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The extension of group-level connectivity methods to individual subjects remains a hurdle for statistical analyses of neuroimaging data. Previous group analyses of positron emission tomography data in clinically depressed patients, for example, have shown that resting-state connectivity prior to therapy predicts how patients eventually respond to pharmacological and cognitive-behavioral therapy. Such applications would be considerably more informative for clinical decision making if these connectivity methods could be extended into the individual subject domain. To test such an extension, 46 treatment-naïve depressed patients were enrolled in an fMRI study to model baseline resting-state functional connectivity. Resting-state fMRI scans were acquired and submitted to exploratory structural equation modeling (SEM) to derive the optimal group connectivity model. Jackknife and split sample tests confirm that group model was highly reproducible, and path weights were consistent across the best five group models. When this model was applied to data from individual subjects, 85% of patients fit the group model. Histogram analysis of individual subjects' paths indicate that some paths are better representative of group membership. These results suggest that exploratory SEM is a viable technique for neuroimaging connectivity analyses of individual subjects' resting-state fMRI data.

This paper reports the results of an across lab metanalysis of effective connectivity in major depression (MDD). Using FDG PET data and Structural Equation Modeling, a formal depression model was created to explicitly test current theories of limbic-cortical dysfunction in MDD and to characterize at the path level potential sources of baseline variability reported in this patient population. A 7-region model consisting of lateral prefrontal cortex (latF9), anterior thalamus (aTh), anterior cingulate (Cg24), subgenual cingulate (Cg25), orbital frontal cortex (OF11), hippocampus (Hc), and medial frontal cortex (mF10) was tested in scans of 119 depressed patients and 42 healthy control subjects acquired during three separate studies at two different institutions. A single model, based on previous theory and supported by anatomical connectivity literature, was stable for the three groups of depressed patients. Within the context of this model, path differences among groups as a function of treatment response characteristics were also identified. First, limbic-cortical connections (latF9-Cg25-OF11-Hc) differentiated drug treatment responders from nonresponders. Second, nonresponders showed additional abnormalities in limbic-subcortical pathways (aTh-Cg24-Cg25-OF11-Hc). Lastly, more limited limbic-cortical (Hc-latF9) and cortical-cortical (OF11-mF10) path differences differentiated responders to cognitive behavioral therapy (CBT) from responders to pharmacotherapy. We conclude that the creation of such models is a first step toward full characterization of the depression phenotype at the neural systems level, with implications for the future development of brain-based algorithms to determine optimal treatment selection for individual patients.

The current study examined limbic-cortical activation under transient emotional stress as a function of personality style. A ventral cingulate (Cg25)-centred limbic-cortical network was identified using positron emission tomography (PET) measures of regional cerebral blood flow (rCBF) during a sad mood challenge that demonstrated differences for individuals selected for specific patterns of Negative and Positive emotional traits, indexed by the NEO Personality Inventory-Revised. Healthy subjects scoring both low on the dispositional Depression facet of Neuroticism (N3) and high on the Positive Emotions facet of Extraversion (E6) were compared to those scoring high on the Depression facet (N3) and low on Positive Emotions (E6), a combination of traits previously linked to normal variations in mood reactivity. Scan analyses were designed to further characterize known variations in Cg25 activity previously reported in studies of negative mood in both healthy subjects and depressed patients. A multivariate technique, partial least squares (PLS) demonstrated a divergent Cg25-mediated network that differentiated temperamentally negative (NAS) from temperamentally positive (PAS) subjects providing a potential neural link between these specific combinations of trait affective styles and vulnerability to depression.