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
Yan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.004 · Cited by 1,461
Behavioral activation treatment for depression: returning to contextual roots
Jacobson, N. S., Martell, C. R., & Dimidjian, S. (2001) · Clinical Psychology: Science and Practice · doi:10.1093/clipsy.8.3.255 · Cited by 1,011 · cited 1×
“Froma clinical perspective, there is the long-standing observation that a subset of depressive behaviors, such as reduced energy and motivation, are related to changes in reinforcement schedules (28, 29).”
Resting-state functional connectivity in major depressive disorder: A review
Mulders, P. C., van Eijndhoven, P. F., Schene, A. H., Beckmann, C. F., & Tendolkar, I. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.07.014 · Cited by 884 · cited 3×
“However, there is a need to understand the distributed brain patterns of perturbations in depression (6), and networkmeasures using iFC arewell suited to this approach.”
Braams, B. R., van Duijvenvoorde, A. C. K., Peper, J. S., & Crone, E. A. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4764-14.2015 · Cited by 617 · cited 1×
“Therefore, since both reward processing and depression prevalence vary with development (2, 35, 36), iFCappearswell suited to their study.”
Brief Screening for Family Psychiatric History: The Family History Screen
Weissman, M. M. (2000) · Archives of General Psychiatry · doi:10.1001/archpsyc.57.7.675 · Cited by 602 · cited 2×
“3% of cases) answered the Family History Screen (17) for 8,012 families representing 9,937 children.”
InfluentialPrefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior
Ferenczi, E. A., Zalocusky, K. A., Liston, C., Grosenick, L., Warden, M. R., Amatya, D., Katovich, K., Mehta, H., Patenaude, B., Ramakrishnan, C., et al. (2016) · Science · doi:10.1126/science.aac9698 · Cited by 535 · cited 1×
“Several experiments demonstrate the key role that dopaminergic signaling in ventral striatal areas plays in reward valuation and effort expended toward reward (31, 32).”
Prevalence of Child and Adolescent Psychiatric Disorders in Southeast Brazil
Fleitlich‐Bilyk, B., & Goodman, R. (2004) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/01.chi.0000120021.14101.ca · Cited by 518 · cited 1×
“5% of cases), including extensive risk factor evaluation and structured psychiatric interview using the Developmental and Well-Being Assessment, Brazilian Portuguese version (18, 19).”
Dopaminergic mechanisms of individual differences in human effort-based decision-making
Treadway, M. T., Buckholtz, J. W., Cowan, R. L., Woodward, N. D., Li, R., Ansari, M. S., Baldwin, R. M., Schwartzman, A. N., Kessler, R. M., & Zald, D. H. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.6459-11.2012 · Cited by 385
Connolly, C. G., Wu, J., Ho, T. C., Hoeft, F., Wolkowitz, O., Eisendrath, S., Frank, G., Hendren, R., Max, J. E., Paulus, M. P., et al. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.05.036 · Cited by 356 · cited 1×
“This suggests that the left ventral striatum is integrating information from various areas of the reward network, including those previously implicated in adolescent depression, such as the anterior cingulate cortex (13, 14).”
Research review: altered reward function in adolescent depression: what, when and how?
Forbes, E. E., & Dahl, R. E. (2012) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2011.02477.x · Cited by 350
Abnormal Amygdala Resting-State Functional Connectivity in Adolescent Depression
Cullen, K. R., Westlund, M. K., Klimes-Dougan, B., Mueller, B. A., Houri, A., Eberly, L. E., & Lim, K. O. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1087 · Cited by 324
Stringaris, A., Vidal-Ribas Belil, P., Artiges, E., Lemaitre, H., Gollier-Briant, F., Wolke, S., Vulser, H., Miranda, R., Penttilä, J., Struve, M., et al. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14101298 · Cited by 322
Self-citationFunctional brain imaging studies of youth depression: A systematic review
Kerestes, R., Davey, C. G., Stephanou, K., Whittle, S., & Harrison, B. J. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.11.009 · Cited by 317 · cited 4×
“However, few studies have adopted network-based approaches, and most are cross-sectional (5).”
InfluentialKessler, R. C., Avenevoli, S., McLaughlin, K. A., Green, J. G., Lakoma, M. D., Petukhova, M. V., Pine, D. S., Sampson, N. A., Zaslavsky, A. M., & Merikangas, K. R. (2012) · Psychological Medicine · doi:10.1017/s0033291712000025 · Cited by 280 · cited 1×
“Lastly, since psychiatric disorders in youth are frequently comorbid (38) andventral striatal dysfunction is implicated in other disorders (39, 40), we examined whether left ventral striatum node strength could predict diagnoses other than depressive disorder.”
Self-citationMaking data sharing work: The FCP/INDI experience
Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.064 · Cited by 280 · cited 1×
“org/projects/fcon_1000/) (21): discard the first four volumes of EPI; skull stripping; head motion correction; despiking; rescale to a grand mean of 10,000; band-pass filtering using classical resting-state band (0.”
Common and Dissociable Dysfunction of the Reward System in Bipolar and Unipolar Depression
Satterthwaite, T. D., Kable, J. W., Vandekar, L., Katchmar, N., Bassett, D. S., Baldassano, C. F., Ruparel, K., Elliott, M. A., Sheline, Y. I., Gur, R. C., et al. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.75 · Cited by 249 · cited 3×
“A previous study found decreased iFC within the rewardnetwork (10), yet this discrepancy could be explained by the fact that these were adults who already had depression and, unlike our adolescents, were on medication.”
Self-citationStriatum-Based Circuitry of Adolescent Depression and Anhedonia
Gabbay, V., Ely, B. A., Li, Q., Bangaru, S. D., Panzer, A. M., Alonso, C. M., Castellanos, F. X., & Milham, M. P. (2013) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2013.04.003 · Cited by 222 · cited 1×
“There is aparticularneed for longitudinal studies of iFC in adolescent depression, to extend promising crosssectional results (12).”
Changing reinforcing events: An approach to the treatment of depression.
Lewinsohn, P. M., Sullivan, J. M., & Grosscup, S. J. (1980) · Psychotherapy: Theory, Research & Practice · doi:10.1037/h0085929 · Cited by 219
Salum, G. A., Gadelha, A., Pan, P. M., Moriyama, T. S., Graeff‐Martins, A. S., Tamanaha, A. C., de Alvarenga, P. G., Krieger, F., Fleitlich‐Bilyk, B., Jackowski, A. P., et al. (2014) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1459 · Cited by 214 · cited 1×
“The first stratum, “the random group” (N=958), included a sample of randomly selected individuals; the second stratum, “the high-risk group” (N=1,553), included children at risk for psychopathology, selected using a validated prioritization algorithm (16).”
Self-citationSchwab‐Stone, M., Fallon, T., Briggs, M., & Crowther, B. (1994) · American Journal of Psychiatry · doi:10.1176/ajp.151.7.1048 · Cited by 199 · cited 2×
“Data suggest that youth report before age 11 is relatively unreliable (20).”
Sonuga-Barke, E. J. S., Cortese, S., Fairchild, G. F., & Stringaris, A. K. (2015) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12496 · Cited by 177 · cited 1×
“Indeed, there is mounting evidence from reward task–based fMRI that reduced activity in the striatum is important in the etiology of depression (3, 5, 33).”
Self-citationBeesdo‐Baum, K., Höfler, M., Leibenluft, E., Lieb, R., Bauer, M., & Pfennig, A. (2009) · Bipolar Disorders · doi:10.1111/j.1399-5618.2009.00738.x · Cited by 162 · cited 3×
“The incidenceofdepression risesmarkedly inadolescence (2), potentially because of maturing reward-system function (3).”
Self-citationSharma, A. K., Wolf, D. H., Ćirić, R., Kable, J. W., Moore, T. M., Vandekar, S., Katchmar, N., Daldal, A., Ruparel, K., Davatzikos, C. A., et al. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.16070774 · Cited by 159 · cited 1×
“While iFC studies implicate reward-network function in depression (10, 11), most studies have examined small, clinically referred samples of adults on medication.”
van Duijvenvoorde, A. C. K., Achterberg, M., Braams, B. R., Peters, S., & Crone, E. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.069 · Cited by 138 · cited 1×
“Therefore, since both reward processing and depression prevalence vary with development (2, 35, 36), iFCappearswell suited to their study.”
Davey, C. G., Whittle, S., Harrison, B. J., Simmons, J. G., Byrne, M. L., Schwartz, O., & Allen, N. B. (2014) · Psychological Medicine · doi:10.1017/s0033291714002001 · Cited by 112 · cited 1×
“This suggests that the left ventral striatum is integrating information from various areas of the reward network, including those previously implicated in adolescent depression, such as the anterior cingulate cortex (13, 14).”
Age effects on the default mode and control networks in typically developing children
Sato, J. R., Salum, G. A., Gadelha, A., Picon, F. A., Pan, P. M., Vieira, G., Zugman, A., Hoexter, M. Q., Anés, M., Moura, L. M., et al. (2014) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2014.07.004 · Cited by 85 · cited 1×
“Following convention (27), we termed these region-of-interest correlations the edges of the reward network (43 edges; see Table S3 in the data supplement).”
Self-citationPannekoek, J. N., van der Werff, S. J. A., van Tol, M., Veltman, D. J., Aleman, A., Zitman, F. G., Rombouts, S. A. R. B., & van der Wee, N. J. A. (2015) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2015.08.002 · Cited by 78 · cited 2×
“One previous longitudinal study (40) showed that stronger cortico-striatal iFC (right dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, and pre–supplementary motor area) predicted earlier onset of alcohol and substance use.”
Validation of a child-friendly version of the monetary incentive delay task
Helfinstein, S. M., Kirwan, M. L., Benson, B. E., Hardin, M. G., Pine, D. S., Monique Ernst, D., & Fox, N. A. (2012) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nss057 · Cited by 58
Self-citationVentral striatal hyperconnectivity during rewarded interference control in adolescents with ADHD
Ma, I., van Holstein, M., Mies, G. W., Mennes, M., Buitelaar, J. K., Cools, R., Cillessen, A. H. N., Krebs, R. M., & Scheres, A. (2016) · Cortex · doi:10.1016/j.cortex.2016.05.021 · Cited by 42
Sato, J. R., Biazoli, C. E., Salum, G. A., Gadelha, A., Crossley, N. A., Vieira, G., Zugman, A., Picon, F. A., Pan, P. M., Hoexter, M. Q., et al. (2016) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2016.05.002 · Cited by 20 · cited 1×
“Our discovery and replication analysis addresses recent concerns regarding the lack of replicability in neuroimaging studies (25).”
Self-citation
Cited by
Sorted by citations
Prefrontal cortex and depression
Pizzagalli, D. A., & Roberts, A. C. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01101-7 · Cited by 667
“An important exception is the recent study by Pan and colleagues [112], who found that resting state functional connectivity within the brain reward pathway predicted the new onset of depressive disorder – but not ADHD, anxiety or substance abuse – 3 years later.”
BackgroundReward Processing in Depression: A Conceptual and Meta-Analytic Review Across fMRI and EEG Studies
Keren, H., O’Callaghan, G., Vidal-Ribas, P., Buzzell, G. A., Brotman, M. A., Leibenluft, E., Pan, P. M., Meffert, L., Kaiser, A., Wolke, S., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17101124 · Cited by 521
“Interestingly, a recent connectivity study (89) demonstrated that increased connectivity of the ventral striatum predicts depression, in keeping with striatal aberrations in this disorder.”
BackgroundSelf-citationPizzagalli, D. A. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20220423 · Cited by 214
“In a notable study involving a large community-based sample of 9-year-old children (N5637), rsFC between the ventral striatum and other key reward hubs (vmPFC, dACC, VTA) predicted new onset of depressive disorders 3 years later, but not attention deficit hyperactivity disorder, anxiety disorders, or substance use dis…”
BackgroundEditorial: What is depression?
Stringaris, A. K. (2017) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12844 · Cited by 155
Self-citationKetamine modulates fronto-striatal circuitry in depressed and healthy individuals
Mkrtchian, A., Evans, J. W., Kraus, C., Yuan, P., Kadriu, B., Nugent, A. C., Roiser, J. P., & Zarate, C. A. (2021) · Molecular Psychiatry · doi:10.1038/s41380-020-00878-1 · Cited by 110
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
