CONTEXT: Despite 25 years of structural imaging in bipolar disorder, brain regions affected in the disorder are ill defined. OBJECTIVES: To use meta-analytical techniques to investigate structural brain changes in bipolar disorder and to assess the effect of medication use and demographic and clinical variables. DATA SOURCES: The MEDLINE, EMBASE, and PsycINFO databases were searched from 1980-2007 for studies using magnetic resonance imaging or x-ray computed tomography to compare brain structure in patients with bipolar disorder and controls. STUDY SELECTION: We identified 1471 unique publications from which 141 studies were included in a database and 98 were selected for meta-analysis. DATA EXTRACTION: Twenty-six demographic and clinical variables were extracted from each study where available. For the meta-analysis, mean structure size and standard deviation were extracted for continuous variables, and numbers of patients and controls with an abnormality were extracted for binary variables. DATA SYNTHESIS: Bipolar disorder was associated with lateral ventricle enlargement (effect size = 0.39; 95% confidence interval, 0.24-0.55; P = 8 x 10(-7)) and increased rates of deep white matter hyperintensities (odds ratio = 2.49; 95% confidence interval, 1.64-3.79; P = 2 x 10(-5)) but not periventricular hyperintensities. Gray matter volume increased among patients when the proportion of patients using lithium increased (P = .004). Calculations from this meta-analysis show current imaging studies (which typically examine 8 regions) have a 34% chance of making a type I error. Type II errors are also appreciable (for example, 70% when measuring the lateral ventricular volume in a typical study involving 25 patients and 33 controls). CONCLUSIONS: The meta-analyses revealed robust but regionally nonspecific changes of brain structure in bipolar disorder. Individual studies will remain underpowered unless sample size is increased or improvements in phenotypic selection and imaging methods are made to reduce within-study heterogeneity. The provision of online databases, as illustrated herein, may facilitate a more refined design and analysis of structural imaging data sets in bipolar disorder.
CONTEXT: Despite 25 years of structural imaging in bipolar disorder, brain regions affected in the disorder are ill defined. OBJECTIVES: To use meta-analytical techniques to investigate structural brain changes in bipolar disorder and to assess the effect of medication use and demographic and clinical variables. DATA SOURCES: The MEDLINE, EMBASE, and PsycINFO databases were searched from 1980-2007 for studies using magnetic resonance imaging or x-ray computed tomography to compare brain structure in patients with bipolar disorder and controls. STUDY SELECTION: We identified 1471 unique publications from which 141 studies were included in a database and 98 were selected for meta-analysis. DATA EXTRACTION: Twenty-six demographic and clinical variables were extracted from each study where available. For the meta-analysis, mean structure size and standard deviation were extracted for continuous variables, and numbers of patients and controls with an abnormality were extracted for binary variables. DATA SYNTHESIS: Bipolar disorder was associated with lateral ventricle enlargement (effect size = 0.39; 95% confidence interval, 0.24-0.55; P = 8 x 10(-7)) and increased rates of deep white matter hyperintensities (odds ratio = 2.49; 95% confidence interval, 1.64-3.79; P = 2 x 10(-5)) but not periventricular hyperintensities. Gray matter volume increased among patients when the proportion of patients using lithium increased (P = .004). Calculations from this meta-analysis show current imaging studies (which typically examine 8 regions) have a 34% chance of making a type I error. Type II errors are also appreciable (for example, 70% when measuring the lateral ventricular volume in a typical study involving 25 patients and 33 controls). CONCLUSIONS: The meta-analyses revealed robust but regionally nonspecific changes of brain structure in bipolar disorder. Individual studies will remain underpowered unless sample size is increased or improvements in phenotypic selection and imaging methods are made to reduce within-study heterogeneity. The provision of online databases, as illustrated herein, may facilitate a more refined design and analysis of structural imaging data sets in bipolar disorder.
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