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Evaluating the evidence for biotypes of depression: attempted replication of Drysdale et.al. 2017

Richard Dinga, Lianne Schmaal, Brenda Penninx, Marie Jose van Tol, Dick J. Veltman, Laura van Velzen, Nic van der Wee, Andre Marquand
doi: https://doi.org/10.1101/416321
Richard Dinga
1Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
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Lianne Schmaal
2Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia
3Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
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Brenda Penninx
1Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
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Marie Jose van Tol
5Cognitive Neuroscience Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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Dick J. Veltman
1Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
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Laura van Velzen
1Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
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Nic van der Wee
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Andre Marquand
4Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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Abstract

Background Psychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechanisms. A possible approach to dissect biological heterogeneity is to look for biologically meaningful subtypes. A recent study Drysdale et al. (2017) showed promising results along this line by simultaneously using resting state fMRI and clinical data and identified four distinct subtypes of depression with different clinical profiles and abnormal resting state fMRI connectivity. These subtypes were predictive of treatment response to transcranial magnetic stimulation therapy.

Objective Here, we attempted to replicate the procedure followed in the Drysdale et al. study and their findings in an independent dataset of a clinically more heterogeneous sample of 187 participants with depression and anxiety. We aimed to answer the following questions: 1) Using the same procedure, can we find a statistically significant and reliable relationship between brain connectivity and clinical symptoms? 2) Is the observed relationship similar to the one found in the original study? 3) Can we identify distinct and reliable subtypes? 4) Do they have similar clinical profiles as the subtypes identified in the original study?

Methods We followed the original procedure as closely as possible, including a canonical correlation analysis to find a low dimensional representation of clinically relevant resting state fMRI features, followed by hierarchical clustering to identify subtypes. We extended the original procedure using additional statistical tests, to test the statistical significance of the relationship between resting state fMRI and clinical data, and the existence of distinct subtypes. Furthermore, we examined the stability of the whole procedure using resampling.

Results and Conclusion We were not able to replicate the findings of the original study. Relationships between brain connectivity and clinical symptoms were not statistically significant and we also did not find clearly distinct subtypes of depression. We argue, that based on our rigorous approach and in-depth review of the original results, that the evidence for the existence of the distinct resting state connectivity based subtypes of depression is weak and should be interpreted with caution.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 14, 2018.
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Evaluating the evidence for biotypes of depression: attempted replication of Drysdale et.al. 2017
Richard Dinga, Lianne Schmaal, Brenda Penninx, Marie Jose van Tol, Dick J. Veltman, Laura van Velzen, Nic van der Wee, Andre Marquand
bioRxiv 416321; doi: https://doi.org/10.1101/416321
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Evaluating the evidence for biotypes of depression: attempted replication of Drysdale et.al. 2017
Richard Dinga, Lianne Schmaal, Brenda Penninx, Marie Jose van Tol, Dick J. Veltman, Laura van Velzen, Nic van der Wee, Andre Marquand
bioRxiv 416321; doi: https://doi.org/10.1101/416321

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