Identifying phronotypes in psychiatry

Biomarker identification in psychiatric disorders: from neuroscience to clinical practice

Patients with psychiatric disorders exhibit several neurobehavioral and neuropsychological alterations compared to healthy controls. However, signature endpoints of these behavioral manifestations have not yet been translated into clinical tests for diagnosis and follow-up measures. Recently, neuroproteomic approaches have been utilized to identify unique signature markers indicative of these disorders. Development of reliable biomarkers has the potential to revolutionize the diagnosis, classification, and monitoring of clinical responses in psychiatric diseases. However, the lack of biological gold standards, the evolving nosology of psychiatric disorders, and the complexity of the nervous system are among the major challenges that have hindered efforts to develop reliable biomarkers in the field of neuropsychiatry and drug abuse. While biomarkers currently have a limited role in the area of neuropsychiatry, several promising biomarkers have been proposed in conditions such as dementia, schizophrenia, depression, suicide, and addiction. One of the primary objectives of this review is to discuss the role of proteomics in the development of biomarkers specific to neuropsychiatry. We discuss and evaluate currently available biomarkers as well as those that are under research for clinical use in the future.

Functional connectivity of brain structures correlates with treatment outcome in major depressive disorder

Identifying biosignatures to assess the probability of response to an antidepressant for patients with major depressive disorder (MDD) is critically needed. Functional connectivity MRI (fcMRI) offers the promise to provide such a measure. Previous work with fcMRI demonstrated that the correlation in signal from one region to another is a measure of functional connectivity. In this pilot work, a baseline non-task fcMRI was acquired in 14 adults with MDD who were free of all medications. Participants were then treated for 8 weeks with an antidepressant and then clinically re-evaluated. Probabilistic anatomic regions of interest (ROI) were defined for 16 brain regions (eight for each hemisphere) previously identified as being important in mood disorders. These ROIs were used to determine mean time courses for each individual's baseline non-task fcMRI. The correlations in time courses between 16 brain regions were calculated. These calculated correlations were considered to signify measures of functional connectivity. The degree of connectivity for each participant was correlated with treatment outcome. Among 13 participants with 8 weeks follow-up data, connectivity measures in several regions, especially the subcallosal cortex, were highly correlated with treatment outcome. These connectivity measures could provide a means to evaluate how likely a patient is to respond to an antidepressant treatment. Further work using larger samples is required to confirm these findings and to assess if measures of functional connectivity can be used to predict differential outcomes between antidepressant treatments.