SOCO Project - social cognition
Background
Major challenges in psychiatry include time-consuming diagnostic processes with low inter-rater reliability and the lack of objective biomarkers. Furthermore, a significant proportion of patients do not respond to psychiatric treatments, with underlying reasons remaining unclear. Consequently, there is a critical need for objective biomarkers to enhance diagnostic reliability and to predict and index treatment responses.
SOCO is a project in collaboration with Robert James Blair from the Research Unit - Child and Adolescent Mental Health Centre (CAMHC), Mental Health Services, Capital Region, tackling this
exigency for biomarkers in psychiatry. In SOCO, a novel machine learning approach is applied on neuroimaging (fMRI), to determine the neural response within specific neuro-cognitive functions in healthy individuals, which can then be used to assess the extent of deviation to this normative response in psychiatric patients. Social cognition is known to be disrupted in adolescent schizophrenia and autism and therefore represents a suitable neuro-cognitive function for the development of a novel biomarker.
Study Design
180 typically developing adolescents in the age range 14-18 years will be included to generate the normative dataset on functional brain response in social cognition tasks (mentalizing and self-referential processing). Later, 100 adolescents with early onset psychosis will be included to test to what extent their neuro-cognitive responses deviate from the norm. Besides the social cognition paradigm, the study further includes structural scans.
Impact
The new class of fMRI biomarkers examined and developed in the SOCO study offer a far more individualized approach to treating adolescent psychiatric patients. They could serve as objective indices aiding reliable diagnosis, allowing the prediction and assessment of treatment response and the provision of treatment targets for novel drug development in psychiatry.
Normative dataset based on 180 typically developing adolescents. Social cognition assessed in task-based fMRI using a mentalizing and a self-referential paradigm. A support vector machine classifier is applied to generate a hyperplane differentiating the BOLD response of two conditions within the social cognition tasks.
DATA Project - Depression and Anxiety Treatment and Assessment
A study to determine task-based neuroimaging to index treatment response and potentially predict treatment response to SSRI treatment in adolescent patients with depression and anxiety.
Background
Major depressive disorder (MDD) is a psychiatric disorder associated with significant adverse life experience and elevated suicide risk. Neurocognitive alterations including atypical neural reward response and aberrant emotional response are known in MDD. Adolescents with MDD are commonly treated with SSRI (fluoxetine), however up to 30% of patients remain with persisting symptoms following treatment. Similarly, fluoxetine offers therapeutic potential in the treatment for adolescents diagnosed with generalized anxiety disorder (GAD), but current insights on efficacy remain limited. Empirical studies on how fluoxetine alters the neuropathophysiology associated to neurocognition in MDD and GAD and how these alterations are linked to symptom alleviation are needed to understand the effect of fluoxetine in the adolescent psychiatric population. Furthermore, predicting the treatment response based on neurofunctional biomarker reflects a crucial goal to improve clinical efficacy of treatment choice.
The DATA project targets this research aims, by using a novel machine learning approach to determine biomarkers derived from functional neuroimaging to both predict and index the response to SSRI treatment. In this collaboration with Robert James Blair from the Research Unit - Child and Adolescent Mental Health Centre (CAMHC), Mental Health Services, Capital Region, the DRCMR contributes with expertise in functional and structural neuroimaging.
Study Design
180 adolescents diagnosed with major depressive disorder (MDD) or generalized anxiety disorder (GAD) in the age range 14-17 years will be included before the start of 12-week SSRI (fluoxetine) therapy. Pre-treatment fMRI, demographic and clinical data will be used to predict the response to SSRI intervention, while post-treatment fMRI will be used to index the treatment response and to determine the association to symptom alleviation. The study includes task-based fMRI (passive avoidance learning and affective stroop task), resting-state fMRI and structural scans.
Impact
The outcomes of the DATA project allow insights into the effect of SSRI on neurocognitive functioning in young psychiatric patients and will reveal objective biomarker to refine the diagnosis and predict the treatment outcome, which essentially allows individualized treatment plans, steering towards tailored patient care.
