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Journal of Neural Transmission

, Volume 124, Issue 5, pp 589–605 | Cite as

Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample

  • Benedikt Sundermann
  • Stephan Feder
  • Heike Wersching
  • Anja Teuber
  • Wolfram Schwindt
  • Harald Kugel
  • Walter Heindel
  • Volker Arolt
  • Klaus Berger
  • Bettina Pfleiderer
Psychiatry and Preclinical Psychiatric Studies - Original Article

Abstract

In small, selected samples, an approach combining resting-state functional connectivity MRI and multivariate pattern analysis has been able to successfully classify patients diagnosed with unipolar depression. Purposes of this investigation were to assess the generalizability of this approach to a large clinically more realistic sample and secondarily to assess the replicability of previously reported methodological feasibility in a more homogeneous subgroup with pronounced depressive symptoms. Two independent subsets were drawn from the depression and control cohorts of the BiDirect study, each with 180 patients with and 180 controls without depression. Functional connectivity either among regions covering the gray matter or selected regions with known alterations in depression was assessed by resting-state fMRI. Support vector machines with and without automated feature selection were used to train classifiers differentiating between individual patients and controls in the entire first subset as well as in the subgroup. Model parameters were explored systematically. The second independent subset was used for validation of successful models. Classification accuracies in the large, heterogeneous sample ranged from 45.0 to 56.1% (chance level 50.0%). In the subgroup with higher depression severity, three out of 90 models performed significantly above chance (60.8–61.7% at independent validation). In conclusion, common classification methods previously successful in small homogenous depression samples do not immediately translate to a more realistic population. Future research to develop diagnostic classification approaches in depression should focus on more specific clinical questions and consider heterogeneity, including symptom severity as an important factor.

Keywords

fMRI Functional connectivity Classification MVPA Depression 

Notes

Acknowledgements

The authors thank all study participants and the entire team of the BiDirect study, including collaborators in associated institutions. Thanks also go to Jens Bode for support with the execution of data analyses.

Compliance with ethical standards

Conflicts of interest

The following authors declared additional financial relationships not directly related to this work: BP has received grants from EU European Social Fund and the German Ministry of Education and Research. VA is board member for Lundbeck, Otsuka, Servier and Tromsdorff and has received honoraria from Lundbeck, Otsuka and Servier.

Funding

BiDirect is funded by a research Grant (FZK: 01ER0816) from the German Federal Ministry of Education and Research (BMBF). This analysis was additionally supported by BMBF Grant 01ER1205.

Ethical approval

The study was approved by the ethics committee of the University of Münster and the Westphalian Chamber of Physicians in Münster. All procedures were done in accordance with the Helsinki Declaration.

Informed consent

Written informed consent for participation in the study was obtained from all participants.

Supplementary material

702_2016_1673_MOESM1_ESM.pdf (87 kb)
Online Resource 1 (ESM_1.pdf): supplementary methods (PDF 87 kb)
702_2016_1673_MOESM2_ESM.pdf (259 kb)
Online Resource 2 (ESM_2.pdf): supplementary Tables 1–6 with detailed classification results and subgroup characteristics (PDF 258 kb)
702_2016_1673_MOESM3_ESM.pdf (25 kb)
Online Resource 3 (ESM_3.pdf): p value histograms of univariate group comparisons (PDF 24 kb)

References

  1. Ackenheil M, Stotz-Ingenlath G, Dietz-Bauer R, Vossen A (1999) Mini International Neuropsychiatric Interview (German version 5.0.0, DSM-IV). Psychiatric University Clinic, MunichGoogle Scholar
  2. Alpaydin E (2010) Introduction to machine learning. The MIT Press, CambridgeGoogle Scholar
  3. Anderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D (2011) Reproducibility of single-subject functional connectivity measurements. AJNR Am J Neuroradiol 32:548–555CrossRefPubMedPubMedCentralGoogle Scholar
  4. Andrade L, Caraveo-Anduaga JJ, Berglund P, Bijl RV, De Graaf R, Vollebergh W, Dragomirecka E, Kohn R, Keller M, Kessler RC, Kawakami N, Kilic C, Offord D, Ustun TB, Wittchen HU (2003) The epidemiology of major depressive episodes: results from the International Consortium of Psychiatric Epidemiology (ICPE) Surveys. Int J Methods Psychiatr Res 12:3–21CrossRefPubMedGoogle Scholar
  5. Arbabshirani MR, Plis S, Sui J, Calhoun VD (2017) Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 145 (part B):137–165. doi: 10.1016/j.neuroimage.2016.02.079
  6. Atluri G, Padmanabhan K, Fang G, Steinbach M, Petrella JR, Lim K, Macdonald A 3rd, Samatova NF, Doraiswamy PM, Kumar V (2013) Complex biomarker discovery in neuroimaging data: finding a needle in a haystack. Neuroimage Clin 3:123–131CrossRefPubMedPubMedCentralGoogle Scholar
  7. Barkhof F, Haller S, Rombouts SA (2014) Resting-state functional mr imaging: a new window to the brain. Radiology 272:29–49CrossRefPubMedGoogle Scholar
  8. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol) 57:289–300Google Scholar
  9. Bhaumik R, Jenkins LM, Gowins JR, Jacobs RH, Barba A, Bhaumik DK, Langenecker SA (2016) Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity. Neuroimage Clin. doi: 10.1016/j.nicl.2016.02.018
  10. Birn RM, Molloy EK, Patriat R, Parker T, Meier TB, Kirk GR, Nair VA, Meyerand ME, Prabhakaran V (2013) The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage 83:550–558CrossRefPubMedPubMedCentralGoogle Scholar
  11. Bowman FD, Drake DF, Huddleston DE (2016) Multimodal imaging signatures of Parkinson’s disease. Front Neurosci 10:131CrossRefPubMedPubMedCentralGoogle Scholar
  12. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, Munafo MR (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14:365–376CrossRefPubMedGoogle Scholar
  13. Calhoun VD, Sui J (2016) Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. Biol Psychiatry Cogn Neurosci Neuroimaging 1:230–244CrossRefPubMedGoogle Scholar
  14. Cao L, Guo S, Xue Z, Hu Y, Liu H, Mwansisya TE, Pu W, Yang B, Liu C, Feng J, Chen EY, Liu Z (2014) Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci 68:110–119CrossRefPubMedGoogle Scholar
  15. Castellanos FX, Di Martino A, Craddock RC, Mehta AD, Milham MP (2013) Clinical applications of the functional connectome. Neuroimage 80:527–540CrossRefPubMedGoogle Scholar
  16. Chang C, Lin C (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27CrossRefGoogle Scholar
  17. Chao-Gan Y, Yu-Feng Z (2010) DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13PubMedPubMedCentralGoogle Scholar
  18. Chu C, Hsu AL, Chou KH, Bandettini P, Lin C, Alzheimer’s Disease Neuroimaging Initiative (2012) Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60:59–70CrossRefPubMedGoogle Scholar
  19. Craddock RC, James GA, Holtzheimer PE 3rd, Hu XP, Mayberg HS (2012) A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 33:1914–1928CrossRefPubMedGoogle Scholar
  20. Craddock RC, Holtzheimer PE 3rd, Hu XP, Mayberg HS (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62:1619–1628CrossRefPubMedPubMedCentralGoogle Scholar
  21. Damoiseaux JS, Beckmann CF, Arigita EJ, Barkhof F, Scheltens P, Stam CJ, Smith SM, Rombouts SA (2008) Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex 18:1856–1864CrossRefPubMedGoogle Scholar
  22. Dannlowski U, Ohrmann P, Konrad C, Domschke K, Bauer J, Kugel H, Hohoff C, Schoning S, Kersting A, Baune BT, Mortensen LS, Arolt V, Zwitserlood P, Deckert J, Heindel W, Suslow T (2009) Reduced amygdala-prefrontal coupling in major depression: association with MAOA genotype and illness severity. Int J Neuropsychopharmacol 12:11–22CrossRefPubMedGoogle Scholar
  23. Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3:185–205CrossRefPubMedGoogle Scholar
  24. Dosenbach NU, Nardos B, Cohen AL, Fair DA, Power JD, Church JA, Nelson SM, Wig GS, Vogel AC, Lessov-Schlaggar CN, Barnes KA, Dubis JW, Feczko E, Coalson RS, Pruett JR Jr, Barch DM, Petersen SE, Schlaggar BL (2010) Prediction of individual brain maturity using fMRI. Science 329:1358–1361CrossRefPubMedPubMedCentralGoogle Scholar
  25. Douaud G, Groves AR, Tamnes CK, Westlye LT, Duff EP, Engvig A, Walhovd KB, James A, Gass A, Monsch AU, Matthews PM, Fjell AM, Smith SM, Johansen-Berg H (2014) A common brain network links development, aging, and vulnerability to disease. Proc Natl Acad Sci USA 111:17648–17653CrossRefPubMedPubMedCentralGoogle Scholar
  26. Douaud G, Menke RA, Gass A, Monsch AU, Rao A, Whitcher B, Zamboni G, Matthews PM, Sollberger M, Smith S (2013) Brain microstructure reveals early abnormalities more than two years prior to clinical progression from mild cognitive impairment to Alzheimer’s disease. J Neurosci 33:2147–2155CrossRefPubMedGoogle Scholar
  27. Dunlop BW, Binder EB, Cubells JF, Goodman MM, Kelley ME, Kinkead B, Kutner M, Nemeroff CB, Newport DJ, Owens MJ, Pace TW, Ritchie JC, Rivera VA, Westen D, Craighead WE, Mayberg HS (2012) Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial. Trials 13:106CrossRefPubMedPubMedCentralGoogle Scholar
  28. Fu CH, Steiner H, Costafreda SG (2013) Predictive neural biomarkers of clinical response in depression: a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies. Neurobiol Dis 52:75–83CrossRefPubMedGoogle Scholar
  29. Graham J, Salimi-Khorshidi G, Hagan C, Walsh N, Goodyer I, Lennox B, Suckling J (2013) Meta-analytic evidence for neuroimaging models of depression: state or trait? J Affect Disord 151:423–431CrossRefPubMedGoogle Scholar
  30. Grieve SM, Korgaonkar MS, Etkin A, Harris A, Koslow SH, Wisniewski S, Schatzberg AF, Nemeroff CB, Gordon E, Williams LM (2013) Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial. Trials 14:224CrossRefPubMedPubMedCentralGoogle Scholar
  31. Guo H, Cheng C, Cao X, Xiang J, Chen J, Zhang K (2014) Resting-state functional connectivity abnormalities in first-onset unmedicated depression. Neural Regen Res 9:153–163CrossRefPubMedPubMedCentralGoogle Scholar
  32. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422CrossRefGoogle Scholar
  33. Hahn T, Kircher T, Straube B, Wittchen HU, Konrad C, Strohle A, Wittmann A, Pfleiderer B, Reif A, Arolt V, Lueken U (2015) Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information. JAMA Psychiatry 72(1):68–74CrossRefPubMedGoogle Scholar
  34. Haller S, Lovblad KO, Giannakopoulos P, Van De Ville D (2014) Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends. Brain Topogr 27:329–337CrossRefPubMedGoogle Scholar
  35. Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatr 23:56–62CrossRefPubMedPubMedCentralGoogle Scholar
  36. Hamilton JP, Farmer M, Fogelman P, Gotlib IH (2015) Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience. Biol Psychiatry 78:224–230CrossRefPubMedPubMedCentralGoogle Scholar
  37. Hermesdorf M, Sundermann B, Feder S, Schwindt W, Minnerup J, Arolt V, Berger K, Pfleiderer B, Wersching H (2016) Major depressive disorder: findings of reduced homotopic connectivity and investigation of underlying structural mechanisms. Hum Brain Mapp 37:1209–1217CrossRefPubMedGoogle Scholar
  38. Heun R, Hardt J, Muller H, Maier W (1997) Selection bias during recruitment of elderly subjects from the general population for psychiatric interviews. Eur Arch Psychiatry Clin Neurosci 247:87–92CrossRefPubMedGoogle Scholar
  39. Hickie IB, Scott J, Hermens DF, Scott EM, Naismith SL, Guastella AJ, Glozier N, McGorry PD (2013) Clinical classification in mental health at the cross-roads: which direction next? BMC Med 11:125CrossRefPubMedPubMedCentralGoogle Scholar
  40. Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2:e124CrossRefPubMedPubMedCentralGoogle Scholar
  41. James G, Witten D, Hastie T (2013) An introduction to statistical learning with applications in R. Springer, New YorkCrossRefGoogle Scholar
  42. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA (2015) Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry 72(6):603–611CrossRefPubMedPubMedCentralGoogle Scholar
  43. Kennedy SH, Downar J, Evans KR, Feilotter H, Lam RW, MacQueen GM, Milev R, Parikh SV, Rotzinger S, Soares C (2012) The Canadian biomarker integration network in depression (CAN-BIND): advances in response prediction. Curr Pharm Des 18:5976–5989CrossRefPubMedGoogle Scholar
  44. Klöppel S, Abdulkadir A, Jack CR Jr, Koutsouleris N, Mourao-Miranda J, Vemuri P (2012) Diagnostic neuroimaging across diseases. Neuroimage 61(2):457–463CrossRefPubMedGoogle Scholar
  45. Krishnan R (2014) Unipolar depression in adults: epidemiology, pathogenesis, and neurobiology. In: Solomon D (ed) UpToDate. Wolters Kluwer Health, Alphen aan de RijnGoogle Scholar
  46. Kupfer DJ, Frank E, Phillips ML (2012) Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet 379:1045–1055CrossRefPubMedGoogle Scholar
  47. Lener MS, Iosifescu DV (2015) In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature. Ann N Y Acad Sci 1344:50–65CrossRefPubMedGoogle Scholar
  48. Li L, Rakitsch B, Borgwardt K (2011) ccSVM: correcting support vector machines for confounding factors in biological data classification. Bioinformatics 27:i342–i348CrossRefPubMedPubMedCentralGoogle Scholar
  49. Lord A, Horn D, Breakspear M, Walter M (2012) Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One 7:e41282CrossRefPubMedPubMedCentralGoogle Scholar
  50. Lui S, Zhou XJ, Sweeney JA, Gong Q (2016) Psychoradiology: the frontier of neuroimaging in psychiatry. Radiology 281:357–372CrossRefPubMedGoogle Scholar
  51. Ma Q, Zeng LL, Shen H, Liu L, Hu D (2013) Altered cerebellar-cerebral resting-state functional connectivity reliably identifies major depressive disorder. Brain Res 1495:86–94CrossRefPubMedGoogle Scholar
  52. Marchetti I, Koster EH, Sonuga-Barke EJ, De Raedt R (2012) The default mode network and recurrent depression: a neurobiological model of cognitive risk factors. Neuropsychol Rev 22:229–251CrossRefPubMedGoogle Scholar
  53. Margulies DS, Bottger J, Long X, Lv Y, Kelly C, Schafer A, Goldhahn D, Abbushi A, Milham MP, Lohmann G, Villringer A (2010) Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. MAGMA 23:289–307CrossRefPubMedGoogle Scholar
  54. McCabe C, Mishor Z (2011) Antidepressant medications reduce subcortical-cortical resting-state functional connectivity in healthy volunteers. Neuroimage 57:1317–1323CrossRefPubMedPubMedCentralGoogle Scholar
  55. Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) YALE: rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 935–940Google Scholar
  56. Murphy K, Birn RM, Bandettini PA (2013) Resting-state fMRI confounds and cleanup. Neuroimage 80:349–359CrossRefPubMedPubMedCentralGoogle Scholar
  57. Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2):229–244CrossRefPubMedPubMedCentralGoogle Scholar
  58. Nejad AB, Fossati P, Lemogne C (2013) Self-referential processing, rumination, and cortical midline structures in major depression. Front Hum Neurosci 7:666CrossRefPubMedPubMedCentralGoogle Scholar
  59. Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A (2012) Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36:1140–1152CrossRefPubMedGoogle Scholar
  60. Patel MJ, Khalaf A, Aizenstein HJ (2016) Studying depression using imaging and machine learning methods. NeuroImage Clin 10:115–123CrossRefPubMedGoogle Scholar
  61. Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45:S199–S209CrossRefPubMedGoogle Scholar
  62. Phillips ML, Chase HW, Sheline YI, Etkin A, Almeida JR, Deckersbach T, Trivedi MH (2015) Identifying predictors, moderators, and mediators of antidepressant response in major depressive disorder: neuroimaging approaches. Am J Psychiatry 172:124–138CrossRefPubMedPubMedCentralGoogle Scholar
  63. Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229CrossRefPubMedPubMedCentralGoogle Scholar
  64. Pyka M, Hahn T, Heider D, Krug A, Sommer J, Kircher T, Jansen A (2013) Baseline activity predicts working memory load of preceding task condition. Hum Brain Mapp 34:3010–3022CrossRefPubMedGoogle Scholar
  65. Qin J, Shen H, Zeng LL, Jiang W, Liu L, Hu D (2015) Predicting clinical responses in major depression using intrinsic functional connectivity. NeuroReport 26:675–680CrossRefPubMedGoogle Scholar
  66. Radloff LS (1977) The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1:385–401CrossRefGoogle Scholar
  67. Ramasubbu R, Brown MR, Cortese F, Gaxiola I, Goodyear B, Greenshaw AJ, Dursun SM, Greiner R (2016) Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage Clin 12:320–331CrossRefPubMedPubMedCentralGoogle Scholar
  68. Rosa MJ, Portugal L, Hahn T, Fallgatter AJ, Garrido MI, Shawe-Taylor J, Mourao-Miranda J (2015) Sparse network-based models for patient classification using fMRI. Neuroimage 105:493–506CrossRefPubMedPubMedCentralGoogle Scholar
  69. Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM (2014) Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 90:449–468CrossRefPubMedPubMedCentralGoogle Scholar
  70. Sarraf S, Tofighi G (2016) Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. arXiv 1603.08631Google Scholar
  71. Schmaal L, Marquand AF, Rhebergen D, van Tol M, Ruhe HG, van der Wee NJ, Veltman DJ, Penninx BWJH (2014) Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: a multivariate pattern recognition study. Biol Psychiatry 78(4):278–286CrossRefPubMedGoogle Scholar
  72. Schneider B, Prvulovic D (2013) Novel biomarkers in major depression. Curr Opin Psychiatry 26:47–53CrossRefPubMedGoogle Scholar
  73. Schouten TM, Koini M, de Vos F, Seiler S, van der Grond J, Lechner A, Hafkemeijer A, Moller C, Schmidt R, de Rooij M, Rombouts SA (2016) Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer’s disease. Neuroimage Clin 11:46–51CrossRefPubMedPubMedCentralGoogle Scholar
  74. Schowe B (2011) Feature selection for high-dimensional data with RapidMiner. In: Proceedings of the 2nd RapidMIner Community Meeting and Conferenc (RCOMM 2011)Google Scholar
  75. Schrouff J, Rosa MJ, Rondina JM, Marquand AF, Chu C, Ashburner J, Phillips C, Richiardi J, Mourao-Miranda J (2013) PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics 11:319–337CrossRefPubMedPubMedCentralGoogle Scholar
  76. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci USA 106:13040–13045CrossRefPubMedPubMedCentralGoogle Scholar
  77. Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, He Y, Yan CG, Zang YF (2011) REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One 6:e25031CrossRefPubMedPubMedCentralGoogle Scholar
  78. Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc Ser B (Statistical Methodology) 64:479–498CrossRefGoogle Scholar
  79. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100:9440–9445CrossRefPubMedPubMedCentralGoogle Scholar
  80. Stuhrmann A, Suslow T, Dannlowski U (2011) Facial emotion processing in major depression: a systematic review of neuroimaging findings. Biol Mood Anxiety Disord 1:10-5380-1-10CrossRefGoogle Scholar
  81. Sundermann B, Herr D, Schwindt W, Pfleiderer B (2014a) Multivariate classification of blood oxygen level-dependent FMRI data with diagnostic intention: a clinical perspective. AJNR Am J Neuroradiol 35:848–855CrossRefPubMedGoogle Scholar
  82. Sundermann B, Olde Lütke Beverborg M, Pfleiderer B (2014b) Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression. Front Hum Neurosci 8:692CrossRefPubMedPubMedCentralGoogle Scholar
  83. Teismann H, Wersching H, Nagel M, Arolt V, Heindel W, Baune BT, Wellmann J, Hense HW, Berger K (2014) Establishing the bidirectional relationship between depression and subclinical arteriosclerosis–rationale, design, and characteristics of the BiDirect Study. BMC Psychiatry 14:174CrossRefPubMedPubMedCentralGoogle Scholar
  84. Teuber A, Sundermann B, Kugel H, Schwindt W, Heindel W, Minnerup J, Dannlowski U, Berger K, Wersching H (2017) MR imaging of the brain in large cohort studies—feasibility report of the population- and patient-based BiDirect study. Eur Radiol 27:231–238Google Scholar
  85. Trivedi M, McGrath PJ, Fava M, Parsey RV, Kurian BT, Phillips ML, Oquendo M, Bruder G, Pizzagalli DA, Toups M, Cooper C, Adams P, Weyandt S, Morris DW, Grannemann BD, Ogden RT, Bucker R, McInnis M, Kramer HC, Petkova E, Carmody T, Weissmann MM (2016) Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): rationale and design. J Psychiatr Res 78:11–23. doi: 10.1016/j.jpsychires.2016.03.001
  86. Ugurbil K, Xu J, Auerbach EJ, Moeller S, Vu AT, Duarte-Carvajalino JM, Lenglet C, Wu X, Schmitter S, Van de Moortele PF, Strupp J, Sapiro G, De Martino F, Wang D, Harel N, Garwood M, Chen L, Feinberg DA, Smith SM, Miller KL, Sotiropoulos SN, Jbabdi S, Andersson JL, Behrens TE, Glasser MF, Van Essen DC, Yacoub E, WU-Minn HCP Consortium (2013) Pushing spatial and temporal resolution for functional and diffusion MRI in the human connectome project. Neuroimage 80:80–104CrossRefPubMedPubMedCentralGoogle Scholar
  87. van den Heuvel MP, Hulshoff Pol HE (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534CrossRefPubMedGoogle Scholar
  88. Van Dijk KR, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL (2010) Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol 103:297–321CrossRefPubMedGoogle Scholar
  89. van Waarde JA, Scholte HS, van Oudheusden LJ, Verwey B, Denys D, van Wingen GA (2015) A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry 20(5):609–614CrossRefPubMedGoogle Scholar
  90. Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer-Verlag, New YorkCrossRefGoogle Scholar
  91. Waites AB, Stanislavsky A, Abbott DF, Jackson GD (2005) Effect of prior cognitive state on resting state networks measured with functional connectivity. Hum Brain Mapp 24:59–68CrossRefPubMedGoogle Scholar
  92. Wee CY, Yap PT, Zhang D, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D (2012) Identification of MCI individuals using structural and functional connectivity networks. Neuroimage 59:2045–2056CrossRefPubMedGoogle Scholar
  93. Williams LM, Rush AJ, Koslow SH, Wisniewski SR, Cooper NJ, Nemeroff CB, Schatzberg AF, Gordon E (2011) International study to predict optimized treatment for depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12:4CrossRefPubMedPubMedCentralGoogle Scholar
  94. Wolfers T, Buitelaar JK, Beckmann CF, Franke B, Marquand AF (2015) From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev 57:328–349CrossRefPubMedGoogle Scholar
  95. Yu Y, Shen H, Zeng LL, Ma Q, Hu D (2013) Convergent and divergent functional connectivity patterns in schizophrenia and depression. PLoS One 8:e68250CrossRefPubMedPubMedCentralGoogle Scholar
  96. Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D (2012) Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135:1498–1507CrossRefPubMedGoogle Scholar
  97. Zeng LL, Shen H, Liu L, Hu D (2014) Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp 35:1630–1641CrossRefPubMedGoogle Scholar
  98. Zhang D, Raichle ME (2010) Disease and the brain’s dark energy. Nat Rev Neurol 6:15–28CrossRefPubMedGoogle Scholar
  99. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Statistical Methodology) 67:301–320CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Benedikt Sundermann
    • 1
  • Stephan Feder
    • 1
  • Heike Wersching
    • 2
  • Anja Teuber
    • 2
  • Wolfram Schwindt
    • 1
  • Harald Kugel
    • 1
  • Walter Heindel
    • 1
  • Volker Arolt
    • 3
  • Klaus Berger
    • 2
  • Bettina Pfleiderer
    • 1
  1. 1.Department of Clinical RadiologyUniversity Hospital MünsterMünsterGermany
  2. 2.Institute of Epidemiology and Social MedicineUniversity of MünsterMünsterGermany
  3. 3.Department of PsychiatryUniversity Hospital MünsterMünsterGermany

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