Neuro MRI Biomarkers for Treatment Navigation in Depression — NEUROTREND  

Depressive Disorder, Major Clinical Trial

Official title: Neuro MRI Biomarkers for Treatment Navigation in Depression

Status Recruiting

Clinical Trial Summary

Background of the study:

Major depressive disorder is a severe neuropsychiatric condition that affects approximately 15% to 18% of people worldwide during their lifetime (Malhi & Mann, 2018). Selection of the optimal treatment is difficult.

A certain correlation (functional / structural, vascular or a mix of both) is expected between clinical data (obtained from psychometric tests such as the HDRS and psychiatric evaluations) and MRI parameters (functional activity, structural connectivity, anatomical variations, perfusion / diffusion etc.).

Objective of the study:

Identification of MRI-based biomarkers to predict clinical outcome of major depressive disorder in comparison with healthy controls. Outcome is defined by level of depressive and cognitive symptomatology and related comorbidity.

Study design:

An independent treating physician will inform a potentially eligible patient and ask whether he/she is interested in voluntary participation in the study. If he/she is interested, the independent treating physician will refer the patient to one of the clinicians from the GGz who is also involved in the Neurotrend study for further steps such as providing the information letter / informed consent and scheduling an intake interview at least one week after receiving all necessary information. Healthy controls will be recruited through public advertisement and via the website www.neurotrend.nl. Pilot subjects will be recruited from the Eindhoven University community and via the website www.neurotrend.nl. Both groups, healthy controls and pilot subjects, will have at least one week to consider and decide on participation. One week later an intake session will take place in which the inclusion and exclusion criteria will be checked. During this session, patients can also ask questions about the study and the informed consent will be signed if the participant is willing to participate voluntarily in the study. Subsequently at the end of the intake session, a starting (baseline) date will be planned for this participant . The actual participation starts at baseline. In total, 120 depressed patients and 60 healthy controls will participate in the study.

Each participant visits Kempenhaeghe twice, whereby each session, is dedicated to complete questionnaires and cognitive tests, such as memory tasks and eye tracking. In the last hour, the participant will be scanned (MRI). Two weeks before each visit, the participant has to fill in some questionnaires that have been sent to the participant.

Study population:

120 patients with major depressive disorder and 60 healthy controls*.

• Inclusion of up to 30 healthy "pilot" participants for technical evaluation. See above.

Primary study parameters/outcome of the study:

• Hamilton Depression Rating Scale (HDRS) scores

• Treatment / medication usage

• MRI metrics (varies per MRI modality, an example is volume per region for a T1-weighted scan and fractional anisotropy for diffusion-weighted scans).

Secondary study parameters/outcome of the study (if applicable):

• Scores of psychometric assessments (e.g. STAI-DY1 - anxiety score)

• Scores of cognitive assessments (e.g. average response time for the eye-tracking task)

Nature and extent of the burden and risks associated with participation, benefit and group relatedness (if applicable):

The participant burden is low and is divided into an intake session and two research sessions. The MRI scan is non-invasive, and subjects can indicate that they want to stop the scan at any time during the scan by squeezing a type of balloon that will lie next to the subject in the case that they feel uncomfortable or for any other reason. Subjects with MRI contraindications (e.g.

claustrophobia, pregnancy or implants not suitable for MRI) are already excluded in advance and will therefore not participate in the study at all. Mostly, the subjects will lie still during the scan, except for one affective task in which they will be asked to match different emotional faces for about 5 minutes.The cognitive tests will only consist of memory, reaction speed, attention, and processing speed tasks which in total, do not last more than 30 minutes. The risks of the MRI scanner (CE-marked) are minimal.

Clinical Trial Description

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. ;

Related Conditions & MeSH terms

• Depressive Disorder
• Depressive Disorder, Major

• NCT number: NCT05701267
• Study type: Observational
• Source: Eindhoven University of Technology
• Status: Recruiting
• Start date: July 1, 2021
• Completion date: July 1, 2024