Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches.

Background: Early diagnosis of major depressive disorder (MDD) could enable timely interventions and effective management which subsequently improve clinical outcomes. However, quantitative and objective assessment tools for the suspected cases who present with depressive symptoms have not been fully established.

Methods: Based on a large-scale dataset (n = 363 subjects) collected with functional near-infrared spectroscopy (fNIRS) measurements during the verbal fluency task (VFT), this study proposed a data representation method for extracting spatiotemporal characteristics of NIRS signals, which emerged as candidate predictors in a two-phase machine learning framework to detect distinctive biomarkers for MDD.

Experiences and challenges faced by community mental health workers when providing care to people with mental health conditions: A qualitative descriptive study.

The global prevalence of mental health conditions and the associated wide treatment gaps have led to increased demand for quality mental healthcare services. In Singapore, despite a shift towards a joint provision of mental healthcare in hospitals and the community, experiences of mental healthcare teams in such hospital-community partnerships have remained unclear. Through a qualitative descriptive approach, this study explored community mental health workers' experiences and perceptions of working with partners from tertiary hospitals and other community organisations.

Effect of Cardiac Rehabilitation on Quality of Life, Depression and Anxiety in Asian Patients.

This study explored the effect of cardiac rehabilitation on quality of life, depression, and anxiety in Asian patients in Singapore. Out of the 194 patients who were recruited into the study, 139 patients (71.6%) completed both the pre- and post-cardiac rehabilitation questionnaires.