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Clinical diagnosis and/or phenomenological description is the cornerstone of both clinical care and research in psychiatry. Clinical management and guidelines are structured around individual diagnoses, with the implicit understanding that such diagnoses mean the same to most clinicians (reliability) and that they have predictive value (validity). Another relevant aspect of diagnosis is its clinical utility--a measure that is difficult to estimate correctly. However, indirect data, such as increasing burden of psychiatric disorders, might be in part explained by diagnostic limitations.

Bipolar disorder (BD) is a lifelong neuropsychiatric disorder that exerts a substantial personal and public health toll, an elevated lifetime risk of suicide, and a remarkable degree of heterogeneity or diversity. Existing studies of heterogeneity in BD tend to focus on variability or clustering along a limited number of features or domains. However, identifying heterogeneity across a limited number of features does not necessarily help us characterize the overall heterogeneity of BD across levels of analysis. Until recently, it was not clear how heterogeneity should best be measured or conceptualized, thereby limiting our ability to understand the diversity of BD in a holistic sense. In addition to these recent advancements in heterogeneity measurement, understanding the diversity of BD requires us to develop research strategies and methods specific to this purpose. Here, Nunes et al argue that understanding the diversity of BD involves characterizing its nature, causes and consequences.

Bipolar disorder is characterized by cyclical alternation between mania and depression, often comorbid with psychosis and suicide. Compared with other medications, the mood stabilizer lithium is the most effective treatment for the prevention of manic and depressive episodes. However, the pathophysiology of bipolar disorder and lithium’s mode of action are yet to be fully understood. Evidence suggests a change in the balance of excitatory and inhibitory activity, favouring excitation in bipolar disorder. In the present study, we sought to establish a holistic understanding of the neuronal consequences of lithium exposure in mouse cortical neurons, and to identify underlying mechanisms of action. We used a range of technical approaches to determine the effects of acute and chronic lithium treatment on mature mouse cortical neurons. We combined RNA screening and biochemical and electrophysiological approaches with confocal immunofluorescence and live-cell calcium imaging. We found that only chronic lithium treatment significantly reduced intracellular calcium flux, specifically by activating metabotropic glutamatergic receptor 5. This was associated with altered phosphorylation of protein kinase C and glycogen synthase kinase 3, reduced neuronal excitability and several alterations to synapse function. Consequently, lithium treatment shifts the excitatory–inhibitory balance toward inhibition. The mechanisms we identified should be validated in future by similar experiments in whole animals and human neurons. Together, the results revealed how lithium dampens neuronal excitability and the activity of the glutamatergic network, both of which are predicted to be overactive in the manic phase of bipolar disorder. Our working model of lithium action enables the development of targeted strategies to restore the balance of overactive networks, mimicking the therapeutic benefits of lithium but with reduced toxicity.

Smaller hippocampal volumes relative to controls are among the most replicated neuroimaging findings in individuals with unipolar but not bipolar depression. Preserved hippocampal volumes in most studies of participants with bipolar disorder may reflect potential neuroprotective effects of lithium (Li). To investigate hippocampal volumes in patients with bipolar disorder while controlling for Li exposure, we performed a meta-analysis of neuroimaging studies that subdivided patients based on the presence or absence of current Li treatment. To achieve the best coverage of literature, we categorized studies based on whether all or a majority, or whether no or a minority of patients were treated with Li. Hippocampal volumes were compared by combining standardized differences between means (Cohen d) from individual studies using random-effects models. Overall, we analyzed data from 101 patients with bipolar disorder in the Li group, 245 patients in the non-Li group and 456 control participants from 16 studies. Both the left and right hippocampal volumes were significantly larger in the Li group than in controls (Cohen d = 0.53, 95% confidence interval [CI] 0.18 to 0.88; Cohen d = 0.51, 95% CI 0.21 to 0.81, respectively) or the non-Li group (Cohen d = 0.93, 95% CI 0.56 to 1.31; Cohen d = 1.07, 95% CI 0.70 to 1.45, respectively), which had smaller left and right hippocampal volumes than the control group (Cohen d = −0.36, 95% CI −0.55 to −0.17; Cohen d = −0.38, 95% CI −0.63 to −0.13, respectively). There was no evidence of publication bias. Missing information about the illness burden or lifetime exposure to Li and polypharmacy in some studies may have contributed to statistical heterogeneity in some analyses. When exposure to Li was minimized, patients with bipolar disorder showed smaller hippocampal volumes than controls or Li-treated patients. Our findings provide indirect support for the negative effects of bipolar disorder on hippocampal volumes and are consistent with the putative neuroprotective effects of Li. The preserved hippocampal volumes among patients with bipolar disorder in most individual studies and all previous meta-analyses may have been related to the inclusion of Li-treated participants.

An important aspect of personalized medicine is its eco- nomic dimension. While personalized medicine should lower the health care cost relative to its benefits, some of the new treatments can be very expensive. For instance, re- cent treatments derived from genetic findings can exceed $100 000 per year in addition to the cost of screening.10 The development of new drugs in general is a costly and lengthy process, and this will be even more the case for treatments targeting smaller patient populations.

Brain imaging is of limited diagnostic use in psychiatry owing to clinical heterogeneity and low sensitivity/specificity of between-group neuroimaging differences. Machine learning (ML) may better translate neuroimaging to the level of individual participants. Studying unaffected offspring of parents with bipolar disorders (BD) decreases clinical heterogeneity and thus increases sensitivity for detection of biomarkers. The present study used ML to identify individuals at genetic high risk (HR) for BD based on brain structure. We studied unaffected and affected relatives of BD probands recruited from 2 sites (Halifax, Canada, and Prague, Czech Republic). Each participant was individually matched by age and sex to controls without personal or family history of psychiatric disorders. We applied support vector machines (SVM) and Gaussian process classifiers (GPC) to structural MRI. We included 45 unaffected and 36 affected relatives of BD probands matched by age and sex on an individual basis to healthy controls. The SVM of white matter distinguished unaffected HR from control participants (accuracy = 68.9%, p = 0.001), with similar accuracy for the GPC (65.6%, p = 0.002) or when analyzing data from each site separately. Differentiation of the more clinically heterogeneous affected familiar group from healthy controls was less accurate (accuracy = 59.7%, p = 0.05). Machine learning applied to grey matter did not distinguish either the unaffected HR or affected familial groups from controls. The regions that most contributed to between-group discrimination included white matter of the inferior/middle frontal gyrus, inferior/middle temporal gyrus and precuneus. Although we recruited 126 participants, ML benefits from even larger samples. Machine learning applied to white but not grey matter distinguished unaffected participants at high and low genetic risk for BD based on regions previously implicated in the pathophysiology of BD.

Despite advancements in research methods and the growth of large international data sharing initiatives, our understanding of the biological underpinnings of psychiatric disorders remains limited. An often cited reason for this stagnation is the presence of \"heterogeneity,\" whether intrinsic to the condition or an artifact of clinical assessment, sampling, experimental protocol, or otherwise. However, for a concept of such longstanding importance to psychiatric research, we have no consistent framework within which to study heterogeneity itself. Here, Nunes et al discuss ways in which heterogeneity could be understood and communicated.

The concept of clinical staging is commonly applied in the management of various medical conditions, including cancer, hypertension, kidney disease or diabetes. In these conditions, the individual stages can be differentiated by clinical presentation and/or by specific biological markers, and they typically inform stage-specific treatment and prognosis. In psychiatry, the model proposes that mental disorders can be described in stages characterized by illness progression. Among the first to propose a staging model for psychiatric disorders were Fava and Kellner. The stages of bipolar disorder have been conceptualized by several authors who all agree on certain key points: the illness moves from an at-risk phase (identifiable by family history of the illness) to early nonspecific symptoms to mild mood symptoms. The stages of bipolar disorder are described in terms of severity of symptoms and quality of remissions as well as associated cognitive and functional impairment. These are often related to one another and to the severity of mood symptoms, and they also may be reversible.

Objective Converging evidence suggests that the subgenual cingulate (SGC) is implicated in regulation of mood and in the pathophysiology of mood disorders. Our objective was to carry out the first meta-analysis of SGC volumes in patients with mood disorders. Methods We reviewed 10 volumetric magnetic resonance imaging studies of SGC volumes in patients with unipolar depression and bipolar disorders. For meta-analysis, we used standardized differences between means (SDMs) and random effects models. In the search for sources of heterogeneity, we subdivided the studies on the basis of diagnosis and presence of family history. Results The volumes of left and right SGC in patients with mood disorders were significantly reduced relative to healthy control subjects (SDM –0.38, 95% confidence interval [CI] –0.67 to –0.1 and SDM –0.2, 95% CI –0.4 to –0.007, respectively). There were significant SGC volume reductions in patients with unipolar (left SGC SDM –0.5, 95% CI –0.92 to –0.07; right SGC SDM –0.33, 95% CI –0.64 to –0.02,), but not bipolar, disorder. Patients with a positive family history of mood disorders showed significant left SGC volume decrease (SDM –0.52, 95% CI –0.96 to –0.07), which was not present among subjects without family history of mood disorders. There was no association between age and SGC volumes. Conclusion The available evidence suggests the existence of left and less robust right SGC volumetric reductions in patients with mood disorders, predominantly in those with unipolar depression. The effect size of this difference was moderate and increased in more homogeneous subgroups of patients with a positive family history. The clustering of SGC abnormalities in patients with a family history, their presence early in the illness course and their lack of progression with age make SGC a candidate for a primary vulnerability marker, although studies in unaffected high-risk subjects are missing.

Cortical folding is essential for healthy brain development. Previous studies have found regional reductions in cortical folding in adult patients with psychotic illness. It is unknown whether these neuroanatomical markers are present in youth with subclinical psychotic symptoms. We collected MRIs and examined the local gyrification index in a sample of 110 youth (mean age ± standard deviation 14.0±3.7 yr; range 9–25 yr) with a family history of severe mental illness: 48 with psychotic symptoms and 62 without. Images were processed using the Human Connectome Pipeline and FreeSurfer. We tested for group differences in local gyrification index using mixed-effects generalized linear models controlling for age, sex and familial clustering. Sensitivity analysis further controlled for intracranial volume, IQ, and stimulant and cannabis use. Youth with psychotic symptoms displayed an overall trend toward lower cortical folding across all brain regions. After adjusting for multiple comparisons and confounders, regional reductions were localized to the frontal and occipital lobes. Specifically, the medial (B = −0.42, pFDR = 0.04) and lateral (B = −0.39, pFDR = 0.04) orbitofrontal cortices as well as the cuneus (B = −0.47, pFDR = 0.03) and the pericalcarine (B = –0.45, pFDR = 0.03) and lingual (B = −0.38, pFDR = 0.04) gyri. Inference about developmental trajectories was limited by the cross-sectional data. Psychotic symptoms in youth are associated with cortical folding deficits, even in the absence of psychotic illness. The current study helps clarify the neurodevelopmental basis of psychosis at an early stage, before medication, drug use and other confounds have had a persistent effect on the brain.