Major Depressive Disorder

FDG-PET as a pre-operative biomarker for predicting and optimizing response to subcallosal cingulate area deep brain stimulation

Background: Deep brain stimulation targeting the subcallosal cingulate area (SCC-DBS) has emerged as a promising therapy for treatment-resistant depression (TRD). However, only half to two thirds of patients experience meaningful clinical response, …

Lack of neuropsychological effects following short-term subcallosal cingulate gyrus deep brain stimulation in treatment-resistant depression: a randomised crossover study

Background: The subcallosal cingulate gyrus (SCG) is integral to cognitive function and mood regulation. Open-label SCG deep brain stimulation (DBS) studies demonstrate improvement or stabilisation of cognitive function in treatment-resistant …

An empirical analysis of structural neuroimaging profiles in a staging model of depression

We examine structural brain characteristics across three diagnostic categories: at risk for serious mental illness; first-presenting episode and recurrent major depressive disorder (MDD). We investigate whether the three diagnostic groups display a …

Resting-state neural mechanisms of capability for suicide and their interaction with pain – A CAN-BIND-05 Study

Background: Suicidal ideation is highly prevalent in Major Depressive Disorder (MDD). However, the factors determining who will transition from ideation to attempt are not established. Emerging research points to suicide capability (SC), which …

Psychological and Mental Health Sequelae of Concussion: Prevalence, Treatment Recommendations, Novel Biomarkers, and Diagnostic Challenges

Psychiatric symptoms following concussion are prevalent and associated with a plethora of negative consequences including elevated post-concussive symptoms, cognitive disturbances, and poorer quality of life. With a primary focus on depression and …

Unpacking Major Depressive Disorder: From Classification to Treatment Selection

In this brief *Perspective* piece, we discuss the heterogeneity of depression, and the limited success of a 'one-size-fits-all' approach to treatment. We explore a personalized medicine approach to psychiatry, which integrates brain circuitry, genetic and molecular markers, and individual clinical symptoms through machine learning approaches to ultimately improve treatment selection.