A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.

OBJECTIVE: A growing literature indicates that unipolar depression and bipolar depression are associated with alterations in grey matter volume. However, it is unclear to what degree these patterns of morphometric change reflect symptom dimensions. Here, we aimed to predict depressive symptoms and hypomanic symptoms based on patterns of grey matter volume using machine learning. METHOD: We used machine learning methods combined with voxel-based morphometry to predict depressive and self-reported hypomanic symptoms from grey matter volume in a sample of 47 individuals with unmedicated unipolar and bipolar depression. RESULTS: We were able to predict depressive severity from grey matter volume in the anteroventral bilateral insula in both unipolar depression and bipolar depression. Self-reported hypomanic symptoms did not predict grey matter loss with a significant degree of accuracy. DISCUSSION: The results of this study suggest that patterns of grey matter volume alteration in the insula are associated with depressive symptom severity across unipolar and bipolar depression. Studies using other modalities and exploring other brain regions with a larger sample are warranted to identify other systems that may be associated with depressive and hypomanic symptoms across affective disorders.

Short-term and long-term measures of cortisol in saliva and hair in atypical and non-atypical depression.

BACKGROUND: Atypical depression may show lowered rather than raised short-term cortisol levels. Atypical major depressive episodes (A-MDE) may also be more closely linked to environmental factors and show overlap with somatic symptom disorders. Hair specimens allow measuring long-term cortisol levels. METHODS: Twenty-seven A-MDE and 44 NA-MDE patients and 40 matched controls were tested. Measures of hair cortisol concentration [HCC] covering the previous 3 months and short-term cortisol parameters (six saliva specimens to assess the cortisol awakening response [CAR] and total daily cortisol output calculated as the area under the curve [AUCg]) were taken alongside measures of environmental factors and clinical variables. RESULTS: There were no differences in HCC between the three groups (P = 0.8), and no difference in the CAR (P = 0.95). However, A-MDE showed lowered short-term cortisol output (AUCg) compared to controls (P = 0.04). A-MDE patients also reported a higher number of daily hassles, and higher levels of fatigue and impaired concentration than NA-MDE. CONCLUSIONS: Normal long-term (HCC) and reduced short-term (AUCg) cortisol levels in A-MDE could suggest a disrupted long-term cortisol rhythm, perhaps affected by environmental factors or by certain symptoms, such as mid-nocturnal insomnia. However, other underlying explanations for these findings should also be investigated in the future.

Instability of default mode network connectivity in major depression: a two-sample confirmation study.

Major depression is associated with altered static functional connectivity in various brain networks, particularly the default mode network (DMN). Dynamic functional connectivity is a novel tool with little application in affective disorders to date, and holds the potential to unravel fluctuations in connectivity strength over time in major depression. We assessed stability of connectivity in major depression between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), key nodes in the DMN that are implicated in ruminative cognitions. Functional connectivity stability between the mPFC and PCC over the course of a resting-state functional magnetic resonance imaging (fMRI) scan was compared between medication-free patients with major depression and healthy controls matched for age, sex and handedness. We tested replicability of the results in an independent sample using multi-echo resting-state fMRI. The primary sample included 20 patients and 19 controls, while the validation sample included 19 patients and 19 controls. Greater connectivity variability was detected in major depression between mPFC and PCC. This was demonstrated in both samples indicating that the results were reliable and were not influenced by the fMRI acquisition approach used. Our results demonstrate that alterations within the DMN in major depression go beyond changes in connectivity strength and extend to reduced connectivity stability within key DMN regions. Findings were robustly replicated across two independent samples. Further research is necessary to better understand the nature of these fluctuations in connectivity and their relationship to the aetiology of major depression.

The relationship between cortisol, stress and psychiatric illness: New insights using hair analysis.

BACKGROUND: Stress is an established important contributor to the development of mental illness and stress related disorders. The biology implicated in the homeostasis of pathological stress mechanisms is not fully established. One of the difficulties with current techniques is the limitation in capturing chronic levels of cortisol as an expression of stress levels in humans. Hair samples can be used to evaluate cortisol levels averaged over relatively long periods of time, therefore providing a more valid measure of chronic levels of this hormone. A highly replicable technique to measure long-term cortisol could prove pivotal in improving our understanding of the role of stress in psychiatric disorders. METHODS: This review synthesises all the published studies relating hair cortisol concentration (HCC) to stress and to psychiatric disorders. It describes and summarises their findings with the aim of providing a summary picture of the current state of this line of research. RESULTS: The strongest finding to date is the replicable increases in hair cortisol associated with stressful life events. Findings in psychiatric disorders are more sparse and inconsistent. There is some support for the presence of raised HCC in major depressive disorders, and for lowered HCC in posttraumatic stress disorder, suggesting chronic hypercortisolaemia and hypocortisolaemia respectively. CONCLUSIONS: HCC is a promising methodology to study chronic cortisol levels with the potential to help characterise psychiatric and stress related disorders. The combination of chronic and acute cortisol measurements has the potential for more accurately determining different aspects of the stress response, and ultimately for the development of a biological marker to aid diagnosis and response to treatment.

Inflammation and clinical response to treatment in depression: A meta-analysis.

The depressive state has been characterised as one of elevated inflammation, which holds promise for better understanding treatment-resistance in affective disorders as well as for future developments in treatment stratification. Aiming to investigate alterations in the inflammatory profiles of individuals with depression as putative biomarkers for clinical response, we conducted meta-analyses examining data from 35 studies that investigated inflammation before and after treatment in depressed patients together with a measure of clinical response. There were sufficient data to analyse IL-6, TNFα and CRP. Levels of IL-6 decreased with antidepressant treatment regardless of outcome, whereas persistently elevated TNFα was associated with prospectively determined treatment resistance. Treatment non-responders tended to have higher baseline inflammation, using a composite measure of inflammatory markers. Our findings suggest that elevated levels of inflammation are contributory to treatment resistance. Combining inflammatory biomarkers might prove a useful tool to improve diagnosis and detection of treatment refractoriness, and targeting persistent inflammation in treatment-resistant depression may offer a potential target for the development of novel intervention strategies.