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

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.

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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.

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Correspondence to Benedikt Sundermann.

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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.

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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.

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

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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

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Keywords

  • fMRI
  • Functional connectivity
  • Classification
  • MVPA
  • Depression