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Role of the Medial Prefrontal Cortex in Impaired Decision Making in Juvenile Attention-Deficit/Hyperactivity Disorder
Open accessAbstract
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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“but the study did not separate positive from negative reward prediction errors.”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Hauser et al., 2014
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“Indeed, adolescents with ADHD showed reduced reward prediction error processing in the medial prefrontal cortex during a reversal learning paradigm compared with adolescents without ADHD (Hauser et al., 2014)”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Hauser et al., 2014
Citations
Cites
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
KAUFMAN, J., BIRMAHER, B., BRENT, D., RAO, U., FLYNN, C., MORECI, P., WILLIAMSON, D., & RYAN, N. (1997) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-199707000-00021 · Cited by 10,148
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Holroyd, C. B., & Coles, M. G. H. (2002) · Psychological Review · doi:10.1037/0033-295x.109.4.679 · Cited by 4,027
Getting formal with dopamine and reward
Schultz, W. (2002) · Neuron · doi:10.1016/s0896-6273(02)00967-4 · Cited by 2,574
Dopamine in motivational control: rewarding, aversive, and alerting
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010) · Neuron · doi:10.1016/j.neuron.2010.11.022 · Cited by 2,396
Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation
Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J. P., Greenstein, D., Clasen, L., Evans, A., Giedd, J., & Rapoport, J. L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707741104 · Cited by 1,819
Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans
Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J., & Frith, C. D. (2006) · Nature · doi:10.1038/nature05051 · Cited by 1,584
Miltner, W. H. R., Braun, C. H., & Coles, M. G. H. (1997) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1997.9.6.788 · Cited by 1,530
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423
Gläscher, J., Daw, N., Dayan, P., & O'Doherty, J. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.016 · Cited by 1,166
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2098 · Cited by 1,121
Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies
Cortese, S., Kelly, C., Chabernaud, C., Proal, E., Di Martino, A., Milham, M. P., & Castellanos, F. X. (2012) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.11101521 · Cited by 1,067
sagvolden, T., johansen, E. B., aase, H., & russell, V. A. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05000075 · Cited by 1,035
Neuroeconomics: decision making and the brain
(2014) · Choice Reviews Online · doi:10.5860/choice.51-5702 · Cited by 1,020
Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis
Glimcher, P. W. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1014269108 · Cited by 955
Evaluating Dopamine Reward Pathway in ADHD: Clinical Implications
Volkow, N. D., Wang, G.-J., Kollins, S. H., Wigal, T. L., Newcorn, J. H., Telang, F., Fowler, J. S., Zhu, W., Logan, J., Ma, Y., et al. (2009) · JAMA · doi:10.1001/jama.2009.1308 · Cited by 728
A Bayesian foundation for individual learning under uncertainty
Mathys, C. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00039 · Cited by 655
Self-citationWalsh, M. M., & Anderson, J. R. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.05.008 · Cited by 577
Functional frontalisation with age: mapping neurodevelopmental trajectories with fMRI
Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S. C. R., Simmons, A., Andrew, C., & Bullmore, E. T. (2000) · Neuroscience & Biobehavioral Reviews · doi:10.1016/s0149-7634(99)00055-x · Cited by 545
Sagvolden, T. (2000) · Neuroscience & Biobehavioral Reviews · doi:10.1016/s0149-7634(99)00058-5 · Cited by 525
Cubillo, A., Halari, R., Smith, A., Taylor, E., & Rubia, K. (2012) · Cortex · doi:10.1016/j.cortex.2011.04.007 · Cited by 520
Double dissociation of value computations in orbitofrontal and anterior cingulate neurons
Kennerley, S. W., Behrens, T. E. J., & Wallis, J. D. (2011) · Nature Neuroscience · doi:10.1038/nn.2961 · Cited by 485
Medial prefrontal cell activity signaling prediction errors of action values
Matsumoto, M., Matsumoto, K., Abe, H., & Tanaka, K. (2007) · Nature Neuroscience · doi:10.1038/nn1890 · Cited by 462
Hauser, T. U., Iannaccone, R., Stämpfli, P., Drechsler, R., Brandeis, D., Walitza, S., & Brem, S. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.028 · Cited by 450
Self-citationSingle-trial EEG–fMRI reveals the dynamics of cognitive function
Debener, S., Ullsperger, M., Siegel, M., & Engel, A. K. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.09.010 · Cited by 428
Plichta, M. M., & Scheres, A. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.07.012 · Cited by 424
Transformation of stimulus value signals into motor commands during simple choice
Hare, T. A., Schultz, W., Camerer, C. F., O’Doherty, J. P., & Rangel, A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1109322108 · Cited by 388
Gläscher, J., Hampton, A. N., & O'Doherty, J. P. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn098 · Cited by 387
Tripp, G., & Wickens, J. R. (2008) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2007.01851.x · Cited by 368
Finger, E. C., Marsh, A. A., Mitchell, D. G., Reid, M. E., Sims, C., Budhani, S., Kosson, D. S., Chen, G., Towbin, K. E., Leibenluft, E., et al. (2008) · Archives of General Psychiatry · doi:10.1001/archpsyc.65.5.586 · Cited by 365
Attention-Deficit/Hyperactivity Disorder and Attention Networks
Bush, G. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2009.120 · Cited by 358
Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain
Niv, Y., Edlund, J. A., Dayan, P., & O'Doherty, J. P. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5498-10.2012 · Cited by 356
Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning
Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.009 · Cited by 342
Self-citationHuman anterior cingulate neurons and the integration of monetary reward with motor responses
Williams, Z. M., Bush, G., Rauch, S. L., Cosgrove, G. R., & Eskandar, E. N. (2004) · Nature Neuroscience · doi:10.1038/nn1354 · Cited by 340
Luman, M., Tripp, G., & Scheres, A. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2009.11.021 · Cited by 335
Methods for Simultaneous EEG-fMRI: An Introductory Review
Huster, R. J., Debener, S., Eichele, T., & Herrmann, C. S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0447-12.2012 · Cited by 328
From maps to mechanisms through neuroimaging of schizophrenia
Meyer‐Lindenberg, A. (2010) · Nature · doi:10.1038/nature09569 · Cited by 325
Predicting free choices for abstract intentions
Soon, C. S., He, A. H., Bode, S., & Haynes, J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1212218110 · Cited by 289
A unique adolescent response to reward prediction errors
Cohen, J. R., Asarnow, R. F., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J., & Poldrack, R. A. (2010) · Nature Neuroscience · doi:10.1038/nn.2558 · Cited by 285
Swanson, J., Baler, R. D., & Volkow, N. D. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2010.160 · Cited by 252
Testing the Reward Prediction Error Hypothesis with an Axiomatic Model
Rutledge, R. B., Dean, M., Caplin, A., & Glimcher, P. W. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.1747-10.2010 · Cited by 244
Liston, C., Cohen, M. M., Teslovich, T., Levenson, D., & Casey, B. J. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.03.022 · Cited by 216
Self-regulation in ADHD: the role of error processing
Shiels, K., & Hawk, L. W. (2010) · Clinical Psychology Review · doi:10.1016/j.cpr.2010.06.010 · Cited by 189
Spatial attention, precision, and Bayesian inference: a study of saccadic response speed
Vossel, S., Mathys, C., Daunizeau, J., Bauer, M., Driver, J., Friston, K. J., & Stephan, K. E. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs418 · Cited by 146
Self-citationNeural and Psychological Maturation of Decision-making in Adolescence and Young Adulthood
Christakou, A., Gershman, S. J., Niv, Y., Simmons, A., Brammer, M. J., & Rubia, K. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00447 · Cited by 134
EEG-fMRI INTEGRATION: A CRITICAL REVIEW OF BIOPHYSICAL MODELING AND DATA ANALYSIS APPROACHES
Rosa, M. J., Jean Daunizeau, A., & Friston, K. J. (2010) · Journal of Integrative Neuroscience · doi:10.1142/s0219635210002512 · Cited by 116
van Meel, C. S., Oosterlaan, J., Heslenfeld, D. J., & Sergeant, J. A. (2005) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2005.03.018 · Cited by 115
Doehnert, M., Brandeis, D., Imhof, K., Drechsler, R., & Steinhausen, H. (2009) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.07.038 · Cited by 108
Self-citationNeuro-anatomic evidence for the maturational delay hypothesis of ADHD
Rubia, K. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0710329105 · Cited by 106
Holroyd, C. B., Baker, T. E., Kerns, K. A., & Müller, U. (2008) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2008.02.011 · Cited by 93
Dopamine, Learning, and Impulsivity: ABiological Account of Attention-Deficit/Hyperactivity Disorder
Williams, J. H., & Dayan, P. (2005) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2005.15.160 · Cited by 85
van Meel, C. S., Heslenfeld, D. J., Oosterlaan, J., Luman, M., & Sergeant, J. A. (2011) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2010.02352.x · Cited by 78
Liechti, M. D., Maurizio, S., Heinrich, H., Jäncke, L., Meier, L., Steinhausen, H., Walitza, S., Drechsler, R., & Brandeis, D. (2012) · Clinical Neurophysiology · doi:10.1016/j.clinph.2012.03.016 · Cited by 68
Self-citationErratum: Computational psychiatry
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.04.003 · Cited by 65
Luman, M., van Meel, C. S., Oosterlaan, J., Sergeant, J. A., & Geurts, H. M. (2009) · Psychiatry Research · doi:10.1016/j.psychres.2008.08.012 · Cited by 63
The feedback‐related negativity ( FRN ) in adolescents
Zottoli, T. M., & Grose‐Fifer, J. (2011) · Psychophysiology · doi:10.1111/j.1469-8986.2011.01312.x · Cited by 59
Chantiluke, K., Barrett, N., Giampietro, V., Brammer, M., Simmons, A., Murphy, D. G., & Rubia, K. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht365 · Cited by 59
Kurzformen des HAWIK-IV: Statistische Bewertung in verschiedenen Anwendungsszenarien
Waldmann, H.-C. (2008) · Diagnostica · doi:10.1026/0012-1924.54.4.202 · Cited by 58
Silvetti, M., Wiersema, J. R., Sonuga-Barke, E. J. S., & Verguts, T. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.05.008 · Cited by 36
Cohen, J. D., & Aston‐Jones, G. (2005) · Nature · doi:10.1038/436471a · Cited by 27
Lidzba, K., Christiansen, H., & Drechsler, R. (2013) · Zurich Open Repository and Archive (University of Zurich) · Cited by 26
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Self-citationThe PhysIO Toolbox for Modeling Physiological Noise in fMRI Data
Kasper, L., Bollmann, S., Diaconescu, A. O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T. U., Sebold, M., Manjaly, Z., Pruessmann, K. P., et al. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.10.019 · Cited by 427
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Adams, R. A., Huys, Q. J. M., & Roiser, J. P. (2015) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2015-310737 · Cited by 285
Silverman, M. H., Jedd, K., & Luciana, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.083 · Cited by 277
Metadata
- Authors
- Hauser, T. U., Iannaccone, R., Ball, J., Mathys, C., Brandeis, D., Walitza, S., & Brem, S.
- Affiliations
- University of Zurich (CH)
- Wellcome Centre for Human Neuroimaging (GB)
- Venue
- JAMA Psychiatry
- Publisher
- American Medical Association (AMA)
- Published
- 2014-10-01
- Volume
- 71
- Issue
- 10
- Pages
- 1165
- ISSN
- 2168-622X (ISSN-L)
- OpenAlex
- W2065928720
- Cited by
- 168
- References
- 64
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 18 · 2023: 15 · 2022: 16 · 2021: 11 · 2020: 16 · 2019: 11 · 2018: 13 · 2017: 6 · 2016: 29 · 2015: 12 · 2014: 2
- FWCI
- 12.46
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- JAMA Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- SDL (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- attention deficit hyperactivity disorder, decision-making, reward prediction error, medial prefrontal cortex, reinforcement learning, probabilistic reversal learning, feedback-related negativity, dopaminergic system, computational modeling, adolescents
- MeSH terms
- Adolescent, Attention Deficit Disorder with Hyperactivity, Brain, Child, Decision Making, Electroencephalography, Female, Humans, Learning, Magnetic Resonance Imaging, Male, Case-Control Studies, Prefrontal Cortex, Functional Neuroimaging
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