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Original Investigation |

Role of the Medial Prefrontal Cortex in Impaired Decision Making in Juvenile Attention-Deficit/Hyperactivity Disorder

Tobias U. Hauser, PhD1,2,3; Reto Iannaccone, MS1,4; Juliane Ball, PhD1; Christoph Mathys, PhD3,5,6; Daniel Brandeis, PhD1,2,7,8; Susanne Walitza, MD1,2,7; Silvia Brem, PhD1,2
[+] Author Affiliations
1University Clinics for Child and Adolescent Psychiatry, University of Zurich, Zurich, Switzerland
2Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
3Wellcome Trust Centre for Neuroimaging, University College London, London, England
4Doctoral Program in Integrative Molecular Medicine, University of Zurich, Zurich, Switzerland
5Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, England
6Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
7Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
8Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
JAMA Psychiatry. 2014;71(10):1165-1173. doi:10.1001/jamapsychiatry.2014.1093.
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Importance  Attention-deficit/hyperactivity disorder (ADHD) has been associated with deficient decision making and learning. Models of ADHD have suggested that these deficits could be caused by impaired reward prediction errors (RPEs). Reward prediction errors are signals that indicate violations of expectations and are known to be encoded by the dopaminergic system. However, the precise learning and decision-making deficits and their neurobiological correlates in ADHD are not well known.

Objective  To determine the impaired decision-making and learning mechanisms in juvenile ADHD using advanced computational models, as well as the related neural RPE processes using multimodal neuroimaging.

Design, Setting, and Participants  Twenty adolescents with ADHD and 20 healthy adolescents serving as controls (aged 12-16 years) were examined using a probabilistic reversal learning task while simultaneous functional magnetic resonance imaging and electroencephalogram were recorded.

Main Outcomes and Measures  Learning and decision making were investigated by contrasting a hierarchical Bayesian model with an advanced reinforcement learning model and by comparing the model parameters. The neural correlates of RPEs were studied in functional magnetic resonance imaging and electroencephalogram.

Results  Adolescents with ADHD showed more simplistic learning as reflected by the reinforcement learning model (exceedance probability, Px = .92) and had increased exploratory behavior compared with healthy controls (mean [SD] decision steepness parameter β: ADHD, 4.83 [2.97]; controls, 6.04 [2.53]; P = .02). The functional magnetic resonance imaging analysis revealed impaired RPE processing in the medial prefrontal cortex during cue as well as during outcome presentation (P < .05, family-wise error correction). The outcome-related impairment in the medial prefrontal cortex could be attributed to deficient processing at 200 to 400 milliseconds after feedback presentation as reflected by reduced feedback-related negativity (ADHD, 0.61 [3.90] μV; controls, −1.68 [2.52] μV; P = .04).

Conclusions and Relevance  The combination of computational modeling of behavior and multimodal neuroimaging revealed that impaired decision making and learning mechanisms in adolescents with ADHD are driven by impaired RPE processing in the medial prefrontal cortex. This novel, combined approach furthers the understanding of the pathomechanisms in ADHD and may advance treatment strategies.

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Figure 1.
Probabilistic Reversal Learning Task and Winning Computational Model

A, The participants played a probabilistic reversal learning task while simultaneous electroencephalogram and functional magnetic resonance imaging were recorded. In each trial, the participants had to select 1 of 2 stimuli: one had a reward probability of 0.8 and the other had a reward probability of 0.2. The participants had to learn the reward probabilities and detect reversals on a trial-and-error basis. B, The hierarchical Gaussian filter model performed best for the healthy controls, but not for the participants with attention-deficit/hyperactivity disorder (ADHD). Markovian states are denoted by x1 to x3, and ϑ, ω, and β describe the free parameters. C, Group difference of the decision steepness parameter β indicates increased exploratory behavior in participants with ADHD compared with the controls. BPA indicates Bayesian parameter average.

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Figure 2.
Main Effects and Group Differences in Reward Prediction Error (RPE) Processing During Cue and Outcome Presentation

Groups showed a different response during cue (A) and outcome (B) in the medial prefrontal cortex. ADHD indicates attention-deficit/hyperactivity disorder.

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Figure 3.
Temporal Aspects of Decision Making: the Feedback-Related Negativity (FRN)

A, Controls, but not participants with attention-deficit/hyperactivity disorder (ADHD), showed a significant FRN (punishment-reward) at electrode Fz. Mean values are presented; limit lines indicate SE. B, FRN was localized to the medial prefrontal cortex in the controls using an electroencephalogram-informed functional magnetic resonance imaging analysis. This cluster overlapped with the group difference in reward prediction error outcome (RPEoutcome), indicating that both measures depict the same impaired process (depicted at P < .005).

aP = .04.

bP = .008.

cP = .52.

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