Amygdala and whole-brain activity to emotional faces distinguishes major depressive disorder and bipolar disorder
ABSTRACT It can be clinically difficult to distinguish depressed individuals with bipolar disorder (BD) and major depressive disorder (MDD). To examine potential biomarkers of difference between the two disorders, the current study examined differences in the functioning of emotion-processing neural regions during a dynamic emotional faces task.
During functional magnetic resonance imaging, healthy control adults (HC) (n = 29) and depressed adults with MDD (n = 30) and BD (n = 22) performed an implicit emotional-faces task in which they identified a color label superimposed on neutral faces that dynamically morphed into one of four emotional faces (angry, fearful, sad, happy). We compared neural activation between the groups in an amygdala region-of-interest and at the whole-brain level.
Adults with MDD showed significantly greater activity than adults with BD in the left amygdala to the anger condition (p = 0.01). Results of whole-brain analyses (at p < 0.005, k ≥ 20) revealed that adults with BD showed greater activity to sad faces in temporoparietal regions, primarily in the left hemisphere, whereas individuals with MDD demonstrated greater activity than those with BD to displays of anger, fear, and happiness. Many of the observed BD-MDD differences represented abnormalities in functioning compared to HC.
We observed a dissociation between depressed adults with BD and MDD in the processing of emerging emotional faces. Those with BD showed greater activity during mood-congruent (i.e., sad) faces, whereas those with MDD showed greater activity for mood-incongruent (i.e., fear, anger, and happy) faces. Such findings may reflect markers of differences between BD and MDD depression in underlying pathophysiological processes.
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ABSTRACT: We survey studies which relate abnormal neurogenesis to major depressive disorder. Clinically, descriptive gene and protein expression analysis and genetic and functional studies revised here show that individual alterations of a complex signaling network, which includes the hypothalamic-pituitary-adrenal axis; the production of neurotrophins and growth factors; the expression of miRNAs; the production of proinflammatory cytokines; and, even, the abnormal delivery of gastrointestinal signaling peptides, are able to induce major mood alterations. Furthermore, all of these factors modulate neurogenesis in brain regions involved in MDD, and are functionally interconnected in such a fashion that initial alteration in one of them results in abnormalities in the others. We highlight data of potential diagnostic significance and the relevance of this information to develop new therapeutic approaches. Controversial issues, such as whether neurogenesis is the basis of the disease or whether it is a response induced by antidepressant treatments, are also discussed.Neural Plasticity 10/2013; 2013:873278. DOI:10.1155/2013/873278 · 3.60 Impact Factor
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ABSTRACT: From a neurobiological perspective there is no such thing as bipolar disorder. Rather, it is almost certainly the case that many somewhat similar, but subtly different, pathological conditions produce a disease state that we currently diagnose as bipolarity. This heterogeneity - reflected in the lack of synergy between our current diagnostic schema and our rapidly advancing scientific understanding of the condition - limits attempts to articulate an integrated perspective on bipolar disorder. However, despite these challenges, scientific findings in recent years are beginning to offer a provisional "unified field theory" of the disease. This theory sees bipolar disorder as a suite of related neurodevelopmental conditions with interconnected functional abnormalities that often appear early in life and worsen over time. In addition to accelerated loss of volume in brain areas known to be essential for mood regulation and cognitive function, consistent findings have emerged at a cellular level, providing evidence that bipolar disorder is reliably associated with dysregulation of glial-neuronal interactions. Among these glial elements are microglia - the brain's primary immune elements, which appear to be overactive in the context of bipolarity. Multiple studies now indicate that inflammation is also increased in the periphery of the body in both the depressive and manic phases of the illness, with at least some return to normality in the euthymic state. These findings are consistent with changes in the hypothalamic-pituitary-adrenal axis, which are known to drive inflammatory activation. In summary, the very fact that no single gene, pathway, or brain abnormality is likely to ever account for the condition is itself an extremely important first step in better articulating an integrated perspective on both its ontological status and pathogenesis. Whether this perspective will translate into the discovery of innumerable more homogeneous forms of bipolarity is one of the great questions facing the field and one that is likely to have profound treatment implications, given that fact that such a discovery would greatly increase our ability to individualize - and by extension, enhance - treatment.Frontiers in Psychiatry 08/2014; 5:98. DOI:10.3389/fpsyt.2014.00098
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ABSTRACT: Functional Magnetic Resonance Imagine (fMRI) is an important assessment tool in longitudinal studies of mental illness and its treatment. Understanding the psychometric properties of fMRI-based metrics, and the factors that influence them, will be critical for properly interpreting the results of these efforts. The current study examined whether the choice among alternative model specifications affects estimates of test-retest reliability in key emotion processing regions across a 6-month interval. Subjects (N = 46) performed an emotional-faces paradigm during fMRI in which neutral faces dynamically morphed into one of four emotional faces. Median voxelwise intraclass correlation coefficients (mvICCs) were calculated to examine stability over time in regions showing task-related activity as well as in bilateral amygdala. Four modeling choices were evaluated: a default model that used the canonical hemodynamic response function (HRF), a flexible HRF model that included additional basis functions, a modified CompCor (mCompCor) model that added corrections for physiological noise in the global signal, and a final model that combined the flexible HRF and mCompCor models. Model residuals were examined to determine the degree to which each pipeline met modeling assumptions. Results indicated that the choice of modeling approaches impacts both the degree to which model assumptions are met and estimates of test-retest reliability. ICC estimates in the visual cortex increased from poor (mvICC = 0.31) in the default pipeline to fair (mvICC = 0.45) in the full alternative pipeline - an increase of 45%. In nearly all tests, the models with the fewest assumption violations generated the highest ICC estimates. Implications for longitudinal treatment studies that utilize fMRI are discussed.PLoS ONE 08/2014; 9(8):e105169. DOI:10.1371/journal.pone.0105169 · 3.53 Impact Factor