The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Testing Computational Models of Dopamine and Noradrenaline Dysfunction in Attention Deficit/Hyperactivity Disorder
Abstract
We test our neurocomputational model of fronto-striatal dopamine (DA) and noradrenaline (NA) function for understanding cognitive and motivational deficits in attention deficit/hyperactivity disorder (ADHD). Our model predicts that low striatal DA levels in ADHD should lead to deficits in 'Go' learning from positive reinforcement, which should be alleviated by stimulant medications, as observed with DA manipulations in other populations. Indeed, while nonmedicated adult ADHD participants were impaired at both positive (Go) and negative (NoGo) reinforcement learning, only the former deficits were ameliorated by medication. We also found evidence for our model's extension of the same striatal DA mechanisms to working memory, via interactions with prefrontal cortex. In a modified AX-continuous performance task, ADHD participants showed reduced sensitivity to working memory contextual information, despite no global performance deficits, and were more susceptible to the influence of distractor stimuli presented during the delay. These effects were reversed with stimulant medications. Moreover, the tendency for medications to improve Go relative to NoGo reinforcement learning was predictive of their improvement in working memory in distracting conditions, suggestive of common DA mechanisms and supporting a unified account of DA function in ADHD. However, other ADHD effects such as erratic trial-to-trial switching and reaction time variability are not accounted for by model DA mechanisms, and are instead consistent with cortical noradrenergic dysfunction and associated computational models. Accordingly, putative NA deficits were correlated with each other and independent of putative DA-related deficits. Taken together, our results demonstrate the usefulness of computational approaches for understanding cognitive deficits in ADHD.
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In the citing paper
“Ziegler et al. (2016) propose that the Basal ganglia model (Frank et al., 2007) predicts decreased positive but increased negative reward prediction errors”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Frank et al., 2007
In the citing paper
“There have been computational studies performed that also show low striatal dopamine levels in ADHD patients which leads to deficits in goal-oriented learning with positive reinforcements, which is often enhanced exogenously with stimulant medications [19].”
From The Impact of Social Media & Technology on Child and Adolescent Mental Health (2025) · cites this paper as [19]
Citations
Cites
Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,842
Parallel organization of functionally segregated circuits linking basal ganglia and cortex
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.09.030186.002041 · Cited by 8,527
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472
Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.02.006 · Cited by 3,568
Separate neural systems value immediate and delayed monetary rewards
McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004) · Science · doi:10.1126/science.1100907 · Cited by 3,089
Getting formal with dopamine and reward
Schultz, W. (2002) · Neuron · doi:10.1016/s0896-6273(02)00967-4 · Cited by 2,574
Molecular genetics of attention-deficit/hyperactivity disorder
Faraone, S. V., Perlis, R. H., Doyle, A. E., Smoller, J. W., Goralnick, J. J., Holmgren, M. A., & Sklar, P. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2004.11.024 · Cited by 2,357
By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism
Frank, M. J., Seeberger, L. C., & O'Reilly, R. C. (2004) · Science · doi:10.1126/science.1102941 · Cited by 1,964
MARTINUSSEN, R., HAYDEN, J., HOGG-JOHNSON, S., & TANNOCK, R. (2005) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/01.chi.0000153228.72591.73 · Cited by 1,297 · cited 1×
“…memory manipulationFwhich requires selective updating of some information while working on a subset of a problemFis typically more impaired in ADHD than maintenance (Martinussen et al, 2005); similar updating/manipulation-specific deficits are observed in Parkinson's disease (see Cools, 2006).”
BackgroundCharacterizing cognition in ADHD: beyond executive dysfunction
Castellanos, F. X., Sonuga-Barke, E. J. S., Milham, M. P., & Tannock, R. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.01.011 · Cited by 1,170
Research works printed but not linked: 1
- Santamaria A, O'Reilly R, Willcutt E (in preparation). Working memory deficits explain timing impairments in ADHD.
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Sorted by citations
O’Reilly, R. C., & Frank, M. J. (2005) · Neural Computation · doi:10.1162/089976606775093909 · Cited by 1,124
Frank, M. J. (2005) · Journal of Cognitive Neuroscience · doi:10.1162/0898929052880093 · Cited by 925
From reinforcement learning models to psychiatric and neurological disorders
Maia, T. V., & Frank, M. J. (2011) · Nature Neuroscience · doi:10.1038/nn.2723 · Cited by 744
Reaction time variability in ADHD: A meta-analytic review of 319 studies
Kofler, M. J., Rapport, M. D., Sarver, D. E., Raiker, J. S., Orban, S. A., Friedman, L. M., & Kolomeyer, E. G. (2013) · Clinical Psychology Review · doi:10.1016/j.cpr.2013.06.001 · Cited by 648
Eagle, D. M., Bari, A., & Robbins, T. W. (2008) · Psychopharmacology · doi:10.1007/s00213-008-1127-6 · Cited by 548
Metadata
- Authors
- Frank, M. J., Santamaria, A., O'Reilly, R. C., & Willcutt, E.
- Affiliations
- University of Arizona (US)
- Alion Science and Technology (United States) (US)
- University of Colorado Boulder (US)
- Corresponding author
- Frank, Michael J
- Venue
- Neuropsychopharmacology
- Published
- 2006-12-13
- Volume
- 32
- Issue
- 7
- Pages
- 1583-1599
- ISSN
- 0893-133X (ISSN-L)
- 1740-634X
- OpenAlex
- W2114186661
- Cited by
- 227
- References
- 97
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 9 · 2023: 9 · 2022: 11 · 2021: 8 · 2020: 8 · 2019: 5 · 2018: 12 · 2017: 8 · 2016: 11 · 2015: 7 · 2014: 19 · 2013: 15 · 2012: 16
- FWCI
- 9.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Neuropsychopharmacology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (R01MH069597, MH 069597-01)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- attention deficit hyperactivity disorder, dopamine, norepinephrine, neurocomputational model, frontostriatal circuits, reinforcement learning, positive reinforcement, stimulant medication, working memory, prefrontal cortex, distractor interference, reaction time variability, cognitive impairment, motivational deficits
- MeSH terms
- Adolescent, Adult, Central Nervous System Stimulants, Attention Deficit Disorder with Hyperactivity, Brain, Brain Chemistry, Computer Simulation, Dopamine, Female, Humans, Learning, Male, Memory, Short-Term, Neural Pathways, Norepinephrine, Reinforcement, Psychology, Prefrontal Cortex
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