The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Atypical Default Network Connectivity in Youth with Attention-Deficit/Hyperactivity Disorder
Open accessAbstract
Background Attention-deficit/hyperactivity disorder (ADHD) is a major public health concern. It has been suggested that the brain's default network may provide a crucial avenue for understanding the neurobiology of attention deficit/hyperactivity disorder. Evaluations of the default network have increased over recent years with the applied technique of resting-state functional connectivity magnetic resonance imaging (rs-fcMRI). These investigations have established that spontaneous activity in this network is highly correlated at rest in young adult populations. This coherence seems to be reduced in adults with ADHD. This is an intriguing finding, as coherence in spontaneous activity within the default network strengthens with age. Thus, the pathophysiology of ADHD might include delayed or disrupted maturation of the default network. If so, it is important to determine whether an altered developmental picture can be detected using rs-fcMRI in children with ADHD.Methods This study used the typical developmental context provided previously by Fair et al. (2008) to examine coherence of brain activity within the default network using rs-fcMRI in children with (n = 23) and without attention deficit/hyperactivity disorder (n = 23).Results We found that functional connections previously shown as developmentally dynamic in the default network were atypical in children with attention deficit/hyperactivity disorder-consistent with perturbation or failure of the maturational processes.Conclusions These findings are consistent with the hypothesis that atypical consolidation of this network over development plays a role in attention deficit/hyperactivity disorder.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Connections to the default network were atypical in ADHD in all the three subgroups, corroborating previous findings of atypical connectivity between the nucleus accumbens and regions of the default network in a group of children with ADHD combined type (which shared some participants with the current study) (Costa Dias et al., 2012), and of atypical integration of this network in ADHD (Castellano”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2010
In the citing paper
“The default mode network is implicated in ADHD (Fair et al., 2010) and is associated with poorer attentional regulation in ADHD (Castellanos et al., 2008).”
From Mindfulness Meditation Training for Attention-Deficit/Hyperactivity Disorder in Adulthood: Current Empirical Support, Treatment Overview, and Future Directions (2015) · cites this paper as Fair et al., 2010
In the citing paper
“Another example includes assessment of the default mode network, which is another promising target for mindfulness (Sood & Jones, 2013) and is implicated in ADHD (Castellanos et al., 2008; Fair et al., 2010)”
From Mindfulness Meditation Training for Attention-Deficit/Hyperactivity Disorder in Adulthood: Current Empirical Support, Treatment Overview, and Future Directions (2015) · cites this paper as Fair et al., 2010
In the citing paper
“in the first section we found that NAcc is atypically connected to several cortical regions in children with ADHD, including regions previously shown to present atypical correlated spontaneous activity in adults and children with the disorder (Castellanos et al., 2008; Fair et al., 2010; Mills et al., 2012).”
From Reward circuit connectivity relates to delay discounting in children with attention-deficit/hyperactivity disorder (2013) · cites this paper as Fair et al., 2010
In the citing paper
“Rs-fcMRI has been used to study brain functional connectivity in ADHD (Castellanos et al., 2008; Fair et al., 2010), but studies focusing on cortical-subcortical connections remain infrequent.”
From Reward circuit connectivity relates to delay discounting in children with attention-deficit/hyperactivity disorder (2013) · cites this paper as Fair et al., 2010
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Co-Planar Stereotaxic Atlas of the Human Brain
Talairach, J., & Tournoux, P. (1988) · Medical Entomology and Zoology · Cited by 11,798
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
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
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Co-planar stereotaxic atlas of the human brain
Laitinen, L. (1989) · Clinical Neurology and Neurosurgery · doi:10.1016/0303-8467(89)90128-5 · Cited by 7,565
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Research works printed but not linked: 2
- Gilbert, 2007, Comment on “Wandering minds: The default network and stimulus-Independent thought.”, Science, 317, 43, 10.1126/science.1140801 · DOI
- Castellanos, 2008, Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder, Biol Psychiatry, 63, 332, 10.1016/j.biopsych.2007.06.025 · DOI · Paper page
Cited by
Sorted by citations
Items 301–353 of 353
Non-stimulant treatment of ADHD
Pedraza, J. D., & Newcorn, J. H. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139035491.022 · Cited by 3
Exploring Functional Connectivity Biomarker in Autism Using Group-Wise Sparse Representation
Ren, Y., & Wang, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33226-6_3 · Cited by 2
Kandel, H., & Pfaff, D. W. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813146-6.00001-1 · Cited by 2
Vidal, L. M., Martínez‐Garcia, M., de Blas, D. M., Navas-Sánchez, F. J., Pretus, C., Ramos‐Quiroga, J. A., Vanesa Richarte, B., Vilarroya, Ó., Sepulcre, J., Desco, M., et al. (2022) · Journal of Attention Disorders · doi:10.1177/10870547221101646 · Cited by 2
Baboli, R., Martin, E., Qiu, Q., Zhao, L., Liu, T.-M., Wu, K., Gao, Y., & Li, X.-B. (2026) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2026.111966
Chen, C., Xu, S., Zhao, J., Xie, Y., Yao, D., Li, F., Liu, W., Wang, Y., Xu, P., & Wang, J. (2026) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.70198 · Cited by 1
Salavert, J. (2015) · TDX (Tesis Doctorals en Xarxa) · Cited by 1
Correlación del funcionamiento ejecutivo y la potencia absoluta del EEG en niños
Galicia-Alvarado, M. A., Laboratorio de Electroencefalografía, I. N. D. R., Flores-Ávalos, B., Sánchez-Quezada, A., Yáñez-Suárez, Ó., Brust-Carmona, H., Servicio de Electrodiagnóstico, I. N. D. R., & Laboratorio de Imagenología, U. A. M.-I. (2016) · Salud Mental · doi:10.17711/sm.0185-3325.2016.031 · Cited by 1
Attention-Deficit/Hyperactivity Disorder and Executive Dysfunction
Cook, N. E., & Forchelli, G. A. (2018) · Current clinical psychiatry · doi:10.1007/978-3-319-98643-2_8 · Cited by 3
Grodd, W., & Beckmann, C. F. (2013) · Springer eBooks · doi:10.1007/978-3-642-29800-4_15 · Cited by 1
Aberrant Functional Connectivity in Core–Periphery Structure Based on WSBM in ADHD
Li, D., Hou, D., Zhang, Y., Zhao, Y., Cui, X., Niu, Y., Xiang, J., & Wang, B. (2023) · Journal of Attention Disorders · doi:10.1177/10870547231214985 · Cited by 1
Brain differences between persistent and remitted attention deficit hyperactivity disorder
Biederman, J., Mattfeld, A. T., Whitfield, S., & Gabrieli, J. D. E. (2016) · European Neuropsychopharmacology · doi:10.1016/s0924-977x(16)31198-1 · Cited by 1
Resting state functional magnetic resonance imaging in attention deficit hyperactivity disorder
Franceschi, M. J. C., Lancheros, J., Ochoa-Gómez, J. F., Nobmann, M. T. R., Gómez, J. R. M., & Carrillo, L. V. M. (2020) · Radiología (English Edition) · doi:10.1016/j.rxeng.2019.11.001 · Cited by 1
ADHD-200 Patient Characterization and Classification using Resting State Networks: A Dissertation
Czerniak, S. (2014) · The Journal of the American Medical Association (JAMA) Network (American Medical Association) · doi:10.13028/m2v591 · Cited by 1
Machine Learning Approaches for Disease State Classification from Neuroimaging Data
Wang, P. (2013) · Cited by 1
Mackenzie, G. (2019) · TSpace (University of Toronto) · Cited by 1
Liu, L., Chen, D., Huang, F., Jia, T., Pan, M., Zhao, M., Bu, X., Wang, Y., Cao, M., Qian, Q., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.14.472533 · Cited by 1
Interparental conflict and neural functioning in infancy: An fMRI study
Graham, A. M. (2014) · Scholars' Bank (University of Oregon)
Benli, Ş. G., İçer, S., Gümüş, K., Özmen, S., Koç, G., Doganay, S., & Öztop, B. D. (2015) · Medical Technologies National Conference · doi:10.1109/tiptekno.2015.7374562
A Developmental Perspective on Attention-Defity Disorder (ADHD)
Campbell, S. B. G., Halperin, J. M., & Sonuga-Barke, E. J. S. (2014)
Jun, J. M., & Castellanos, F. X. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139035491.017
Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders
Nakubulwa, M. A. (2016) · Aston Publications Explorer (Aston University) · doi:10.48780/publications.aston.ac.uk.00028813
Ben‐Pazi, H. (2016) · Journal of Neurology and Neurobiology · doi:10.16966/2379-7150.129
ADHD NEUROIMAGING: FINDINGS FROM STRUCTURAL AND FUNCTIONAL MAGNETIC RESONANCE IMAGING
Picon, F. A. (2014) · Debates em Psiquiatria · doi:10.25118/2236-918x-4-2-4 · Cited by 2
Dias, T. G. D. C. (2018) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2018.tde-14032018-123829
Self-citationMethylphenidate for Ego-Syntonic Daydreaming
Naguy, A., Alamiri, B., & AlDhaen, M. N. (2018) · American Journal of Therapeutics · doi:10.1097/mjt.0000000000000742
Kittel‐Schneider, S. (2018) · Springer eBooks · doi:10.1007/978-3-662-45995-9_18-1
Broken Brains or Flawed Studies? an Update on Leo and Cohen's Critical Review of ADHD Neuroimaging
Marley, C., O'Leary, A. M., & Nikopoulou, V. A. (2018) · The Journal of mind and behavior
Resonancia magnética funcional de reposo en el trastorno por déficit de atención e hiperactividad
Franceschi, M. J. C., Lancheros, J., Ochoa-Gómez, J. F., Nobmann, M. T. R., Gómez, J. R. M., & Carrillo, L. V. M. (2019) · Radiología · doi:10.1016/j.rx.2019.07.001
Predicting Individual Brain Maturity Using Window-Based Dynamic Functional Connectivity
Hu, D., & Zeng, L. (2019) · Springer eBooks · doi:10.1007/978-981-32-9523-0_4
Song, Z., Li, W., Bi, H., Wang, S., & Zou, L. (2020) · PubMed Central · doi:10.7507/1001-5515.201912058
Norr, M. E. (2020) · eScholarship (California Digital Library)
Awareness of ADHD in primary carePreprint
French, B. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/waqfd
Sørensen, L., Chung, Y. S., Khadka, S., & Stevens, M. C. G. (2022) · medRxiv · doi:10.1101/2022.01.31.22270169
Attention Deficit Hyperactivity Disorder
Pearson, G. S., & Leung, C. Y. (2021) · Wiley eBooks · doi:10.1002/9781119487593.ch10
Psychiatric Disturbances of Attention
Cohen, R. A. (2013) · Springer eBooks · doi:10.1007/978-0-387-72639-7_17
(2022) · Carrefour des psychothérapies · doi:10.3917/dbu.zylow.2022.01.0249
Tang, X., Ma, Z., Kat, S., Xu, L., Liu, Q., Yang, L., Wang, Y., Cao, Q., Li, X., & Liu, J. (2024) · Journal of Attention Disorders · doi:10.1177/10870547241233207
Functional connectivity differences in adult’s ADHD – a MEG studyPreprint
Mouseli, P., Dunkley, B. T., & Moayedi, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.09.663963
Default mode network : self, laterização e patologia
Santos, P. J. H. (2014) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Blasco-Fontecilla, H., Sanchez‐Cerezo, J., Gómez, I., Abreu-Fernández, G., Ortiz, S., Villoria, J. F., Blanco, M., García, A., Ballesteros, J., Martínez, R., et al. (2026) · JMIR Research Protocols · doi:10.2196/79150
Paradigm free regularization for fMRI brain activation recovery
Costantini, I. (2020) · theses.fr (ABES) · doi:10.70675/9c93f23cza1e7z49e8zb02dz0f0b5204502f
Marqueurs de développement de l'attention exécutive chez l'enfant d'âge préscolaire
Anquetil, M. (2023) · theses.fr (ABES) · doi:10.70675/25e00a02z4bd8z433dzbaabzf50530808cab
Li, X., Tang, L., Kang, C., Xu, F., & Ma, N. (2026) · Brain Science and Child Development · doi:10.1016/j.bscd.2026.100007
Gated edge-node interaction graph convolutional network for ADHD classification
Chen, Y., Li, Y., Li, Z., Meng, X., & Tang, Y. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae6bf0
Schoenberg, P. L. A., Zargar, R. M., & Hepark, S. (2026) · Brain Topography · doi:10.1007/s10548-026-01227-1
Early Detection of Neurodevelopmental Disorders Through Dynamic System Models and Machine Learning
Awasthi, A. K., Garov, A. K., & Kumari, R. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-3311-1.ch004
Pandey, P., Doodipala, R. R., Eranki, P., Torres-Rojas, C., Saikia, M. J., & Sitaram, R. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111941
Brain network localization of gray matter alterations and executive dysfunction in pediatric ADHD
Zeng, Y., Cheng, J., Xia, M., Yang, L., & Cao, Q. (2026) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.70245
Cross-Modal Masked Connectome Transformer for multimodal EEG-fMRI learning in ADHD diagnosis
Guo, J., Wang, M., & 郭晓静 (2026) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2026.110922
Cocchi, L., Bramati, I. E., Zalesky, A., Furukawa, E., Fontenelle, L. F., Moll, J., Tripp, G., & Mattos, P. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.3272-12.2012 · Cited by 151
Metadata
- Authors
- Fair, D. A., Posner, J., Nagel, B. J., Bathula, D., Dias, T. G. C., Mills, K. L., Blythe, M. S., Giwa, A., Schmitt, C. F., & Nigg, J. T.
- Affiliations
- Oregon Health & Science University (US)
- New York State Psychiatric Institute (US)
- Corresponding author
- Fair, Damien A.
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2010-12
- Volume
- 68
- Issue
- 12
- Pages
- 1084-1091
- ISSN
- 0006-3223 (ISSN-L)
- PMC
- PMC2997893
- OpenAlex
- W2082502102
- Cited by
- 354
- References
- 77
- Citations by year
- 2026: 11 · 2025: 5 · 2024: 13 · 2023: 12 · 2022: 16 · 2021: 18 · 2020: 22 · 2019: 29 · 2018: 21 · 2017: 27 · 2016: 26 · 2015: 31 · 2014: 48 · 2013: 35 · 2012: 28
- FWCI
- 16.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biological Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Oregon Clinical and Translational Research Institute
- Medical Research Foundation
- UNCF Merck postdoctoral fellowship
- Ford Foundation
- Portland Alcohol Research Center pilot funds (P60 AA010760)
- Oregon Health and Science University Neuropsychiatric Institute
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Mind wandering and attention
- Keywords
- attention deficit hyperactivity disorder, default mode network, resting-state functional connectivity, children, brain maturation, developmental trajectories, spontaneous brain activity, neurodevelopmental hypothesis
- MeSH terms
- Adolescent, Age Factors, Attention Deficit Disorder with Hyperactivity, Brain, Brain Mapping, Child, Female, Humans, Magnetic Resonance Imaging, Male, Neural Pathways
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
