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Predicting clinical outcome from reward circuitry function and white matter structure in behaviorally and emotionally dysregulated youth

Abstract

Behavioral and emotional dysregulation in childhood may be understood as prodromal to adult psychopathology. Additionally, there is a critical need to identify biomarkers reflecting underlying neuropathological processes that predict clinical/behavioral outcomes in youth. We aimed to identify such biomarkers in youth with behavioral and emotional dysregulation in the Longitudinal Assessment of Manic Symptoms (LAMS) study. We examined neuroimaging measures of function and white matter in the whole brain using 80 youth aged 14.0 (s.d.=2.0) from three clinical sites. Linear regression using the LASSO (Least Absolute Shrinkage and Selection Operator) method for variable selection was used to predict severity of future behavioral and emotional dysregulation measured by the Parent General Behavior Inventory-10 Item Mania Scale (PGBI-10M)) at a mean of 14.2 months follow-up after neuroimaging assessment. Neuroimaging measures, together with near-scan PGBI-10M, a score of manic behaviors, depressive behaviors and sex, explained 28% of the variance in follow-up PGBI-10M. Neuroimaging measures alone, after accounting for other identified predictors, explained ~1/3 of the explained variance, in follow-up PGBI-10M. Specifically, greater bilateral cingulum length predicted lower PGBI-10M at follow-up. Greater functional connectivity in parietal-subcortical reward circuitry predicted greater PGBI-10M at follow-up. For the first time, data suggest that multimodal neuroimaging measures of underlying neuropathologic processes account for over a third of the explained variance in clinical outcome in a large sample of behaviorally and emotionally dysregulated youth. This may be an important first step toward identifying neurobiological measures with the potential to act as novel targets for early detection and future therapeutic interventions.

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References

  1. Berkman ET, Falk EB . Beyond brain mapping using neural measures to predict real-world outcomes. Curr Direct Psychol Sci 2013; 22: 45–50.

    Article  Google Scholar 

  2. Pizzagalli DA . Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology 2010; 36: 183–206.

    Article  Google Scholar 

  3. Fu CH, Steiner H, Costafreda SG . Predictive neural biomarkers of clinical response in depression: a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies. Neurobiol Dis 2013; 52: 75–83.

    Article  CAS  Google Scholar 

  4. Shin LM, Davis FC, VanElzakker MB, Dahlgren MK, Dubois SJ . Neuroimaging predictors of treatment response in anxiety disorders. Biol Mood Anxiety Disord 2013; 3: 15.

    Article  Google Scholar 

  5. Forbes EE, Olino TM, Ryan ND, Birmaher B, Axelson D, Moyles DL et al. Reward-related brain function as a predictor of treatment response in adolescents with major depressive disorder. Cogn Affect Behav Neurosci 2010; 10: 107–118.

    Article  Google Scholar 

  6. Masten CL, Eisenberger NI, Borofsky LA, McNealy K, Pfeifer JH, Dapretto M . Subgenual anterior cingulate responses to peer rejection: a marker of adolescents' risk for depression. Dev Psychopathol 2011; 23: 283–292.

    Article  Google Scholar 

  7. Morgan JK, Olino TM, McMakin DL, Ryan ND, Forbes EE . Neural response to reward as a predictor of increases in depressive symptoms in adolescence. Neurobiol Dis 2013; 52: 66–74.

    Article  Google Scholar 

  8. McClure EB, Adler A, Monk CS, Cameron J, Smith S, Nelson EE et al. fMRI predictors of treatment outcome in pediatric anxiety disorders. Psychopharmacology 2007; 191: 97–105.

    Article  CAS  Google Scholar 

  9. Hum KM, Manassis K, Lewis MD . Neurophysiological markers that predict and track treatment outcomes in childhood anxiety. J Abnorm Child Psychol 2013; 41: 1243–1255.

    Article  Google Scholar 

  10. Kohannim O, Hibar DP, Jahanshad N, Stein JL, Hua X, Toga AW et al. Predicting temporal lobe volume on MRI from genotypes using L(1)–L(2) regularized regression. Proc IEEE Int Symp Biomed Imag 2012; 1160–1163; 2-5 May 2012.

  11. Wang Z, Xu W, Liu Y . Integrating full spectrum of sequence features into predicting functional microRNA–mRNA interactions. Bioinformatics 2015; 31: 3529–3536, 30.

    Article  CAS  Google Scholar 

  12. Zemmour C, Bertucci F, Finetti P, Chetrit B, Birnbaum D, Filleron T et al. Prediction of early breast cancer metastasis from DNA microarray data using high-dimensional Cox regression models. Cancer Inform 2015; 14 (Suppl. 2): 129–138.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Kohannim O, Hibar DP, Stein JL, Jahanshad N, Hua X, Rajagopalan P et al. Discovery and replication of gene influences on brain structure using LASSO regression. Front Neurosci 2012; 6: 115.

    Article  CAS  Google Scholar 

  14. Luo Y, McShan D, Kong F, Schipper M, Haken RT . TH-AB-304-07: a two-stage signature-based data fusion mechanism to predict radiation pneumonitis in patients with non-small-cell lung cancer (NSCLC). Med Phys 2015; 42: 4926122.

    Google Scholar 

  15. Christensen JA, Zoetmulder M, Koch H, Frandsen R, Arvastson L, Christensen SR et al. Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease. J Neurosci Methods 2014; 235: 262–276.

    Article  Google Scholar 

  16. Yan S, Tsurumi A, Que YA, Ryan CM, Bandyopadhaya A, Morgan AA et al. Prediction of multiple infections after severe burn trauma: a prospective cohort study. Ann Surg 2015; 261: 781–792.

    Article  Google Scholar 

  17. Findling RL, Youngstrom EA, Fristad MA, Birmaher B, Kowatch RA, Arnold LE et al. Characteristics of children with elevated symptoms of mania: the Longitudinal Assessment of Manic Symptoms (LAMS) study. J Clin Psychiatry 2010; 71: 1664.

    Article  Google Scholar 

  18. Horwitz SM, Demeter C, Pagano ME, Youngstrom EA, Fristad MA, Arnold LE et al. Longitudinal Assessment of Manic Symptoms (LAMS) Study: background, design and initial screening results. J Clin Psychiatry 2010; 71: 1511.

    Article  Google Scholar 

  19. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine D, Quinn K et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010; 167: 748–751.

    Article  Google Scholar 

  20. Insel TR, Gogtay N . National institute of mental health clinical trials: new opportunities, new expectations. JAMA Psychiatry 2014; 71: 745–746.

    Article  Google Scholar 

  21. Youngstrom E, Meyers O, Demeter C, Youngstrom J, Morello L, Piiparinen R et al. Comparing diagnostic checklists for pediatric bipolar disorder in academic and community mental health settings. Bipolar Disord 2005; 7: 507–517.

    Article  Google Scholar 

  22. Youngstrom EA, Frazier TW, Demeter C, Calabrese JR, Findling RL . Developing a Ten Item Mania Scale from the Parent General Behavior Inventory for Children and Adolescents. J Clin Psychiatry 2008; 69: 831.

    Article  Google Scholar 

  23. Frazier TW, Youngstrom EA, Horwitz SM, Demeter CA, Fristad MA, Arnold LE et al. The relationship of persistent manic symptoms to the diagnosis of pediatric bipolar disorder. J Clin Psychiatry 2011; 72: 846.

    Article  Google Scholar 

  24. Versace A, Acuff H, Bertocci MA, Bebko G, Almeida JR, Perlman SB et al. White matter structure in youth with behavioral and emotional dysregulation disorders: a probabilistic tractographic study. JAMA Psychiatry 2015; 72: 367–376.

    Article  Google Scholar 

  25. Bebko G, Bertocci MA, Fournier JC, Hinze AK, Bonar L, Almeida JR et al. Parsing dimensional vs diagnostic category-related patterns of reward circuitry function in behaviorally and emotionally dysregulated youth in the Longitudinal Assessment of Manic Symptoms Study. JAMA Psychiatry 2013; 71: 71–80.

    Article  Google Scholar 

  26. Versace A, Andreazza AC, Young LT, Fournier JC, Almeida JR, Stiffler RS et al. Elevated serum measures of lipid peroxidation and abnormal prefrontal white matter in euthymic bipolar adults: toward peripheral biomarkers of bipolar disorder. Mol Psychiatry 2014; 19: 200–208.

    Article  CAS  Google Scholar 

  27. Loeber R, Green SM, Keenan K, Lahey BB . Which boys will fare worse? Early predictors of the onset of conduct disorder in a Six-Year Longitudinal Study. J Am Acad Child Adolesc Psychiatry 1995; 34: 499–509.

    Article  CAS  Google Scholar 

  28. Leibenluft E, Cohen P, Gorrindo T, Brook JS, Pine DS . Chronic versus episodic irritability in youth: a community-based, longitudinal study of clinical and diagnostic associations. J Child Adolesc Psychopharmacol 2006; 16: 456–466.

    Article  Google Scholar 

  29. Kaufman J, Birmaher B, Brent DA, Rao U, Flynn C, Moreci P et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 1997; 36: 980–988.

    Article  CAS  Google Scholar 

  30. Axelson DA, Birmaher B, Brent DA, Wassick S, Hoover C, Bridge J et al. A preliminary study of the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children mania rating scale for children and adolescents. J Child Adolesc Psychopharmacol 2003; 13: 463–470.

    Article  Google Scholar 

  31. Forbes EE, Hariri AR, Martin SL, Silk JS, Moyles DL, Fisher PM et al. Altered striatal activation predicting real-world positive affect in adolescent major depressive disorder. Am J Psychiatry 2009; 166: 64.

    Article  Google Scholar 

  32. Di Martino A, Scheres A, Margulies D, Kelly A, Uddin L, Shehzad Z et al. Functional connectivity of human striatum: a resting state FMRI study. Cerebral Cortex 2008; 18: 2735–2747.

    Article  CAS  Google Scholar 

  33. Postuma RB, Dagher A . Basal ganglia functional connectivity based on a meta-analysis of 126 positron emission tomography and functional magnetic resonance imaging publications. Cerebral Cortex 2006; 16: 1508–1521.

    Article  Google Scholar 

  34. Yendiki A, Panneck P, Srinivasan P, Stevens A, Zöllei L, Augustinack J et al. Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front Neuroinform 2011; 5: 23.

    Article  Google Scholar 

  35. Segall JM, Turner JA, van Erp TG, White T, Bockholt HJ, Gollub RL et al. Voxel-based morphometric multisite collaborative study on schizophrenia. Schizophr Bull 2009; 35: 82–95.

    Article  Google Scholar 

  36. Magnotta VA, Friedman L . Measurement of signal-to-noise and contrast-to-noise in the fBIRN Multicenter Imaging Study. J Digital Imag 2006; 19: 140–147.

    Article  Google Scholar 

  37. Eklund A, Andersson M, Josephson C, Johannesson M, Knutsson H . Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets. NeuroImage 2012; 61: 565–578.

    Article  Google Scholar 

  38. Friedman L, Glover GH, The FC . Reducing interscanner variability of activation in a multicenter fMRI study: controlling for signal-to-fluctuation-noise-ratio (SFNR) differences. NeuroImage 2006; 33: 471–481.

    Article  Google Scholar 

  39. Pujol J, Soriano-Mas C, Alonso P et al. Mapping structural brain alterations in obsessive-compulsive disorder. Arch Gen Psychiatry 2004; 61: 720–730.

    Article  Google Scholar 

  40. Goldin PR, Manber-Ball T, Werner K, Heimberg R, Gross JJ . Neural mechanisms of cognitive reappraisal of negative self-beliefs in social anxiety disorder. Biol Psychiatry 2009; 66: 1091–1099.

    Article  Google Scholar 

  41. Surguladze S, Brammer MJ, Keedwell P, Giampietro V, Young AW, Travis MJ et al. A differential pattern of neural response toward sad versus happy facial expressions in major depressive disorder. Biol Psychiatry 2005; 57: 201–209.

    Article  Google Scholar 

  42. Lynn M, Demanet J, Krebs R, Van Dessel P, Brass M . Voluntary inhibition of pain avoidance behavior: an fMRI study. Brain Struct Funct 2014; 1–12; doi:10.1007/s00429-014-0972-9.

    Article  Google Scholar 

  43. Woo C-W, Krishnan A, Wager TD . Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. NeuroImage 2014; 91: 412–419.

    Article  Google Scholar 

  44. Reeck C, Egner T . Emotional task management: neural correlates of switching between affective and non-affective task-sets. Soc Cogn Affect Neurosci 2014; 10: 1045–1053.

    Article  Google Scholar 

  45. Somerville LH, Jones RM, Ruberry EJ, Dyke JP, Glover G, Casey BJ . The medial prefrontal cortex and the emergence of self-conscious emotion in adolescence. Psychol Sci 2013; 24: 1554–1562.

    Article  Google Scholar 

  46. Ladouceur CD, Farchione T, Diwadkar V, Pruitt P, Radwan J, Axelson DA et al. Differential Patterns of abnormal activity and connectivity in the amygdala–prefrontal circuitry in bipolar-I and bipolar-NOS youth. J Am Acad Child Adolesc Psychiatry. 2011; 50: 1275–89.e2.

    Article  Google Scholar 

  47. Friedman J, Hastie T, Simon N, Tibshirani R . GLMNET. 2.0-2 ed, 2014; http://www.jstatsoft.org/v33/i01/; last accessed December 2015.

  48. Tibshirani R . Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodological) 1996; 58: 267–288.

    Google Scholar 

  49. Friedman J, Hastie T, Tibshirani R . Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010; 33: 1–22.

    Article  Google Scholar 

  50. Revolutionary Analytics. Trevor Hastie presents glmnet: lasso and elastic-net regularization in R, 2013; blog.revolutionaryanalytics.com/2013/05/hastie-glmnet.html; last accessed December 2015.

  51. Wu TT, Lange K . Coordinate decent algorithms for lasso penalized regression. Ann Appl Stat 2008; 2: 224–244.

    Article  Google Scholar 

  52. Lockhart R, Taylor J, Tibshirani RJ, Tibshirani R . A significance test for the lasso. Ann Stat 2014; 42: 413–468.

    Article  Google Scholar 

  53. Peters J, Büchel C . The neural mechanisms of inter-temporal decision-making: understanding variability. Trends Cogn Sci 2011; 15: 227–239.

    Article  Google Scholar 

  54. Chen MY, Jimura K, White CN, Maddox WT, Poldrack RA . Multiple brain networks contribute to the acquisition of bias in perceptual decision-making. Front Neurosci 2015; 9: 63.

    PubMed  PubMed Central  Google Scholar 

  55. Boettiger CA, Mitchell JM, Tavares VC, Robertson M, Joslyn G, D'Esposito M et al. Immediate reward bias in humans: fronto-parietal networks and a role for the catechol-O-methyltransferase 158(Val/Val) genotype. J Neurosci 2007; 27: 14383–14391.

    Article  CAS  Google Scholar 

  56. Jarbo K, Verstynen TD . Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum. J Neurosci 2015; 35: 3865–3878.

    Article  CAS  Google Scholar 

  57. Urosevic S, Abramson LY, Alloy LB, Nusslock R, Harmon-Jones E, Bender R et al. Increased rates of events that activate or deactivate the behavioral approach system, but not events related to goal attainment, in bipolar spectrum disorders. J Abnorm Psychol 2010; 119: 610–615.

    Article  Google Scholar 

  58. Phillips ML, Swartz HA . A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and a road map for future research. Am J Psychiatry 2014; 171: 829–843.

    Article  Google Scholar 

  59. Paillere Martinot ML, Lemaitre H, Artiges E, Miranda R, Goodman R, Penttila J et al. White-matter microstructure and gray-matter volumes in adolescents with subthreshold bipolar symptoms. Mol Psychiatry 2014; 19: 462–470.

    Article  Google Scholar 

  60. Schmahmann JD, Pandya DN . Fiber Pathways of the Brain. Oxford University Press: Cary, NC, USA, 2009.

    Google Scholar 

  61. Heilbronner SR, Haber SN . Frontal cortical and subcortical projections provide a basis for segmenting the cingulum bundle: implications for neuroimaging and psychiatric disorders. J Neurosci 2014; 34: 10041–10054.

    Article  CAS  Google Scholar 

  62. Mufson EJ, Pandya DN . Some observations on the course and composition of the cingulum bundle in the rhesus monkey. J Comp Neurol 1984; 225: 31–43.

    Article  CAS  Google Scholar 

  63. Tibshirani R . The lasso method for variable selection in the cox model. Stat Med 1997; 16: 385–395.

    Article  CAS  Google Scholar 

  64. Brain Development Cooperative Group. Total and regional brain volumes in a population-based normative sample from 4 to 18 Years: The NIH MRI Study of Normal Brain Development. Cerebral Cortex 2012; 22: 1–12.

    Article  Google Scholar 

  65. Singh MK, Kelley RG, Howe ME, Reiss AL, Gotlib IH, Chang KD . Reward processing in healthy offspring of parents with bipolar disorder. JAMA Psychiatry 2014; 71: 1148–1156.

    Article  Google Scholar 

  66. Whelan R, Garavan H . When optimism hurts: inflated predictions in psychiatric neuroimaging. Biol Psychiatry 2013; 15: 00457–5.

    Google Scholar 

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Acknowledgements

This work was supported by the National Institute of Mental Health Grants 2R01 MH73953 (Dr Boris Birmaher and Dr Mary L Phillips, University of Pittsburgh), 2R01 MH73816 (Dr Scott Holland, Children’s Hospital Medical Center), 2R01 MH73967 (Dr Robert Findling, Case Western Reserve University), 2R01 MH73801(Dr Mary Fristad, Ohio State University), and the Pittsburgh Foundation (Mary L Phillips). The funding agency was not involved in the design and conduct of the study, the collection, management, analysis or interpretation of the data, or the preparation, review or approval of the manuscript. We would like to acknowledge Richard White, Gary Ciuffetelli, Eric Rodriguez and Christine Demeter for their contributions to the study.

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Correspondence to M A Bertocci.

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Bertocci, Bebko, Olino, Fournier, Iyengar, Horwitz, Axelson, Holland, Schirda, Versace, Almeida, Perlman, Diwadkar, Travis, Bonar, Gill and Forbes have no financial interests or potential conflict of interest. Dr Findling receives or has received research support, acted as a consultant and/or served on a speaker's bureau for Alcobra, American Academy of Child & Adolescent Psychiatry, American Physician Institute, American Psychiatric Press, AstraZeneca, Bracket, Bristol-Myers Squibb, CogCubed, Cognition Group, Coronado Biosciences, Dana Foundation, Elsevier, Forest, GlaxoSmithKline, Guilford Press, Johns Hopkins University Press, Johnson and Johnson, Jubilant Clinsys, KemPharm, Lilly, Lundbeck, Merck, NIH, Neurim, Novartis, Noven, Otsuka, Oxford University Press, Pfizer, Physicians Postgraduate Press, Purdue, Rhodes Pharmaceuticals, Roche, Sage, Shire, Sunovion, Supernus Pharmaceuticals, Transcept Pharmaceuticals, Tris, Validus, and WebMD. Dr Frazier has received federal funding or research support from, acted as a consultant to, received travel support from and/or received a speaker’s honorarium from the Cole family research fund, the Simons Foundation, Ingalls Foundation, Forest Laboratories, Ecoeos, IntegraGen, Kugona LLC, Shire Development, Bristol-Myers Squibb, National Institutes of Health and the Brain and Behavior Research Foundation. Dr Arnold has received research funding from Curemark, Forest, Lilly, Neuropharm, Novartis, Noven, Shire, Supernus, and YoungLiving (as well as NIH and Autism Speaks) and has consulted with or been on advisory boards for Arbor, Gowlings, Ironshore, Neuropharm, Novartis, Noven, Organon, Otsuka, Pfizer, Roche, Seaside Therapeutics, Sigma Tau, Shire, Tris Pharma, and Waypoint and received travel support from Noven. Dr Youngstrom has consulted with Pearson, Lundbeck and Otsuka about assessment, as well as having grant support from the NIH. Dr Fristad receives royalties from Guilford Press, APPI, CFPSI and is a consultant to Physicians Postgraduate Press and Western Psychological Services. Dr Birmaher receives royalties from for publications from Random House (New hope for children and teens with bipolar disorder) and Lippincott Williams & Wilkins (Treating Child and Adolescent Depression). He is employed by the University of Pittsburgh and the University of Pittsburgh Medical Center and receives research funding from NIMH. Dr Kowatch is a consultant for Forest Pharmaceutical and the REACH Foundation. He is employed by the Ohio State Wexner Medical Center. Dr Sunshine receives research support from Siemens Healthcare, Dr Phillips is a consultant for Roche.

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Bertocci, M., Bebko, G., Versace, A. et al. Predicting clinical outcome from reward circuitry function and white matter structure in behaviorally and emotionally dysregulated youth. Mol Psychiatry 21, 1194–1201 (2016). https://doi.org/10.1038/mp.2016.5

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