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BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Abstract Background Neurocognitive impairment is frequently described in a number of psychiatric disorders and may be a direct consequence of serotonergic dysfunction. As impairments in executive functions are some of the most frequently described, the purpose of this study was to examine the performance of normal volunteers on a range of executive tasks following a transient reduction of central serotonin (5-HT) levels using the method of acute tryptophan depletion (ATD). Methods Fifteen healthy male subjects participated in a within-subject, double-blind, counterbalanced crossover study. ATD was induced by ingestion of a 100 g amino-acid drink. Executive function was evaluated using the Wisconsin Card Sorting Test, Stroop, Verbal Fluency and Trail Making. Visual analogue scales were administered to assess mood. Results Plasma free and total tryptophan concentrations were significantly reduced by the depleting drink (P < 0.001). ATD selectively improved motor speed/ attention on the Trails A test (P = 0.027), with no effect on subjective ratings of mood. Interaction effects between drink and the order of drink administration were observed on most neurocognitive tests. Conclusions The improvement in simple motor speed/ attention following ATD is in keeping with the ascribed role of 5-HT in the cortex, however performance on tests of executive function is not robustly altered. The presence of interaction effects on most tasks suggests that subtle changes may occur but are masked, possibly by simple learning effects, in the context of a crossover design. This has implications for the design of future studies, particularly those examining executive functions.