AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts 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. METHODS: We 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. RESULTS: We 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. CONCLUSION: We 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.

Adjunctive home-based transcranial direct current stimulation treatment for major depression with real-time remote supervision: An open-label, single-arm feasibility study with long term outcomes.

Current treatments for major depressive disorder (MDD) have limited effectiveness and acceptability. Transcranial direct current stimulation (tDCS) is a novel non-invasive brain stimulation method that has demonstrated treatment efficacy in MDD. tDCS requires daily sessions, however clinical trials have been conducted in research centers requiring repeated visits. As tDCS is portable and safe, it could be provided at home. We developed a home-based protocol with real-time supervision, and we examined the clinical outcomes, acceptability and feasibility. Participants were 26 MDD (19 women), mean age 40.9 ± 14.2 years, in current depressive episode of moderate to severe severity (mean 17-item Hamilton Rating Scale for Depression (HAMD) score 19.12 ± 2.12). tDCS was provided in a bilateral frontal montage, F3 anode, F4 cathode, 2 mA, each session 30 min, in a 6-week trial, for a total 21 sessions. Participants maintained their current treatment (antidepressant medication, psychotherapy, or were enrolled in online CBT). Two tDCS device brands were used, and a research team member was present in person or by real-time video call at each session. 92.3% MDD participants (n = 24) completed the 6-week treatment. Attrition rate was 7.7%. There was a significant improvement in depressive symptoms following treatment (mean HAMD 5.33 ± 2.33), which was maintained at 6 months (mean HAMD 5.43 ± 2.73). Acceptability was endorsed as "very acceptable" or "quite acceptable" by all participants. Due to the open-label feasibility design, efficacy findings are preliminary. In summary, home-based tDCS with real-time supervision was associated with significant clinical improvements and high acceptability which were maintained in the long term.