Issues in the Evaluation and Treatment of Anxiety and Depression in Migrant Women in the Perinatal Period.

Issues in the evaluation and treatment of anxiety and depression in migrant women in the perinatal period.

Australas Psychiatry. 2013 Apr 24;
Neale A, Wand A

OBJECTIVES: Perinatal depression and anxiety are prevalent in migrant women. The main aims of this literature review were to understand the psychosocial determinants of depression and anxiety in migrant women antenatally and to explore common clinical presentations. In addition, we aimed to identify barriers and facilitating factors for help-seeking and treatment, in order to identify directions for service development and improvement. METHODS: A review of the literature was performed using electronic databases. RESULTS: Depression and anxiety are over represented in migrant women in the perinatal period. Somatic symptoms are common presentations for emotional distress. An identified difficulty is a perceived lack of support, often as a result of cultural dislocation and social isolation. Significant barriers to help-seeking include the perception that limited assistance is available and reluctance to share personal information with an unknown clinician. Preferred therapeutic approaches include the use of clinical consultants from the same or similar culture, as well as practical and emotional support rather than medical management of symptoms. CONCLUSION: Clinicians should be aware of psychosocial issues in this vulnerable population. Group programmes with a focus on education about mental and physical health, available supports, and socialisation are effective in engaging and assisting pregnant migrant women. HubMed – depression

Unsupervised classification of major depression using functional connectivity MRI.

Hum Brain Mapp. 2013 Apr 24;
Zeng LL, Shen H, Liu L, Hu D

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self-reported symptoms and clinical signs may be prone to patients’ behaviors and psychiatrists’ bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting-state functional magnetic resonance imaging scans in the absence of clinical information. Twenty-four medication-naive patients with major depression and 29 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting-state functional connectivity patterns and showed that a maximum margin clustering-based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group-level clustering consistency of 92.5% and an individual-level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering-based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc. HubMed – depression

Insomnia in Patients With Advanced Cancer.

Am J Hosp Palliat Care. 2013 Apr 23;
Davis MP, Khoshknabi D, Walsh D, Lagman R, Platt A

Introduction:Insomnia is underrecognized in patients with cancer. By identifying clinical correlations and predisposing factors of insomnia, interventions may be initiated to treat insomnia. METHODS: Consecutive patients referred to palliative medicine services were screened with a single question. Patients answering affirmatively completed the Insomnia Severity Index (ISI). Patients were screened for depression, fatigue, and pain. Spearman correlation was performed for associations. RESULTS: Of 715 consecutive patients, 102 had sleep problems and 64 had clinical insomnia by the ISI criteria. Insomnia correlated with depression (r = .32), pain (r = .29), and tiredness (r = .40) but not with age or precipitating factors.Discussion:Insomnia severity moderately correlates with depression, pain, and tiredness. We found no association of insomnia severity with age or medications.Conclusion:Insomnia, pain, depression, and tiredness are a symptom cluster. HubMed – depression

Depression: Treatment – Part 2 Final Cut.