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Bipolar and major depressive disorder: neuroimaging the developmental-degenerative divide.

ABSTRACT: Both major depressive disorder and bipolar disorder are the subject of a voluminous imaging and genetics literature. Here, we attempt a comprehensive review of MRI and metabolic PET studies conducted to date on these two disorders, and interpret our findings from the perspective of developmental and degenerative models of illness. Elevated activity and volume loss of the hippocampus, orbital and ventral prefrontal cortex are recurrent themes in the literature. In contrast, dorsal aspects of the PFC tend to display hypometabolism. Ventriculomegaly and white matter hyperintensities are intimately associated with depression in elderly populations and likely have a vascular origin. Important confounding influences are medication, phenotypic and genetic heterogeneity, and technological limitations. We suggest that environmental stress and genetic risk variants interact with each other in a complex manner to alter neural circuitry and precipitate illness. Imaging genetic approaches hold out promise for advancing our understanding of affective illness.

SUBMITTER: Savitz J

PROVIDER: S-EPMC2858318 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Bipolar and major depressive disorder: neuroimaging the developmental-degenerative divide.

Savitz Jonathan J Drevets Wayne C WC

Neuroscience and biobehavioral reviews 20090121 5

Both major depressive disorder and bipolar disorder are the subject of a voluminous imaging and genetics literature. Here, we attempt a comprehensive review of MRI and metabolic PET studies conducted to date on these two disorders, and interpret our findings from the perspective of developmental and degenerative models of illness. Elevated activity and volume loss of the hippocampus, orbital and ventral prefrontal cortex are recurrent themes in the literature. In contrast, dorsal aspects of the ...[more]

PMID: 19428491

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Project description:Family and genetic studies indicate overlapping liability for major depressive disorder and bipolar disorder. The purpose of the present study was to determine whether this shared genetic liability influences clinical presentation.A polygenic risk score for bipolar disorder, derived from a large genome-wide association meta-analysis, was generated for each subject of European-American ancestry (n = 1,274) in the Sequential Treatment Alternatives to Relieve Depression study (STAR*D) outpatient major depressive disorder cohort. A hypothesis-driven approach was used to test for association between bipolar disorder risk score and features of depression associated with bipolar disorder in the literature. Follow-up analyses were performed in two additional cohorts.A generalized linear mixed model including seven features hypothesized to be associated with bipolar spectrum illness was significantly associated with bipolar polygenic risk score [F = 2.07, degrees of freedom (df) = 7, p = 0.04]. Features included early onset, suicide attempt, recurrent depression, atypical depression, subclinical mania, subclinical psychosis, and severity. Post-hoc univariate analyses demonstrated that the major contributors to this omnibus association were onset of illness at age ? 18 years [odds ratio (OR) = 1.2, p = 0.003], history of suicide attempt (OR = 1.21, p = 0.03), and presence of at least one manic symptom (OR = 1.16, p = 0.02). The maximal variance in these traits explained by polygenic score ranged from 0.8% to 1.1%. However, analyses in two replication cohorts testing a five-feature model did not support this association.Bipolar genetic loading appeared to be associated with bipolar-like presentation in major depressive disorder in the primary analysis. However, the results were at most inconclusive because of lack of replication. Replication efforts were challenged by different ascertainment and assessment strategies in the different cohorts. The methodological approach described here may prove useful in applying genetic data to clarify psychiatric nosology in future studies.

Project description:Background and Objectives: Depressive symptoms are prominent in both major depressive disorder (MDD) and bipolar disorder (BD). However, comparative research on the network structure of depressive symptoms in these two diagnostic groups has been limited. This study aims to compare the network structure of depressive symptoms in MDD and BD, providing a deeper understanding of the depressive symptomatology of each disorder. Materials and Methods: The Zung Self-Rating Depressive Scale, a 20-item questionnaire, was administered to assess the depressive symptoms in individuals with MDD (n = 322) and BD (n = 516). A network analysis was conducted using exploratory graph analysis (EGA), and the network structure was analyzed using regularized partial correlation models. To validate the dimensionality of the Zung SDS, principal component analysis (PCA) was adopted. Centrality measures of the depressive symptoms within each group were assessed, followed by a network comparison test between the two groups. Results: In both diagnostic groups, the network analysis revealed four distinct categories, aligning closely with the PCA results. "Depressed affect" emerged as the most central symptom in both MDD and BD. Furthermore, non-core symptoms, "Personal devaluation" in MDD and "Confusion" in BD, displayed strong centrality. The network comparison test did not reveal significant differences in the network structure between MDD and BD. Conclusions: The absence of significant differences in the network structures between MDD and BD suggests that the underlying mechanisms of depressive symptoms may be similar across these disorders. The identified central symptoms, including "Depressed affect", in both disorders and the distinct non-core symptoms in each highlight the complexity of the depressive symptomatology. Future research should focus on validating these symptoms as therapeutic targets and incorporate various methodologies, including non-metric dimension reduction techniques or canonical analysis.

Project description:There is compelling evidence that epigenetic factors contribute to the manifestation of depression, in which microRNA132 (miR-132) is suggested to play a pivotal role in the pathogenesis and neuronal mechanisms underlying the symptoms of depression. Additionally, several depression-associated genes [MECP2, ARHGAP32 (p250GAP), CREB, and period genes] were experimentally validated as miR-132 targets. However, most studies regarding miR-132 in major depressive disorder are based on post-mortem, animal models or genetic comparisons. This work will be the first attempt to investigate how miR-132 dysregulation may impact covariation of multimodal brain imaging data in 81 unmedicated major depressive patients and 123 demographically-matched healthy controls, as well as in a medication-naïve subset of major depressive patients. MiR-132 values in blood (patients > controls) was used as a prior reference to guide fusion of three MRI features: fractional amplitude of low frequency fluctuations, grey matter volume, and fractional anisotropy. The multimodal components correlated with miR-132 also show significant group difference in loadings. Results indicate that (i) higher miR-132 levels in major depressive disorder are associated with both lower fractional amplitude of low frequency fluctuations and lower grey matter volume in fronto-limbic network; and (ii) the identified brain regions linked with increased miR-132 levels were also associated with poorer cognitive performance in attention and executive function. Using a data-driven, supervised-learning method, we determined that miR-132 dysregulation in major depressive disorder is associated with multi-facets of brain function and structure in fronto-limbic network (the key network for emotional regulation and memory), which deepens our understanding of how miR-132 dysregulation in major depressive disorders contribute to the loss of specific brain areas and is linked to relevant cognitive impairments.

Project description:ImportanceCurrently, fewer than 40% of patients treated for major depressive disorder achieve remission with initial treatment. Identification of a biological marker that might improve these odds could have significant health and economic impact.ObjectiveTo identify a candidate neuroimaging "treatment-specific biomarker" that predicts differential outcome to either medication or psychotherapy.DesignBrain glucose metabolism was measured with positron emission tomography prior to treatment randomization to either escitalopram oxalate or cognitive behavior therapy for 12 weeks. Patients who did not remit on completion of their phase 1 treatment were offered enrollment in phase 2 comprising an additional 12 weeks of treatment with combination escitalopram and cognitive behavior therapy.SettingMood and anxiety disorders research program at an academic medical center.ParticipantsMen and women aged 18 to 60 years with currently untreated major depressive disorder.InterventionRandomized assignment to 12 weeks of treatment with either escitalopram oxalate (10-20 mg/d) or 16 sessions of manual-based cognitive behavior therapy.Main outcome and measureRemission, defined as a 17-item Hamilton depression rating scale score of 7 or less at both weeks 10 and 12, as assessed by raters blinded to treatment.ResultsPositive and negative predictors of remission were identified with a 2-way analysis of variance treatment (escitalopram or cognitive behavior therapy) × outcome (remission or nonresponse) interaction. Of 65 protocol completers, 38 patients with clear outcomes and usable positron emission tomography scans were included in the primary analysis: 12 remitters to cognitive behavior therapy, 11 remitters to escitalopram, 9 nonresponders to cognitive behavior therapy, and 6 nonresponders to escitalopram. Six limbic and cortical regions were identified, with the right anterior insula showing the most robust discriminant properties across groups (effect size = 1.43). Insula hypometabolism (relative to whole-brain mean) was associated with remission to cognitive behavior therapy and poor response to escitalopram, while insula hypermetabolism was associated with remission to escitalopram and poor response to cognitive behavior therapy.Conclusions and relevanceIf verified with prospective testing, the insula metabolism-based treatment-specific biomarker defined in this study provides the first objective marker, to our knowledge, to guide initial treatment selection for depression.Trial registrationRegistered at clinicaltrials.gov (NCT00367341).

Dataset Information

Bipolar and major depressive disorder: neuroimaging the developmental-degenerative divide.

Publications

Bipolar and major depressive disorder: neuroimaging the developmental-degenerative divide.

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