Ventral Striatum Functional Connectivity as a Predictor of Adolescent Depressive Disorder in a Longitudinal Community-Based Sample

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art_PAN_Ventral_Striatum_Functional_Connectivity_as_a_Predictor_of_2017.PDF (968.7 KB)
Citações na Scopus
100
Tipo de produção
article
Data de publicação
2017
Editora
AMER PSYCHIATRIC PUBLISHING, INC
DOI
Indexadores
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Autores
PAN, Pedro Mario
SALUM, Giovanni A.
GADELHA, Ary
ZUGMAN, Andre
MARI, Jair
JACKOWSKI, Andrea
PICON, Felipe
Autor de Grupo de pesquisa
Editores
Coordenadores
Organizadores
Citação
AMERICAN JOURNAL OF PSYCHIATRY, v.174, n.11, p.1112-1119, 2017
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
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.
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URI
https://observatorio.fm.usp.br/handle/OPI/24346
Referências
  1. Bartra O, 2013, NEUROIMAGE, V76, P412, DOI 10.1016/j.neuroimage.2013.02.063
  2. Beesdo K, 2009, BIPOLAR DISORD, V11, P637, DOI 10.1111/j.1399-5618.2009.00738.x
  3. Braams BR, 2015, J NEUROSCI, V35, P7226, DOI 10.1523/JNEUROSCI.4764-14.2015
  4. Connolly CG, 2013, BIOL PSYCHIAT, V74, P898, DOI 10.1016/j.biopsych.2013.05.036
  5. Cullen KR, 2014, JAMA PSYCHIAT, V71, P1138, DOI 10.1001/jamapsychiatry.2014.1087
  6. Davey CG, 2015, PSYCHOL MED, V45, P1001, DOI 10.1017/S0033291714002001
  7. Dimidjian S, 2006, J CONSULT CLIN PSYCH, V74, P658, DOI 10.1037/0022-006X.74.4.658
  8. Drysdale AT, 2017, NAT MED, V23, P28, DOI 10.1038/nm.4246
  9. Ferenczi EA, 2016, SCIENCE, V351, DOI 10.1126/science.aac9698
  10. Ferrari AJ, 2013, PLOS MED, V10, DOI 10.1371/journal.pmed.1001547
  11. Fleitlich-Bilyk B, 2004, J AM ACAD CHILD PSY, V43, P727, DOI 10.1097/01.chi.0000120021.14101.ca
  12. Forbes EE, 2012, J CHILD PSYCHOL PSYC, V53, P3, DOI 10.1111/j.1469-7610.2011.02477.x
  13. Gabbay V, 2013, J AM ACAD CHILD PSY, V52, P628, DOI 10.1016/j.jaac.2013.04.003
  14. Goodman R, 2000, J CHILD PSYCHOL PSYC, V41, P645, DOI 10.1111/j.1469-7610.2000.tb02345.x
  15. Haber SN, 2010, NEUROPSYCHOPHARMACOL, V35, P4, DOI 10.1038/npp.2009.129
  16. Helfinstein SM, 2013, SOC COGN AFFECT NEUR, V8, P720, DOI 10.1093/scan/nss057
  17. Jacobson NS, 2001, CLIN PSYCHOL-SCI PR, V8, P255, DOI 10.1093/clipsy/8.3.255
  18. Kaiser RH, 2015, JAMA PSYCHIAT, V72, P603, DOI 10.1001/jamapsychiatry.2015.0071
  19. Kerestes R, 2014, NEUROIMAGE-CLIN, V4, P209, DOI 10.1016/j.nicl.2013.11.009
  20. Kessler RC, 2012, PSYCHOL MED, V42, P1997, DOI 10.1017/S0033291712000025
  21. LEWINSOHN PM, 1980, PSYCHOTHER-THEOR RES, V17, P322, DOI 10.1037/h0085929
  22. Ma I, 2016, CORTEX, V82, P225, DOI 10.1016/j.cortex.2016.05.021
  23. Mennes M, 2013, NEUROIMAGE, V82, P683, DOI 10.1016/j.neuroimage.2012.10.064
  24. Mulders PC, 2015, NEUROSCI BIOBEHAV R, V56, P330, DOI 10.1016/j.neubiorev.2015.07.014
  25. Pannekoek JN, 2015, EUR NEUROPSYCHOPHARM, V25, P1933, DOI 10.1016/j.euroneuro.2015.08.002
  26. Power JD, 2014, NEUROIMAGE, V84, P320, DOI 10.1016/j.neuroimage.2013.08.048
  27. Power JD, 2012, NEUROIMAGE, V59, P2142, DOI 10.1016/j.neuroimage.2011.10.018
  28. Rubinov M, 2010, NEUROIMAGE, V52, P1059, DOI 10.1016/j.neuroimage.2009.10.003
  29. Salum GA, 2015, INT J METH PSYCH RES, V24, P58, DOI 10.1002/mpr.1459
  30. Sato JR, 2016, DEV COGN NEUROS-NETH, V20, P2, DOI 10.1016/j.dcn.2016.05.002
  31. Sato JR, 2014, J PSYCHIATR RES, V58, P89, DOI 10.1016/j.jpsychires.2014.07.004
  32. Satterthwaite TD, 2015, NEUROPSYCHOPHARMACOL, V40, P2258, DOI 10.1038/npp.2015.75
  33. SCHWABSTONE M, 1994, AM J PSYCHIAT, V151, P1048
  34. Sharma A, 2017, AM J PSYCHIAT, V174, P657, DOI 10.1176/appi.ajp.2016.16070774
  35. Sonuga-Barke EJS, 2016, J CHILD PSYCHOL PSYC, V57, P321, DOI 10.1111/jcpp.12496
  36. Stringaris A, 2015, AM J PSYCHIAT, V172, P1215, DOI 10.1176/appi.ajp.2015.14101298
  37. Treadway MT, 2012, J NEUROSCI, V32, P6170, DOI 10.1523/JNEUROSCI.6459-11.2012
  38. van Duijvenvoorde ACK, 2016, NEUROIMAGE, V124, P409, DOI 10.1016/j.neuroimage.2015.04.069
  39. Weissman MM, 2000, ARCH GEN PSYCHIAT, V57, P675, DOI 10.1001/archpsyc.57.7.675
  40. Yan CG, 2013, NEUROIMAGE, V76, P183, DOI 10.1016/j.neuroimage.2013.03.004

Coleções
Artigos e Materiais de Revistas Científicas - FM/MPS
Artigos e Materiais de Revistas Científicas - LIM/21
Artigos e Materiais de Revistas Científicas - LIM/44