Stimulant medications affect arousal and reward, not attention networks

Benjamin P Kay¹, Muriah D Wheelock², Joshua S Siegel³, Ryan V Raut⁴, Roselyne J Chauvin⁵, Athanasia Metoki⁵, Aishwarya Rajesh⁶, Andrew Eck², Jim Pollaro², Anxu Wang⁵, Vahdeta Suljic⁵, Babatunde Adeyemo⁵, Noah J Baden⁵, Kristen M Scheidter⁵, Julia S Monk⁵, Forrest I Whiting⁵, Nadeshka Ramirez-Perez⁷, Samuel R Krimmel⁵, Russell T Shinohara⁸, Brenden Tervo-Clemmens⁹, Robert J M Hermosillo¹⁰, Steven M Nelson¹⁰, Timothy J Hendrickson¹¹, Thomas Madison¹⁰, Lucille A Moore¹², Óscar Miranda-Domínguez¹⁰, Anita Randolph¹³, Eric Feczko¹⁰, Jarod L Roland¹⁴, Ginger E Nicol¹⁵, Timothy O Laumann¹⁶, Scott Marek⁶, Evan M Gordon⁶, Marcus E Raichle¹⁷, Deanna M Barch¹⁸, Damien A Fair¹⁹, Nico U F Dosenbach²⁰

Affiliations

¹ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA. Electronic address: benjamin.kay@wustl.edu.
² Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
³ Department of Psychiatry, New York University Langone Center for Psychedelic Medicine, New York, NY, USA.
⁴ Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Allen Institute, Seattle, WA, USA; Department of Physiology & Biophysics, University of Washington, Seattle, WA, USA.
⁵ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA.
⁶ Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
⁷ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Taylor Family Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA.
⁸ Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, Philadelphia, PA, USA; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, Philadelphia, PA, USA; Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, Philadelphia, PA, USA.
⁹ Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA.
¹⁰ Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
¹¹ Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA.
¹² Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA.
¹³ Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
¹⁴ Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Taylor Family Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA.
¹⁵ Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
¹⁶ Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
¹⁷ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
¹⁸ Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA.
¹⁹ Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
²⁰ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Program in Occupational Therapy, Washington University in St. Louis, St. Louis, MO, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA.

PMID: 41448140
PMCID: PMC12834599
DOI: 10.1016/j.cell.2025.11.039

Stimulant medications affect arousal and reward, not attention networks

Benjamin P Kay et al. Cell. 2025.

. 2025 Dec 24;188(26):7529-7546.e20.

doi: 10.1016/j.cell.2025.11.039.

Authors

Affiliations

¹ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA. Electronic address: benjamin.kay@wustl.edu.
² Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
³ Department of Psychiatry, New York University Langone Center for Psychedelic Medicine, New York, NY, USA.
⁴ Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Allen Institute, Seattle, WA, USA; Department of Physiology & Biophysics, University of Washington, Seattle, WA, USA.
⁵ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA.
⁶ Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
⁷ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Taylor Family Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA.
⁸ Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, Philadelphia, PA, USA; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, Philadelphia, PA, USA; Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, Philadelphia, PA, USA.
⁹ Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA.
¹⁰ Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
¹¹ Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA.
¹² Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA.
¹³ Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
¹⁴ Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Taylor Family Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA.
¹⁵ Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
¹⁶ Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
¹⁷ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
¹⁸ Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA.
¹⁹ Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
²⁰ Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Allied Labs for Imaging Guided Neurotherapies (ALIGN), Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Program in Occupational Therapy, Washington University in St. Louis, St. Louis, MO, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA.

PMID: 41448140
PMCID: PMC12834599
DOI: 10.1016/j.cell.2025.11.039

Abstract

Prescription stimulants (e.g., methylphenidate) are thought to improve attention, but evidence from prior fMRI studies is conflicted. We utilized resting-state fMRI data from the Adolescent Brain Cognitive Development Study (n = 11,875; 8-11 years old) and validated the functional connectivity findings in a precision imaging drug trial with highly sampled (n = 5, 165-210 min each) healthy adults (methylphenidate 40 mg). Stimulant-related connectivity differences in sensorimotor regions matched fMRI patterns of daytime arousal, sleeping longer at night, and norepinephrine transporter expression. Taking stimulants reversed the effects of sleep deprivation on connectivity and school grades. Connectivity was also changed in salience and parietal memory networks, which are important for dopamine-mediated, reward-motivated learning, but not the brain's attention systems (e.g., dorsal attention network). The combined noradrenergic and dopaminergic effects of stimulants may drive brain organization towards a more wakeful and rewarded configuration, improving task effort and persistence without effects on attention networks.

Keywords: ADHD; arousal; brain networks; brain-wide association studies; fMRI; functional connectivity; methylphenidate; resting state; reward; stimulants.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests D.A.F. and N.U.F.D. have a financial interest in Turing Medical and may financially benefit if the company is successful in marketing FIRMM motion-monitoring software products. D.A.F. and N.U.F.D. may receive royalty income based on FIRMM technology developed at Washington University School of Medicine and Oregon Health and Sciences University and licensed to Turing Medical Inc. D.A.F. and N.U.F.D. are co-founders of Turing Medical Inc. These potential conflicts of interest have been reviewed and are managed by Washington University School of Medicine, Oregon Health and Sciences University and the University of Minnesota.

Figures

**Figure 1.. Stimulant-related functional connectivity differences**
ABCD Study data, n = 5,795 children, n = 337 taking a stimulant. Stimulant-related findings are color-coded red. (A) Magnitude (root-mean-square) of functional connectivity (FC) difference shown on the Gordon-Laumann cortical parcellation. The color scale is thresholded between the 50^th and 95^th percentiles to facilitate visual comparison between figures. (B) Differences in FC with an exemplar (most affected by stimulants) seed parcel in the motor-hand region (purple dot). (C and D) Significant (FWER p < 0.05) differences in FC between network pairs using network level analysis (NLA). (E) Magnitude (NLA, Welch’s t-statistic) of FC differences in whole networks relative to the whole connectome. Significant (FWER p < 0.05) differences are indicated by a *. DMN, default mode; VIS, visual; FPN, fronto-parietal; DAN, dorsal attention; VAN, ventral attention; SAL, salience; PMN, parietal memory; AMN, action-mode; SM, somato-cognitive action/motor; AUD, auditory; CAN, context association; HC, hippocampus; AMYG, amygdala; BG, basal ganglia; THAL, thalamus; CERB, cerebellum. See also Figures S1–S8.

**Figure 2.. Stimulant effects validated in precision imaging drug trial**
(A) Magnitude (root-mean-square) of functional connectivity (FC) differences shown on the Gordon-Laumann cortical parcellation for 337 children taking stimulants in the ABCD Study (total n = 5,795). The color scale is thresholded between the 50^th and 95^th percentiles to facilitate visual comparison. (B) Magnitude of acute FC differences in adult participants (n = 5) given methylphenidate 40 mg in a controlled study. The cortical maps are correlated at r = 0.41 (spin test p < 0.001). See also Figure S8.

**Figure 3.. Sleep-duration-related functional connectivity differences**
ABCD Study data, n = 5,795 children. Sleep-related findings are color-coded blue. (A) Magnitude (root-mean-square) of functional connectivity (FC) differences shown on the Gordon-Laumann cortical parcellation. The color scale is thresholded between the 50^th and 95^th percentiles to facilitate visual comparison between figures. (B) Differences in FC with an exemplar seed parcel in the somatomotor hand region (purple dot). (C and D) Significant (FWER p < 0.05) differences in FC between network pairs using NLA. (E) Magnitude (Welch’s t-statistic) of FC difference in whole networks relative to the whole connectome. Significant (NLA, FWER p < 0.05) changes are indicated by a *. DMN, default mode; VIS, visual; FPN, fronto-parietal; DAN, dorsal attention; VAN, ventral attention; SAL, salience; PMN, parietal memory; AMN, action-mode; SM, somato-cognitive action/motor; AUD, auditory; CAN, context association; HC, hippocampus; AMYG, amygdala; BG, basal ganglia; THAL, thalamus; CERB, cerebellum. See also Figures S1, S2, S7, and S9.

**Figure 4.. Sleep duration effects validated against independent brain maps of arousal**
(A) Magnitude (root-mean-square) of functional connectivity (FC) differences related to sleep duration shown on the Gordon-Laumann cortical parcellation (ABCD Study, n = 5,795). The color scale is thresholded between the 50^th and 95^th percentiles to facilitate visual comparison. (B) Arousal template obtained by correlating EEG alpha slow wave index (alpha/delta power ratio) with fMRI signal intensity (n = 10).^, (C) Arousal map obtained from coherence between respiratory variation and fMRI signal intensity based on Human Connectome Project (n = 190). (D) Non-displaceable binding potential for 11C-MRB (methylreboxetine) in a positron emission tomography (PET) study (n = 20).^, Correlations between cortical maps are shown in gray arrows and summarized in Table S2. The correlation between the EEG- and respiration-derived arousal maps was r = 0.60 (spin test p < 0.0001). See also Figure S10.

**Figure 5.. Sleep duration and stimulant use’s interacting brain effects**
ABCD Study data, n = 5,795 children, n = 337 taking a stimulant. (A and B) Functional connectivity (FC) difference magnitude (root-mean-square) for sleep shown on the Gordon-Laumann cortical parcellation in children (A) not taking stimulants (n = 5,458) and (B) taking stimulants (n = 337). A more liberal t value threshold was used in (B) to show detail. (C) Significant (FWER p < 0.05) differences in FC between network pairs in children not taking stimulants. (D) Magnitude (Welch’s t-statistic) of FC differences in whole networks, relative to the whole connectome, for sleep in children not taking stimulants and taking stimulants. Significant (FWER p < 0.05) changes are indicated by a *. (E) Significant (FWER p < 0.05) differences in FC between network pairs in children taking stimulants. DMN, default mode; VIS, visual; FPN, fronto-parietal; DAN, dorsal attention; VAN, ventral attention; SAL, salience; PMN, parietal memory; AMN, action-mode; SM, somato-cognitive action/motor; AUD, auditory; CAN, context association; HC, hippocampus; AMYG, amygdala; BG, basal ganglia; THAL, thalamus; CERB, cerebellum. See also Figures S1, S9, S11, andS12.

See this image and copyright information in PMC

Update of

Stimulant medications affect arousal and reward, not attention.
Kay BP, Wheelock MD, Siegel JS, Raut R, Chauvin RJ, Metoki A, Rajesh A, Eck A, Pollaro J, Wang A, Suljic V, Adeyemo B, Baden NJ, Scheidter KM, Monk J, Ramirez-Perez N, Krimmel SR, Shinohara RT, Tervo-Clemmens B, Hermosillo RJM, Nelson SM, Hendrickson TJ, Madison T, Moore LA, Miranda-Domínguez Ó, Randolph A, Feczko E, Roland JL, Nicol GE, Laumann TO, Marek S, Gordon EM, Raichle ME, Barch DM, Fair DA, Dosenbach NUF. Kay BP, et al. bioRxiv [Preprint]. 2025 May 22:2025.05.19.654915. doi: 10.1101/2025.05.19.654915. bioRxiv. 2025. Update in: Cell. 2025 Dec 24;188(26):7529-7546.e20. doi: 10.1016/j.cell.2025.11.039. PMID: 40475604 Free PMC article. Updated. Preprint.

References

1. Rasmussen N (2015). Chapter two - amphetamine-type stimulants: The early history of their medical and non-medical uses. In The neuropsychiatric complications of stimulant abuse International Review of Neurobiology, Taba P, Lees A, and Sikk K, eds. (Academic Press; ), pp. 9–25. 10.1016/bs.irn.2015.02.001. - DOI - PubMed
1. Heal DJ, Smith SL, Gosden J, and Nutt DJ (2013). Amphetamine, past and present – a pharmacological and clinical perspective. J. Psychopharmacol 27, 479–496. 10.1177/0269881113482532. - DOI - PMC - PubMed
1. Faraone SV (2018). The pharmacology of amphetamine and methylphenidate: Relevance to the neurobiology of attention-deficit/hyperactivity disorder and other psychiatric comorbidities. Neurosci. Biobehav. Rev 87, 255–270. 10.1016/j.neubiorev.2018.02.001. - DOI - PMC - PubMed
1. Hales CM, Kit BK, Gu Q, and Ogden CL (2018). Trends in prescription medication use among children and adolescents—united states, 1999–2014. JAMA 319, 2009–2020. 10.1001/jama.2018.5690. - DOI - PMC - PubMed
1. Board AR, Guy G, Jones CM, and Hoots B (2020). Trends in stimulant dispensing by age, sex, state of residence, and prescriber specialty — United States, 2014–2019. Drug Alcohol Depend. 217, 108297. 10.1016/j.drugalcdep.2020.108297. - DOI - PMC - PubMed

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Stimulant medications affect arousal and reward, not attention networks

Affiliations

Stimulant medications affect arousal and reward, not attention networks

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources