Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen’s d =−0.293; P = 1.71 × 10−21), left fusiform gyrus (d =−0.288; P = 8.25 × 10−21) and left rostral middle frontal cortex (d =−0.276; P =2.99 × 10−19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Objectives Functional neuroimaging methods have proliferated in recent years, such that functional magnetic resonance imaging, in particular, is now widely used to study bipolar disorder. However, discrepant findings are common. A workgroup was organized by the Department of Psychiatry, University of Cincinnati (Cincinnati, OH, USA) to develop a consensus functional neuroanatomic model of bipolar I disorder based upon the participants’ work as well as that of others. Methods Representatives from several leading bipolar disorder neuroimaging groups were organized to present an overview of their areas of expertise as well as focused reviews of existing data. The workgroup then developed a consensus model of the functional neuroanatomy of bipolar disorder based upon these data. Results Among the participants, a general consensus emerged that bipolar I disorder arises from abnormalities in the structure and function of key emotional control networks in the human brain. Namely, disruption in early development (e.g., white matter connectivity, prefrontal pruning) within brain networks that modulate emotional behavior leads to decreased connectivity among ventral prefrontal networks and limbic brain regions, especially amygdala. This developmental failure to establish healthy ventral prefrontal–limbic modulation underlies the onset of mania and ultimately, with progressive changes throughout these networks over time and with affective episodes, a bipolar course of illness. Conclusions This model provides a potential substrate to guide future investigations and areas needing additional focus are identified.

Considerable uncertainty exists about the defining brain changes associated with bipolar disorder (BD). Understanding and quantifying the sources of uncertainty can help generate novel clinical hypotheses about etiology and assist in the development of biomarkers for indexing disease progression and prognosis. Here we were interested in quantifying case–control differences in intracranial volume (ICV) and each of eight subcortical brain measures: nucleus accumbens, amygdala, caudate, hippocampus, globus pallidus, putamen, thalamus, lateral ventricles. In a large study of 1710 BD patients and 2594 healthy controls, we found consistent volumetric reductions in BD patients for mean hippocampus (Cohen's d=−0.232; P=3.50 × 10−7) and thalamus (d=−0.148; P=4.27 × 10−3) and enlarged lateral ventricles (d=−0.260; P=3.93 × 10−5) in patients. No significant effect of age at illness onset was detected. Stratifying patients based on clinical subtype (BD type I or type II) revealed that BDI patients had significantly larger lateral ventricles and smaller hippocampus and amygdala than controls. However, when comparing BDI and BDII patients directly, we did not detect any significant differences in brain volume. This likely represents similar etiology between BD subtype classifications. Exploratory analyses revealed significantly larger thalamic volumes in patients taking lithium compared with patients not taking lithium. We detected no significant differences between BDII patients and controls in the largest such comparison to date. Findings in this study should be interpreted with caution and with careful consideration of the limitations inherent to meta-analyzed neuroimaging comparisons.

Context Diffusion tensor imaging (DTI) studies in adults with bipolar disorder (BD) indicate altered white matter (WM) in the orbitomedial prefrontal cortex (OMPFC), potentially underlying abnormal prefrontal corticolimbic connectivity and mood dysregulation in BD. Objective To use tract-based spatial statistics (TBSS) to examine WM skeleton (ie, the most compact whole-brain WM) in subjects with BD vs healthy control subjects. Design Cross-sectional, case-control, whole-brain DTI using TBSS. Setting University research institute. Participants Fifty-six individuals, 31 having a DSM-IV diagnosis of BD type I (mean age, 35.9 years [age range, 24-52 years]) and 25 controls (mean age, 29.5 years [age range, 19-52 years]). Main Outcome Measures Fractional anisotropy (FA) longitudinal and radial diffusivities in subjects with BD vs controls (covarying for age) and their relationships with clinical and demographic variables. Results Subjects with BD vs controls had significantly greater FA (t>3.0, P≤.05 corrected) in the left uncinate fasciculus (reduced radial diffusivity distally and increased longitudinal diffusivity centrally), left optic radiation (increased longitudinal diffusivity), and right anterothalamic radiation (no significant diffusivity change). Subjects with BD vs controls had significantly reduced FA (t>3.0, P≤.05 corrected) in the right uncinate fasciculus (greater radial diffusivity). Among subjects with BD, significant negative correlations (P<.01) were found between age and FA in bilateral uncinate fasciculi and in the right anterothalamic radiation, as well as between medication load and FA in the left optic radiation. Decreased FA (P<.01) was observed in the left optic radiation and in the right anterothalamic radiation among subjects with BD taking vs those not taking mood stabilizers, as well as in the left optic radiation among depressed vs remitted subjects with BD. Subjects having BD with vs without lifetime alcohol or other drug abuse had significantly decreased FA in the left uncinate fasciculus. Conclusions To our knowledge, this is the first study to use TBSS to examine WM in subjects with BD. Subjects with BD vs controls showed greater WM FA in the left OMPFC that diminished with age and with alcohol or other drug abuse, as well as reduced WM FA in the right OMPFC. Mood stabilizers and depressed episode reduced WM FA in left-sided sensory visual processing regions among subjects with BD. Abnormal right vs left asymmetry in FA in OMPFC WM among subjects with BD, likely reflecting increased proportions of left-sided longitudinally aligned and right-sided obliquely aligned myelinated fibers, may represent a biologic mechanism for mood dysregulation in BD.

Objective Bipolar disorder may be characterized by a hypersensitivity to reward-relevant stimuli, potentially underlying the emotional lability and dysregulation that characterizes the illness. In parallel, research highlights the predominant role of striatal and orbitofrontal cortical (OFC) regions in reward-processing and approach-related affect. We aimed to examine whether bipolar disorder, relative to healthy, participants displayed elevated activity in these regions during reward processing. Methods Twenty-one euthymic bipolar I disorder and 20 healthy control participants with no lifetime history of psychiatric disorder underwent functional magnetic resonance imaging (fMRI) scanning during a card-guessing paradigm designed to examine reward-related brain function to anticipation and receipt of monetary reward and loss. Data were collected using a 3T Siemens Trio scanner. Results Region-of-interest analyses revealed that bipolar disorder participants displayed greater ventral striatal and right-sided orbitofrontal [Brodmann area (BA) 11] activity during anticipation, but not outcome, of monetary reward, relative to healthy controls (p < 0.05, corrected). Wholebrain analyses indicated that bipolar disorder, relative to healthy, participants also displayed elevated left-lateral OFC activity (BA 47) activity during reward anticipation (p < 0.05, corrected). Conclusions Elevated ventral striatal and OFC activity during reward anticipation may represent a neural mechanism for predisposition to expansive mood and hypo/mania in response to reward-relevant cues that characterizes bipolar disorder. Our findings contrast with research reporting blunted activity in the ventral striatum during reward processing in unipolar depressed individuals, relative to healthy controls. Examination of reward-related neural activity in bipolar disorder is a promising research focus to facilitate identification of biological markers of the illness.

Background-Bipolar disorder is frequently misdiagnosed as major depressive disorder delaying appropriate treatment and worsening outcome for many bipolar individuals. Emotion dysregulation is a core feature of bipolar disorder. Measures of dysfunction in neural systems supporting emotion regulation may therefore help discriminate bipolar from major depressive disorder.

Objective-To examine abnormal patterns of frontal cortical-subcortical activity in response to emotional stimuli in euthymic individuals with bipolar disorder type I in order to identify trait-like, pathophysiologic mechanisms of the disorder. We examined potential confounding effects of total psychotropic medication load and illness variables upon neural abnormalities.Method-We analyzed neural activity in 19 euthymic bipolar and 24 healthy individuals to mild and intense happy, fearful and neutral faces.Results-Relative to healthy individuals, bipolar subjects had significantly increased left striatal activity in response to mild happy faces (p < 0.05, corrected), decreased right dorsolateral prefrontal cortical (DLPFC) activity in response to neutral, mild and intense happy faces, and decreased left DLPFC activity in response to neutral, mild and intense fearful faces (p < 0.05, corrected). Bipolar and healthy individuals did not differ in amygdala activity in response to either emotion. In bipolar individuals, there was no significant association between medication load and abnormal activity in these regions, but a negative relationship between age of illness onset and amygdala activity in response to mild fearful faces (p = 0.007). Relative to those without comorbidities, bipolar individuals with comorbidities showed a trend increase in left striatal activity in response to mild happy faces.Conclusions-Abnormally increased striatal activity in response to potentially rewarding stimuli and decreased DLPFC activity in response to other emotionally salient stimuli may underlie mood instabilities in euthymic bipolar individuals, and are more apparent in those with comorbid diagnoses. No relationship between medication load and abnormal neural activity in bipolar individuals suggests that our findings may reflect pathophysiologic mechanisms of the illness rather than medication confounds. Future studies should examine whether this pattern of abnormal neural activity could distinguish bipolar from unipolar depression. Bipolar disorder is one of the most debilitating illnesses worldwide (1). Bipolar disorder type I, in particular, is characterized by abnormalities in psychosocial and cognitive function as well as emotion and mood regulation that can persist outside of episodes of mania and depression, during remission (2-6), and likely reflect pathophysiologic mechanisms of the illness (7) that are not mood state dependant. The research agenda for DSM-V emphasizes a need to translate basic and clinical neuroscience findings into a new classification system for all psychiatric disorders based upon pathophysiologic and etiological processes (8,9). Examining neural system abnormalities in euthymic individuals with bipolar disorder type I during paradigms specifically designed to measure emotion processing is therefore a first stage toward identifying biomarkers of bipolar disorder that reflect pathophysiologic neural mechanisms of the disorder (10). These, in turn, can then be included in future diagnostic cl...