Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Depressive symptoms in youth before and during the COVID-19 pandemic: longitudinal investigation of patterns dependent on age, sex, and family history of mental illness.

BACKGROUND: Cross-sectional studies report high levels of depressive symptoms during the COVID-19 pandemic, especially in youth and females. However, longitudinal research comparing depressive symptoms before and during the pandemic is lacking. Little is known about how the pandemic affected individuals with familial history of mental illness. The present study examines the impact of the pandemic on youth depressive symptoms, including offspring of parents with major mood and psychotic disorders. METHODS: Between March 2018 and February 2020, we measured depressive symptoms in 412 youth aged 5-25 years. We measured depressive symptoms again in 371 (90%) of these youth between April 2020 and May 2022. Two thirds (249) participants had a biological parent with a major mood or psychotic disorder. We tested the effect of the pandemic by comparing depression symptoms before and after March 2020. We examined age, sex, and family history as potential moderators. RESULTS: We found an overall small increase in youth depressive symptoms (b = 0.07, 95% CI -0.01 to 0.15, p = 0.062). This was driven by an increase in female youth without familial history of mental illness (b = 0.35, 95% CI 0.14 to 0.56, p = 0.001). There was no change in depressive symptoms among offspring of parents with mental illness or males. CONCLUSIONS: Our results provide reassurance about the wellbeing of children of parents with mental illness during a period of restricted access to resources outside the family. Rather than increasing symptoms in established risk groups, the pandemic led to a redistribution of depression burden towards segments of the youth population that were previously considered to be low-risk.

Immunoglobulin genes expressed in lymphoblastoid cell lines discern and predict lithium response in bipolar disorder patients.

Bipolar disorder (BD) is a neuropsychiatric mood disorder manifested by recurrent episodes of mania and depression. More than half of BD patients are non-responsive to lithium, the first-line treatment drug, complicating BD clinical management. Given its unknown etiology, it is pertinent to understand the genetic signatures that lead to variability in lithium response. We discovered a set of differentially expressed genes (DEGs) from the lymphoblastoid cell lines (LCLs) of 10 controls and 19 BD patients belonging mainly to the immunoglobulin gene family that can be used as potential biomarkers to diagnose and treat BD. Importantly, we trained machine learning algorithms on our datasets that predicted the lithium response of BD subtypes with minimal errors, even when used on a different cohort of 24 BD patients acquired by a different laboratory. This proves the scalability of our methodology for predicting lithium response in BD and for a prompt and suitable decision on therapeutic interventions.

Challenges in defining treatment-resistant mania in adults: A systematic review.

OBJECTIVES: To review the definitions of treatment-resistant mania (TRM) in the literature and propose criteria for an operationalized definition. METHODS: A systematic search of five databases (MEDLINE, EMBASE, PsychInfo, Cochrane Central, and CINAHL) and data extraction of eligible articles. RESULTS: In total, 47 articles addressing the concept of TRM were included, comprising 16 case reports, 11 case series, 3 randomized clinical trials, 8 open-label clinical trials, 1 experimental study, 7 narrative reviews, and 1 systematic review. While reviews discussed several challenges in defining TRM, definitions varied substantially based on different criteria for severity of mania, duration of mania, and use of specific therapeutic agents with minimal dosages and duration of treatment. Only a handful of the reviewed articles operationalized these criteria. CONCLUSION: While the concept of TRM has been discussed in the literature for over three decades, we could not find an agreed-upon operationalized definition based on specific criteria. We propose and discuss a possible definition that could be used by clinicians to guide their practice and by researchers to assess the prevalence of TRM and develop and test interventions targeting TRM.

Phenotypic clustering of bipolar disorder supports stratification by lithium responsiveness over diagnostic subtypes.

INTRODUCTION: The aim of this study was to determine whether the clinical profiles of bipolar disorder (BD) patients could be differentiated more clearly using the existing classification by diagnostic subtype or by lithium treatment responsiveness. METHODS: We included adult patients with BD-I or II (N = 477 across four sites) who were treated with lithium as their principal mood stabilizer for at least 1 year. Treatment responsiveness was defined using the dichotomized Alda score. We performed hierarchical clustering on phenotypes defined by 40 features, covering demographics, clinical course, family history, suicide behaviour, and comorbid conditions. We then measured the amount of information that inferred clusters carried about (A) BD subtype and (B) lithium responsiveness using adjusted mutual information (AMI) scores. Detailed phenotypic profiles across clusters were then evaluated with univariate comparisons. RESULTS: Two clusters were identified (n = 56 and n = 421), which captured significantly more information about lithium responsiveness (AMI range: 0.033 to 0.133) than BD subtype (AMI: 0.004 to 0.011). The smaller cluster had disproportionately more lithium responders (n = 47 [83.8%]) when compared to the larger cluster (103 [24.4%]; p = 0.006). CONCLUSIONS: Phenotypes derived from detailed clinical data may carry more information about lithium responsiveness than the current classification of diagnostic subtype. These findings support lithium responsiveness as a valid approach to stratification in clinical samples.

Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

BACKGROUND: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. METHODS: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. RESULTS: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. CONCLUSIONS: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier.

AIM: Bipolar disorder (BD) is a mood disorder with a high morbidity and death rate. Lithium (Li), a prominent mood stabilizer, is often used as a first-line treatment. However, clinical studies have shown that Li is fully effective in roughly 30% of BD patients. Our goal in this study was to use features derived from information theory to improve the prediction of the patient's response to Li as well as develop a diagnostic algorithm for the disorder. METHODS: We have performed electrophysiological recordings in patient-derived dentate gyrus (DG) granule neurons (from a total of 9 subjects) for three groups: 3 control individuals, 3 BD patients who respond to Li treatment (LR), and 3 BD patients who do not respond to Li treatment (NR). The recordings were analyzed by the statistical tools of modern information theory. We used a Support Vector Machine (SVM) and Random forest (RF) classifiers with the basic electrophysiological features with additional information theory features. RESULTS: Information theory features provided further knowledge about the distribution of the electrophysiological entities and the interactions between the different features, which improved classification schemes. These newly added features significantly improved our ability to distinguish the BD patients from the control individuals (an improvement from 60% to 74% accuracy) and LR from NR patients (an improvement from 81% to 99% accuracy). CONCLUSION: The addition of Information theory-derived features provides further knowledge about the distribution of the parameters and their interactions, thus significantly improving the ability to discriminate and predict the LRs from the NRs and the patients from the controls.

Lithium in breast milk transiently affects the renal electrolytic balance of infants.

BACKGROUND: The use of lithium during breast-feeding has not been comprehensively investigated in humans due to concerns about lithium toxicity. PROCEDURE: We analyzed lithium in the kidneys of nursed pups of lithium medicated mothers, using analytical spectroscopy in a novel rat model. The mothers were healthy rats administered lithium via gavage (1000 mg/day Li2 CO3 per 50 kg body weight). RESULTS: Lithium was detected in the breast milk, and in the blood of pups (0.08 mM), of lithium-exposed dams at post-natal day 18 (P18), during breast-feeding. No lithium was detected after breast-feeding, at P25 (4 days after cessation of nursing). The lithium pups blood had elevated urea nitrogen at P18 and reduced total T4 at P18 and P25, indicating a longer-term effect on the kidneys and the thyroid gland. Multivariate machine-learning analysis of spectroscopy data collected from the excised kidneys of pups showed elevated potassium in lithium-exposed animals both during- and after breast-feeding. The elevated renal potassium was associated with low nephrin expression in the kidneys measured immunohistochemically during breast-feeding. After lithium exposure is stopped, the filtration of lithium from the kidneys reverses these effects. Our study showed that breastfeeding during lithium use has an effect on the kidneys of the offspring in rats.

Essential data dimensions for prospective international data collection in older age bipolar disorder (OABD): Recommendations from the GAGE-BD group.

BACKGROUND: By 2030, over 50% of individuals living with bipolar disorder (BD) are expected to be aged ≥50 years. However, older age bipolar disorder (OABD) remains understudied. There are limited large-scale prospectively collected data organized in key dimensions capable of addressing several fundamental questions about BD affecting this subgroup of patients. METHODS: We developed initial recommendations for the essential dimensions for OABD data collection, based on (1) a systematic review of measures used in OABD studies, (2) a Delphi consensus of international OABD experts, (3) experience with harmonizing OABD data in the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD, n ≥ 4500 participants), and (4) critical feedback from 34 global experts in geriatric mental health. RESULTS: We identified 15 key dimensions and variables within each that are relevant for the investigation of OABD: (1) demographics, (2) core symptoms of depression and (3) mania, (4) cognition screening and subjective cognitive function, (5) elements for BD diagnosis, (6) descriptors of course of illness, (7) treatment, (8) suicidality, (9) current medication, (10) psychiatric comorbidity, (11) psychotic symptoms, (12) general medical comorbidities, (13) functioning, (14) family history, and (15) other. We also recommend particular instruments for capturing some of the dimensions and variables. CONCLUSION: The essential data dimensions we present should be of use to guide future international data collection in OABD and clinical practice. In the longer term, we aim to establish a prospective consortium using this core set of dimensions and associated variables to answer research questions relevant to OABD.

Familial traits of bipolar disorder: A systematic review and meta-analysis.

BACKGROUND: Genetic studies of bipolar disorder (BD) have shown varied results, which is in part because of the heterogeneity of the disorder. Identifying clinical phenotypes of BD could reduce variability and benefit research. Since BD has a robust genetic component, studies can investigate clinical traits that cluster in families to identify phenotypes with a probable genetic basis. METHODS: We conducted a systematic review of the current literature on familial clinical traits of BD. Text screening and data extraction were performed independently by two reviewers, and random effects meta-analysis was used. RESULTS: Of 1117 unique records, 16 studies met inclusion criteria. These studies indicated 14 potentially familial traits of BD: age of onset (OR: 4.50; 95% CI: [3.25, 6.22]), bipolar type (OR: 2.05 [1.50, 2.79]), lithium response (OR: 3.71 [1.28, 10.82]), polarity at onset (OR: 1.17 [1.03, 1.34]), psychotic features (OR: 2.20 [1.51, 3.20]), mood-incongruent psychosis (OR: 2.52 [1.66, 3.83]), puerperal psychosis (OR: 6.54 [2.55, 16.77]), rapid cycling (OR: 4.95 [0.96, 25.40]), suicide attempt (OR: 1.04 [0.65, 1.67]), alcoholism (OR: 1.53 [1.09, 2.16]), obsessive-compulsive disorder (OR: 3.10 [1.31; 7.09]), panic disorder (OR: 2.69 [1.12; 6.48]), social anxiety disorder (OR: 1.00 [0.39, 2.55]), and specific phobia (OR: 1.94 [0.95; 3.96]). For most traits, tests of heterogeneity were significant and publication bias was likely. CONCLUSION: The results of our review and meta-analysis highlight the lack of studies investigating familial clinical traits of BD, despite the need to address heterogeneity. The large degree of variability between studies must be reduced for future research.

Longitudinal Digital Mood Charting in Bipolar Disorder: Experiences with ChronoRecord Over 20 Years.

INTRODUCTION: Longitudinal study is an essential methodology for understanding disease trajectories, treatment effects, symptom changes, and long-term outcomes of affective disorders. Daily self-charting of mood and other illness-related variables is a commonly recommended intervention. With the widespread acceptance of home computers in the early 2000s, automated tools were developed for patient mood charting, such as ChronoRecord, a software validated by patients with bipolar disorder. The purpose of this study was to summarize the daily mood, sleep, and medication data collected with ChronoRecord, and highlight some of the key research findings. Lessons learned from implementing a computerized tool for patient self-reporting are also discussed. METHODS: After a brief training session, ChronoRecord software for daily mood charting was installed on a home computer and used by 609 patients with affective disorders. RESULTS: The mean age of the patients was 40.3±11.8 years, a mean age of onset was 22±11.2 years, and 71.4% were female. Patients were euthymic for 70.8% of days, 15.1% had mild depression, 6.6% had severe depression, 6.6% had hypomania, and 0.8% had mania. Among all mood groups, 22.4% took 1-2 medications, 37.2% took 3-4 medications, 25.7 took 5-6 medications, 11.6% took 7-8 medications, and 3.1% took >8 medications. CONCLUSION: The daily mood charting tool is a useful tool for increasing patient involvement in their care, providing detailed patient data to the physician, and increasing understanding of the course of illness. Longitudinal data from patient mood charting was helpful in both clinical and research settings.

Synaptotagmin-7 is a key factor for bipolar-like behavioral abnormalities in mice.

The pathogenesis of bipolar disorder (BD) has remained enigmatic, largely because genetic animal models based on identified susceptible genes have often failed to show core symptoms of spontaneous mood cycling. However, pedigree and induced pluripotent stem cell (iPSC)-based analyses have implicated that dysfunction in some key signaling cascades might be crucial for the disease pathogenesis in a subpopulation of BD patients. We hypothesized that the behavioral abnormalities of patients and the comorbid metabolic abnormalities might share some identical molecular mechanism. Hence, we investigated the expression of insulin/synapse dually functioning genes in neurons derived from the iPSCs of BD patients and the behavioral phenotype of mice with these genes silenced in the hippocampus. By these means, we identified synaptotagmin-7 (Syt7) as a candidate risk factor for behavioral abnormalities. We then investigated Syt7 knockout (KO) mice and observed nocturnal manic-like and diurnal depressive-like behavioral fluctuations in a majority of these animals, analogous to the mood cycling symptoms of BD. We treated the Syt7 KO mice with clinical BD drugs including olanzapine and lithium, and found that the drug treatments could efficiently regulate the behavioral abnormalities of the Syt7 KO mice. To further verify whether Syt7 deficits existed in BD patients, we investigated the plasma samples of 20 BD patients and found that the Syt7 mRNA level was significantly attenuated in the patient plasma compared to the healthy controls. We therefore concluded that Syt7 is likely a key factor for the bipolar-like behavioral abnormalities.

In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

Circadian rhythms in bipolar disorder patient-derived neurons predict lithium response: preliminary studies.

Bipolar disorder (BD) is a neuropsychiatric illness defined by recurrent episodes of mania/hypomania, depression and circadian rhythm abnormalities. Lithium is an effective drug for BD, but 30-40% of patients fail to respond adequately to treatment. Previous work has demonstrated that lithium affects the expression of "clock genes" and that lithium responders (Li-R) can be distinguished from non-responders (Li-NR) by differences in circadian rhythms. However, circadian rhythms have not been evaluated in BD patient neurons from Li-R and Li-NR. We used induced pluripotent stem cells (iPSCs) to culture neuronal precursor cells (NPC) and glutamatergic neurons from BD patients characterized for lithium responsiveness and matched controls. We identified strong circadian rhythms in Per2-luc expression in NPCs and neurons from controls and Li-R, but NPC rhythms in Li-R had a shorter circadian period. Li-NR rhythms were low amplitude and profoundly weakened. In NPCs and neurons, expression of PER2 was higher in both BD groups compared to controls. In neurons, PER2 protein levels were higher in BD than controls, especially in Li-NR samples. In single cells, NPC and neuron rhythms in both BD groups were desynchronized compared to controls. Lithium lengthened period in Li-R and control neurons but failed to alter rhythms in Li-NR. In contrast, temperature entrainment increased amplitude across all groups, and partly restored rhythms in Li-NR neurons. We conclude that neuronal circadian rhythm abnormalities are present in BD and most pronounced in Li-NR. Rhythm deficits in BD may be partly reversible through stimulation of entrainment pathways.

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles  and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

Smartphone-based interventions in bipolar disorder: Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force.

BACKGROUND: The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established. OBJECTIVES: To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators. METHODS: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668). RESULTS: The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators. CONCLUSION: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.

Correction of depression-associated circadian rhythm abnormalities is associated with lithium response in bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is characterized by episodes of depression and mania and disrupted circadian rhythms. Lithium is an effective therapy for BD, but only 30%-40% of patients are fully responsive. Preclinical models show that lithium alters circadian rhythms. However, it is unknown if the circadian rhythm effects of lithium are essential to its therapeutic properties. METHODS: In secondary analyses of a multi-center, prospective, trial of lithium for BD, we examined the relationship between circadian rhythms and therapeutic response to lithium. Using standardized instruments, we measured morningness, diurnal changes in mood, sleep, and energy (circadian rhythm disturbances) in a cross-sectional study of 386 BD subjects with varying lithium exposure histories. Next, we tracked symptoms of depression and mania prospectively over 12 weeks in a subset of 88 BD patients initiating treatment with lithium. Total, circadian, and affective mood symptoms were scored separately and analyzed. RESULTS: Subjects with no prior lithium exposure had the most circadian disruption, while patients stable on lithium monotherapy had the least. Patients who were stable on lithium with another drug or unstable on lithium showed intermediate levels of disruption. Treatment with lithium for 12 weeks yielded significant reductions in total and affective depression symptoms. Lithium responders (Li-Rs) showed improvement in circadian symptoms of depression, but non-responders did not. There was no difference between Li-Rs and nonresponders in affective, circadian, or total symptoms of mania. CONCLUSIONS: Exposure to lithium is associated with reduced circadian disruption. Lithium response at 12 weeks was selectively associated with the reduction of circadian depressive symptoms. We conclude that stabilization of circadian rhythms may be an important feature of lithium's therapeutic effects. CLINICAL TRIALS REGISTRY: NCT0127253.

Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals.

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.