Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold

By James F. Cavanagh, Thomas V. Wiecki, Michael X. Cohen, C. M. Figueroa, Johan Samanta, Scott J. Sherman, Michael J Frank. Nature Neuroscience, 2011

611 citations recorded by the source corpus.

It takes effort and time to tame one's impulses. Although medial prefrontal cortex (mPFC) is broadly implicated in effortful control over behavior, the subthalamic nucleus (STN) is specifically thought to contribute by acting as a brake on cortico-striatal function during decision conflict, buying time until the right decision can be made. Using the drift diffusion model of decision making, we found that trial-to-trial increases in mPFC activity (EEG theta power, 4–8 Hz) were related to an increased threshold for evidence accumulation (decision threshold) as a function of conflict. Deep brain stimulation of the STN in individuals with Parkinson's disease reversed this relationship, resulting in impulsive choice. In addition, intracranial recordings of the STN area revealed increased activity (2.5–5 Hz) during these same high-conflict decisions. Activity in these slow frequency bands may reflect a neural substrate for cortico–basal ganglia communication regulating decision processes.

View Paper: https://www.semanticscholar.org/paper/85d4413e3c280aef9047d18b8ee3a2bc761e0270

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Citations in the corpus

Oscillations and the basal ganglia: Motor control and beyond (2013, unknown): “Theta activity in the STN LFP is also sensitive to conflict during decision making paradigms (Cavanagh et al., 2011; Fumagalli et al., 2011).”
Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy (2013, result): “Moreover, intracranial EEG recordings from DBS electrodes have revealed differences in STN oscillatory activity between high-conﬂict and low-conﬂict trials (Cavanagh et al., 2011), thus arguing for a contribution of STN to conﬂict processing, in line with Frank et al.’s (2007) prediction.”
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python (2013, methodology): “In this example we will be using data collected in a reward-based decision-making experiment in our lab (Cavanagh et al., 2011).”
The expected value of control: An integrative theory of anterior cingulate cortex function (2013, unknown): “Several studies have shown that dACC interacts directly with structures proposed to implement changes of threshold, such as the subthalamic nucleus (Aron et al., 2007; Aron and Poldrack, 2006; Cavanagh et al., 2011; Jahfari et al., 2011; Wiecki and Frank, 2013), as well as those thought to influence”
Computational psychiatry as a bridge from neuroscience to clinical applications (2016, background): “Impulsive behaviors that result from reduced decision thresholds are observed in patients with Parkinson’s disease treated with STN deep brain stimulation (DBS) and are linked to disruption of normal communication between frontal cortex and STN 140 .”
A computational model of inhibitory control in frontal cortex and basal ganglia. (2011, methodology): “Evidence for conflict-induced decision threshold adjustment via the hyperdirect pathway has been recently described in a reinforcement-based decision making task (Cavanagh et al., 2011).”
Neurophysiology of performance monitoring and adaptive behavior. (2014, background): “Indeed, high-frequency DBS in the STN for PD results in more impulsive decisions (reflected in shorter reaction times in highconflict conditions) in decision making and more errors of commission in simple motor Go/NoGo tasks, respectively (17, 80, 187).”
Lead-DBS: A toolbox for deep brain stimulation electrode localizations and visualizations (2015, unknown): “…scientific approaches using depth recordings from DBS target regions of the basal ganglia (Brown and Williams, 2005) or stimulation protocols to evaluate the involvement of the anatomical DBS target region in information processing (Cavanagh et al., 2011; Frank et al., 2007; Green et al., 2013).”
Cognitive control over learning: creating, clustering, and generalizing task-set structure. (2013, unknown): “…and drift diffusion modeling are consistent with this depiction of frontal-STN communication (Aron, Behrens, Smith, Frank, & Poldrack, 2007; Cavanagh et al., 2011; Frank, Moustafa, et al., 2007; Isoda & Hikosaka, 2008; Wylie, Ridderinkhof , Bashore, & van den Wildenberg, 2010; Zaghloul et…”