Limitation to research to date

Heterogeneous findings of current studies may indicate that MDD has been approached too much as a homogeneous concept, whereas analysing subtypes might be more useful. The literature supports the existence of different MDD subtypes based on biological variables and/or clinical features. Research has distinguished 3 types of MDD based on depressive, symptomatology: a severe melancholic class, a severe atypical class and a class of moderate severity. Both depression severity (moderate vs severe) and the nature of depressive symptoms (melancholic vs atypical) were found to be important differentiators between those subtypes. Other subgroups of relevance may be remission vs. non-remission, early onset vs. late onset depression. The relation with neuroimaging findings is scarce and inconsistent. As depressive symptoms may also strongly fluctuate over time, repeated MRI and clinical data acquisition at different points in time is warranted. There is a relative scarcity of longitudinal MRI studies on MDD. Large representative samples are warranted, addressing multiple factors such as sociodemographic characteristics, clinical history (incl. childhood trauma), co-morbidity and monitoring of treatment, including type and duration. This will elucidate causative mechanisms as well as the role of treatment, which may pave the path towards diagnostic subtyping and treatment navigation. Moreover, brain alterations are usually described on a structural, functional and vascular level and seldom from an integral approach, let alone in a longitudinal design. A multi-modal approach whereby the three levels are considered might thus increase the reproducibility of findings. In addition, there is an urgent need to find biomarkers that are meaningful on the individual patient level. Machine learning is a technique that can be utilized to allow imaging risk markers to be identified at the level of subtypes and ultimately on the individual level, which is crucial for clinical applicability.

Current study

The Neurotrend study will contribute to the existing literature by aiming to predict outcome in MDD using multi-modal imaging with an observational study design. Two scanning sessions with one year in between will be held for patients with MDD and controls, as well as a broad assessment of clinical, cognitive and demographic variables (at baseline and 1-year follow-up). Predicting outcome at the level of subtypes/individuals in a naturalistic clinical setting is a different approach to identify biomarkers than most studies have done so far. For this purpose, machine learning will be used, which is a powerful method to identify biomarkers from the different MRI modalities. For the first time, IVIM and T2\*-weighted imaging will be included in the MRI protocol to yield more insight into the microvascularity and microbleeds of the brain. In addition, other novel techniques will be applied to increase the chance of finding meaningful biomarkers. For instance, with respect to the brain alterations on a structural level, high spatial resolutions (using multiband imaging) and better automated segmentation methods will be used to examine the substructures of brain regions with high accuracy as substructural imaging is becoming more popular and could identify more reliable biomarkers. For the brain alterations on a functional level, multiband and multi-echo imaging lead to higher spatial/temporal resolution which will help to reduce the noise and artifacts to which fMRI is very susceptible.