The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Ventral Striatum Functional Connectivity as a Predictor of Adolescent Depressive Disorder in a Longitudinal Community-Based Sample
Open accessAbstract
OBJECTIVE: Previous studies have implicated aberrant reward processing in the pathogenesis of adolescent depression. However, no study has used functional connectivity within a distributed reward network, assessed using resting-state functional MRI (fMRI), to predict the onset of depression in adolescents. This study used reward network-based functional connectivity at baseline to predict depressive disorder at follow-up in a community sample of adolescents. METHOD: A total of 637 children 6-12 years old underwent resting-state fMRI. Discovery and replication analyses tested intrinsic functional connectivity (iFC) among nodes of a putative reward network. Logistic regression tested whether striatal node strength, a measure of reward-related iFC, predicted onset of a depressive disorder at 3-year follow-up. Further analyses investigated the specificity of this prediction. RESULTS: Increased left ventral striatum node strength predicted increased risk for future depressive disorder (odds ratio=1.54, 95% CI=1.09-2.18), even after excluding participants who had depressive disorders at baseline (odds ratio=1.52, 95% CI=1.05-2.20). Among 11 reward-network nodes, only the left ventral striatum significantly predicted depression. Striatal node strength did not predict other common adolescent psychopathology, such as anxiety, attention deficit hyperactivity disorder, and substance use. CONCLUSIONS: Aberrant ventral striatum functional connectivity specifically predicts future risk for depressive disorder. This finding further emphasizes the need to understand how brain reward networks contribute to youth depression.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Across studies, over-recruitment of PFC regions (mPFC, dlPFC) during reward processing has been interpreted as suggesting over-compensation for reduced striatal responses to rewards [104, 111, 112].”
From Prefrontal cortex and depression (2022) · cites this paper as [112]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Highlighting specificity, this metric did not predict the emergence of ADHD, anxiety or substance abuse over the same time period.”
From Prefrontal cortex and depression (2022) · cites this paper as [112]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Critically, in a community-based sample of 6-12 years children (N = 637), rsFC strength between the ventral striatum and other reward-related regions (vmPFC, dACC, VTA) predicted the new onset of depressive disorder 3 years later [112].”
From Prefrontal cortex and depression (2022) · cites this paper as [112]
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
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 · cited 3×
“Such work quantifies the degree to which brain “nodes” (9) facilitate signal integration among network components.”
InfluentialPower, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429 · cited 2×
“Finally, iFC data are sensitive to head motion (22, 23).”
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748 · cited 2×
“Finally, iFC data are sensitive to head motion (22, 23).”
The reward circuit: linking primate anatomy and human imaging
Haber, S. N., & Knutson, B. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2009.129 · Cited by 3,718 · cited 1×
“Our first step was to show resting-state coupling between brain regions typically activated during rewardrelatedbehaviors (3,26,37).”
Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B., Freedman, G., Murray, C. J. L., Vos, T., & Whiteford, H. A. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001547 · Cited by 3,115
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.0071 · Cited by 2,281
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170
Bartra, O., McGuire, J. T., & Kable, J. W. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.063 · Cited by 1,927 · cited 2×
“Our first step was to show resting-state coupling between brain regions typically activated during rewardrelatedbehaviors (3,26,37).”
Goodman, R., Ford, T., Richards, H., Gatward, R., & Meltzer, H. (2000) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2000.tb02345.x · Cited by 1,755
Dimidjian, S., Hollon, S. D., Dobson, K. S., Schmaling, K. B., Kohlenberg, R. J., Addis, M. E., Gallop, R., McGlinchey, J. B., Markley, D. K., Gollan, J. K., et al. (2006) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.74.4.658 · Cited by 1,607
Cited by
Sorted by citations
Items 101–172 of 172
An Overview of Machine Learning Applications in Mood Disorders
Topolski, N., Jeong, S. H., & Mwangi, B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.017 · Cited by 4
Qu, Y. L., Rappaport, B. I., Luby, J. L., & Barch, D. M. (2021) · Neuroimage Reports · doi:10.1016/j.ynirp.2021.100036 · Cited by 4
An Empirical Analysis of Structural Neuroimaging Profiles in a Staging Model of Depression
Nogovitsyn, N., Ballester, P. L., Lasby, M., Dunlop, K., Ceniti, A. K., Squires, S. D., Rowe, J., Ho, K. T., Suh, J. S., Hassel, S., et al. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.246 · Cited by 5
Anhedonia in Depression: Mechanisms, Assessment, and Therapeutics
Beauchaine, T. P., Klein, D. N., Knapton, E., & Zisner, A. R. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813333-0.00005-6 · Cited by 5
Emotion regulation in self-injurious youth: A tale of two circuits.
Santana-Gonzalez, C., Ranatunga, J., Nguyen, G. H., Greiskalns, B., Das, N., Lattimer, E., Maurice, M., Yi, G., Zietlow, A.-L., Eckstein, M., et al. (2024) · Psychiatry research. Neuroimaging · doi:10.1016/j.pscychresns.2024.111944 · Cited by 6
(2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018 · Cited by 3
Zhao, Z., Wang, Y.-C., Chen, P., Tzeng, S., Chen, P.-S., & Kuo, Y. (2025) · Behavioral and Brain Functions · doi:10.1186/s12993-025-00269-y · Cited by 3
“Disruption of FS circuit is considered a correlate [15], a predictor [16], or even a cause [17] of depression.”
MRI Insights Into Adolescent Neurocircuitry—A Vision for the Future
Tymofiyeva, O., Zhou, V. X., Lee, C. M., Xu, D., Hess, C. P., & Yang, T. T. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00237 · Cited by 6
Busatto, G. F., Rosa, P. G. P., Serpa, M. H., Squarzoni, P., & Duran, F. (2020) · Brazilian Journal of Psychiatry · doi:10.1590/1516-4446-2019-0757 · Cited by 3
Neuroimaging Studies of Effects of Psychotherapy in Depression
Bauer, I. E., & Meyer, T. D. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.021 · Cited by 2
Dugre, J. R., & Potvin, S. (2023) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-023-01143-z · Cited by 4
“…of the NAcc is associated with numerous psychopathologies during adolescence such as anxiety and depressive symptoms (Dorfman et al., 2016; Pan et al., 2017), impulsive decision-making (Costa Dias et al., 2013), substance misuse (Huntley et al., 2020; Morales et al., 2021), and social…”
BackgroundWang, Y.-M., Chen, L., Wang, C.-L., Yan, C., Xie, G., & Yang, X. (2024) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2024.111887 · Cited by 3
Neuroimaging and Circuit Mechanisms of Depression
Romer, A. L., Bertocci, M. A., Phillips, M. L., & Pizzagalli, D. A. (2025) · doi:10.1093/med/9780197640654.003.0029 · Cited by 2
Electrophysiological Biomarkers for Mood Disorders
Ramakrishnan, N., Murphy, N., Selvaraj, S., & Cho, R. Y. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.015 · Cited by 2
Disturbed hierarchy and mediation in reward-related circuits in depression
Yang, R., Chen, J., Yue, S., Yu, Y., Fan, J., Luo, Y., He, H., Duan, M., Jiang, S., Yao, D., et al. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103739 · Cited by 1
Neonatal striatal volume is associated with infant anhedonia
Nevarez-Brewster, M., Demers, C. H., Irwin, J. L., Glynn, L. M., Styner, M. A., Haase, M. H., Hoffman, M. C., Pizzagalli, D. A., Hankin, B. L., & Davis, E. P. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120211 · Cited by 1
“Although not specifically assessing anhedonia, other prospective findings exist linking striatal structure and function in childhood and adolescence to depression, of which anhedonia is a cardinal symptom (Ec et al., 2019; Morgan et al., 2013; Pan et al., 2017).”
Cao, J., Chen, X., Chen, J., Li, X., Ai, M., Wang, W., Gan, Y., He, J.-L., Qiu, H., & Kuang, L. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-07695-2 · Cited by 1
“Pan et al. [45] investigated the iFC of the VS within the reward network in a community sample of adolescents.”
Chen, X., Chen, H., Li, S., Tang, H., Zhou, J., Cheng, B., Jiang, Z., Ye, Y., Liu, J., Lu, F., et al. (2025) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2025.111597 · Cited by 1
Santoya-Montes, Y., Gutiérrez-Ruiz, K., Cotes, R. Z., & Rozo, P. J. P. (2020) · Developmental Neuropsychology · doi:10.1080/87565641.2020.1825719 · Cited by 1
Zang, Z., Qiao, K., Chen, X., Zhou, J., Yang, Z., & Wang, G. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100523 · Cited by 1
“…consistently associated with serotonin pro fi les, whereas putamen ALFF dispersion correlated with both dopamine and serotonin pro fi les. rs-fMRI studies have signi fi cantly advanced our understanding of striatal abnormalities in MDD, from differential diagnosis (44) to treatment prediction (45,46).”
Brain Imaging and the Mechanisms of Antidepressant Action
Godlewska, B. R., Selvaraj, S., & Cowen, P. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.020 · Cited by 1
Effects of Lithium on Brain Structure in Bipolar Disorder
Kaur, J., Kafantaris, V., & Szeszko, P. R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.018 · Cited by 1
Functional Connectome in Bipolar Disorder
Cha, J., & Anand, A. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.008 · Cited by 1
Liu, L., Margoni, F., He, Y., & Liu, H. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/jfchd · Cited by 2
Zhang, S., He, J., Wang, Y., Zhong, G., Zhao, Y.-N., Li, S., Xiao, X., Yang, Z., Zhao, B., Wang, L., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1837921/v1 · Cited by 1
Traumatic Brain Injury and Cognition
Hedges, D., Farrer, T., Bigler, E., & Hopkins, R. (2019) · The Brain at Risk · doi:10.1007/978-3-030-14260-5_13
Vrijen, C., Hartman, C. A., de Jonge, P., & Oldehinkel, A. J. (2020) · Neuropraxis · doi:10.1007/s12474-019-00245-z
Imaging Genetic and Epigenetic Markers in Mood Disorders
Poletti, S., Mazza, E. A., Vai, B., & Benedetti, F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.012
Brain Imaging of Reward Dysfunction in Unipolar and Bipolar Disorders
Kumar, P., Jiang, Y., & Whitton, A. E. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.006
Resting-State Functional Connectivity in Unipolar Depression
Chen, Z., & Gong, Q. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.007
Brain Imaging Methods in Mood Disorders
Selvaraj, S., Brambilla, P., & Soares, J. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.002
Kious, B. M., & Renshaw, P. F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.009
Neuroimaging Brain Inflammation in Mood Disorders
Meyer, J. H. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.011
Functional Near-Infrared Spectroscopy Studies in Mood Disorders
Matsuo, K., & Matsubara, T. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.014
Neuroanatomical Findings in Unipolar Depression and the Role of the Hippocampus
Arnone, D. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.003
Neuroimaging Biomarkers in Pediatric Mood Disorders
West, M. P., Tang, W. J., & Singh, M. K. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.005
fMRI Neurofeedback as Treatment for Depression
Skottnik, L., & Linden, D. E. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.013
Neuroanatomical Findings in Bipolar Disorder
Tronchin, G., & McDonald, C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.004
Imaging Glutamatergic and GABAergic Abnormalities in Mood Disorders
Scotti-Muzzi, E., Otaduy, M. C. G., Soeiro‐de‐Souza, M. G., & Machado‐Vieira, R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.010
Molecular Imaging of Dopamine and Antipsychotics in Bipolar Disorder
Jauhar, S. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.019
Magnetoencephalography Studies in Mood Disorders
Nugent, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.016
Timing in psychiatry: exploring resonance in biological systems for treatment of mental disorders.Preprint
Argôlo, F. C., Garcia‐Dias, R., Pan, P. M., Gadelha, A., Quarantini, L. D. C., & Bressan, R. A. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/84ndb · Cited by 1
Self-citationSelvaraj, S., Brambilla, P., & Soares, J. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.022
Selvaraj, S., Brambilla, P., & Soares, J. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108623018.001
Qu, Y. L., Rappaport, B. I., Luby, J. L., & Barch, D. M. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/7gzkj
Njoroge, W., Maina, R., Frank, E., Atwoli, L., Wu, Z., Ngugi, A. K., Sen, S., Wang, J., Wong, S. P. Y., Baker, J. E., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2458763/v1
“Such methods have been known to reduce false positive and false negative rates [37] in inferring the relations among mobile phenotypes and between the mobile phenotypes and the daily mood scores.”
Oliveira, W. L. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.100.2023.tde-24102023-212532
Roelofs, E. F., Bas‐Hoogendam, J. M., Winkler, A. M., van der Wee, N. J. A., & Vermeiren, R. R. J. M. (2024) · Neuroscience Applied · doi:10.1016/j.nsa.2024.104090
“…seven resting-state networks derived from ICA: default mode, frontoparietal (separated in a left and right component), executive control, dorsal attention, salience and affective network (Smith et al., 2009a; Bas-et al., 2021; Barkhof et al., 2014; Witt et al., 2021; Pan et al., 2017; Zhang, 2017).”
Chapter 5. Neuroscience of Early-Onset Depression
Rao, U., & Lei, T. (2019) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379200.lg05
Pawlak, M., Bray, S., Burles, F., & Kopala‐Sibley, D. C. (2025) · Neuroimage Reports · doi:10.1016/j.ynirp.2025.100291
“In clinical populations, greater activation of the NAcc at rest has been associated with increased levels of anhedonia and may reflect impairments in integrating reward information (Pan et al., 2017; Pisoni et al., 2021).”
Consummatory deficits in close social reward predict inflammation-induced depressed mood
Boyle, C. C., Rahal, D., Meinert, J. C., Ko, V., Olmstead, R., Eisenberger, N. I., & Irwin, M. R. (2025) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2025.107673
Altered reward-related resting-state network properties in adolescent cannabis use and depression
Nguyen, T. N. B., Tobe, R. H., Ely, B. A., & Gabbay, V. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111839
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727160
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727163
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727148
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727145
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727124
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727151
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727154
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727157
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727166
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727118
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727115
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727139
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727136
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727133
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727130
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727121
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727142
Huang, D., Wu, Y., Yue, J., & Wang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26727127
Exploring the molecular determinants of behavioral changes induced by ketamine at the nanoscale
Uyttersprot, F. (2024) · theses.fr (ABES) · doi:10.70675/df438baaz92d0z488aza38cz4227b46c9acb
Metadata
- Authors
- Pan, P. M., Sato, J. R., Salum, G. A., Rohde, L. A., Gadelha, A., Zugman, A., Mari, J., Jackowski, A., Picon, F., Miguel, E. C., Pine, D. S., Leibenluft, E., Bressan, R. A., & Stringaris, A.
- Affiliations
- From the Department of Psychiatry, Laboratório Interdisciplinar de Neurociências Clínicas, Universidade Federal de São Paulo, São Paulo, Brazil; the Mood, Brain, and Development Unit, Emotion and Development Branch, NIMH, Bethesda, Md.; Center for Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil; the Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; the Department and Institute of...
- Universidade Federal do Rio Grande do Sul (BR)
- ORCID
- Venue
- American Journal of Psychiatry
- Published
- 2017-11-01
- Volume
- 174
- Issue
- 11
- Pages
- 1112-1119
- ISSN
- 0002-953X (ISSN-L)
- 1535-7228
- PMC
- PMC10752293
- OpenAlex
- W2758286034
- Cited by
- 156
- References
- 40
- Citations by year
- 2026: 1 · 2025: 13 · 2024: 14 · 2023: 14 · 2022: 21 · 2021: 38 · 2020: 23 · 2019: 19 · 2018: 11 · 2017: 2
- FWCI
- 8.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Funders
- Eli Lilly and Company
- Wellcome Trust
- University of Cambridge
- University College London
- University of Oxford
- Fundação de Amparo à Pesquisa do Estado de São Paulo (2014/, 14/50917-0, 13/08531-5)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (2014‐2, 2013/08531-5, 465550/2014-2, CNPq 465550/2014-2)
- H. Lundbeck A/S
- National Institutes of Health
- National Institute of Mental Health (Z01MH002781, Z01MH002782)
- Daiichi Sankyo Europe
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- ventral striatum, functional connectivity, adolescent depression, reward processing, reward network, intrinsic functional connectivity, risk prediction, children and adolescents, longitudinal prediction
- MeSH terms
- Adolescent, Brain, Brazil, Child, Major Depressive Disorder, Depressive Disorder, Female, Follow-Up Studies, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Reward, Cohort Studies, Odds Ratio, Risk Assessment, Functional Neuroimaging, Ventral Striatum
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
