Skip to main content

Advertisement

SpringerLink
Log in

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

  • Psychiatry and Preclinical Psychiatric Studies - Original Article
  • Published:
Journal of Neural Transmission Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • 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, Munich

  • Alpaydin E (2010) Introduction to machine learning. The MIT Press, Cambridge

    Google Scholar 

  • Anderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D (2011) Reproducibility of single-subject functional connectivity measurements. AJNR Am J Neuroradiol 32:548–555

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 12:3–21

    Article  PubMed  Google Scholar 

  • 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

  • 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 3:123–131

    Article  PubMed  PubMed Central  Google Scholar 

  • Barkhof F, Haller S, Rombouts SA (2014) Resting-state functional mr imaging: a new window to the brain. Radiology 272:29–49

    Article  PubMed  Google Scholar 

  • 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) 57:289–300

    Google Scholar 

  • 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

  • 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 83:550–558

    Article  PubMed  PubMed Central  Google Scholar 

  • Bowman FD, Drake DF, Huddleston DE (2016) Multimodal imaging signatures of Parkinson’s disease. Front Neurosci 10:131

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 14:365–376

    Article  CAS  PubMed  Google Scholar 

  • 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 1:230–244

    Article  PubMed  Google Scholar 

  • 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 68:110–119

    Article  PubMed  Google Scholar 

  • Castellanos FX, Di Martino A, Craddock RC, Mehta AD, Milham MP (2013) Clinical applications of the functional connectome. Neuroimage 80:527–540

    Article  CAS  PubMed  Google Scholar 

  • Chang C, Lin C (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27

    Article  Google Scholar 

  • Chao-Gan Y, Yu-Feng Z (2010) DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13

    PubMed  PubMed Central  Google Scholar 

  • 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 60:59–70

    Article  PubMed  Google Scholar 

  • 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 33:1914–1928

    Article  PubMed  Google Scholar 

  • Craddock RC, Holtzheimer PE 3rd, Hu XP, Mayberg HS (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62:1619–1628

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 18:1856–1864

    Article  CAS  PubMed  Google Scholar 

  • 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 12:11–22

    Article  CAS  PubMed  Google Scholar 

  • Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3:185–205

    Article  CAS  PubMed  Google Scholar 

  • 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 329:1358–1361

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 111:17648–17653

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 33:2147–2155

    Article  CAS  PubMed  Google Scholar 

  • 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 13:106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 52:75–83

    Article  CAS  PubMed  Google Scholar 

  • 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 151:423–431

    Article  PubMed  Google Scholar 

  • 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 14:224

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 9:153–163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422

    Article  Google Scholar 

  • 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):68–74

    Article  PubMed  Google Scholar 

  • 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 27:329–337

    Article  PubMed  Google Scholar 

  • Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatr 23:56–62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hamilton JP, Farmer M, Fogelman P, Gotlib IH (2015) Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience. Biol Psychiatry 78:224–230

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 37:1209–1217

    Article  PubMed  Google Scholar 

  • 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 247:87–92

    Article  CAS  PubMed  Google Scholar 

  • 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 11:125

    Article  PubMed  PubMed Central  Google Scholar 

  • Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2:e124

    Article  PubMed  PubMed Central  Google Scholar 

  • James G, Witten D, Hastie T (2013) An introduction to statistical learning with applications in R. Springer, New York

    Book  Google Scholar 

  • 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):603–611

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 18:5976–5989

    Article  CAS  PubMed  Google Scholar 

  • Klöppel S, Abdulkadir A, Jack CR Jr, Koutsouleris N, Mourao-Miranda J, Vemuri P (2012) Diagnostic neuroimaging across diseases. Neuroimage 61(2):457–463

    Article  PubMed  Google Scholar 

  • Krishnan R (2014) Unipolar depression in adults: epidemiology, pathogenesis, and neurobiology. In: Solomon D (ed) UpToDate. Wolters Kluwer Health, Alphen aan de Rijn

    Google Scholar 

  • Kupfer DJ, Frank E, Phillips ML (2012) Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet 379:1045–1055

    Article  PubMed  Google Scholar 

  • 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 1344:50–65

    Article  CAS  PubMed  Google Scholar 

  • Li L, Rakitsch B, Borgwardt K (2011) ccSVM: correcting support vector machines for confounding factors in biological data classification. Bioinformatics 27:i342–i348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lord A, Horn D, Breakspear M, Walter M (2012) Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One 7:e41282

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lui S, Zhou XJ, Sweeney JA, Gong Q (2016) Psychoradiology: the frontier of neuroimaging in psychiatry. Radiology 281:357–372

    Article  PubMed  Google Scholar 

  • 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 1495:86–94

    Article  CAS  PubMed  Google Scholar 

  • 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 22:229–251

    Article  PubMed  Google Scholar 

  • 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 23:289–307

    Article  PubMed  Google Scholar 

  • McCabe C, Mishor Z (2011) Antidepressant medications reduce subcortical-cortical resting-state functional connectivity in healthy volunteers. Neuroimage 57:1317–1323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 935–940

  • Murphy K, Birn RM, Bandettini PA (2013) Resting-state fMRI confounds and cleanup. Neuroimage 80:349–359

    Article  PubMed  PubMed Central  Google Scholar 

  • Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2):229–244

    Article  PubMed  PubMed Central  Google Scholar 

  • Nejad AB, Fossati P, Lemogne C (2013) Self-referential processing, rumination, and cortical midline structures in major depression. Front Hum Neurosci 7:666

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 36:1140–1152

    Article  PubMed  Google Scholar 

  • Patel MJ, Khalaf A, Aizenstein HJ (2016) Studying depression using imaging and machine learning methods. NeuroImage Clin 10:115–123

    Article  PubMed  Google Scholar 

  • Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45:S199–S209

    Article  PubMed  Google Scholar 

  • 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 172:124–138

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 8:229

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 34:3010–3022

    Article  PubMed  Google Scholar 

  • Qin J, Shen H, Zeng LL, Jiang W, Liu L, Hu D (2015) Predicting clinical responses in major depression using intrinsic functional connectivity. NeuroReport 26:675–680

    Article  CAS  PubMed  Google Scholar 

  • Radloff LS (1977) The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1:385–401

    Article  Google Scholar 

  • 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 12:320–331

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 105:493–506

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 90:449–468

    Article  PubMed  PubMed Central  Google Scholar 

  • Sarraf S, Tofighi G (2016) Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. arXiv 1603.08631

  • 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):278–286

    Article  PubMed  Google Scholar 

  • Schneider B, Prvulovic D (2013) Novel biomarkers in major depression. Curr Opin Psychiatry 26:47–53

    Article  PubMed  Google Scholar 

  • 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 11:46–51

    Article  PubMed  PubMed Central  Google Scholar 

  • Schowe B (2011) Feature selection for high-dimensional data with RapidMiner. In: Proceedings of the 2nd RapidMIner Community Meeting and Conferenc (RCOMM 2011)

  • 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 11:319–337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 106:13040–13045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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:e25031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc Ser B (Statistical Methodology) 64:479–498

    Article  Google Scholar 

  • Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100:9440–9445

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stuhrmann A, Suslow T, Dannlowski U (2011) Facial emotion processing in major depression: a systematic review of neuroimaging findings. Biol Mood Anxiety Disord 1:10-5380-1-10

    Article  Google Scholar 

  • 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 35:848–855

    Article  CAS  PubMed  Google Scholar 

  • 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 8:692

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 14:174

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 27:231–238

  • 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

  • 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 80:80–104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • van den Heuvel MP, Hulshoff Pol HE (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534

    Article  PubMed  Google Scholar 

  • 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 103:297–321

    Article  PubMed  Google Scholar 

  • 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):609–614

    Article  PubMed  Google Scholar 

  • Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer-Verlag, New York

    Book  Google Scholar 

  • 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 24:59–68

    Article  PubMed  Google Scholar 

  • 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 59:2045–2056

    Article  PubMed  Google Scholar 

  • 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 12:4

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 57:328–349

    Article  PubMed  Google Scholar 

  • Yu Y, Shen H, Zeng LL, Ma Q, Hu D (2013) Convergent and divergent functional connectivity patterns in schizophrenia and depression. PLoS One 8:e68250

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 135:1498–1507

    Article  PubMed  Google Scholar 

  • Zeng LL, Shen H, Liu L, Hu D (2014) Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp 35:1630–1641

    Article  PubMed  Google Scholar 

  • Zhang D, Raichle ME (2010) Disease and the brain’s dark energy. Nat Rev Neurol 6:15–28

    Article  PubMed  Google Scholar 

  • Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Statistical Methodology) 67:301–320

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany

    Benedikt Sundermann, Stephan Feder, Wolfram Schwindt, Harald Kugel, Walter Heindel & Bettina Pfleiderer

  2. Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany

    Heike Wersching, Anja Teuber & Klaus Berger

  3. Department of Psychiatry, University Hospital Münster, Münster, Germany

    Volker Arolt

Authors
  1. Benedikt Sundermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephan Feder
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heike Wersching
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anja Teuber
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wolfram Schwindt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Harald Kugel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Walter Heindel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Volker Arolt
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Klaus Berger
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bettina Pfleiderer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benedikt Sundermann.

Ethics declarations

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.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online Resource 1 (ESM_1.pdf): supplementary methods (PDF 87 kb)

702_2016_1673_MOESM2_ESM.pdf

Online Resource 2 (ESM_2.pdf): supplementary Tables 1–6 with detailed classification results and subgroup characteristics (PDF 258 kb)

Online Resource 3 (ESM_3.pdf): p value histograms of univariate group comparisons (PDF 24 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sundermann, B., Feder, S., Wersching, H. et al. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm 124, 589–605 (2017). https://doi.org/10.1007/s00702-016-1673-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00702-016-1673-8

Keywords

Search

Navigation