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Size matters; but so does what you do with it!

Using meta-analysis techniques in a large neuroimaging data set derived from 1728 major depressive disorder (MDD) patients and 7199 healthy controls, across 15 centres worldwide, Schmaal et al.1 attempted to identify subcortical brain alterations associated with MDD. The impetus for this, as emphasized by the authors themselves, is that current neuroimaging studies that have tackled this question suffer from three main problems: (1) limited statistical power due to small sample sizes, (2) disease heterogeneity in clinical samples and (3) complex interactions between clinical characteristics and brain morphology. Puzzlingly, the authors predominantly focus on the first of these problems despite having coalesced structural neuroimaging data from a large number of subjects, and essentially report reduced hippocampal volume in patients with MDD. But this is not a new finding and indeed it has been replicated and supported by several previous neuroimaging meta-analyses.2, 3

The scientific merit of a research finding is governed not only by the sample size but also by how moderating factors are managed within the analyses, to reveal meaningful relationships between variables of interest. Disease heterogeneity for example is inevitable when sampling real-world clinical populations and, rather than attempting to achieve homogeneity through statistical control, a large sample size allows for distinction of clinically meaningful subtypes, such as melancholia, addressing problem (2) above. Large data sets, such as those garnered by Schmaal et al.,1 often afford such an opportunity; unfortunately the authors did not take full advantage of the large data set to address these issues.

When structurally mapping hippocampal volume, extant research findings point to several moderating factors that warrant careful consideration, such as gender,4 recurrence of mood episodes3, 5 and the effects of medication, in particular the impact of antidepressants,6 given that each of these can produce discernible volumetric change. And while disentangling the individual effects of clinical factors upon neuroimaging parameters is a significant challenge, it is one that can be purposefully attempted in a large data set by robustly delineating subgroups defined on the basis of clinical characteristics, instead of regressing out these features or only examining these factors individually. The authors may have the ability to partition their large sample into interactive cells (for example, diagnosis x sex x recurrence x onset) and to analyse for between-subject differences across these complex interactions. In doing so, they would have the ability to show how clinical complexity plays a role in hippocampal volume in MDD, addressing problem (3) above. This type of approach is capable of revealing pertinent new findings that can potentially inform both the aetiology and treatment of MDD. In the absence of such granular analyses, reiteration of non-specific volumetric changes does little to advance our knowledge of the illness and instead risks reifying a clinically inconsequential finding.

Our concern is exemplified by the finding that there was no association between MDD severity and hippocampal volume change, and that first-episode MDD did not differentiate patients from healthy controls with respect to hippocampal size. This raises some interesting questions: does hippocampal structural change become evident early in the development of MDD? And to what extent is hippocampal volume moderated by clinical complexity? Schmaal et al.1 examined early- versus late-onset MDD, but the real impact of the illness is difficult to gauge without detailed knowledge of its duration, course and clinical pattern.

The architects of ENIGMA are to be commended for achieving sufficiently large sample sizes for neuroimaging analysis through multicentre collaboration. But by not attending to the complexity of clinical manifestations, the study fails to exploit its full potential, and the reported hippocampal volume changes fail to provide a deeper understanding of MDD.

References

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Correspondence to G S Malhi.

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Malhi, G., Das, P. & Outhred, T. Size matters; but so does what you do with it!. Mol Psychiatry 21, 725–726 (2016). https://doi.org/10.1038/mp.2015.200

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Further reading

  • Response to Dr Fried & Dr Kievit, and Dr Malhi et al.

    • L Schmaal
    • , D J Veltman
    • , T G M van Erp
    • , P G Sämann
    • , T Frodl
    • , N Jahanshad
    • , E Loehrer
    • , M W Vernooij
    • , W J Niessen
    • , M A Ikram
    • , K Wittfeld
    • , H J Grabe
    • , A Block
    • , K Hegenscheid
    • , D Hoehn
    • , M Czisch
    • , J Lagopoulos
    • , S N Hatton
    • , I B Hickie
    • , R Goya-Maldonado
    • , B Krämer
    • , O Gruber
    • , B Couvy-Duchesne
    • , M E Rentería
    • , L T Strike
    • , M J Wright
    • , G I de Zubicaray
    • , K L McMahon
    • , S E Medland
    • , N A Gillespie
    • , G B Hall
    • , L S van Velzen
    • , M-J van Tol
    • , N J van der Wee
    • , I M Veer
    • , H Walter
    • , E Schramm
    • , C Normann
    • , D Schoepf
    • , C Konrad
    • , B Zurowski
    • , A M McIntosh
    • , H C Whalley
    • , J E Sussmann
    • , B R Godlewska
    • , F H Fischer
    • , B W J H Penninx
    • , P M Thompson
    •  & D P Hibar

    Molecular Psychiatry (2016)

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