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Associations between children’s family environment, spontaneous brain oscillations, and emotional and behavioral problems

  • João Ricardo SatoEmail author
  • Claudinei Eduardo BiazoliJr.
  • Giovanni Abrahão Salum
  • Ary Gadelha
  • Nicolas Crossley
  • Gilson Vieira
  • André Zugman
  • Felipe Almeida Picon
  • Pedro Mario Pan
  • Marcelo Queiroz Hoexter
  • Edson AmaroJr.
  • Mauricio Anés
  • Luciana Monteiro Moura
  • Marco Antonio Gomes Del’Aquilla
  • Philip Mcguire
  • Luis Augusto Rohde
  • Euripedes Constantino Miguel
  • Rodrigo Affonseca Bressan
  • Andrea Parolin Jackowski
Original Contribution
  • 177 Downloads

Abstract

The family environment in childhood has a strong effect on mental health outcomes throughout life. This effect is thought to depend at least in part on modifications of neurodevelopment trajectories. In this exploratory study, we sought to investigate whether a feasible resting-state fMRI metric of local spontaneous oscillatory neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), is associated with the levels of children’s family coherence and conflict. Moreover, we sought to further explore whether spontaneous activity in the brain areas influenced by family environment would also be associated with a mental health outcome, namely the incidence of behavioral and emotional problems. Resting-state fMRI data from 655 children and adolescents (6–15 years old) were examined. The quality of the family environment was found to be positively correlated with fALFF in the left temporal pole and negatively correlated with fALFF in the right orbitofrontal cortex. Remarkably, increased fALFF in the temporal pole was associated with a lower incidence of behavioral and emotional problems, whereas increased fALFF in the orbitofrontal cortex was correlated with a higher incidence.

Keywords

Development Family environment Neuroimaging Psychopathology Resting state 

Notes

Acknowledgements

The opinions, hypotheses, conclusions, and recommendations of this study are those of the authors and do not necessarily represent the opinions of the funding agencies. The authors are grateful to FAPESP (grants 2013/10498-6 and 2013/00506-1 to J.R.S. and grant 2013/08531-5 to A.J.) and the National Institute of Developmental Psychiatry for Children and Adolescents, a science and technology institute funded by CNPq and FAPESP (grant 573974/2008-0).

Compliance with ethical standards

Conflict of interest

Dr. Luis Augusto Rohde has been on the speakers’ bureau/advisory board and/or acted as a consultant for Eli-Lilly, Janssen-Cilag, Novartis, and Shire in the last 3 years. The ADHD and Juvenile Bipolar Disorder Outpatient programs chaired by Dr. Rhode have also received unrestricted educational and research support from the following pharmaceutical companies in the last 3 years: Eli-Lilly, Janssen-Cilag, Novartis, and Shire. Dr. Rohde has also received travel grants from Shire for participation in the 2014 American Physiological Association and 2015 World Federation of ADHD congresses. Finally, he receives authorship royalties from Oxford Press and ArtMed. Dr. Rodrigo Affonseca Bressan has been on the speakers’ bureau/advisory board of AstraZeneca, Bristol, Janssen, and Lundbeck. Dr. Bressan has also received research grants from Janssen, Eli-Lilly, Lundbeck, Novartis, Roche, FAPESP, CNPq, CAPES, Fundação E.J. Safra, and Fundação ABAHDS. He is also a shareholder in Biomolecular Technology Ltd. Dr. Edson Amaro Jr. has received research grants from FAPESP, CNPq, CAPES, Fundação E.J. Safra, and Fundação ABAHDS. Dr. Pedro Pan received a PhD Scholarship from CNPq.

References

  1. 1.
    Achenbach TM, Rescorla LA (2001) Manual for the ASEBA school-age forms and profiles. University of Vermont, Research Center for Children, Youth, and Families, BurlingtonGoogle Scholar
  2. 2.
    Aiello M, Salvatore E, Cachia A, Pappatà S, Cavaliere C, Prinster A et al (2015) Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: a PET/MR hybrid scanner study. Neuroimage 113:111–121CrossRefGoogle Scholar
  3. 3.
    Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL (2010) Functional-anatomic fractionation of the brain’s default network. Neuron 65(4):550–562CrossRefGoogle Scholar
  4. 4.
    Barbas H (2015) General cortical and special prefrontal connections: principles from structure to function. Annu Rev Neurosci 38:269–289CrossRefGoogle Scholar
  5. 5.
    Barrett LF, Simmons WK (2015) Interoceptive predictions in the brain. Nat Rev Neurosci 16:419–429CrossRefGoogle Scholar
  6. 6.
    Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541CrossRefGoogle Scholar
  7. 7.
    Biswal BB, Mennes M, Zuo XN et al (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107:4734–4739CrossRefGoogle Scholar
  8. 8.
    Blair C, Raver CC (2016) Poverty, stress, and brain development: new directions for prevention and intervention. Acad Pediatr 16(3):S30–S36CrossRefGoogle Scholar
  9. 9.
    Brunner M, Nagy G, Wilhelm O (2012) A tutorial on hierarchically structured constructs. J Pers 80(4):796–846CrossRefGoogle Scholar
  10. 10.
    Chanes L, Barrett LF (2016) Redefining the role of limbic areas in cortical processing. Trends Cogn Sci 20(2):96–106CrossRefGoogle Scholar
  11. 11.
    Cirulli F, Berry A, Alleva E (2003) Early disruption of the mother–infant relationship: effects on brain plasticity and implications for psychopathology. Neurosci Biobehav Rev 27(1):73–82CrossRefGoogle Scholar
  12. 12.
    Cordes D, Haughton VM, Arfanakis K et al (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR 22:1326–1333PubMedGoogle Scholar
  13. 13.
    Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173CrossRefGoogle Scholar
  14. 14.
    Davidson RJ, McEwen BS (2012) Social influences on neuroplasticity: stress and interventions to promote well-being. Nat Neurosci 15(5):689–695CrossRefGoogle Scholar
  15. 15.
    Davies PT, Lindsay LL (2004) Interparental conflict and adolescent adjustment: why does gender moderate early adolescent vulnerability? J Fam Psychol 18(1):160–170CrossRefGoogle Scholar
  16. 16.
    Di X, Kim EH, Huang CC, Tsai SJ, Lin CP, Biswal BB (2013) The influence of the amplitude of low-frequency fluctuations on resting-state functional connectivity. Front Hum Neurosci 7:118PubMedPubMedCentralGoogle Scholar
  17. 17.
    Di Martino A, Fair DA, Kelly C et al (2014) Unraveling the miswired connectome: a developmental perspective. Neuron 83(6):1335–1353CrossRefGoogle Scholar
  18. 18.
    Fair DA, Dosenbach NU, Church JA et al (2007) Development of distinct control networks through segregation and integration. Proc Natl Acad Sci 104(33):13507–13512CrossRefGoogle Scholar
  19. 19.
    Fair DA, Cohen AL, Dosenbach NU et al (2008) The maturing architecture of the brain’s default network. Proc Natl Acad Sci 105(10):4028–4032CrossRefGoogle Scholar
  20. 20.
    Ferro MA, Boyle MH (2015) The impact of chronic physical illness, maternal depressive symptoms, family functioning, and self-esteem on symptoms of anxiety and depression in children. J Abnorm Child Psychol 43(1):177–187CrossRefGoogle Scholar
  21. 21.
    Formoso D, Gonzales NA, Aiken LS (2000) Family conflict and children’s internalizing and externalizing behavior: protective factors. Am J Community Psychol 28:175–199CrossRefGoogle Scholar
  22. 22.
    Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711CrossRefGoogle Scholar
  23. 23.
    Geng X, Li G, Lu Z, Gao W, Wang L, Shen D et al (2017) Structural and maturational covariance in early childhood brain development. Cereb Cortex 27(3):1795–1807PubMedGoogle Scholar
  24. 24.
    Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A et al (1999) Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 2(10):861–863CrossRefGoogle Scholar
  25. 25.
    Gilmore JH, Shi F, Woolson SL, Knickmeyer RC, Short SJ, Lin W et al (2011) Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb Cortex 22(11):2478–2485CrossRefGoogle Scholar
  26. 26.
    Goodkind M, Eickhoff SB, Oathes DJ et al (2015) Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72(4):305–315CrossRefGoogle Scholar
  27. 27.
    Graham AM, Pfeifer JH, Fisher PA, Lin W, Gao W, Fair DA (2015) The potential of infant fMRI research and the study of early life stress as a promising exemplar. Dev Cogn Neurosci 12:12–39CrossRefGoogle Scholar
  28. 28.
    Graham AM, Pfeifer JH, Fisher PA, Carpenter S, Fair DA (2015) Early life stress is associated with default system integrity and emotionality during infancy. J Child Psychol Psychiatry 56(11):1212–1222CrossRefGoogle Scholar
  29. 29.
    Grayson DS, Fair DA (2017) Development of large-scale functional networks from birth to adulthood: a guide to the neuroimaging literature. NeuroImage 160:15–31CrossRefGoogle Scholar
  30. 30.
    Gu S, Satterthwaite TD, Medaglia JD, Yang M, Gur RE, Gur RC, Bassett DS (2015) Emergence of system roles in normative neurodevelopment. Proc Natl Acad Sci 112(44):13681–13686CrossRefGoogle Scholar
  31. 31.
    Hamilton E, Carr A (2016) Systematic review of self-report family assessment measures. Fam Process 55(1):16–30CrossRefGoogle Scholar
  32. 32.
    Han Y, Wang J, Zhao Z et al (2011) Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study. Neuroimage 55(1):287–295CrossRefGoogle Scholar
  33. 33.
    Harold GT, Leve LD, Barrett D, Elam K, Neiderhiser JM, Natsuaki MN, Shaw DS, Reiss D, Thapar A (2013a) Biological and rearing mother influences on child ADHD symptoms: revisiting the developmental interface between nature and nurture. J Child Psychol Psychiatry 54(10):1038–1046.  https://doi.org/10.1111/jcpp.12100 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Harold GT, Leve LD, Elam KK, Thapar A, Neiderhiser JM, Natsuaki MN, Shaw DS, Reiss D (2013b) The nature of nurture: disentangling passive genotype-environment correlation from family relationship influences on children’s externalizing problems. J Fam Psychol 27(1):12–21.  https://doi.org/10.1037/a0031190 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Hayes AF (2013) Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. The Guilford, New YorkGoogle Scholar
  36. 36.
    Holtmaat A, Svoboda K (2009) Experience-dependent structural synaptic plasticity in the mammalian brain. Nat Rev Neurosci 10(9):647–658CrossRefGoogle Scholar
  37. 37.
    Hou J, Wu W, Lin Y et al (2012) Localization of cerebral functional deficits in patients with obsessive–compulsive disorder: a resting-state fMRI study. J Affect Disord 138(3):313–321CrossRefGoogle Scholar
  38. 38.
    Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) FSL. NeuroImage 62(2):782–790CrossRefGoogle Scholar
  39. 39.
    Johnson SB, Riis JL, Noble KG (2016) State of the art review: poverty and the developing brain. Pediatrics, peds-2015Google Scholar
  40. 40.
    Kaufmann T, Alnæs D, Brandt CL, Doan NT, Kauppi K, Bettella F, Lagerberg TV, Berg AO, Djurovic S, Agartz I, Melle IS, Ueland T, Andreassen OA, Westlye LT (2017) Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets. Neuroimage 147:243–252.  https://doi.org/10.1016/j.neuroimage.2016.11.073 CrossRefPubMedGoogle Scholar
  41. 41.
    Kieling C, Baker-Henningham H, Belfer M et al (2011) Child and adolescent mental health worldwide: evidence for action. Lancet 378(9801):1515–1525CrossRefGoogle Scholar
  42. 42.
    Kim P, Evans GW, Angstadt M et al (2013) Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proc Natl Acad Sci 110(46):18442–18447CrossRefGoogle Scholar
  43. 43.
    Kong F, Hu S, Wang X, Song Y, Liu J (2015) Neural correlates of the happy life: the amplitude of spontaneous low frequency fluctuations predicts subjective well-being. Neuroimage 107:136–145CrossRefGoogle Scholar
  44. 44.
    Lendval B, Stern EA, Chen B, Svoboda K (2000) Experience-dependent plasticity of dendritic spines in the developing rat barrel cortex in vivo. Nature 404(6780):876CrossRefGoogle Scholar
  45. 45.
    Leve LD, Kim HK, Pears KC (2005) Childhood temperament and family environment as predictors of internalizing and externalizing trajectories from ages 5 to 17. J Abnorm Child Psychol 33:505–520CrossRefGoogle Scholar
  46. 46.
    Li F, He N, Li Y et al (2014) Intrinsic brain abnormalities in attention deficit hyperactivity disorder: a resting-state functional MR imaging study. Radiology 272(2):514–523CrossRefGoogle Scholar
  47. 47.
    Liu D, Diorio J, Day JC, Francis DD, Meaney MJ (2000) Maternal care, hippocampal synaptogenesis and cognitive development in rats. Nat Neurosci 3(8):799–806CrossRefGoogle Scholar
  48. 48.
    Liu J, Ren L, Womer FY et al (2014) Alterations in amplitude of low frequency fluctuation in treatment-naïve major depressive disorder measured with resting-state fMRI. Hum Brain Mapp 35(10):4979–4988CrossRefGoogle Scholar
  49. 49.
    Luby J, Belden A, Botteron K, Marrus N, Harms MP, Babb C et al (2013) The effects of poverty on childhood brain development: the mediating effect of caregiving and stressful life events. JAMA Pediatr 167(12):1135–1142CrossRefGoogle Scholar
  50. 50.
    Lucia VC, Breslau N (2006) Family cohesion and children’s behavior problems: a longitudinal investigation. Psychiatry Res 141:141–149CrossRefGoogle Scholar
  51. 51.
    Lyall AE, Shi F, Geng X, Woolson S, Li G, Wang L et al (2014) Dynamic development of regional cortical thickness and surface area in early childhood. Cereb Cortex 25(8):2204–2212CrossRefGoogle Scholar
  52. 52.
    Maughan A, Cicchetti D (2002) Impact of child maltreatment and interadult violence on children’s emotion regulation abilities and socioemotional adjustment. Child Dev 73(5):1525–1542CrossRefGoogle Scholar
  53. 53.
    Meda SA, Wang Z, Ivleva EI et al (2015) Frequency-specific neural signatures of spontaneous low-frequency resting state fluctuations in psychosis: evidence from bipolar-schizophrenia network on intermediate phenotypes (B-SNIP) consortium. Schizophr Bull 41(6):1336–1348CrossRefGoogle Scholar
  54. 54.
    Meyer-Lindenberg A, Weinberger DR (2006) Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat Rev Neurosci 7(10):818–827CrossRefGoogle Scholar
  55. 55.
    Moos RH, Moos BS (1994) Family environment scale manual. Consulting Psychologists, Palo AltoGoogle Scholar
  56. 56.
    Neville HJ, Stevens C, Pakulak E et al (2013) Family-based training program improves brain function, cognition, and behavior in lower socioeconomic status preschoolers. Proc Natl Acad Sci 110(29):12138–12143CrossRefGoogle Scholar
  57. 57.
    Noble KG, Houston SM, Kan E, Sowell ER (2012) Neural correlates of socioeconomic status in the developing human brain. Dev Sci 15(4):516–527CrossRefGoogle Scholar
  58. 58.
    Noble KG, Houston SM, Brito NH et al (2015) Family income, parental education and brain structure in children and adolescents. Nat Neurosci 18(5):773–778CrossRefGoogle Scholar
  59. 59.
    Piccolo LR, Merz EC, He X, Sowell ER, Noble KG (2016) Age-related differences in cortical thickness vary by socioeconomic status. PLoS One 11(9):e0162511CrossRefGoogle Scholar
  60. 60.
    Power JD, Fair DA, Schlaggar BL, Petersen SE (2010) The development of human functional brain networks. Neuron 67(5):735–748CrossRefGoogle Scholar
  61. 61.
    Repetti RL, Taylor SE, Seeman TE (2002) Risky families: family social environments and the mental and physical health of offspring. Psychol Bull 128(2):330–366CrossRefGoogle Scholar
  62. 62.
    Richmond MK, Stocker CM (2006) Associations between family cohesion and adolescent siblings’ externalizing behavior. J Fam Psychol 20:663–669CrossRefGoogle Scholar
  63. 63.
    Salum GA, Gadelha A, Pan PM et al (2015) High risk cohort study for psychiatric disorders in childhood: rationale, design, methods and preliminary results. Int J Methods Psychiatr Res 24(1):58–73CrossRefGoogle Scholar
  64. 64.
    Satterthwaite TD, Wolf DH, Loughead J et al (2012) Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage 60:623–632CrossRefGoogle Scholar
  65. 65.
    Satterthwaite TD, Baker JT (2015) How can studies of resting-state functional connectivity help us understand psychosis as a disorder of brain development? Curr Opin Neurobiol 30:85–91CrossRefGoogle Scholar
  66. 66.
    Sato JR, Salum GA, Gadelha A et al (2014) Age effects on the default mode and control networks in typically developing children. J Psychiatr Res 58:89–95CrossRefGoogle Scholar
  67. 67.
    Sato JR, Biazoli CE, Salum GA, Gadelha A, Crossley N, Satterthwaite TD et al (2015) Temporal stability of network centrality in control and default mode networks: specific associations with externalizing psychopathology in children and adolescents. Hum Brain Mapp 36(12):4926–4937CrossRefGoogle Scholar
  68. 68.
    Sato JR, Salum GA, Gadelha A et al (2016) Default mode network maturation and psychopathology in children and adolescents. J Child Psychol Psychiatry.  https://doi.org/10.1111/jcpp.12444 CrossRefPubMedGoogle Scholar
  69. 69.
    Sheridan MA, Sarsour K, Jutte D, D’Esposito M, Boyce WT (2012) The impact of social disparity on prefrontal function in childhood. PLoS One 7(4):e35744CrossRefGoogle Scholar
  70. 70.
    Shonkoff JP (2012) Leveraging the biology of adversity to address the roots of disparities in health and development. Proc Natl Acad Sci 109(S2):17302–17307CrossRefGoogle Scholar
  71. 71.
    Smith SM, Fox PT, Miller KL et al (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci USA 106:13040–13045CrossRefGoogle Scholar
  72. 72.
    Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW (2004) Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci 24(38):8223–8231CrossRefGoogle Scholar
  73. 73.
    Sripada RK, Swain JE, Evans GW, Welsh RC, Liberzon I (2014) Childhood poverty and stress reactivity are associated with aberrant functional connectivity in default mode network. Neuropsychopharmacology 39(9):2244–2251CrossRefGoogle Scholar
  74. 74.
    Stalnaker TA, Cooch NK, Schoenbaum G (2015) What the orbitofrontal cortex does not do. Nat Neurosci 18(5):620–627CrossRefGoogle Scholar
  75. 75.
    Tellegen A, Briggs PF (1967) Old wine in new skins: grouping Wechsler subtests into new scales. J Consult Psychol 31(5):499–506CrossRefGoogle Scholar
  76. 76.
    Toga AW, Thompson PM, Sowell ER (2006) Mapping brain maturation. Trends Neurosci 29(3):148–159CrossRefGoogle Scholar
  77. 77.
    Tost H, Champagne FA, Meyer-Lindenberg A (2015) Environmental influence in the brain, human welfare and mental health. Nat Neurosci 18(10):1421–1431CrossRefGoogle Scholar
  78. 78.
    Uddin LQ, Supekar K, Menon V (2010) Typical and atypical development of functional human brain networks: insights from resting-state FMRI. Front Syst Neurosci 4:21PubMedPubMedCentralGoogle Scholar
  79. 79.
    Van Den Heuvel MP, Pol HEH (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534CrossRefGoogle Scholar
  80. 80.
    Wagner F, Martel MM, Cogo-Moreira H et al (2016) Attention-deficit/hyperactivity disorder dimensionality: the reliable ‘g’ and the elusive ‘s’ dimensions. Eur Child Adolesc Psychiatry 25(1):83–90CrossRefGoogle Scholar
  81. 81.
    Wang GZ, Belgard TG, Mao D et al (2015) Correspondence between resting-state activity and brain gene expression. Neuron 88(4):659–666CrossRefGoogle Scholar
  82. 82.
    Wang L, Kong Q, Li K et al (2016) Frequency-dependent changes in amplitude of low-frequency oscillations in depression: a resting-state fMRI study. Neurosci Lett 614:105–111CrossRefGoogle Scholar
  83. 83.
    Wechsler D (1991) Wechsler intelligence scale for children-third edition (WISC-III): manual. Psychological Corporation, San AntonioGoogle Scholar
  84. 84.
    Weissman MM, Wickramaratne P, Adams P, Wolk S, Verdeli H, Olfson M (2000) Brief screening for family psychiatric history: the family history screen. Arch Gen Psychiatry 57:675–682CrossRefGoogle Scholar
  85. 85.
    Whittle S, Vijayakumar N, Simmons JG, Dennison M, Schwartz O, Pantelis C et al (2017) Role of positive parenting in the association between neighborhood social disadvantage and brain development across adolescence. JAMA Psychiatry 74:824–832CrossRefGoogle Scholar
  86. 86.
    Wyman PA, Cowen EL, Work WC, Hoyt-Meyers L, Magnus KB, Fagen DB (1999) Caregiving and developmental factors differentiating young at-risk urban children showing resilient versus stress-affected outcomes: a replication and extension. Child Dev 70(3):645–659CrossRefGoogle Scholar
  87. 87.
    Xu K, Liu H, Li H et al (2014) Amplitude of low-frequency fluctuations in bipolar disorder: a resting state fMRI study. J Affect Disord 152–154:237–242CrossRefGoogle Scholar
  88. 88.
    Yan CG, Craddock RC, Zuo XN, Zang YF, Milham MP (2013) Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage 80:246–262CrossRefGoogle Scholar
  89. 89.
    Yan CG, Cheung B, Kelly C et al (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76:183–201CrossRefGoogle Scholar
  90. 90.
    Yan X, Brown AD, Lazar M et al (2013) Spontaneous brain activity in combat related PTSD. Neurosci Lett 547:1–5CrossRefGoogle Scholar
  91. 91.
    Yang H, Wu QZ, Guo LT et al (2011) Abnormal spontaneous brain activity in medication-naive ADHD children: a resting state fMRI study. Neurosci Lett 502:89–93CrossRefGoogle Scholar
  92. 92.
    Yu R, Chien YL, Wang HL et al (2014) Frequency-specific alternations in the amplitude of low-frequency fluctuations in schizophrenia. Hum Brain Mapp 35(2):627–637CrossRefGoogle Scholar
  93. 93.
    Zou QH, Zhu CZ, Yang Y et al (2008) An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 172(1):137–141CrossRefGoogle Scholar
  94. 94.
    Zuo XN, Di Martino A, Kelly C et al (2010) The oscillating brain: complex and reliable. Neuroimage 49(2):1432–1445CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • João Ricardo Sato
    • 1
    • 3
    • 7
    • 8
    • 9
    Email author
  • Claudinei Eduardo BiazoliJr.
    • 1
    • 7
  • Giovanni Abrahão Salum
    • 2
    • 8
  • Ary Gadelha
    • 3
    • 8
  • Nicolas Crossley
    • 6
  • Gilson Vieira
    • 5
    • 7
  • André Zugman
    • 3
    • 8
  • Felipe Almeida Picon
    • 2
    • 8
  • Pedro Mario Pan
    • 3
    • 8
  • Marcelo Queiroz Hoexter
    • 3
    • 4
    • 8
  • Edson AmaroJr.
    • 9
  • Mauricio Anés
    • 2
    • 8
  • Luciana Monteiro Moura
    • 3
    • 8
  • Marco Antonio Gomes Del’Aquilla
    • 3
    • 8
  • Philip Mcguire
    • 6
  • Luis Augusto Rohde
    • 2
    • 8
  • Euripedes Constantino Miguel
    • 4
    • 8
  • Rodrigo Affonseca Bressan
    • 3
    • 8
  • Andrea Parolin Jackowski
    • 3
    • 8
  1. 1.Center of Mathematics, Computation, and CognitionUniversidade Federal do ABCSanto AndréBrazil
  2. 2.Hospital de Clinicas de Porto Alegre and Department of PsychiatryFederal University of Rio Grande do SulPorto AlegreBrazil
  3. 3.Interdisciplinary Lab for Clinical Neurosciences (LiNC)Universidade Federal de Sao Paulo (UNIFESP)São PauloBrazil
  4. 4.Department of Psychiatry, School of MedicineUniversity of Sao PauloSão PauloBrazil
  5. 5.Bioinformatics Program, Institute of Mathematics and StatisticsUniversity of Sao PauloSão PauloBrazil
  6. 6.Institute of PsychiatryKing’s College LondonLondonUK
  7. 7.Department of Radiology, School of MedicineUniversity of Sao PauloSão PauloBrazil
  8. 8.National Institute of Developmental Psychiatry for Children and Adolescents (CNPq)São PauloBrazil
  9. 9.Institute of Radiology (InRad), School of MedicineUniversity of Sao PauloSão PauloBrazil

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