Evaluating the evidence for biotypes of depression: Methodological replication and extension of

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 a different clinical population and a 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 As in the original study, we found extremely high canonical correlations between functional connectivity and clinical symptoms, and an optimal three-cluster solution. However, neither canonical correlations nor clusters were statistically significant. On the basis of our extensive evaluations of the analysis methodology used and within the limits of comparison of our sample relative to the sample used in Drysdale et al., we argue that the evidence for the existence of the distinct resting state connectivity-based subtypes of depression should be interpreted with caution.

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

“…Pursuant to this, there has been considerable interest in identifying clinically relevant subgroups based on brain imaging, with initially encouraging results (Drysdale et al, ). However, the robustness and generalizability of such studies have been brought into question (Dinga et al, ), which may be partly due to substantial brain heterogeneity within groups, which has been illustrated in terms of morphometry in schizophrenia (Alnæs et al, ). Alternatively, dimensional measures such as brain age prediction (Kaufmann et al, In press) and normative modeling (Marquand et al, ; Marquand, Rezek, Buitelaar, & Beckmann, ) have shown promising results in elucidating brain heterogeneity in mental disorders such as schizophrenia (Wolfers et al, ) and attention deficit/hyperactivity disorder (Wolfers et al, ).…”

Subtypes of functional connectivity associate robustly with ASD diagnosis

“…Our systematic evaluation of the robustness of subtype maps, and the discrete or continuous assignments of individuals to them, establishes a foundation on which to understand previous incidental findings on the robustness ( Easson et al, 2019 ) or non-reproducibility ( Dinga et al, 2019 ) of subtype analyses. The FC patterns of the subtypes identified in our analysis were found to be robust to perturbations of the discovery data set.…”

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

“…Pursuant to this, there has been considerable interest in identifying clinically relevant subgroups based on brain imaging, with initially encouraging results (Drysdale et al, 2017). However, the robustness and generalizability of such studies have been brought into question (Dinga et al, 2019), which may be partly due to substantial brain heterogeneity within groups, which has been illustrated in terms of morphometry in schizophrenia . Alternatively, dimensional measures such as brain age prediction and normative modelling Marquand, Rezek, Buitelaar, & Beckmann, 2016) have shown promising results in elucidating brain heterogeneity in mental disorders such as schizophrenia (Wolfers et al, 2018) and attention deficit/hyperactivity disorder .…”