The nested hierarchical model of self and its non-relational vs relational posttraumatic manifestation: an fMRI meta-analysis of emotional processing.

Different kinds of traumatic experiences like natural catastrophes vs. relational traumatic experiences (e.g., sex/physical abuse, interpersonal partner violence) are involved in the development of the self and PTSD psychopathological manifestations. Looking at a neuroscience approach, it has been proposed a nested hierarchical model of self, which identifies three neural-mental networks: (i) interoceptive; (ii) exteroceptive; (iii) mental. However, it is still unclear how the self and its related brain networks might be affected by non-relational vs relational traumatic experiences. Departing from this background, the current study aims at conducting a meta-analytic review of task-dependent fMRI studies (i.e., emotional processing task) among patients with PTSD due to non-relational (PTSD-NR) and relational (PTSD-R) traumatic experiences using two approaches: (i) a Bayesian network meta-analysis for a region-of-interest-based approach; (ii) a coordinated-based meta-analysis. Our findings suggested that the PTSD-NR mainly recruited areas ascribed to the mental self to process emotional stimuli. Whereas, the PTSD-R mainly activated regions associated with the intero-exteroceptive self. Accordingly, the PTSD-R compared to the PTSD-NR might not reach a higher symbolic capacity to process stimuli with an emotional valence. These results are also clinically relevant in support of the development of differential treatment approaches for non-relational vs. relational PTSD.

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Penetrant-induced plasticization in microporous polymer membranes.

Penetrant-induced plasticization has prevented the industrial deployment of many polymers for membrane-based gas separations. With the advent of microporous polymers, new structural design features and unprecedented property sets are now accessible under controlled laboratory conditions, but property sets can often deteriorate due to plasticization. Therefore, a critical understanding of the origins of plasticization in microporous polymers and the development of strategies to mitigate this effect are needed to advance this area of research. Herein, an integrative discussion is provided on seminal plasticization theory and gas transport models, and these theories and models are compared to an exhaustive database of plasticization characteristics of microporous polymers. Correlations between specific polymer properties and plasticization behavior are presented, including analyses of plasticization pressures from pure-gas permeation tests and mixed-gas permeation tests for pure polymers and composite films. Finally, an evaluation of common and current state-of-the-art strategies to mitigate plasticization is provided along with suggestions for future directions of fundamental and applied research on the topic.

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.

Choroid plexus volume is increased in mood disorders and associates with circulating inflammatory cytokines.

Depressed patients exhibit altered levels of immune-inflammatory markers both in the peripheral blood and in the cerebrospinal fluid (CSF) and inflammatory processes have been widely implicated in the pathophysiology of mood disorders. The Choroid Plexus (ChP), located at the base of each of the four brain ventricles, regulates the exchange of substances between the blood and CSF and several evidence supported a key role for ChP as a neuro-immunological interface between the brain and circulating immune cells. Given the role of ChP as a regulatory gate between periphery, CSF spaces and the brain, we compared ChP volumes in patients with bipolar disorder (BP) or major depressive disorder (MDD) and healthy controls, exploring their association with history of illness and levels of circulating cytokines. Plasma levels of inflammatory markers and MRI scans were acquired for 73 MDD, 79 BD and 72 age- and sex-matched healthy controls (HC). Patients with either BD or MDD had higher ChP volumes than HC. With increasing age, the bilateral ChP volume was larger in patients, an effect driven by the duration of illness; while only minor effects were observed in HC. Right ChP volumes were proportional to higher levels of circulating cytokines in the clinical groups, including IFN-γ, IL-13 and IL-17. Specific effects in the two diagnostic groups were observed when considering the left ChP, with positive association with IL-1ra, IL-13, IL-17, and CCL3 in BD, and negative associations with IL-2, IL-4, IL-1ra, and IFN-γ in MDD. These results suggest that ChP could represent a reliable and easy-to-assess biomarker to evaluate the brain effects of inflammatory status in mood disorders, contributing to personalized diagnosis and tailored treatment strategies.

Low-dose interleukin 2 antidepressant potentiation in unipolar and bipolar depression: Safety, efficacy, and immunological biomarkers.

Immune-inflammatory mechanisms are promising targets for antidepressant pharmacology. Immune cell abnormalities have been reported in mood disorders showing a partial T cell defect. Following this line of reasoning we defined an antidepressant potentiation treatment with add-on low-dose interleukin 2 (IL-2). IL-2 is a T-cell growth factor which has proven anti-inflammatory efficacy in autoimmune conditions, increasing thymic production of naïve CD4 + T cells, and possibly correcting the partial T cell defect observed in mood disorders. We performed a single-center, randomised, double-blind, placebo-controlled phase II trial evaluating the safety, clinical efficacy and biological responses of low-dose IL-2 in depressed patients with major depressive (MDD) or bipolar disorder (BD). 36 consecutively recruited inpatients at the Mood Disorder Unit were randomised in a 2:1 ratio to receive either aldesleukin (12 MDD and 12 BD) or placebo (6 MDD and 6 BD). Active treatment significantly potentiated antidepressant response to ongoing SSRI/SNRI treatment in both diagnostic groups, and expanded the population of T regulatory, T helper 2, and percentage of Naive CD4+/CD8 + immune cells. Changes in cell frequences were rapidly induced in the first five days of treatment, and predicted the later improvement of depression severity. No serious adverse effect was observed. This is the first randomised control trial (RCT) evidence supporting the hypothesis that treatment to strengthen the T cell system could be a successful way to correct the immuno-inflammatory abnormalities associated with mood disorders, and potentiate antidepressant response.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Higher Seasonal Variation of Systemic Inflammation in Bipolar Disorder.

Seasonal rhythms affect the immune system. Evidence supports the involvement of immuno-inflammatory mechanisms in bipolar disorder (BD), with the neutrophil to lymphocyte ratio (NLR), and the systemic immune-inflammatory index (SII; platelets × neutrophils/lymphocytes) consistently reported to be higher in patients with BD than in HC, but seasonal rhythms of innate and adaptive immunity have never been studied. We retrospectively studied NLR and SII in 824 participants divided into three groups: 321 consecutively admitted inpatients affected by a major depressive episode in course of BD, and 255 consecutively admitted inpatients affected by obsessive-compulsive disorder (OCD; positive psychiatric control), and 248 healthy controls (HC). Patients with BD showed markedly higher markers of systemic inflammation in autumn and winter, but not in spring and summer, in respect to both HC and patients with OCD, thus suggesting a specific effect of season on inflammatory markers in BD, independent of a shared hospital setting and drug treatment. Given that systemic inflammation is emerging as a new marker and as target for treatment in depressive disorders, we suggest that seasonal rhythms should be considered for tailoring antidepressant immuno-modulatory treatments in a precision medicine approach.

A Microporous Poly(Arylene Ether) Platform for Membrane-Based Gas Separation.

Membrane-based gas separations are crucial for an energy-efficient future. However, it is difficult to develop membrane materials that are high-performing, scalable, and processable. Microporous organic polymers (MOPs) combine benefits for gas sieving and solution processability. Herein, we report membrane performance for a new family of microporous poly(arylene ether)s (PAEs) synthesized via Pd-catalyzed C-O coupling reactions. The scaffold of these microporous polymers consists of rigid three-dimensional triptycene and stereocontorted spirobifluorene, endowing these polymers with micropore dimensions attractive for gas separations. This robust PAE synthesis method allows for the facile incorporation of functionalities and branched linkers for control of permeation and mechanical properties. A solution-processable branched polymer was formed into a submicron film and characterized for permeance and selectivity, revealing lab data that rivals property sets of commercially available membranes already optimized for much thinner configurations. Moreover, the branching motif endows these materials with outstanding plasticization resistance, and their microporous structure and stability enables benefits from competitive sorption, increasing CO2 /CH4 and (H2 S+CO2 )/CH4 selectivity in mixture tests as predicted by the dual-mode sorption model. The structural tunability, stability, and ease-of-processing suggest that this new platform of microporous polymers provides generalizable design strategies to form MOPs at scale for demanding gas separations in industry.

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.

A Delphi-method-based consensus guideline for definition of treatment-resistant depression for clinical trials.

Criteria for treatment-resistant depression (TRD) and partially responsive depression (PRD) as subtypes of major depressive disorder (MDD) are not unequivocally defined. In the present document we used a Delphi-method-based consensus approach to define TRD and PRD and to serve as operational criteria for future clinical studies, especially if conducted for regulatory purposes. We reviewed the literature and brought together a group of international experts (including clinicians, academics, researchers, employees of pharmaceutical companies, regulatory bodies representatives, and one person with lived experience) to evaluate the state-of-the-art and main controversies regarding the current classification. We then provided recommendations on how to design clinical trials, and on how to guide research in unmet needs and knowledge gaps. This report will feed into one of the main objectives of the EUropean Patient-cEntric clinicAl tRial pLatforms, Innovative Medicines Initiative (EU-PEARL, IMI) MDD project, to design a protocol for platform trials of new medications for TRD/PRD.

Insulin resistance disrupts white matter microstructure and amplitude of functional spontaneous activity in bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is linked to several structural and functional brain alterations. In addition, BD patients have a three-fold increased risk of developing insulin resistance, which is associated with neural changes and poorer BD outcomes. Therefore, we investigated the effects of insulin and two derived measures (insulin resistance and sensitivity) on white matter (WM) microstructure, resting-state (rs) functional connectivity (FC), and fractional amplitude of low-frequency fluctuation (fALFF). METHODS: BD patients (n = 92) underwent DTI acquisition, and a subsample (n = 22) underwent rs-fMRI. Blood samples were collected to determine insulin and glucose levels. The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) were computed. DTI data were analyzed via tract-based spatial statistics and threshold-free cluster enhancement. From rs-fMRI data, both ROI-to-ROI FC matrices and fALFF maps were extracted. RESULTS: Insulin showed a widespread negative association with fractional anisotropy (FA) and a positive effect on radial diffusivity (RD) and mean diffusivity (MD). HOMA-IR exerted a significant effect on RD in the right superior longitudinal fasciculus, whereas QUICKI was positively associated with FA and negatively with RD and MD in the left superior longitudinal fasciculus, left anterior corona radiata, and forceps minor. fALFF was negatively modulated by insulin and HOMA-IR and positively associated with QUICKI in the precuneus. No significant results were found in the ROI-to-ROI analysis. CONCLUSION: Our findings suggest that WM microstructure and functional alterations might underlie the effect of IR on BD pathophysiology, even if the causal mechanisms need to be further investigated.

The self and our perception of its synchrony - Beyond internal and external cognition.

The self is the core of our mental life which connects one's inner mental life with the external perception. Since synchrony is a key feature of the biological world and its various species, what role does it play for humans? We conducted a large-scale psychological study (n = 1072) combining newly developed visual analogue scales (VAS) for the perception of synchrony and internal and external cognition complemented by several psychological questionnaires. Overall, our findings showed close connection of the perception of synchrony of the self with both internal (i.e., body and cognition) and external (i.e., others, environment/nature) synchrony being associated positively with adaptive and negatively with maladaptive traits of self. Moreover, we have demonstrated how external (i.e., life events like the COVID-19 pandemic) variables modulate the perception of the self's internal-external synchrony. These findings suggest how synchrony with self plays a central role during times of uncertainty.

Cognitive remediation therapy for post-acute persistent cognitive deficits in COVID-19 survivors: A proof-of-concept study.

Cognitive impairments figure prominently in COVID-19 survivors. Cognitive remediation therapy (CRT) improves functional outcomes reducing long-term cognitive deficits in several neurological and psychiatric conditions. Our case-control study investigates the efficacy of a CRT programme administered to COVID-19 survivors in the post-acute phase of the illness. Seventy-three COVID-19 survivors presenting cognitive impairments at one-month follow-up were enrolled. Among them, 15 patients were treated with a two-month CRT programme, and 30 non-treated patients were matched conditional to their baseline cognitive functioning. Cognitive functions were assessed before and after treatment. Depression and quality of life were also evaluated. Mixed model ANOVA revealed a significant effect over time of the CRT programme on global cognitive functioning (F = 4.56, p = 0.039), while no significant effect was observed in the untreated group. We observed a significant effect of the improvement in verbal fluency (χ2 = 7.20, p = 0.007) and executive functions (χ2 = 13.63, p < 0.001) on quality of life. A positive significant correlation was found between depressive symptomatology and verbal fluency (r = -0.35), working memory (r = -0.44), psychomotor coordination (r = -0.42), and executive functions (r = -0.33). Our results could pave the way to a plausible innovative treatment targeting cognitive impairments and ameliorating the quality of life of COVID-19 survivors.

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.

An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration.

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

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.

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.

MOF-Based Membranes for Gas Separations.

Metal-organic frameworks (MOFs) represent the largest known class of porous crystalline materials ever synthesized. Their narrow pore windows and nearly unlimited structural and chemical features have made these materials of significant interest for membrane-based gas separations. In this comprehensive review, we discuss opportunities and challenges related to the formation of pure MOF films and mixed-matrix membranes (MMMs). Common and emerging separation applications are identified, and membrane transport theory for MOFs is described and contextualized relative to the governing principles that describe transport in polymers. Additionally, cross-cutting research opportunities using advanced metrologies and computational techniques are reviewed. To quantify membrane performance, we introduce a simple membrane performance score that has been tabulated for all of the literature data compiled in this review. These data are reported on upper bound plots, revealing classes of MOF materials that consistently demonstrate promising separation performance. Recommendations are provided with the intent of identifying the most promising materials and directions for the field in terms of fundamental science and eventual deployment of MOF materials for commercial membrane-based gas separations.