permission from Society for Neuroscience, from Haynes and Haber (2013); permission conveyed through Copyright Clearance Center, Inc.

Figure 2.

Oscillatory dynamics in the STN during motor and nonmotor inhibition, and conflict. (A) STN beta power decreases from onset of GO cue and during movement. If a STOP signal appears after the GO cue (ST(s) condition), beta power increases relative to the GO trials without STOP signal (GO(lm) condition). (B) STN oscillatory dynamics during sensorimotor decisions with low (i) and high (ii) conflict. Theta power increases only during high conflict trials, while perimotor beta power decreases similarly in both conditions. (C) STN oscillatory dynamics during nonmotor memory decisions. Beta power decreases during memory encoding both in the lateral prefrontal cortex (PFC) (i) and the STN (ii), while theta power increases only in the lateral PFC. PFC-STN beta coherence (iii) decreases during encoding of target trials, which are supposed to be encoded, but increases during distractor trials which are not supposed to be encoded. Reproduced with permission from (A) Benis and others (2014), (B) Zavala and others (2017a), (C) Zavala and others (2017b).

Figure 3.

Evidence accumulation and decision thresholds. Schematic illustration of the evidence accumulation process, indicating how accumulated evidence increases over time toward the decision threshold for the correct decision. When the accumulated evidence reaches the decision threshold, a response is made. Upper panel displays high and low drift rates of the accumulated evidence. In this case, both conditions eventually accumulate the same level of evidence and therefore will have similar accuracy, but it takes longer time when the drift rate is low, such as during a difficult task. Lower panel displays high and low decision thresholds. Drift rate is slower in the low-threshold condition, but since the decision threshold is lower, the response is made at the same time as the high-threshold condition, but with less accumulated evidence and therefore lower accuracy level.

Figure 4.

Decision thresholds in the STN. (A, B) Decision making during high and low conflicts. In the moving dot task (A, left), participants are asked to indicate the overall direction of dot movement, which becomes gradually more coherent. Sensory information is sequentially sampled and integrated over time, as evidence for the correct response is accumulated. During high-conflict trials in this study (A, right), some dots moved in the opposite direction of the overall movement direction. STN theta power (B, i) and mPFC-STN theta coherence (B, ii) increased selectively in the high-conflict trials. In this task, increased STN theta power was related to increased decision thresholds in a drift diffusion model, and mPFC-STN theta coupling predicted threshold modulation (Herz and others 2016). (C) Decision making during instructed speed-accuracy tradeoffs. In this task, participants were instructed to be either fast or accurate in their responses, which should indicate the overall direction of moving dots with either high or low movement coherence. Increase in STN theta power predicted decision thresholds only after accuracy instructions (C, left). Decrease in STN beta during high dot moving coherence predicted decreased decision thresholds regardless of instructions (C, right). Reproduced from (A, B) Zavala and others (2014) and (C) Herz and others (2017), licensed under CC BY.

Figure 5.

Neuronal activity in the STN during response inhibition, decisional conflict, and reward. (A) Single neuron responses from a monkey performing an inhibitory control task which disentangles proactive Go-NoGo responses and reactive switch-responses: switch-go (stop-to-go) and switch-stop (go-to-stop). (i) Response of two different cells to switch-go trials and switch-stop trials. (ii) Response of two different cells to Go-trials and NoGo-trials in the Go-NoGo task. (iii) Stop and Switch cells were separate populations during reactive trials, but each of these could be both Go and NoGo cells during proactive control trials. (B) STN population rate during associative decisions. Firing rate increases during high conflict trials relative to low conflict trials. (C) During a sensorimotor task, STN cell responses with increased firing rate aligned either to the cue (“Early cells”) or to the response (“Late cells”). The cue-aligned cells increased in firing rate during high conflict trials relative to the low conflict trials. (D) STN neuronal responses following a “Go for Reward” cue for neurons that increased (red) or decreased (blue) in firing rate. (A) Reproduced with permission from Pasquereau and Turner (2017), (B) Republished with permission of Society for Neuroscience, from Zaghloul and others (2012); permission conveyed through Copyright Clearance Center, Inc., (C) Reproduced with permission from Zavala and others (2017a), (D) Reproduced with permission from Rossi and others (2017).

Figure 6.

Action control in the STN. Simplified scheme of possible conceptual routes of information flow between the frontal cortex, STN, and GPI/SNR, based on functional anatomical or dynamic connections. Color gradients represent topographical organization of functions from the blue “motor” part in the most posterior dorsolateral STN to the red “limbic” part in the most anterior ventromedial STN, with connected areas in the frontal cortex and GPI/SNR (Alexander and others 1990, Haynes and Haber 2013). Similar principles could be relevant for the striato-pallido-subthalamic (indirect) pathway, but the figure does not address how this input is integrated with cortical input in the STN. All of these schemes may be relevant, in parallel or depending on context. (a) Competing input model: Competing action plans reaching the STN cause a global excitation of GPI/SNR, conveying a “wait” signal to withhold a response until the optimal action is selected by the striatum (Bogacz and Gurney 2007; Frank 2006). (b) Specific input model: A specific signal from the PFC to the STN (conveying for example “Stop” or “Conflict”) causes global excitation of GPI/SNR (Aron and others 2016). (c) Parallel/spiral model: Parallel circuits in the basal ganglia loop process different types of information, though the inhibitory effect of STN on basal ganglia output may be similar regardless of information type (Alexander and others 1986). Open and closed loops across the basal ganglia may allow information to spiral from ventral to dorsal loops (Haber 2003; Haynes and Haber 2013; Kelly and Strick 2004). (d) Integration model: Integration of information modalities to specific neurons across the STN (Espinosa-Parrilla and others 2013; Janssen and others 2017). This could be anatomically supported by nucleus-wide dendrites (Sato and others 2000), intranuclear axon collaterals (Kita and others 1983) or STN-Gpe interactions. STN neurons convey a specific but integrated (modular) signal to the GPI/SNR. (e) Dynamic model: The frontal cortex conveys different signals to the STN through directed oscillatory coupling. For example, a “conflict” signal from the PFC can be transmitted through increased theta coherence, and a “go” signal from sensorimotor cortex can be transmitted through decreased beta coherence. In the STN, parts of the local processing by neurons and high-frequency oscillations can entrain to these rhythms, and the participating ensemble of neurons transmits a collective but patterned signal to the GPI/SNR (Aron and others 2016; Brittain and others 2014; Zavala and others 2015; Zavala and others 2017a).