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

On the Empirics of Institutions and Quality of Growth: Evidence for Developing Countries

We explore a newly available dataset on quality of growth to investigate the effect of institutions on growth quality in 93 developing countries for the period 1990 to 2011. Quality of institutions is measured in term of political risk. The empirical evidence is based on: (i) Ordinary Least Squares (OLS) and Two Stage Least Squares (2SLS) and (ii) cross-sectional and panel data structures. In order to avail room for more policy implications, the dataset is further disaggregated into income levels, namely: Lower middle income (LMIC), low income (LI) and upper middle income (UMIC). Three main findings are established. First, institutions are positively related to the quality of growth. Second, institutions have significantly contributed to growth quality in increasing order during the following time intervals: 2005-2011, 1995-1999 and 2000-2004. Third, the positive nexus between institutions and growth quality is fundamentally driven by LMIC. Policy implications are discussed