Evaluating the evidence for biotypes of depression: Methodological replication and extension of Drysdale et al. (2017)

Highlights

Using methods of Drysdale et al. (2017) extremely high canonical correlations and optimal 3 cluster solution were obtained.

These, however, were not statistically significant after performing sufficiently rigorous statistical tests.

The researchers in this study argue that the methods used in Drysdale et al. (2017) do not provide convincing evidence for biotypes of depression.

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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.

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Objective

Here, it was attempted to replicate the procedure followed in the Drysdale et al. study and their findings in a different clinical population and a more heterogeneous sample of 187 participants with depression and anxiety.

The aim was to answer the following questions:

1) Using the same procedure, can a statistically significant and reliable relationship between brain connectivity and clinical symptoms be found?

2) Is the observed relationship similar to the one found in the original study?

3) Can distinct and reliable subtypes be identified?

4) Do they have similar clinical profiles as the subtypes identified in the original study?

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Methods

The original procedure was followed 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.

The original procedure was extended 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, the stability of the whole procedure was examined using resampling.

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Results and conclusion

As in the original study, extremely high canonical correlations between functional connectivity and clinical symptoms, and an optimal three-cluster solution were found.

However, neither canonical correlations nor clusters were statistically significant.

On the basis of the extensive evaluations of the analysis methodology used and within the limits of comparison of this sample relative to the sample used in Drysdale et al., it is argued that the evidence for the existence of the distinct resting state connectivity-based subtypes of depression should be interpreted with caution.

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Read the full publication here.

Dinga R., Schmaal L., Penninx B.W.J.H., van Tol M.J., Veltman D.J., van Velzen L., Mennes M., van der Wee N.J.A., Marquand A.F. Evaluating the evidence for biotypes of depression: Methodological replication and extension of Drysdale et al. (2017). Neuroimage Clin. 2019; 22:101796.

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