Mads Lund Pedersen

• Pedersen, Mads Lund; Mowinckel, Athanasia Monika; Ziegler, Sigurd; Fredriksen, Mats; Bjørnerud, Atle & Endestad, Tor [Show all 7 contributors for this article] (2026). Selective enhancement of cost sensitivity by methylphenidate in adult ADHD: a randomized placebo-controlled trial. Psychological Medicine. ISSN 0033-2917. 56. doi: 10.1017/S0033291726104954. Full text in Research Archive Show summary

  Background Attention-deficit hyperactivity disorder (ADHD) is associated with impairments in real-life decisions, many involving balancing costs and benefits. Increasing dopamine and norepinephrine with stimulant medication improves decision-making in ADHD. However, it is unclear whether stimulant medication affects sensitivity to costs and/or benefits or alters the speed-accuracy tradeoff. Here, we applied the drift diffusion model (DDM) to assess how the stimulant medication methylphenidate (MPH) affects subprocesses of cost-benefit decision-making in ADHD. Methods Sixty-one adult participants with ADHD and 63 healthy controls completed a cost-benefit decision-making task in a randomized double-blind placebo-controlled design. ADHD participants were tested once on and once off MPH. Control participants performed the task in two sessions without medication. The task entailed deciding whether to accept or reject a stimulus based on learned cost and benefit value associations. The effect of ADHD and medication on subprocesses of decision-making was disentangled by estimating task performance with DDM parameters drift rate, starting point bias, and decision threshold, representing evidence accumulation, a priori accept/reject bias, and the speed-accuracy tradeoff, respectively. Primary analyses used a modified intention-to-treat sample, including participants with main-phase data from both sessions. Results The DDM analysis identified impaired evidence accumulation combined with premature responding in participants with ADHD, indicated by reduced sensitivity to costs and benefits and narrower decision thresholds. MPH altered the cost-benefit balance by improving sensitivity to costs. Lastly, lower cost sensitivity was associated with greater symptom severity and higher prescribed dosage in ADHD. Conclusions Methylphenidate alters cost-benefit decision-making in ADHD by increasing sensitivity to costs.

• Pedersen, Mads Lund; Frogner, Erik Rimestad; Westlye, Lars Tjelta & Moberget, Torgeir (2026). Neural decision dynamics underlying reinforcement learning and working memory. iScience. 29(5). doi: 10.1016/j.isci.2026.115471. Full text in Research Archive
• Khuntia, Adyasha; Popovic, David; Sarisik, Elif; Buciuman, Madalina O.; Pedersen, Mads Lund & Westlye, Lars Tjelta [Show all 22 contributors for this article] (2025). The BMIgap tool to quantify transdiagnostic brain signatures of current and future weight. Nature Mental Health. 3(11), p. 1395–1406. doi: 10.1038/s44220-025-00522-3. Full text in Research Archive Show summary

  Understanding the neurobiological underpinnings of weight gain could reduce excess mortality and improve long-term trajectories of psychiatric disorders. Using brain scans from healthy individuals ( n  = 1,504), we trained a model to predict body mass index (BMI) and applied it to individuals with schizophrenia ( n  = 146), clinical high-risk states for psychosis ( n  = 213) and recent-onset depression (ROD, n  = 200). We computed BMIgap (BMI predicted  − BMI measured ), interrogated its brain-level overlaps with schizophrenia and explored whether BMIgap predicted weight gain at the 1-year and 2-year follow-ups. Schizophrenia (BMIgap = 1.05 kg m − 2 ) and clinical high-risk individuals (BMIgap = 0.51 kg m − 2 ) showed increased BMIgap and individuals with ROD (BMIgap = −0.82 kg m − 2 ) showed decreased BMIgap. Shared brain patterns of BMI and schizophrenia were linked to illness duration, disease onset and hospitalization frequency. Higher BMIgap predicted future weight gain, particularly in younger individuals with ROD, and at 2-year follow-up. Here we show that BMIgap can serve as a potential brain-derived measure to stratify at-risk individuals and deliver tailored interventions for better metabolic risk control.

• Frogner, Erik R.; Dahl, Andreas; Kjelkenes, Rikka; Moberget, Torgeir; Collins, Anne G.E. & Westlye, Lars Tjelta [Show all 7 contributors for this article] (2025). Linking reinforcement learning, working memory, and choice dynamics to age and symptoms of anxiety and depression in adolescence. Developmental Cognitive Neuroscience. ISSN 1878-9293. 76. doi: 10.1016/j.dcn.2025.101626. Full text in Research Archive
• Trøstheim, Martin Ostenfeldt; Pedersen, Mads Lund; Leknes, Siri; Hama, Lennja Majid; Roland, Mathias Nikolai & Lobmaier, Philipp Paul [Show all 10 contributors for this article] (2025). Reward Sensitivity in Patients Receiving Opioid Agonist and Antagonist Treatment for Opioid Use Disorder: An Observational Study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. ISSN 2451-9022. 10(11), p. 1166–1176. doi: 10.1016/j.bpsc.2025.04.013. Full text in Research Archive Show summary

  Background Disrupted reward processing is a core component in neurobiological theories of addictions, including opioid use disorder (OUD). While acute opioid agonist and antagonist administration can modulate reward behavior and experiences, it remains unclear how typical long-term OUD treatment with these medications impacts patients’ sensitivity to substance-free rewards. Therefore, we conducted a cross-sectional study of reward sensitivity in opioid agonist– and antagonist–treated patients with OUD and healthy volunteers. Methods Ninety-six patients with OUD on extended-release naltrexone (n = 45) or opioid agonists (n = 51) and 50 healthy volunteers completed a probabilistic reward task (PRT) and self-report measures of anhedonia, depression, preoccupation with immediate consequences, substance craving, and life satisfaction in a single session. We used signal detection analysis and drift diffusion modeling to derive behavioral reward bias measures from PRT performance. Group differences were modeled with beta and linear regression. Results Patients reported significantly greater anhedonia (Cohen’s ds ≥ 0.64), depression (ds ≥ 0.53), and preoccupation with immediate consequences (ds ≥ 0.54) than healthy volunteers, but differences between naltrexone- and opioid agonist–treated patients were nonsignificant (ds ≤ 0.26). Group differences in behavioral reward bias were small and nonsignificant (ps = 1, Bayes factor [BF]01s ≥ 84.13). Anhedonia was significantly associated with lower life satisfaction (odds ratio [95% CI], 1.10 [1.04 to 1.17]). There were no other significant associations between reward sensitivity measures and life satisfaction or craving (ps ≥ .31, BF01s ≥ 2.58). Conclusions These data support an association between OUD and reduced well-being irrespective of opioid agonist or antagonist treatment, highlighting patients’ need for psychosocial support and/or adjunct interventions. Major detrimental effects of naltrexone treatment on well-being seem unlikely from these and previous results.

• Berthet, Pierre; Haatveit, Beathe; Kjelkenes, Rikka; Worker, Amanda; Kia, Seyed Mostafa & Wolfers, Thomas [Show all 21 contributors for this article] (2024). A 10-Year Longitudinal Study of Brain Cortical Thickness in People with First-Episode Psychosis Using Normative Models. Schizophrenia Bulletin. ISSN 0586-7614. 51(1), p. 95–107. doi: 10.1093/schbul/sbae107. Full text in Research Archive Show summary

  Background Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms. Design Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms. Results LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores. Conclusions This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.

• Wiker, Thea; Alnæs, Dag; Pedersen, Mads Lund; Nordbom, Linn Bonaventure; Boer, Olga & Kjelkenes, Rikka [Show all 13 contributors for this article] (2024). Shared Patterns of Cognitive Control Behavior and Electrophysiological Markers in Adolescence. Journal of Cognitive Neuroscience. ISSN 0898-929X. 37(2), p. 372–413. doi: 10.1162/jocn_a_02272. Full text in Research Archive
• Sanders, Anne-Marthe; Richard, Geneviève; Kolskår, Knut-Kristian; Ulrichsen, Kristine Moe; Alnæs, Dag & Dørum, Erlend Solberg [Show all 10 contributors for this article] (2024). Exploring the associations between physical activity level, cognitive performance, and response to computerized cognitive training among chronic stroke patients. Brain and Behavior. 14(2). doi: 10.1002/brb3.3406. Full text in Research Archive Show summary

  Background: Post-stroke attentional and working memory deficits are common and represent relevant predictors of long-term functional recovery and outcome. The individual responses to cognitive rehabilitation and interventions vary between patients and are influenced by multiple factors. Recently, a link between the level of engagement in physical activities and cognitive rehabilitation has been suggested. However, few previous studies have tested the predictive value of physical activity on cognitive performance and response to cognitive training among chronic stroke patients. There is also a lack of knowledge concerning the prognostic value of index stroke characteristics on physical activity in chronic phase. Method: In this cross-sectional and longitudinal study, including stroke survivors suffering mild-to-moderate strokes (n = 52, mean age = 70 years), we used Bayesian regression to test the association between cognitive performance and response to a 3-week intervention with a commonly used computerized cognitive training (CCT) system and baseline physical activity level measured with International Physical Activity Questionnaire. We also tested the association between physical activity level in chronic phase and stroke characteristics, including stroke severity (National Institutes of Health Stroke Scale), ischemic stroke etiology (Trial of Org 10172 in Acute Stroke Treatment), and stroke location (n = 66, mean age = 68 years). For descriptive purposes, we included 104 sex- and age-matched healthy controls (mean age = 69 years). Results: The analyses revealed anecdotal evidence of a positive association between overall cognitive performance and daily minutes of sedentary behavior, indicating that better cognitive performance was associated with more daily hours of sitting still. We found no support for an association between cognitive performance and response to CCT with activity level. In addition, the analysis showed group differences in sedentary behavior between patients with small-vessel disease (n = 20) and cardioembolism (n = 7), indicating more sedentary behavior in patients with small-vessel disease. There was no further support for a predictive value of index stroke characteristics on physical activity level. Conclusion: The results do not support that baseline physical activity level is a relevant predictor of the overall performance or response to CCT in this sample of chronic stroke patients. Similarly, the analyses revealed little evidence for an association between index stroke characteristics and future activity level in patients surviving mild-to-moderate stroke.

• Wiker, Thea; Pedersen, Mads Lund; Ferschmann, Lia; Beck, Dani; Norbom, Linn Christin Bonaventure & Dahl, Andreas [Show all 13 contributors for this article] (2023). Assessing the Longitudinal Associations Between Decision-Making Processes and Attention Problems in Early Adolescence. Research on Child and Adolescent Psychopathology. ISSN 2730-7166. 52, p. 803–817. doi: 10.1007/s10802-023-01148-8. Full text in Research Archive Show summary

  Abstract Cognitive functions and psychopathology develop in parallel in childhood and adolescence, but the temporal dynamics of their associations are poorly understood. The present study sought to elucidate the intertwined development of decision-making processes and attention problems using longitudinal data from late childhood (9–10 years) to mid-adolescence (11–13 years) from the Adolescent Brain Cognitive Development (ABCD) Study (n = 8918). We utilised hierarchical drift-diffusion modelling of behavioural data from the stop-signal task, parent-reported attention problems from the Child Behavior Checklist (CBCL), and multigroup univariate and bivariate latent change score models. The results showed faster drift rate was associated with lower levels of inattention at baseline, as well as a greater reduction of inattention over time. Moreover, baseline drift rate negatively predicted change in attention problems in females, and baseline attention problems negatively predicted change in drift rate. Neither response caution (decision threshold) nor encoding- and responding processes (non-decision time) were significantly associated with attention problems. There were no significant sex differences in the associations between decision-making processes and attention problems. The study supports previous findings of reduced evidence accumulation in attention problems and additionally shows that development of this aspect of decision-making plays a role in developmental changes in attention problems in youth.

• Bø, Ragnhild; Kraft, Brage; Pedersen, Mads Lund; Joormann, Jutta; Jonassen, Rune & Osnes, Kåre [Show all 8 contributors for this article] (2023). The effect of attention bias modification on depressive symptoms in a comorbid sample: a randomized controlled trial. Psychological Medicine. ISSN 0033-2917. 53(13), p. 6389–6396. doi: 10.1017/S0033291722003956. Full text in Research Archive Show summary

  Background Studies investigating the long-term effect of attention bias modification (ABM) in clinical samples are lacking. This study investigates the 6-months follow-up effect of ABM on depressive symptoms in participant with major depressive disorder with and without comorbid disorders. Methods We conducted a double-blind randomized sham-controlled trial in 101 participants between 19 November 2019, and 17 August 2021. Follow-up ended 3 April 2022. Participants were allocated to ABM or sham condition twice daily for 14 consecutive days. Primary outcomes were the total score on the Beck Depression Inventory-II (BDI-II) at 6 months, mean Brief State Rumination Inventory (BSRI) score post-treatment and reduction in BSRI post-treatment. Secondary outcome was change in attentional bias (AB). The trial was preregistered in ClinicalTrials.gov (#NCT 04137367). Results A total of 118 patients aged 18–65 years were assessed for eligibility, and 101 were randomized and subjected to intention-to-treat analyses. At 6 months, ABM had no effect on depression and anxiety compared to a sham condition. While rumination decreased during the intervention, there was no effect of condition on rumination and AB. Predictor analysis did not reveal differences between participants with ongoing major depressive episode or comorbid anxiety. Conclusion Compared to sham training, there was no effect of ABM on depressive symptoms at 6-months follow-up. Since the intervention failed at modifying AB, it is unclear whether changes in AB are related to long-term outcomes.

• Sanders, Anne-Marthe; Richard, Geneviève; Kolskår, Knut-Kristian; Ulrichsen, Kristine Moe; Alnæs, Dag & Beck, Dani [Show all 14 contributors for this article] (2023). Associations between everyday activities and arterial spin labeling-derived cerebral blood flow: A longitudinal study in community-dwelling elderly volunteers. Human Brain Mapping. ISSN 1065-9471. 44, p. 3377–3393. doi: 10.1002/hbm.26287. Full text in Research Archive Show summary

  Cerebral blood flow (CBF) is critical for brain metabolism and function. Age-related changes in CBF are associated with increased risk of neurocognitive disorders and vascular events such as stroke. Identifying correlates and positive modifiers of age- related changes in CBF before the emergence of incipient clinical decline may inform public health advice and clinical practice. Former research has been inconclusive regarding the association between regular physical activity and CBF, and there is a lack of studies on the association between level of everyday activities and CBF, in older adults. To investigate these relationships, 118 healthy community-dwelling adults (65–89 years) underwent pseudo-continuous arterial spin labeling (ASL) MRI, neurocognitive, physical, and activity assessments at baseline. Eighty-six participants completed a follow-up ASL MRI, on average 506 (SD = 113) days after the baseline scan. Cross-sectional analysis revealed credible evidence for positive associations between time spent on low intensity physical activity and CBF in multiple cortical and subcortical regions, time spent on moderate to vigorous intensity physical activity and accumbens CBF, participation in social activity and CBF in multiple cortical regions, and between reading and thalamic CBF, indicating higher regional CBF in more active adults. Longitudinal analysis revealed anecdotal evidence for an interac- tion between time and baseline level of gardening on occipital and parietal CBF, and baseline reading on pallidum CBF, indicating more change in CBF in adults with lower level of activity. The findings support that malleable lifestyle factors contribute to healthy brain aging, with relevance for public health guidelines.

• Bø, Ragnhild; Kraft, Brage; Jonassen, Rune; Pedersen, Mads Lund; Harmer, Catherine & Landrø, Nils Inge (2023). The long-term effects of ABM on symptom severity in patients with recurrent depression: A randomized sham-controlled trial. Journal of Affective Disorders. ISSN 0165-0327. 340, p. 886–892. doi: 10.1016/j.jad.2023.08.024. Full text in Research Archive Show summary

  Background: The present study reports on long-term outcomes of ABM over one year in self-reported and clinician-rated depression symptoms, anxiety symptoms, and relapse rates. Methods: We conducted a double-blind randomized sham-controlled trial in 301 participants with recurrent major depression disorder between January 2015 and October 2016 (#NCT02658682). Participants were allo- cated to ABM or sham condition twice daily for 14 consecutive days. Long-term effects of ABM were assessed by BDI-II, HDRS and BAI at one-, six-, and 12-months follow-up. Relapse rates at 12-months follow-up were also assessed. Results: There was no long-term effect of ABM (as compared to sham) on clinician-rated depression symptoms, on anxiety symptoms, nor in relapse rates. By 12 months follow-up, there was a small effect on self-reported depression favoring ABM over sham. Limitations: The lack of an assessment-only condition hinders comparison to natural trajectories of depression symptoms. Conclusions: The overall long-term effect of ABM was limited, and currently there is no convincing evidence for implementing this as a viable treatment option in clinical populations. We speculate if the sham condition should be replaced by another control condition when investigating the clinical utility of ABM.

• Kjelkenes, Rikka; Wolfers, Thomas; Alnæs, Dag; Meer, Dennis van der; Pedersen, Mads Lund & Dahl, Andreas [Show all 12 contributors for this article] (2023). Mapping Normative Trajectories of Cognitive Function and Its Relation to Psychopathology Symptoms and Genetic Risk in Youth. Biological Psychiatry: Global Open Science. 3(2), p. 255–263. doi: 10.1016/j.bpsgos.2022.01.007. Full text in Research Archive Show summary

  Background Adolescence hosts a sharp increase in the incidence of mental disorders. The prodromal phases are often characterized by cognitive deficits that predate disease onset by several years. Characterization of cognitive performance in relation to normative trajectories may have value for early risk assessment and monitoring. Methods Youth aged 8 to 21 years (N = 6481) from the Philadelphia Neurodevelopmental Cohort were included. Performance scores from a computerized neurocognitive battery were decomposed using principal component analysis, yielding a general cognitive score. Items reflecting various aspects of psychopathology from self-report questionnaires and collateral caregiver information were decomposed using independent component analysis, providing individual domain scores. Using normative modeling and Bayesian statistics, we estimated normative trajectories of cognitive function and tested for associations between cognitive deviance and psychopathological domain scores. In addition, we tested for associations with polygenic scores for mental and behavioral disorders often involving cognition, including schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, and Alzheimer’s disease. Results More negative normative cognitive deviations were associated with higher general psychopathology burden and domains reflecting positive and prodromal psychosis, attention problems, norm-violating behavior, and anxiety. In addition, better performance was associated with higher joint burden of depression, suicidal ideation, and negative psychosis symptoms. The analyses revealed no evidence for associations with polygenic scores. Conclusions Our results show that cognitive performance is associated with general and specific domains of psychopathology in youth. These findings support the close links between cognition and psychopathology in youth and highlight the potential of normative modeling for early risk assessment.

• Cao, Han; Zhang, Youcheng; Baumbach, Jan; Burton, Paul R.; Dwyer, Dominic & Koutsouleris, Nikolaos [Show all 32 contributors for this article] (2022). dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning. Bioinformatics. ISSN 1367-4803. 38(21), p. 4919–4926. doi: 10.1093/bioinformatics/btac616. Full text in Research Archive
• Fernandez-Cabello, Sara; Alnæs, Dag; Meer, Dennis van der; Dahl, Andreas; Holm, Madelene Christin & Kjelkenes, Rikka [Show all 12 contributors for this article] (2022). Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9–11. NeuroImage. ISSN 1053-8119. 263. doi: 10.1016/j.neuroimage.2022.119611. Full text in Research Archive Show summary

  Psychiatric disorders are highly heritable and polygenic, and many have their peak onset in late childhood and adolescence, a period of tremendous changes. Although the neurodevelopmental antecedents of mental illness are widely acknowledged, research in youth population cohorts is still scarce, preventing our progress towards the early characterization of these disorders. We included 7,124 children (9–11 years old) from the Adolescent Brain and Cognitive Development Study to map the associations of structural and diffusion brain imaging with common genetic variants and polygenic scores for psychiatric disorders and educational attainment. We used principal component analysis to derive imaging components, and calculated their heritability. We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA). Most imaging components had moderate heritability. Univariate models showed limited evidence and small associations of polygenic scores with brain structure at this age. CCA revealed two significant modes of covariation. The first mode linked higher polygenic scores for educational attainment with less externalizing problems and larger surface area. The second mode related higher polygenic scores for schizophrenia, bipolar disorder, and autism spectrum disorder to higher global cortical thickness, smaller white matter volumes of the fornix and cingulum, larger medial occipital surface area and smaller surface area of lateral and medial temporal regions. While cross-validation suggested limited generalizability, our results highlight the potential of multivariate models to better understand the transdiagnostic and distributed relationships between mental health and brain structure in late childhood.

• Rise, Henning Hoel; Brune, Synne; Chien, Claudia; Berge, Tone; Bos, Steffan Daniel & Andorrà, Magí [Show all 23 contributors for this article] (2022). Brain disconnectome mapping derived from white matter lesions and serum neurofilament light levels in multiple sclerosis: A longitudinal multicenter study. NeuroImage: Clinical. 35, p. 1–11. doi: 10.1016/j.nicl.2022.103099. Full text in Research Archive Show summary

  Background and Objectives Connectivity-based approaches incorporating the distribution and magnitude of the extended brain network aberrations caused by lesions may offer higher sensitivity for axonal damage in patients with multiple sclerosis (MS) than conventional lesion characteristics. Using individual brain disconnectome mapping, we tested the longitudinal associations between putative imaging-based brain network aberrations and levels of serum neurofilament light chain (NfL) as a neuroaxonal injury biomarker. Methods MS patients (n = 312, mean age 42.9 years, 71 % female) and healthy controls (HC) (n = 59, mean age 39.9 years, 78 % female) were prospectively enrolled at four European MS centres, and reassessed after two years (MS, n = 242; HC, n = 30). Post-processing of 3 Tesla (3 T) MRI data was performed at one centre using a harmonized pipeline, and disconnectome maps were calculated using BCBtoolkit based on individual lesion maps. Global disconnectivity (GD) was defined as the average disconnectome probability in each patient’s white matter. Serum NfL concentrations were measured by single molecule array (Simoa). Robust linear mixed models (rLMM) with GD or T2-lesion volume (T2LV) as dependent variables, patient as a random factor, serum NfL, age, sex, timepoint for visit, diagnosis, treatment, and center as fixed factors were run. Results rLMM revealed significant associations between GD and serum NfL (t = 2.94, p = 0.003), age (t = 4.21, p = 2.5 × 10−5), and longitudinal changes in NfL (t = -2.29, p = 0.02), but not for sex (t = 0.63, p = 0.53) or treatments (t = 0.80–0.83, p = 0.41–0.42). Voxel-wise analyses revealed significant associations between dysconnectivity in cerebellar and brainstem regions and serum NfL (t = 7.03, p < 0.001). Discussion In our prospective multi-site MS cohort, rLMMs demonstrated that the extent of global and regional brain disconnectivity is sensitive to a systemic biomarker of axonal damage, serum NfL, in patients with MS. These findings provide a neuroaxonal correlate of advanced disconnectome mapping and provide a platform for further investigations of the functional and potential clinical relevance of brain disconnectome mapping in patients with brain disorders.

• Fengler, Alexander; Bera, Krishn; Pedersen, Mads Lund & Frank, Michael J. (2022). Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM. Journal of Cognitive Neuroscience. ISSN 0898-929X. 34(10), p. 1780–1805. doi: 10.1162/jocn_a_01902. Full text in Research Archive
• Schindler, Louise S; Subramaniapillai, Sivaniya; Barth, Claudia; Meer, Dennis van der; Pedersen, Mads Lund & Kaufmann, Tobias [Show all 18 contributors for this article] (2022). Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women. NeuroImage: Clinical. 36. doi: 10.1016/j.nicl.2022.103239. Full text in Research Archive Show summary

  The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.

• Beck, Dani; Lange, Ann-Marie Glasø de; Alnæs, Dag; Maximov, Ivan; Pedersen, Mads Lund & Leinhard, Olof Dahlqvist [Show all 20 contributors for this article] (2022). Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults. NeuroImage: Clinical. 33. doi: 10.1016/j.nicl.2022.102949. Full text in Research Archive Show summary

  There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18–94 years (mean ± standard deviation (SD) at baseline: 46.8 ± 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19–86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) – the difference between actual age and the prediction of the brain’s biological age – at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

• Ulrichsen, Kristine Moe; Kolskår, Knut-Kristian; Richard, Geneviève; Pedersen, Mads Lund; Alnæs, Dag & Dørum, Erlend Solberg [Show all 13 contributors for this article] (2022). No add-on effect of tDCS on fatigue and depression in chronic stroke patients: A randomized sham-controlled trial combining tDCS with computerized cognitive training. Brain and Behavior. 12(7). doi: 10.1002/brb3.2643. Full text in Research Archive Show summary

  Background Fatigue and emotional distress rank high among self-reported unmet needs in life after stroke. Transcranial direct current stimulation (tDCS) may have the potential to alleviate these symptoms for some patients, but the acceptability and effects for chronic stroke survivors need to be explored in randomized controlled trials. Methods Using a randomized sham-controlled parallel design, we evaluated whether six sessions of 1 mA tDCS (anodal over F3, cathodal over O2) combined with computerized cognitive training reduced self-reported symptoms of fatigue and depression. Among the 74 chronic stroke patients enrolled at baseline, 54 patients completed the intervention. Measures of fatigue and depression were collected at five time points spanning a 2 months period. Results While symptoms of fatigue and depression were reduced during the course of the intervention, Bayesian analyses provided evidence for no added beneficial effect of tDCS. Less severe baseline symptoms were associated with higher performance improvement in select cognitive tasks, and study withdrawal was higher in patients with more fatigue and younger age. Time-resolved symptom analyses by a network approach suggested higher centrality of fatigue items (except item 1 and 2) than depression items. Conclusion The results reveal no add-on effect of tDCS on fatigue or depression but support the notion of fatigue as a relevant clinical symptom with possible implications for treatment adherence and response.

• Pedersen, Mads Lund; Alnæs, Dag; Meer, Dennis van der; Fernandez-Cabello, Sara; Berthet, Pierre Guillaume Raphael & Dahl, Andreas [Show all 12 contributors for this article] (2022). Computational Modeling of the n-Back Task in the ABCD Study: Associations of Drift Diffusion Model Parameters to Polygenic Scores of Mental Disorders and Cardiometabolic Diseases. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. ISSN 2451-9022. doi: 10.1016/j.bpsc.2022.03.012. Full text in Research Archive Show summary

  Background Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with polygenic risk scores (PRSs) for mental disorders and comorbid cardiometabolic diseases. Methods We used the drift diffusion model to infer latent computational processes underlying decision making and working memory during the n-back task in 3707 children ages 9 to 10 years from the Adolescent Brain Cognitive Development (ABCD) Study. Single nucleotide polymorphism–based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated n-back task performance, and neurocognitive assessments. PRSs were calculated for Alzheimer’s disease, bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia, and type 2 diabetes. Results Heritability estimates of cognitive phenotypes ranged from 12% to 38%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRSs for CAD and schizophrenia. Longer nondecision time was associated with higher PRSs for Alzheimer’s disease and lower PRSs for CAD. Narrower decision threshold was associated with higher PRSs for CAD. Load-dependent effects on nondecision time and decision threshold were associated with PRSs for Alzheimer’s disease and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRSs for any of the mental or cardiometabolic phenotypes. Conclusions We identified distinct associations between computational cognitive processes and genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

• Rolle, Camarin; Pedersen, Mads Lund; Johnson, Noriah; Amemori, Ken-ichi; Ironside, Maria & Graybiel, Ann M [Show all 8 contributors for this article] (2021). The Role of the Dorsal-Lateral Prefrontal Cortex in Reward Sensitivity During Approach-Avoidance Conflict. Cerebral Cortex. ISSN 1047-3211. 32(6), p. 1269–1285. doi: 10.1093/cercor/bhab292. Full text in Research Archive
• Beck, Dani; Lange, Ann-Marie G. de; Pedersen, Mads L.; Alnæs, Dag; Maximov, Ivan & Voldsbekk, Irene [Show all 18 contributors for this article] (2021). Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Human Brain Mapping. ISSN 1065-9471. p. 1–21. doi: 10.1002/hbm.25680. Full text in Research Archive Show summary

  The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.

• Pedersen, Mads Lund; Ironside, Maria; Amemori, Ken-ichi; McGrath, Callie L.; Kang, Min S. & Graybiel, Ann M [Show all 8 contributors for this article] (2021). Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder. PLoS Computational Biology. ISSN 1553-734X. doi: 10.1371/journal.pcbi.1008955. Full text in Research Archive Show summary

  Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM) suited to quantify how offers of reward and aversiveness, and neural correlates thereof, are dynamically integrated to form decisions, and how such processes are altered in MDD. Hierarchical parameter estimation from the DDM demonstrated that the MDD group differed in three distinct reward-related parameters driving approach-based decision making. First, MDD was associated with reduced reward sensitivity, measured as the impact of offered reward on evidence accumulation. Notably, this effect was replicated in a follow-up study. Second, the MDD group showed lower starting point bias towards approaching offers. Third, this starting point was influenced in opposite directions by Pavlovian effects and by nucleus accumbens activity across the groups: greater accumbens activity was related to approach bias in controls but avoid bias in MDD. Cross-validation revealed that the combination of these computational biomarkers were diagnostic of patient status, with accumbens influences being particularly diagnostic. Finally, within the MDD group, reward sensitivity and nucleus accumbens parameters were differentially related to symptoms of perceived stress and depression. Collectively, these findings establish the promise of computational psychiatry approaches to dissecting approach-avoidance decision dynamics relevant for affective disorders.

• Ulrichsen, Kristine Moe; Alnæs, Dag; Kolskår, Knut-Kristian; Richard, Geneviève; Sanders, Anne-Marthe & Dørum, Erlend Solberg [Show all 11 contributors for this article] (2020). Dissecting the cognitive phenotype of post-stroke fatigue using computerized assessment and computational modeling of sustained attention. European Journal of Neuroscience. ISSN 0953-816X. 52(7), p. 3828–3845. doi: 10.1111/ejn.14861. Full text in Research Archive Show summary

  Post‐stroke fatigue (PSF) is prevalent among stroke patients, but its mechanisms are poorly understood. Many patients with PSF experience cognitive difficulties, but studies aiming to identify cognitive correlates of PSF have been largely inconclusive. With the aim of characterizing the relationship between subjective fatigue and attentional function, we collected behavioral data using the attention network test (ANT) and self‐reported fatigue scores using the fatigue severity scale (FSS) from 53 stroke patients. In order to evaluate the utility and added value of computational modeling for delineating specific underpinnings of response time (RT) distributions, we fitted a hierarchical drift diffusion model (hDDM) to the ANT data. Results revealed a relationship between fatigue and RT distributions. Specifically, there was a positive interaction between FSS score and elapsed time on RT. Group analyses suggested that patients without PSF increased speed during the course of the session, while patients with PSF did not. In line with the conventional analyses based on observed RT, the best fitting hDD model identified an interaction between elapsed time and fatigue on non‐decision time, suggesting an increase in time needed for stimulus encoding and response execution rather than cognitive information processing and evidence accumulation. These novel results demonstrate the significance of considering the sustained nature of effort when defining the cognitive phenotype of PSF, intuitively indicating that the cognitive phenotype of fatigue entails an increased vulnerability to sustained effort, and suggest that the use of computational approaches offers a further characterization of specific processes underlying behavioral differences.

• Pedersen, Mads Lund & Frank, Michael J. (2020). Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data. Computational Brain & Behavior. ISSN 2522-0861. doi: 10.1007/s42113-020-00084-w. Full text in Research Archive Show summary

  Cognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions can be well captured when the choice function is replaced with a sequential sampling model such as the drift diffusion model. Hierarchical Bayesian parameter estimation further enhances the identifiability of distinct learning and choice parameters. One caveat is that these models can be time-consuming to build, sample from, and validate, especially when models include links between neural activations and model parameters. Here we describe a novel extension to the widely used hierarchical drift diffusion model (HDDM) toolbox, which facilitates flexible construction, estimation, and evaluation of the reinforcement learning drift diffusion model (RLDDM) using hierarchical Bayesian methods. We describe the types of experiments most applicable to the model and provide a tutorial to illustrate how to perform quantitative data analysis and model evaluation. Parameter recovery confirmed that the method can reliably estimate parameters with varying numbers of synthetic subjects and trials. We also show that the simultaneous estimation of learning and choice parameters can improve the sensitivity to detect brain–behavioral relationships, including the impact of learned values and fronto-basal ganglia activity patterns on dynamic decision parameters.

• Ironside, Maria; Amemori, Ken-ichi; McGrath, Callie; Pedersen, Mads Lund; Kang, Min Su & Amemori, Satoko [Show all 9 contributors for this article] (2020). Approach-Avoidance Conflict in Major Depressive Disorder: Congruent Neural Findings in Humans and Nonhuman Primates. Biological Psychiatry. ISSN 0006-3223. doi: 10.1016/j.biopsych.2019.08.022. Full text in Research Archive
• Schwarz, Emanuel; Alnæs, Dag; Andreassen, Ole Andreas; Cao, Han; Chen, Junfang & Degenhardt, Franziska [Show all 27 contributors for this article] (2020). Identifying multimodal signatures underlying the somatic comorbidity of psychosis: the COMMITMENT roadmap. Molecular Psychiatry. ISSN 1359-4184. doi: 10.1038/s41380-020-00915-z. Full text in Research Archive
• Eikemo, Marie; Lobmaier, Philipp Paul; Pedersen, Mads Lund; Kunøe, Nikolaj; Matziorinis, Anna Maria & Leknes, Siri [Show all 7 contributors for this article] (2019). Intact responses to non-drug rewards in long-term opioid maintenance treatment. Neuropsychopharmacology. ISSN 0893-133X. 44(8), p. 1456–1463. doi: 10.1038/s41386-019-0377-9. Full text in Research Archive Show summary

  Disruption of non-drug reward processing in addiction could stem from long-term drug use, addiction-related psychosocial stress, or a combination of these. It remains unclear whether long-term opioid maintenance treatment (OMT) disrupts reward processing. Here, we measured subjective and objective reward responsiveness in 26 previously heroin-addicted mothers in >7 years stable OMT with minimal psychosocial stress and illicit drug use. The comparison group was 30 healthy age-matched mothers (COMP). Objective reward responsiveness was assessed in a two-alternative forced-choice task with skewed rewards. Results were also compared to performance from an additional 968 healthy volunteers (meta-analytic approach). We further compared subprocesses of reward-based decisions across groups using computational modelling with a Bayesian drift diffusion model of decision making. Self-reported responsiveness to non-drug rewards was high for both groups (means: OMT = 6.59, COMP = 6.67, p = 0.84, BF10 = 0.29), yielding moderate evidence against subjective anhedonia in this OMT group. Importantly, the mothers in OMT also displayed robust reward responsiveness in the behavioral task (t19 = 2.72, p = 0.013, BF10 = 3.98; d = 0.61). Monetary reward changed their task behavior to the same extent as the local comparison group (reward bias OMT = 0.12, COMP = 0.12, p = 0.96, BF10 = 0.18) and in line with data from 968 healthy controls previously tested. Computational modelling revealed that long-term OMT did not even change decision subprocesses underpinning reward behavior. We conclude that reduced sensitivity to rewards and anhedonia are not necessary consequences of prolonged opioid use.

• Mowinckel, Athanasia Monika; Alnæs, Dag; Pedersen, Mads Lund; Ziegler, Sigurd; Fredriksen, Mats & Kaufmann, Tobias [Show all 10 contributors for this article] (2017). Increased default-mode variability is related to reduced task-performance and is evident in adults with ADHD. NeuroImage: Clinical. 16, p. 369–382. doi: 10.1016/j.nicl.2017.03.008. Full text in Research Archive Show summary

  Insufficient suppression and connectivity of the default mode network (DMN) is a potential mediator of cognitive dysfunctions across various disorders, including attention deficit/hyperactivity disorder (ADHD). However, it remains unclear if alterations in sustained DMN suppression, variability and connectivity during prolonged cognitive engagement are implicated in adult ADHD pathophysiology, and to which degree methylphenidate (MPH) remediates any DMN abnormalities. This randomized, double-blinded, placebo-controlled, cross-over clinical trial of MPH (clinicaltrials.gov/ct2/show/NCT01831622) explored large-scale brain network dynamics in 20 adults with ADHD on and off MPH, compared to 27 healthy controls, while performing a reward based decision-making task. DMN task-related activation, variability, and connectivity were estimated and compared between groups and conditions using independent component analysis, dual regression, and Bayesian linear mixed models. The results show that the DMN exhibited more variable activation patterns in unmedicated patients compared to healthy controls. Group differences in functional connectivity both between and within functional networks were evident. Further, functional connectivity between and within attention and DMN networks was sensitive both to task performance and case-control status. MPH altered within-network connectivity of the DMN and visual networks, but not between-network connectivity or temporal variability. This study thus provides novel fMRI evidence of reduced sustained DMN suppression in adults with ADHD during value-based decision-making, a pattern that was not alleviated by MPH. We infer from multiple analytical approaches further support to the default mode interference hypothesis, in that higher DMN activation variability is evident in adult ADHD and associated with lower task performance. This research was first published in the journal NeuroImage: Clinical. © Elsevier.

• Ziegler, Sigurd; Pedersen, Mads Lund; Mowinckel, Athanasia Monika & Biele, Guido (2016). Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning. Neuroscience and Biobehavioral Reviews. ISSN 0149-7634. 71, p. 633–656. doi: 10.1016/j.neubiorev.2016.09.002. Full text in Research Archive
• Pedersen, Mads Lund; Frank, Michael J. & Biele, Guido (2016). The drift diffusion model as the choice rule in reinforcement learning. Psychonomic Bulletin & Review. ISSN 1069-9384. p. 18–18. doi: 10.3758/s13423-016-1199-y. Full text in Research Archive
• Pedersen, Mads Lund; Endestad, Tor & Biele, Guido (2015). Evidence Accumulation and Choice Maintenance Are Dissociated in Human Perceptual Decision Making. PLOS ONE. 10(10). doi: 10.1371/journal.pone.0140361. Full text in Research Archive Show summary

  Perceptual decision making in monkeys relies on decision neurons, which accumulate evidence and maintain choices until a response is given. In humans, several brain regions have been proposed to accumulate evidence, but it is unknown if these regions also maintain choices. To test if accumulator regions in humans also maintain decisions we compared delayed and self-paced responses during a face/house discrimination decision making task. Computational modeling and fMRI results revealed dissociated processes of evidence accumulation and decision maintenance, with potential accumulator activations found in the dorsomedial prefrontal cortex, right inferior frontal gyrus and bilateral insula. Potential maintenance activation spanned the frontal pole, temporal gyri, precuneus and the lateral occipital and frontal orbital cortices. Results of a quantitative reverse inference meta-analysis performed to differentiate the functions associated with the identified regions did not narrow down potential accumulation regions, but suggested that response-maintenance might rely on a verbalization of the response.

• Mowinckel, Athanasia Monika; Pedersen, Mads Lund; Eilertsen, Espen Moen & Biele, Guido (2015). A Meta-Analysis of Decision-Making and Attention in Adults With ADHD. Journal of Attention Disorders. ISSN 1087-0547. 19(5), p. 355–367. doi: 10.1177/1087054714558872. Full text in Research Archive Show summary

  Abstract Objective: Deficient reward processing has gained attention as an important aspect of ADHD, but little is known about reward-based decision-making (DM) in adults with ADHD. This article summarizes research on DM in adult ADHD and contextualizes DM deficits by comparing them to attention deficits. Method: Meta-analytic methods were used to calculate average effect sizes for different DM domains and continuous performance task (CPT) measures. Results: None of the 59 included studies (DM: 12 studies; CPT: 43; both: 4) had indications of publication bias. DM and CPT measures showed robust, small to medium effects. Large effect sizes were found for a drift diffusion model analysis of the CPT. Conclusion: The results support the existence of DM deficits in adults with ADHD, which are of similar magnitude as attention deficits. These findings warrant further examination of DM in adults with ADHD to improve the understanding of underlying neurocognitive mechanisms

View all works in NVA

• Sanders, Anne-Marthe; Richard, Geneviève; Kolskår, Knut K.; Ulrichsen, Kristine Moe; Alnæs, Dag & Beck, Dani [Show all 14 contributors for this article] (2022). Associations between everyday activities and arterial spin labelling-derived cerebral blood flow: A longitudinal study in community-dwelling elderly volunteers. medRxiv. doi: 10.1101/2022.09.22.22280237. Full text in Research Archive
• Pedersen, Mads Lund & Biele, Guido (2017). Computational modeling as a tool for understanding learning and decision making processes in ADHD. Universitetet i Oslo. Full text in Research Archive Show summary

  Computational models have been instrumental in describing underlying cognitive and neural mechanisms of learning and decision making. The current thesis centers on the use of such models in order to better understand the processes leading to impaired decision making and learning in attention-deficit hyperactivity disorder (ADHD). Low signaling of the neurotransmitters dopamine (DA) and noradrenaline (NA) in ADHD are hypothesized to be the cause of the impairments, but how suboptimal signaling of DA and NA in ADHD leads to problems in decision making and learning is unclear. Computational modeling can aid in filling this explanatory gap by providing mechanistic explanations to observed behavior. Paper 2 and 3 of the thesis use computational models to analyze data from adult patients with ADHD on simple forced choice decision making and instrumental learning tasks, respectively. Both papers assessed performance in ADHD on and off stimulant medication, the first-line pharmacological treatment of ADHD, which effectively increases signaling of DA and NA. Importantly, the scientific contribution of a computational model hinges on its ability to accurately describe data, and to be a plausible description of underlying processes. In paper 1 we attempt to link behavior to its neural processes based on predictions from models of decision making, while paper 3 describes how models of instrumental learning can be extended to describe choosing between reinforced stimuli. In all three papers of the thesis we use the drift diffusion model (DDM) to explain mechanisms of simple two-choice decision making. Much of the popularity of the DDM was driven by the discovery that neural firing in monkeys performing decision making tasks closely resembled the firing patterns predicted by the model. Extensive studies on this subject have revealed that clusters of neurons in monkeys accumulate evidence until reaching a decision threshold. In addition to revealing the firing patterns of decision neurons, studies on monkeys have shown that these same neurons remain active when decisions have to be maintained, thus suggesting a dual process for decision neurons in monkeys; both accumulating evidence and maintaining choices in simple decision making. In paper 1, we set out to test if decision neurons in humans also maintain evidence, by comparing neural activity, measured with functional Magnetic Resonance Imaging (fMRI), during immediate and delayed response conditions. No brain region displayed activation across response conditions and difficulty levels consistent with predictions for a combined accumulation and maintenance region. Instead, our results supported results from previous studies on decision processes in humans, suggesting that evidence accumulation is performed across fronto-parietal regions. The results thus indicate a dissociation of evidence accumulation and decision maintenance in humans, contrary to reports on monkeys. In paper 2 we used a randomized placebo-controlled design to measure the effect of methylphenidate, a central stimulant increasing signaling of DA and NA, on decision making in adult patients with ADHD. Patients were further compared to age-matched healthy controls. DA and NA are involved in several sub-processes of decision making. These sub-processes can in turn be linked to parameters in the DDM. Our aim was therefore to use the DDM to better describe the mechanisms leading to impaired decision making in ADHD, and how these mechanisms are altered by stimulant medication. We found that compared to healthy controls, patients with ADHD responded prematurely and were slow at accumulating evidence, both on and off methylphenidate. Methylphenidate increased the rate of evidence accumulation, but did not improve premature responding. Our results thus indicate a selective effect of methylphenidate in ADHD improving evidence accumulation during decision making. Reinforcement learning models describe learning processes in classical conditioning and instrumental learning. These models have played a vital role in discovering that DA potentially signals the reward prediction error, which drives learning by updating beliefs when events violate expectations. Instrumental learning, in contrast to classical conditioning, involves an active choice process. Current reinforcement learning models usually describe choice processes in learning through a single parameter, which potentially captures several independent decision processes. In paper 3 we created a model that potentially could disentangle these decision processes; by describing a reinforcement learning model that explains choosing in instrumental learning with the DDM. The extended model approach was validated by comparing fit to data across models with different expressions of parameters. The best-fitting model was further shown to accurately recover generated data, thus suggesting that the model parameters captured the mechanisms they were intended to describe. ADHD is associated with impaired learning. Despite strong connections between ADHD and DA on one side, and DA and learning on the other, few studies have directly used reinforcement learning models to test the effects of central stimulants on instrumental learning in ADHD. Using data collected for a previous study, we used the newly developed reinforcement learning drift diffusion model to measure effects of stimulant medication on instrumental learning in adult ADHD. Results indicated that stimulant medication resulted in improved evidence accumulation, as well as more deliberate choice processes, extended motor-processes, and slightly lower updating of beliefs from unexpected rewards. Together, the current thesis implicate that central stimulants improve evidence accumulation in ADHD across both simple decision making and instrumental learning. The thesis further discusses the potential benefit of using computational models to understand cognitive mechanisms, and their alteration, in ADHD. Finally, the work presented herein highlights the importance of continuously improving computational models’ ability to describe observed behavior, and how mechanisms proposed by the models can be linked to neural data.

View all works in NVA