Nursing assessment of differential needs and their correlates across neuroimaging-based depression subtypes: research project

Abstract

Introduction: Depression is one of the most prevalent and incident mental disorders worldwide. Its heterogeneity complicates both diagnosis and treatment approach difficult. Recently, neuroimaging has allowed the identification of differentiated brain patterns based on anatomical structures and physiological functions, which suggests the existence of subtypes of depression, opening new doors for a more precise classification of the disorder and for treatment selection. In this context, psychoradiology emerges as a field that merges psychiatry and radiology to study these neurobiological differences. This project explores how, from a nursing perspective, through a structured evaluation using Gordon's functional health patterns, it might be possible to identify specific needs according to depression subtype. This could contribute to the development of more effective and focused nursing interventions, promoting person-centred and evidence-based care, moving away from the current generalist paradigm in depressive disorders. Objectives: To identify whether there are distinguishable specific needs among depression subtypes through nursing assessment based on Gordon’s patterns. To explore the relationship between the needs and the neurobiological patterns observed in neuroimaging, which could be used as a starting point to propose specific nursing interventions. Methodology: Quantitative, observational, and cross-sectional study, not completed. The estimated sample will consist of 370 participants diagnosed with major depressive disorder, according to DSM-5-TR criteria. Participants will be asked to complete several questionnaires. First, a sociodemographic questionnaire. Then, the FHPAST (Functional Health Pattern Assessment Screening Tool), which evaluates the needs based on the 11 functional health patterns by Gordon. Finally, the PHQ-9 will be used to assess the severity of depressive symptomatology. Functional and structural neuroimaging data will also be collected. This information will allow the classification of participants into four subtypes of depression, based on neurobiological patterns. The data will be analysed using descriptive statistics to identify significant associations between depression subtypes and detected needs. Bivariate analysis will also be conducted to study significant relationships between variables