Skip to main content

Abstract

Bipolar disorder is a heterogeneous, complex, multidimensional neuropsychiatric condition characterized by episodes of (hypo)mania and depression. The neurobiology of bipolar disorder is undoubtedly complicated, and much remains to be discovered regarding the neural and genetic correlates of this disorder. In the present work, we present an update on what is currently known regarding the neurobiology of bipolar disorder with a focus on data from neuroimaging and genetic studies. Magnetic resonance imaging studies suggest alterations in both the structure and function of prefrontal and cortical regions implicated in emotion regulation and cognition. Genetic studies have identified several risk variants for the disorder, yet the functional relevance of these variants remains to be clarified. Future studies that take into account the phenotypic heterogeneity of bipolar disorder are likely to advance our understanding of the neurobiology of this complicated neuropsychiatric illness.

Introduction and Overview

Bipolar disorder (BD) remains one of the most poorly understood of the neuropsychiatric disorders and one of the more potentially disabling. Lifetime estimates for both bipolar I and bipolar II disorders are approximately 1%–2% (1), and rates of significant functional impairment are high (2). The neurobiology of BD is undoubtedly extremely complex, and it is likely that the BD phenotype is a result of potentially many distinct etiologies. Furthermore, the inability to directly assay the brain in vivo places severe limitations on the ability to understand what processes may be involved in producing BD. Nevertheless, recent advances in neuroimaging, genetics, and epigenetics have added to our knowledge of these underlying processes. In this selective review, we highlight what is currently known about the neurobiology of BD from the perspective of structural and functional neural alterations as well as genetic and epigenetic processes.
It is important to note that relatively few studies to date have directly compared participants with BD to participants with other psychiatric disorders; as such, it is often difficult to determine whether a particular finding is specific to BD or whether it represents a trans-diagnostic finding. This is in line with larger issues related to categorical versus dimensional diagnosis; it is likely that bipolar and unipolar depression, for example, share many neurobiological correlates, and there may indeed be more neurobiological similarities across disorders than between them. Nevertheless, as every behavior and symptom cluster is, on some level, a result of a neurobiological process, the unique aspects of bipolar disorder such as mania and prominent circadian dysfunction are likely to be reflected by distinct neural and genetic aberrations that may not be present in other disorders.

Structural Brain Alterations

Decades of magnetic resonance imaging (MRI) studies in BD have produced often inconsistent results; however, as methodology and image resolution have improved, somewhat more consistent findings have emerged. In general, it appears that many, though not all, patients with BD may demonstrate a subtle reduction in cortical gray matter volume within several specific regions. A recent meta-analysis of 15 structural MRI studies in patients with BD identified clusters of significantly reduced gray matter volume within the right anterior cingulate, the right precentral gyrus, and the inferior frontal gyrus bilaterally (3) compared with healthy controls. Using an alternative meta-analytic method, another recent report analyzing 21 structural MRI studies in BD reported gray matter reduction in left anterior cingulate and right fronto-insular cortex (4), again in comparison to control participants. These findings are broadly consistent with a meta-analysis of 14 structural MRI studies comparing patients with BD or schizophrenia to controls that found reduced gray matter volume in the anterior cingulate and insula bilaterally in patients with BD compared with healthy controls (5). These regions of volume reduction generally overlapped with regions of smaller volume in patients with schizophrenia compared with controls, although there was a region of volume reduction in the anterior cingulate cortex that was present in patients with BD but not in those with schizophrenia. Overall, volume reductions were more widespread in schizophrenia compared with bipolar disorder when both groups were compared with controls.
Few studies have directly compared patients with BD to those with unipolar depression; a recent review of eight such studies suggests that patients with BD demonstrate smaller habenula volumes compared with patients with unipolar depression (6). A very recent study comparing patients with BD and unipolar depression reported that patients with BD demonstrated smaller hippocampal and amygdalar volume compared with patients with unipolar depression, whereas patients with unipolar depression evidenced volume reductions in the anterior cingulate gyrus compared with patients with BD (7). Moreover, the researchers were able to use machine learning to correctly distinguish between unipolar depression and BD with close to 80% accuracy, suggesting that such approaches may improve clinical diagnosis in the future.
One of the most commonly replicated MRI findings in BD is an increase in the number of white matter hyperintensities, bright spots appearing within the white matter in T2-weighted images (8). These findings, although nonspecific, appear to be more prevalent in BD compared with other disorders. However, it remains unknown what the cause or consequence of these nonspecific findings may be in BD. Nevertheless, an increased prevalence of white matter hyperintensities in BD suggests that white matter is particularly relevant to the neurobiology of the disorder. The advent of diffusion tensor imaging (DTI), an MRI methodology that allows for the examination and quantification of white matter integrity, has allowed for a closer inspection of this tissue in BD. The evidence to date suggests that white matter integrity, commonly indexed as fractional anisotropy, appears to be altered in BD (9) as well as in schizophrenia (10), unipolar depression (11), and many other neuropsychiatric disorders (12).
As with gray matter findings, white matter findings in BD are also varied and somewhat inconsistent. In general, studies find that patients with BD tend to have less white matter integrity than healthy controls. Specific clusters of significantly lower fractional anisotropy have been reported throughout the brain, many of which are located within fronto-limbic circuitry (9, 13). In one of the largest DTI studies of BD to date (14), patients with BD were found to have lower mean generalized fractional anisotropy compared with healthy controls within the corpus callosum as well as along a portion of the left anterior cingulum bundle that connects the anterior cingulate and the posterior cingulate cortices. In addition, white matter integrity was lower in patients in the anterior segment of the left arcuate fasciculus, which connects the inferior frontal gyrus and the superior temporal cortex (14). Moreover, patients with a history of psychosis demonstrated lower white matter integrity along the corpus callosum than patients without a history of psychosis (14). The specific pattern of white matter abnormalities that distinguish BD from other neuropsychiatric disorders has not been fully characterized, although the evidence to date suggests that patients with BD demonstrate more widespread white matter connectivity abnormalities compared with patients with unipolar depression (6). The functional relevance of these alterations, however, remains largely unknown.

Functional Brain Alterations

Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) allow for the examination of functional activity within the brain, either at rest or during the completion of a task. PET is currently used less frequently than fMRI because of the invasive nature of PET as well as decreased temporal resolution compared with fMRI. PET studies (e.g., references 1517) and fMRI studies (e.g., references 1820) suggest that, overall, patients with BD demonstrate decreased prefrontal activation compared with controls, particularly in the ventral and medial lateral prefrontal regions, and increased amygdala activation (21). There are, of course, inconsistencies in the findings reported; these are likely due to the heterogeneity of the disorder (and indeed, studies that include only patients diagnosed with BD I appear to have more consistent results than those that include BD II and BD NOS subtypes), differences in methodology, and mood state of the participants.
Nevertheless, the evidence to date suggests a model of BD in which aberrant neurodevelopmental processes, such as altered white matter connectivity and/or deficient neuronal pruning, may be associated with deficits in prefrontal modulation of subcortical emotional processing (21, 22). Consistent with this model, a recent quantitative meta-analysis of fMRI studies in BD found evidence for decreased inferior frontal cortex activation in response to both emotional and cognitive stimuli as well as increased limbic activity, including the amygdala, in response to emotional but not cognitive stimuli compared with controls (23).
It should be noted that many of the circuits involved in BD appear to be implicated in other psychiatric disorders, indicating a potential lack of specificity. For example, a model of emotional undermodulation, with hypoactive frontal regions and hyperactive limbic regions, is consistent with the findings in major depressive disorder (24), posttraumatic stress disorder (25), and borderline personality disorder (26). Further work is required to delineate circuits that are specific to BD, as well as to understand commonalities in circuit alterations across disorders.

Evidence from Magnetic Resonance Spectroscopy Studies

In addition to structural and functional information, magnetic resonance technology is able to provide information about the concentrations of several metabolites in the brain through the use of magnetic resonance spectroscopy (MRS). The evidence to date suggests that there are alterations in several key neuroregulators in BD, although further evidence is required to resolve or clarify inconsistencies in results (2729). A recent meta-analysis suggests that glutamate and glutamine concentrations are increased in the frontal cortex in patients with BD compared with controls (28), which may point to alterations in N-methyl-d-aspartate (NMDA) receptor function. N-acetyl aspartate (NAA) is a metabolite thought to index neuronal integrity and, in particular, axon functioning; evidence suggests that NAA levels may be decreased in the basal ganglia (30) as well as in the frontal lobe (29) in BD. Few studies have used MRS to compare patients with BD to patients with other psychiatric disorders; one such study found that although patients with both BD and unipolar depression demonstrated lower NAA/creatine ratios in the white matter of the left prefrontal cortex compared with controls, only patients with unipolar depression evidenced lower NAA/creatine ratios in the right prefrontal white matter compared with controls (31). The evidence for alterations in choline and creatine levels in BD is less strong, although this may be due to the relatively small number of studies that have been conducted.

Postmortem Morphometric Alterations

Morphometric analyses of postmortem brain tissue from patients with BD provide further evidence of alterations in frontal and subcortical regions known to be involved in emotion regulation (32). In particular, several studies have reported decreased neuronal size (3335) and decreased glial (3537) and neuronal density (36, 38) within the dorsolateral prefrontal cortex in patients with BD compared with controls. Similar alterations have been reported in the anterior cingulate cortex (39, 40), hippocampus (41), and amygdala (42), although negative studies have also been reported (reviewed in reference 32). The small number of studies that have been conducted, and the small samples sizes included in such studies, preclude any firm conclusions regarding the effect size or the significance of these alterations. Few postmortem investigations have directly compared samples from patients with bipolar disorder to samples from other disorders. In those that have, sample sizes have generally been too small to detect changes among groups that may be more similar than dissimilar (e.g., reference 43). Nevertheless, abnormalities in neuronal and glial density and size in prefrontal and limbic regions support the view that alterations at the cellular level are apparent in BD in regions that are critical for the cognitive control of emotion.

Genetic Findings

Given the high heritability of the disorder (79%–93%) (44), BD has long been known to have a strong genetic component. Nevertheless, understanding the nature of the genetic abnormalities in BD has proven to be quite difficult, and the precise genetic underpinnings of the disorder remain unknown. The evidence to date suggests that BD, like other neuropsychiatric disorders, is unlikely to be caused by one or even just a few genetic mutations. Rather, BD is likely to have a strong polygenic component (45) wherein the BD phenotype is the result of many genetic factors.
Evidence from several different methodologies suggests that the development of BD is under considerable genetic control. Twin studies demonstrate that monozygotic twins have a higher concordance rate (38.5%–43%) for BD than dizygotic twins (4.5%–5.6%) (4648), and family studies show that adopted children with a biological parent with BD have a higher relative risk of developing the disorder themselves than adopted children whose biological parents do not have BD (4.3 versus 1.3) (49).
Several different methodologies are used to try to understand the genetics of BD, such as linkage analysis, candidate gene association studies, and genome-wide association studies (GWAS). In a linkage analysis study, genetic information is collected from pedigrees in an attempt to identify the chromosomal region wherein susceptibility genes may be located. Many markers spread across the genome are examined, with the goal of identifying genomic areas that appear to be inherited along with the disorder within each family. This methodology is particularly well suited, however, to identifying the genetic causes of disorders in which a small number of genes contribute to a large degree of risk for developing the disorder across families. As BD does not appear to be inherited in this way in the majority of cases, evidence from the many linkage analysis studies that have been carried out in BD has been largely inconclusive (44, 50, 51).

Genetic Association Studies

Candidate gene association studies are those in which a single or small number of a priori selected genes are examined in cases and controls. Results from these studies have generally been inconsistent; a large meta-analysis of 487 candidate gene studies in BD did not find any genes that were significantly associated with the disorder after correction for multiple comparisons (52). However, the lack of any significant findings may be the result of methodological limitations such as insufficient sample sizes. Despite these negative results, it may be productive to examine candidate genes and their association with discrete phenotypic aspects of BD, rather than with the disorder as whole.
Genome-wide association studies (GWAS) are simultaneous investigations of hundreds of thousands of single-nucleotide polymorphisms (SNPs), with the goal of identifying SNPs that occur more or less often in one group compared with a control group. Approximately 12 GWAS have now been conducted in BD, and half of these have identified SNPs that are significantly associated with the disorder. The largest GWAS conducted to date in BD identified two SNPs that attained genome-wide statistical significance: CACNA1C, which encodes for the alpha subunit of the l-type calcium channel, and ODZ4, which is involved in cell surface signaling and neuronal pathfinding (53). CACNA1C was also identified in a previous GWAS (54), suggesting that this SNP is likely to be relevant to BD. ANK3 (encoding ankyrin 3) has also been identified in two BD GWAS (54, 55). Other SNPs that have been found to be significantly associated with BD include NCAN (encoding neurocan) (56), TRPC4AP (57), and TRANK3, PTGFR, and LMAN2L (55). Replication in larger samples will be required to confirm these results, as well as to discover additional SNPs that may be associated with BD. The specificity of these results to BD remains largely unknown; however, recent work suggests that several genes appear to be associated with a range of disorders, rather than being specific to a single disorder. In a large-scale GWAS including samples from patients with unipolar depression, attention-deficit hyperactivity disorder, autism spectrum disorder, BD, and schizophrenia, three SNPs (located near CACNB2, AS3MT, and ITIH3, among others) were associated with all five disorders (58). In the same study, CACNA1B was associated with both BD and schizophrenia (58). These results suggest that there is likely to be considerable genetic overlap across disorders, as well as genetic findings that are specific to distinct disorders.

Pathway Analysis

The discovery of SNPs that appear to be associated with BD is critical and exciting; nevertheless, the results are difficult to interpret, and the neurobiological significance of many of the identified SNPs is not well understood. Further work will undoubtedly clarify these findings and will hopefully lead to the elucidation of processes that are directly involved in the development or maintenance of BD. In the meantime, recent advances in genetic analysis allow for the examination of entire pathways of biologically related genes. For example, researchers can now investigate the particular genes that are known to be involved in specific biochemical functions. In BD, the results of the first pathway analysis study suggest that there is enrichment in three calcium channel subunits (CACNB3, CACNA1D, and CACNA1C) that are related to voltage-gated calcium channel activity (53). These findings are consistent with evidence that l-type calcium channel blockers are effective in treating BD, given that CACNA1C and CACNA1D encode for the major l-type alpha subunits in the brain. Enrichment of CACNA1C is also consistent with data from GWAS studies (53) and postmortem brain tissue gene expression studies (52). A recent meta-analysis investigated biological pathways contributing to the risk of BD using 4 published GWAS studies for a total of 5253 BD patients and 6874 controls. Based on this analysis, 17 significant canonical pathways were identified, of which 6 showed significant association with BD in both initial and replication data sets. The six pathways were driven by calcium channel genes, glutamate receptor genes, and genes involved in the second messenger system and in hormone regulation. Specifically, they were: corticotropin-releasing hormone (CRH) signaling, cardiac β-adrenergic signaling, phospholipase C (PLC) signaling, glutamate receptor signaling, endothelin 1 signaling, and cardiac hypertrophy signaling (59).

Alterations in Gene Expression

The examination of gene expression profiles within either postmortem brain tissue or peripheral tissue from living participants allows for additional investigation into the genetics of BD. In studies of gene expression, groups of genes are examined to identify shared mechanisms of regulation as well as commonalities in molecular, biological, or structural functions (60). One such study reported evidence for the downregulation of oligodenodrocyte-related genes (61) in BD compared with controls, which may be consistent with neuroimaging evidence of altered white matter in BD. In addition, gene expression studies in BD have reported an upregulation of genes related to inflammation and the immune response (60, 6264) compared with controls. Finally, such studies have also provided evidence of a downregulation in genes involved in mitochondrial function and energy metabolism (60, 6567). This last finding is consistent with evidence from multiple lines of investigation linking abnormal mitochondrial functioning and BD, such as magnetic resonance spectroscopy findings of lower pH in the frontal lobes of patients with BD compared with healthy controls (68, 69).

Structural Genetic Alterations

In addition to identifying differences in gene expression, it is also possible to investigate the genetics of BD by examining structural alterations in the genome. These alterations, such as copy number variations (CNVs), are likely to be rare but to have a larger effect on risk for developing BD compared with the common SNPs identified through GWAS. Although only a few studies of rare variation have been conducted in BD, the evidence suggests that there is an enrichment of rare CNVs in patients with BD compared with controls (7072). However, the effects reported are small, and it appears that CNVs do not play as large of a role in the development of BD as they do in the development of schizophrenia or autism spectrum disorders (70), where structural variation appears to play a larger role. Studies that examine families, rather than individuals, can examine the rate of CNVs that are present in an individual but that are not present in either parent. These de novo CNVs appear to be increased in BD (4.3% rate of de novo CNVs in BD versus 0.9% for controls), particularly in patients with an early onset of BD (71). In fact, for individuals who had an early onset of BD, the rate of de novo CNVs was similar to that found in individuals with schizophrenia, a disorder known to have a higher burden of such structural variation (71).

Epigenetics

In addition to studying genes themselves, researchers have begun more recently to investigate epigenetics, or the regulation of gene expression; this often involves a complex interaction between genes and environment. Much of the work in epigenetics has been carried out in nonhuman animals, although a few preliminary human studies have been conducted to examine several key epigenetic processes in BD. One of these processes, chromatin remodeling, refers to the ways in which gene transcription may be regulated through alterations within the chromatin, the complex of DNA and associated histone proteins. Several studies suggest that there are chromatin alterations in people with mood disorders (73, 74) compared with healthy controls. However, it is difficult to study chromatin remodeling in humans as such modifications are very sensitive to changes in experience and vary considerably across time. There is currently no way to directly assess chromatin modification in vivo in the brain in humans; rather, studies have been carried out on postmortem brain tissue or on peripheral cells.
Several studies in BD have examined histone deacetylases (HDACs), enzymes that are integral to chromatin remodeling in that they tend to repress transcription (7577). One such study found a reduction in the expression of several HDACs in patients with major depressive disorder or BD during a depressive episode and found no difference in these expression levels during a period of affective remission (73). A study of the expression of 11 different types of HDACs found that patients with BD demonstrated an increase in the expression of HDAC4 mRNA during a depressive episode and a decrease in the expression of HDAC6 and HDAC8 mRNA during both the depressive and remitted states (74). Although the particular downstream biological or behavioral significance of these HDAC alterations is currently unknown, there is evidence that abnormalities in HDAC expression are associated with a range of mood and cognitive phenotypes (78). Moreover, the mood stabilizer valproate, a first-line treatment for BD, appears to act as an HDAC inhibitor, further implicating epigenetic factors in the neurobiology of BD and suggesting that novel treatments may arise that target HDACs and other factors associated with chromatin remodeling (79).
HDACs are but one of many epigenetic factors that are likely to be critically important in the development and maintenance of mood disorder symptoms, and research in this area is relatively new. Epigenetic targets appear to be a promising avenue for the development of novel therapeutic agents and perhaps the application of personalized medicine in the treatment of BD.

Circadian Abnormalities

One area in which there is growing evidence for the influence of epigenetic factors on the BD phenotype is in the domain of circadian alterations, a prominent feature of BD and one that is notoriously difficult to treat. During acute mood episodes, more than 90% of people with BD report sleep abnormalities; even during periods of affective remission, alterations in sleep and daytime activity level persist for many patients (80). Moreover, abnormalities in the sleep/wake cycle are predictive of an impending mood episode, and such circadian symptoms are closely associated with a diminished quality of life (81).
A genetic basis for circadian disruptions has long been posited, with evidence that even unaffected children of patients with BD demonstrate alterations in sleep and activity levels compared with healthy controls with no family history of BD (82). However, candidate gene association studies have been limited by small sample sizes, and results are often inconsistent. Nevertheless, there is some evidence that a polymorphism in the CLOCK gene is associated with delayed sleep onset and altered levels of evening activity in patients with BD depression (83), as well as with a tendency toward an “eveningness” chronotype in healthy people (84).
Recently, the CLOCK gene was found to code for another class of enzymes involved in chromatin remodeling, a histone acetyltransferase (HAT) (85). In contrast to HDACs, this class of enzymes tends to modify chromatin in such a way that DNA is more accessible for transcription (7577). The HAT encoded by the CLOCK gene appears to interact with a network of genes including BMAL1, PER1–3, and CRY1 and CRY2 to generate circadian rhythms using a transcriptional-translational feedback loop (86, 87). Alterations to the CLOCK gene in mice result in maniclike behavior that can be treated with lithium administration (88); complete knockdown of the CLOCK gene in the ventral tegmental area resulted in a phenotype resembling a mixed manic-depressive episode in mice (89). Further work is required to investigate the ways in which circadian functioning and mood are related in humans, as a focus on the circadian system may lead to novel treatment strategies for BD (e.g., reference 90).

Summary

Much remains to be discovered regarding the neurobiology of BD. Evidence from several different methodologies and modalities suggests that there are characteristic alterations in prefrontal–cortical functional relationships, as well as structural alterations in regions implicated in emotional regulation and cognition in BD. The genetics of BD are likely to be complex and to vary considerably across individuals and populations. To date, several GWAS investigations have reported single-locus risk alleles for BD; many of these SNPs are also implicated in other neuropsychiatric disorders (58), suggesting that the genes that are involved in BD are likely to have more general effects that increase the risk for the development of a number of different disorders. There is substantial evidence for a strong polygenic component to BD, pointing toward the importance of pathway analyses in uncovering the genetic architecture of this disorder. It is important to note that the functional significance of many of the risk variants that have been identified to date remains unknown; future work investigating the function of such variants is also critically important.
Understanding the neurobiology of BD is critical in developing novel treatment strategies. Currently available pharmacological and psychotherapeutic treatments leave much to be desired in terms of symptom remission and prevention. For example, evidence suggests that many patients with bipolar disorder experience significant interepisode symptoms, and that even when treated, many patients continue to experience episodes of depression, hypomania, or mania (91). More recently, the persistence and significance of neurocognitive and circadian dysfunction in BD have been recognized. There are currently no effective treatments for these potentially disabling and chronic symptoms of the disorder.
A major hurdle to overcome in seeking to understand the neurobiology of BD is the large degree of phenotypic heterogeneity inherent in the disorder. Among individuals with BD, there are considerable variations in important illness dimensions such as predominant mood state, psychosis, circadian dysfunction, neurocognitive impairment, and interepisode recovery. It is likely that each of these dimensions is associated with both distinct and overlapping neurobiological correlates. As such, future work that takes such heterogeneity into account by means of thorough phenotypic assessment is likely to provide increased understanding of the neurobiology of BD.

Footnote

Katie Mahon, Ph.D., Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
Manuela Russo, Ph.D., Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
M. Mercedes Perez-Rodriguez, M.D., Ph.D., Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY; The Mental Health Patient Care Center and the Mental Illness Research Education and Clinical Center (MIRECC), Bronx, NY; CIBERSAM, Autonoma University of Madrid, Fundacion Jimenez Diaz Hospital, Madrid, Spain
Katherine E. Burdick, Ph.D., Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY; The Mental Health Patient Care Center and the Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters Veterans Affairs Medical Center, Bronx, NY

References

1.
Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE: Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005; 62:593–602
2.
Altshuler LL, Post RM, Black DO, Keck PE Jr, Nolen WA, Frye MA, Suppes T, Grunze H, Kupka RW, Leverich GS, McElroy SL, Walden J, Mintz J: Subsyndromal depressive symptoms are associated with functional impairment in patients with bipolar disorder: results of a large, multisite study. J Clin Psychiatry 2006; 67:1551–1560
3.
Houenou J, Frommberger J, Carde S, Glasbrenner M, Diener C, Leboyer M, Wessa M: Neuroimaging-based markers of bipolar disorder: evidence from two meta-analyses. J Affect Disord 2011; 132:344–355
4.
Bora E, Fornito A, Yücel M, Pantelis C: Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder. Biol Psychiatry 2010; 67:1097–1105
5.
Ellison-Wright I, Bullmore E: Anatomy of bipolar disorder and schizophrenia: a meta-analysis. Schizophr Res 2010; 117:1–12
6.
Cardoso de Almeida JR, Phillips ML: Distinguishing between unipolar depression and bipolar depression: current and future clinical and neuroimaging perspectives. Biol Psychiatry 2013; 73:111–118
7.
Redlich R, Almeida JJR, Grotegerd D, Opel N, Kugel H, Heindel W, Arolt V, Phillips ML, Dannlowski U: Brain morphometric biomarkers distinguishing unipolar and bipolar depression: a voxel-based morphometry-pattern classification approach. JAMA Psychiatry 2014; 71:1222–1230
8.
Ahn KH, Lyoo IK, Lee HK, Song IC, Oh JS, Hwang J, Kwon J, Kim MJ, Kim M, Renshaw PF: White matter hyperintensities in subjects with bipolar disorder. Psychiatry Clin Neurosci 2004; 58:516–521
9.
Mahon K, Burdick KE, Szeszko PR: A role for white matter abnormalities in the pathophysiology of bipolar disorder. Neurosci Biobehav Rev 2010; 34:533–554
10.
Canu E, Agosta F, Filippi M: A selective review of structural connectivity abnormalities of schizophrenic patients at different stages of the disease. Schizophr Res 2014. Available at doi:
11.
Tham MW, Woon PS, Sum MY, Lee T-S, Sim K: White matter abnormalities in major depression: evidence from post-mortem, neuroimaging and genetic studies. J Affect Disord 2011; 132:26–36
12.
Shizukuishi T, Abe O, Aoki S: Diffusion tensor imaging analysis for psychiatric disorders. Magn Reson Med Sci 2013; 12:153–159
13.
Brambilla P, Bellani M, Yeh P-H, Soares JC, Tansella M: White matter connectivity in bipolar disorder. Int Rev Psychiatry 2009; 21:380–386
14.
Sarrazin S, Poupon C, Linke J, Wessa M, Phillips M, Delavest M, Versace A, Almeida J, Guevara P, Duclap D, Duchesnay E, Mangin JF, Le Dudal K, Daban C, Hamdani N, D’Albis MA, Leboyer M, Houenou J: A multicenter tractography study of deep white matter tracts in bipolar I disorder: psychotic features and interhemispheric disconnectivity. JAMA Psychiatry 2014; 71:388–396
15.
Ketter TA, Kimbrell TA, George MS, Dunn RT, Speer AM, Benson BE, Willis MW, Danielson A, Frye MA, Herscovitch P, Post RM: Effects of mood and subtype on cerebral glucose metabolism in treatment-resistant bipolar disorder. Biol Psychiatry 2001; 49:97–109
16.
Blumberg HP, Stern E, Ricketts S, Martinez D, de Asis J, White T, Epstein J, Isenberg N, McBride PA, Kemperman I, Emmerich S, Dhawan V, Eidelberg D, Kocsis JH, Silbersweig DA: Rostral and orbital prefrontal cortex dysfunction in the manic state of bipolar disorder. Am J Psychiatry 1999; 156:1986–1988
17.
Brooks JO 3rd, Wang PW, Bonner JC, Rosen AC, Hoblyn JC, Hill SJ, Ketter TA: Decreased prefrontal, anterior cingulate, insula, and ventral striatal metabolism in medication-free depressed outpatients with bipolar disorder. J Psychiatr Res 2009; 43:181–188
18.
Foland LC, Altshuler LL, Bookheimer SY, Eisenberger N, Townsend J, Thompson PM: Evidence for deficient modulation of amygdala response by prefrontal cortex in bipolar mania. Psychiatry Res 2008; 162:27–37
19.
Foland-Ross LC, Bookheimer SY, Lieberman MD, Sugar CA, Townsend JD, Fischer J, Torrisi S, Penfold C, Madsen SK, Thompson PM, Altshuler LL: Normal amygdala activation but deficient ventrolateral prefrontal activation in adults with bipolar disorder during euthymia. Neuroimage 2012; 59:738–744
20.
Blumberg HP, Leung H-C, Skudlarski P, Lacadie CM, Fredericks CA, Harris BC, Charney DS, Gore JC, Krystal JH, Peterson BS: A functional magnetic resonance imaging study of bipolar disorder: state- and trait-related dysfunction in ventral prefrontal cortices. Arch Gen Psychiatry 2003; 60:601–609
21.
Strakowski SM, Adler CM, Almeida J, Altshuler LL, Blumberg HP, Chang KD, DelBello MP, Frangou S, McIntosh A, Phillips ML, Sussman JE, Townsend JD: The functional neuroanatomy of bipolar disorder: a consensus model. Bipolar Disord 2012; 14:313–325
22.
Phillips ML, Ladouceur CD, Drevets WC: A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Mol Psychiatry 2008; 13:829-, 833–857
23.
Chen C-H, Suckling J, Lennox BR, Ooi C, Bullmore ET: A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disord 2011; 13:1–15
24.
Davidson RJ, Irwin W, Anderle MJ, Kalin NH: The neural substrates of affective processing in depressed patients treated with venlafaxine. Am J Psychiatry 2003; 160:64–75
25.
Lanius RA, Vermetten E, Loewenstein RJ, Brand B, Schmahl C, Bremner JD, Spiegel D: Emotion modulation in PTSD: Clinical and neurobiological evidence for a dissociative subtype. Am J Psychiatry 2010; 167:640–647
26.
Perez-Rodriguez MM, Zaluda L, New AS: Biological advances in personality disorders. Focus. 2013; 11:146–154
27.
Gigante AD, Bond DJ, Lafer B, Lam RW, Young LT, Yatham LN: Brain glutamate levels measured by magnetic resonance spectroscopy in patients with bipolar disorder: a meta-analysis. Bipolar Disord 2012; 14:478–487
28.
Chitty KM, Lagopoulos J, Lee RSC, Hickie IB, Hermens DF: A systematic review and meta-analysis of proton magnetic resonance spectroscopy and mismatch negativity in bipolar disorder. Eur Neuropsychopharmacol 2013; 23:1348–1363
29.
Yildiz-Yesiloglu A, Ankerst DP: Neurochemical alterations of the brain in bipolar disorder and their implications for pathophysiology: a systematic review of the in vivo proton magnetic resonance spectroscopy findings. Prog Neuropsychopharmacol Biol Psychiatry 2006; 30:969–995
30.
Kraguljac NV, Reid M, White D, Jones R, den Hollander J, Lowman D, Lahti AC: Neurometabolites in schizophrenia and bipolar disorder - a systematic review and meta-analysis. Psychiatry Res 2012; 203:111–125
31.
Zhong S, Wang Y, Zhao G, Xiang Q, Ling X, Liu S, Huang L, Jia Y: Similarities of biochemical abnormalities between major depressive disorder and bipolar depression: a proton magnetic resonance spectroscopy study. J Affect Disord 2014; 168:380–386
32.
Gigante AD, Young LT, Yatham LN, Andreazza AC, Nery FG, Grinberg LT, Heinsen H, Lafer B: Morphometric post-mortem studies in bipolar disorder: possible association with oxidative stress and apoptosis. Int J Neuropsychopharmacol 2011; 14:1075–1089
33.
Cotter D, Mackay D, Chana G, Beasley C, Landau S, Everall IP: Reduced neuronal size and glial cell density in area 9 of the dorsolateral prefrontal cortex in subjects with major depressive disorder. Cereb Cortex 2002; 12:386–394
34.
Law AJ, Harrison PJ: The distribution and morphology of prefrontal cortex pyramidal neurons identified using anti-neurofilament antibodies SMI32, N200 and FNP7. Normative data and a comparison in subjects with schizophrenia, bipolar disorder or major depression. J Psychiatr Res 2003; 37:487–499
35.
Vostrikov VM, Uranova NA, Orlovskaya DD: Deficit of perineuronal oligodendrocytes in the prefrontal cortex in schizophrenia and mood disorders. Schizophr Res 2007; 94:273–280
36.
Rajkowska G, Halaris A, Selemon LD: Reductions in neuronal and glial density characterize the dorsolateral prefrontal cortex in bipolar disorder. Biol Psychiatry 2001; 49:741–752
37.
Uranova NA, Vostrikov VM, Orlovskaya DD, Rachmanova VI: Oligodendroglial density in the prefrontal cortex in schizophrenia and mood disorders: a study from the Stanley Neuropathology Consortium. Schizophr Res 2004; 67:269–275
38.
Sakai T, Oshima A, Nozaki Y, Ida I, Haga C, Akiyama H, Nakazato Y, Mikuni M: Changes in density of calcium-binding-protein-immunoreactive GABAergic neurons in prefrontal cortex in schizophrenia and bipolar disorder. Neuropathology 2008; 28:143–150
39.
Benes FM, Vincent SL, Todtenkopf M: The density of pyramidal and nonpyramidal neurons in anterior cingulate cortex of schizophrenic and bipolar subjects. Biol Psychiatry 2001; 50:395–406
40.
Bouras C, Kövari E, Hof PR, Riederer BM, Giannakopoulos P: Anterior cingulate cortex pathology in schizophrenia and bipolar disorder. Acta Neuropathol 2001; 102:373–379
41.
Benes FM, Kwok EW, Vincent SL, Todtenkopf MS: A reduction of nonpyramidal cells in sector CA2 of schizophrenics and manic depressives. Biol Psychiatry 1998; 44:88–97
42.
Berretta S, Pantazopoulos H, Lange N: Neuron numbers and volume of the amygdala in subjects diagnosed with bipolar disorder or schizophrenia. Biol Psychiatry 2007; 62:884–893
43.
Matthews PR, Harrison PJ: A morphometric, immunohistochemical, and in situ hybridization study of the dorsal raphe nucleus in major depression, bipolar disorder, schizophrenia, and suicide. J Affect Disord 2012; 137:125–134
44.
Barnett JH, Smoller JW: The genetics of bipolar disorder. Neuroscience 2009; 164:331–343
45.
Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, Sklar P; International Schizophrenia Consortium: Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009; 460:748–752
46.
Kendler KS, Pedersen NL, Neale MC, Mathé AA: A pilot Swedish twin study of affective illness including hospital- and population-ascertained subsamples: results of model fitting. Behav Genet 1995; 25:217–232
47.
Kieseppä T, Partonen T, Haukka J, Kaprio J, Lönnqvist J: High concordance of bipolar I disorder in a nationwide sample of twins. Am J Psychiatry 2004; 161:1814–1821
48.
Smoller JW, Finn CT: Family, twin, and adoption studies of bipolar disorder. Am J Med Genet C Semin Med Genet 2003; 123C:48–58
49.
Lichtenstein P, Yip BH, Björk C, Pawitan Y, Cannon TD, Sullivan PF, Hultman CM: Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 2009; 373:234–239
50.
Craddock N, Sklar P: Genetics of bipolar disorder. Lancet 2013; 381:1654–1662
51.
Craddock N, Sklar P: Genetics of bipolar disorder: successful start to a long journey. Trends Genet 2009; 25:99–105
52.
Seifuddin F, Pirooznia M, Judy JT, Goes FS, Potash JB, Zandi PP: Systematic review of genome-wide gene expression studies of bipolar disorder. BMC Psychiatry 2013; 13:213
53.
Psychiatric GWAS Consortium Bipolar Disorder Working Group: Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 2011; 43:977–983
54.
Ferreira MAR, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L, Fan J, Kirov G, Perlis RH, Green EK, Smoller JW, Grozeva D, Stone J, Nikolov I, Chambert K, Hamshere ML, Nimgaonkar VL, Moskvina V, Thase ME, Caesar S, Sachs GS, Franklin J, Gordon-Smith K, Ardlie KG, Gabriel SB, Fraser C, Blumenstiel B, Defelice M, Breen G, Gill M, Morris DW, Elkin A, Muir WJ, McGhee KA, Williamson R, MacIntyre DJ, MacLean AW, St CD, Robinson M, Van Beck M, Pereira AC, Kandaswamy R, McQuillin A, Collier DA, Bass NJ, Young AH, Lawrence J, Ferrier IN, Anjorin A, Farmer A, Curtis D, Scolnick EM, McGuffin P, Daly MJ, Corvin AP, Holmans PA, Blackwood DH, Gurling HM, Owen MJ, Purcell SM, Sklar P, Craddock N; Wellcome Trust Case Control Consortium: Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 2008; 40:1056–1058
55.
Chen DT, Jiang X, Akula N, Shugart YY, Wendland JR, Steele CJM, Kassem L, Park JH, Chatterjee N, Jamain S, Cheng A, Leboyer M, Muglia P, Schulze TG, Cichon S, Nöthen MM, Rietschel M, McMahon FJ, Farmer A, McGuffin P, Craig I, Lewis C, Hosang G, Cohen-Woods S, Vincent JB, Kennedy JL, Strauss J; BiGS: Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder. Mol Psychiatry 2013; 18:195–205
56.
Cichon S, Mühleisen TW, Degenhardt FA, Mattheisen M, Miró X, Strohmaier J, Steffens M, Meesters C, Herms S, Weingarten M, Priebe L, Haenisch B, Alexander M, Vollmer J, Breuer R, Schmäl C, Tessmann P, Moebus S, Wichmann HE, Schreiber S, Müller-Myhsok B, Lucae S, Jamain S, Leboyer M, Bellivier F, Etain B, Henry C, Kahn JP, Heath S, Hamshere M, O’Donovan MC, Owen MJ, Craddock N, Schwarz M, Vedder H, Kammerer-Ciernioch J, Reif A, Sasse J, Bauer M, Hautzinger M, Wright A, Mitchell PB, Schofield PR, Montgomery GW, Medland SE, Gordon SD, Martin NG, Gustafsson O, Andreassen O, Djurovic S, Sigurdsson E, Steinberg S, Stefansson H, Stefansson K, Kapur-Pojskic L, Oruc L, Rivas F, Mayoral F, Chuchalin A, Babadjanova G, Tiganov AS, Pantelejeva G, Abramova LI, Grigoroiu-Serbanescu M, Diaconu CC, Czerski PM, Hauser J, Zimmer A, Lathrop M, Schulze TG, Wienker TF, Schumacher J, Maier W, Propping P, Rietschel M, Nöthen MM; Bipolar Disorder Genome Study (BiGS) Consortium: Genome-wide association study identifies genetic variation in neurocan as a susceptibility factor for bipolar disorder. Am J Hum Genet 2011; 88:372–381
57.
Green EK, Hamshere M, Forty L, Gordon-Smith K, Fraser C, Russell E, Grozeva D, Kirov G, Holmans P, Moran JL, Purcell S, Sklar P, Owen MJ, O’Donovan MC, Jones L, Jones IR, Craddock N; WTCCC: Replication of bipolar disorder susceptibility alleles and identification of two novel genome-wide significant associations in a new bipolar disorder case-control sample. Mol Psychiatry 2013; 18:1302–1307
58.
Cross-Disorder Group of the Psychiatric Genomics Consortium: Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 2013; 381:1371–1379
59.
Nurnberger JI Jr, Koller DL, Jung J, Edenberg HJ, Foroud T, Guella I, Vawter MP, Kelsoe JR; Psychiatric Genomics Consortium Bipolar Group: Identification of pathways for bipolar disorder: a meta-analysis. JAMA Psychiatry 2014; 71:657–664
60.
Konradi C, Sillivan SE, Clay HB: Mitochondria, oligodendrocytes and inflammation in bipolar disorder: evidence from transcriptome studies points to intriguing parallels with multiple sclerosis. Neurobiol Dis 2012; 45:37–47
61.
Tkachev D, Mimmack ML, Ryan MM, Wayland M, Freeman T, Jones PB, Starkey M, Webster MJ, Yolken RH, Bahn S: Oligodendrocyte dysfunction in schizophrenia and bipolar disorder. Lancet 2003; 362:798–805
62.
Ryan MM, Lockstone HE, Huffaker SJ, Wayland MT, Webster MJ, Bahn S: Gene expression analysis of bipolar disorder reveals downregulation of the ubiquitin cycle and alterations in synaptic genes. Mol Psychiatry 2006; 11:965–978
63.
Shao L, Vawter MP: Shared gene expression alterations in schizophrenia and bipolar disorder. Biol Psychiatry 2008; 64:89–97
64.
Rao JS, Harry GJ, Rapoport SI, Kim HW: Increased excitotoxicity and neuroinflammatory markers in postmortem frontal cortex from bipolar disorder patients. Mol Psychiatry 2010; 15:384–392
65.
Konradi C, Eaton M, MacDonald ML, Walsh J, Benes FM, Heckers S: Molecular evidence for mitochondrial dysfunction in bipolar disorder. Arch Gen Psychiatry 2004; 61:300–308
66.
Iwamoto K, Bundo M, Kato T: Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis. Hum Mol Genet 2005; 14:241–253
67.
Sun X, Wang J-F, Tseng M, Young LT: Downregulation in components of the mitochondrial electron transport chain in the postmortem frontal cortex of subjects with bipolar disorder. J Psychiatry Neurosci 2006; 31:189–196
68.
Kato T, Takahashi S, Shioiri T, Inubushi T: Alterations in brain phosphorous metabolism in bipolar disorder detected by in vivo 31P and 7Li magnetic resonance spectroscopy. J Affect Disord 1993; 27:53–59
69.
Kato T, Murashita J, Kamiya A, Shioiri T, Kato N, Inubushi T: Decreased brain intracellular pH measured by 31P-MRS in bipolar disorder: a confirmation in drug-free patients and correlation with white matter hyperintensity. Eur Arch Psychiatry Clin Neurosci 1998; 248:301–306
70.
Grozeva D, Kirov G, Ivanov D, Jones IR, Jones L, Green EK, St Clair DM, Young AH, Ferrier N, Farmer AE, McGuffin P, Holmans PA, Owen MJ, O’Donovan MC, Craddock N; Wellcome Trust Case Control Consortium: Rare copy number variants: a point of rarity in genetic risk for bipolar disorder and schizophrenia. Arch Gen Psychiatry 2010; 67:318–327
71.
Malhotra D, McCarthy S, Michaelson JJ, Vacic V, Burdick KE, Yoon S, Cichon S, Corvin A, Gary S, Gershon ES, Gill M, Karayiorgou M, Kelsoe JR, Krastoshevsky O, Krause V, Leibenluft E, Levy DL, Makarov V, Bhandari A, Malhotra AK, McMahon FJ, Nöthen MM, Potash JB, Rietschel M, Schulze TG, Sebat J: High frequencies of de novo CNVs in bipolar disorder and schizophrenia. Neuron 2011; 72:951–963
72.
Priebe L, Degenhardt FA, Herms S, Haenisch B, Mattheisen M, Nieratschker V, Weingarten M, Witt S, Breuer R, Paul T, Alblas M, Moebus S, Lathrop M, Leboyer M, Schreiber S, Grigoroiu-Serbanescu M, Maier W, Propping P, Rietschel M, Nöthen MM, Cichon S, Mühleisen TW: Genome-wide survey implicates the influence of copy number variants (CNVs) in the development of early-onset bipolar disorder. Mol Psychiatry 2012; 17:421–432
73.
Abe N, Uchida S, Otsuki K, Hobara T, Yamagata H, Higuchi F, Shibata T, Watanabe Y: Altered sirtuin deacetylase gene expression in patients with a mood disorder. J Psychiatr Res 2011; 45:1106–1112
74.
Hobara T, Uchida S, Otsuki K, Matsubara T, Funato H, Matsuo K, Suetsugi M, Watanabe Y: Altered gene expression of histone deacetylases in mood disorder patients. J Psychiatr Res 2010; 44:263–270
75.
Berndsen CE, Denu JM: Catalysis and substrate selection by histone/protein lysine acetyltransferases. Curr Opin Struct Biol 2008; 18:682–689
76.
Selvi BR, Cassel J-C, Kundu TK, Boutillier A-L: Tuning acetylation levels with HAT activators: therapeutic strategy in neurodegenerative diseases. Biochim Biophys Acta 2010; 1799:840–853
77.
Langley B, Gensert JM, Beal MF, Ratan RR: Remodeling chromatin and stress resistance in the central nervous system: histone deacetylase inhibitors as novel and broadly effective neuroprotective agents. Curr Drug Targets CNS Neurol Disord 2005; 4:41–50
78.
Machado-Vieira R, Ibrahim L, Zarate CA Jr: Histone deacetylases and mood disorders: epigenetic programming in gene-environment interactions. CNS Neurosci Ther 2011; 17:699–704
79.
Phiel CJ, Zhang F, Huang EY, Guenther MG, Lazar MA, Klein PS: Histone deacetylase is a direct target of valproic acid, a potent anticonvulsant, mood stabilizer, and teratogen. J Biol Chem 2001; 276:36734–36741
80.
Plante DT, Winkelman JW: Sleep disturbance in bipolar disorder: therapeutic implications. Am J Psychiatry 2008; 165:830–843
81.
Gruber J, Harvey AG, Wang PW, Brooks JO 3rd, Thase ME, Sachs GS, Ketter TA: Sleep functioning in relation to mood, function, and quality of life at entry to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). J Affect Disord 2009; 114:41–49
82.
Ankers D, Jones SH: Objective assessment of circadian activity and sleep patterns in individuals at behavioural risk of hypomania. J Clin Psychol 2009; 65:1071–1086
83.
Benedetti F, Dallaspezia S, Fulgosi MC, Lorenzi C, Serretti A, Barbini B, Colombo C, Smeraldi E: Actimetric evidence that CLOCK 3111 T/C SNP influences sleep and activity patterns in patients affected by bipolar depression. Am J Med Genet B Neuropsychiatr Genet 2007; 144B:631–635
84.
Katzenberg D, Young T, Finn L, Lin L, King DP, Takahashi JS, Mignot E: A CLOCK polymorphism associated with human diurnal preference. Sleep 1998; 21:569–576
85.
Doi M, Hirayama J, Sassone-Corsi P: Circadian regulator CLOCK is a histone acetyltransferase. Cell 2006; 125:497–508
86.
Brown SA, Kowalska E, Dallmann R: (Re)inventing the circadian feedback loop. Dev Cell 2012; 22:477–487
87.
Hirayama J, Sahar S, Grimaldi B, Tamaru T, Takamatsu K, Nakahata Y, Sassone-Corsi P: CLOCK-mediated acetylation of BMAL1 controls circadian function. Nature 2007; 450:1086–1090
88.
Roybal K, Theobold D, Graham A, DiNieri JA, Russo SJ, Krishnan V, Chakravarty S, Peevey J, Oehrlein N, Birnbaum S, Vitaterna MH, Orsulak P, Takahashi JS, Nestler EJ, Carlezon WA Jr, McClung CA: Mania-like behavior induced by disruption of CLOCK. Proc Natl Acad Sci USA 2007; 104:6406–6411
89.
Mukherjee S, Coque L, Cao J-L, Kumar J, Chakravarty S, Asaithamby A, Graham A, Gordon E, Enwright JF 3rd, DiLeone RJ, Birnbaum SG, Cooper DC, McClung CA: Knockdown of Clock in the ventral tegmental area through RNA interference results in a mixed state of mania and depression-like behavior. Biol Psychiatry 2010; 68:503–511
90.
McClung CA: How might circadian rhythms control mood? Let me count the ways. Biol Psychiatry 2013; 74:242–249
91.
Gitlin M: Treatment-resistant bipolar disorder. Mol Psychiatry 2006; 11:227–240

Information & Authors

Information

Published In

History

Published in print: Winter 2015
Published online: 23 February 2015

Authors

Details

M. Mercedes Perez-Rodriguez, M.D., Ph.D.
Katherine E. Burdick, Ph.D.

Notes

Address correspondence to Katherine E. Burdick, Ph.D., Icahn School of Medicine at Mount Sinai, Psychiatry Box # 1230, 1 Gustave L. Levy Place, New York, NY 10029; email: [email protected]

Funding Information

This work was performed at the Department of Psychiatry, Icahn School of Medicine at Mount Sinai, and MIRECC at James J Peters VAMC, NY.
All authors report no competing interests.

Metrics & Citations

Metrics

Citations

Export Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Format
Citation style
Style
Copy to clipboard

View Options

View options

PDF/EPUB

View PDF/EPUB

Get Access

Login options

Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.

Personal login Institutional Login Open Athens login
Purchase Options

Purchase this article to access the full text.

PPV Articles - Focus

PPV Articles - Focus

Not a subscriber?

Subscribe Now / Learn More

PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.

Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).

Media

Figures

Other

Tables

Share

Share

Share article link

Share