4.7 Review

Distinguishing between Unipolar Depression and Bipolar Depression: Current and Future Clinical and Neuroimaging Perspectives

Journal

BIOLOGICAL PSYCHIATRY
Volume 73, Issue 2, Pages 111-118

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.biopsych.2012.06.010

Keywords

Bipolar disorder; functional imaging; magnetic resonance imaging; major depressive disorder; major depressive episode; mood disorder; neuroimaging; structural imaging

Funding

  1. Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) [U01MH092221-0]
  2. Longitudinal Assessment of Manic Symptoms [LAMS] [R01 MH076971-01, RO1 MH073953]
  3. Pittsburgh Bipolar Offspring Study [BIOS] [2RO1 MH060952-11]
  4. EMBARC [U01MH092221-01]
  5. Medical Research Council [G0801418B] Funding Source: researchfish

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Differentiating bipolar disorder (BD) from recurrent unipolar depression (UD) is a major clinical challenge. Main reasons for this include the higher prevalence of depressive relative to hypo/manic symptoms during the course of BD illness and the high prevalence of subthreshold manic symptoms in both BD and UD depression. Identifying objective markers of BD might help improve accuracy in differentiating between BD and UD depression, to ultimately optimize clinical and functional outcome for all depressed individuals. Yet, only eight neuroimaging studies to date have directly compared UD and BD depressed individuals. Findings from these studies suggest more widespread abnormalities in white matter connectivity and white matter hyperintensities in BD than UD depression, habenula volume reductions in BD but not UD depression, and differential patterns of functional abnormalities in emotion regulation and attentional control neural circuitry in the two depression types. These findings suggest different pathophysiologic processes, especially in emotion regulation, reward, and attentional control neural circuitry in BD versus UD depression. This review thereby serves as a call to action to highlight the pressing need for more neuroimaging studies, using larger samples sizes, comparing BD and UD depressed individuals. These future studies should also include dimensional approaches, studies of at-risk individuals, and more novel neuroimaging approaches, such as connectivity analysis and machine learning. Ultimately, these approaches might provide biomarkers to identify individuals at future risk for BD versus UD and biological targets for more personalized treatment and new treatment developments for BD and UD depression.

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