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Abstract The progression of Parkinson’s disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences. Diffusion-weighted MRI data from 1654 participants diagnosed with PD (age: 20–89 years; 33% female) and 885 controls (age: 19–84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate white matter microstructure. Skeletonized maps of fractional anisotropy (FA) and mean diffusivity (MD) were compared across Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter alterations at different stages. We found an enhanced, more widespread pattern of microstructural alterations with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest: Cohen’s d effect sizes reached d = −1.01 for FA differences in the fornix at PD HY Stage 4/5. The early PD signature in HY stage 1 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with motor and non-motor clinical dysfunction. While overridden by degenerative changes in the later stages of PD, early PD is associated with paradoxically higher FA and lower MD in PD, consistent with early compensatory changes associated with the disorder.

Abstract Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal ‘trajectory’ of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Abstract. Introduction:. Neuropathic pain (NP) poses significant challenges for individuals with spinal cord injury (SCI), often inadequately managed by current interventions. Immersive virtual reality (IVR) has emerged as a promising approach for pain modulation, yet robust evidence is lacking. Objectives:. This pilot study investigated the analgesic effects of different IVR environments (scenic, somatic) compared with a control environment, and explored psychomotor properties influencing pain attenuation. Methods:. Twenty-two participants with NP caused by SCI were randomized into 3 IVR environments: somatic (n = 8), scenic (n = 7), and control (n = 8), undergoing 3 weekly sessions over 4 weeks with baseline, postintervention, and one-month follow-ups. Results:. There was a significant interaction effect between VR environment and time point on Neuropathic Pain Symptom Inventory scores (F(4,37.0) = 2.80, P = 0.04). Scenic VR participants exhibited reduced scores postintervention and at follow-up, with no significant changes in somatic VR or control environments. Similar trends were observed in secondary measures, such as Neuropathic Pain Scale and pain numeric rating scale. Enjoyment and presence were associated with decreased pain-change scores (F(1, 252) = 4.68, P = 0.03 for enjoyment; F(1, 223.342) = 7.92, P = 0.005 for presence), although not significantly influenced by VR environment or time point. Conclusion:. Both environments showed reduced pain outcomes, underscoring the need for personalized IVR pain therapies and informing further technology development for NP management.

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer’s disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer’s treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer’s Disease Neuroimaging Initiative. We evaluated each model’s balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

Dysregulation of the dopamine (DA) system is a hallmark of substance use disorders, including alcohol use disorder (AUD). Of the DA receptor subtypes, the DA D2 receptors (D2Rs) play a key role in the reinforcing effects of alcohol. D2Rs are expressed in numerous brain regions associated with the regulation of appetitive behaviors. One such region is the bed nucleus of the stria terminalis (BNST), which has been linked to the development and maintenance of AUD. Recently, we identified alcohol withdrawal-related neuroadaptations in the periaqueductal gray/dorsal raphe to BNST DA circuit in male mice. However, the role of D2R-expressing BNST neurons in voluntary alcohol consumption is not well characterized. In this study, we used a CRISPR-Cas9-based viral approach, to selectively reduce the expression of D2Rs in BNST GABA neurons (BNSTvgat Drd2) and interrogated the impact on alcohol-related behaviors. In male mice, reduced BNSTvgat Drd2 expression potentiated the stimulatory effects of alcohol and increased voluntary consumption of 20% w/v alcohol in a two-bottle choice intermittent access paradigm. This effect was not specific to alcohol, as BNSTvgat Drd2 knockdown also increased sucrose intake in male mice. Interestingly, reduction in BNSTvgat Drd2 expression in female mice did not alter alcohol-related behaviors but lowered the threshold for mechanical pain sensitivity. Collectively, our findings suggest a role for postsynaptic BNST D2Rs in the modulation of sex-specific behavioral responses to alcohol and sucrose.

Abstract Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

Host resistance to a common protozoan parasite Toxoplasma gondii relies on a coordinated immune response involving multiple cell types, including macrophages. Embryonically seeded tissue-resident macrophages (TRMs) play a critical role in maintaining tissue homeostasis, but their role in parasite clearance is poorly understood. In this study, we uncovered a crucial aspect of host defense against T. gondii mediated by TRMs. Through the use of neutralizing antibodies and conditional IFN-γ receptor-deficient mice, we demonstrated that IFN-γ directly mediated the elimination of TRMs. Mechanistically, IFN-γ stimulation in vivo rendered macrophages unresponsive to macrophage colony-stimulating factor (M-CSF) and inactivated mTOR signaling by causing the shedding of CD115 (CSFR1), the receptor for M-CSF. Further experiments revealed the essential role of macrophage IFN-γ responsiveness in host resistance to T. gondii. The elimination of peritoneal TRMs emerged as an additional host defense mechanism aimed at limiting the parasite's reservoir. The identified mechanism, involving IFN-γ-induced suppression of CD115-dependent mTOR signaling in macrophages, provides insights into the adaptation of macrophage subsets during infection and highlights a crucial aspect of host defense against intracellular pathogens.

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Background: Many smokers report attempting to quit each year, yet most relapse, in part due to exposure to smoking-related cues. It is hypothesized that extinction of the cue-drug association could be facilitated through random nicotine delivery (RND), thus making it easier for smokers to quit. The current study aimed to evaluate the effects of RND on smoking cessation-related outcomes including cigarettes per day (CPD) and exhaled carbon monoxide (CO). Methods: Participants were current smokers (>9 CPD) interested in quitting. Novel trans-mucosal, orally dissolving nicotine films, developed by Bionex Pharmaceuticals, were used in the study. The pharmacokinetic profile of these films was assessed in single (Experiment 1) and multiple-dose (Experiment 2) administrations prior to the smoking cessation study (Experiment 3). In Experiment 3, participants were randomized 1:1:1 to recieve 4 nicotine films per day of either: placebo delivery (0 mg), steady-state delivery (2 mg), or random nicotine delivery (RND) (0 mg or 4 mg). After two weeks, participants were advised to quit (target quit date, TQD) and were followed up 4 weeks later to collect CPD and CO and to measure dependence (Penn State Cigarette Dependence Index; PSCDI) and craving (Questionnaire of Smoking Urges; QSU-Brief). Means and frequencies were used to describe the data and repeated measures ANOVA was used to determine differences between groups. Results: The pharmacokinetic studies (Experiment 1 and 2) demonstrated that the films designed for this study delivered nicotine as expected, with the 4 mg film delivering a nicotine boost of approximately 12.4 ng/mL across both the single and the multiple dose administration studies. The films reduced craving for a cigarette and were well-tolerated, overall, and caused no changes in blood pressure or heart rate. Using these films in the cessation study (Experiment 3) (n = 45), there was a significant overall reduction in cigarettes smoked per day (CPD) and in exhaled CO, with no significant differences across groups (placebo, steady-state, RND). In addition, there were no group differences in dependence or craving. Adverse events included heartburn, hiccups, nausea, and to a lesser extent, vomiting and anxiety and there were no differences across groups. Conclusion: Overall, this pilot study found that RND via orally dissolving films was feasible and well tolerated by participants. However, RND participants did not experience a greater reduction in self-reported CPD and exhaled CO, compared with participants in the steady-state and placebo delivery groups. Future studies to evaluate optimal RND parameters with larger sample sizes are needed to fully understand the effect of RND on smoking cessation-related outcomes.

Individuals across six major psychiatric conditions from the ENIGMA consortium reveals a shared morphological effect, following a sensory-fugal axis, which is related to microstructural gradient and neurotransmitter axes.

Changes to structural and functional connectivity can give rise to neurodegeneration and neurodevelopmental diseases. Here the authors investigate molecular and connectomic patterns in 13 different neurological, psychiatric and neurodevelopmental diseases from the ENIGMA consortium.

Abstract Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the ‘normativeness’ of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.

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Introduction: Compliance with the Government’s lockdown policy is required to curtail community transmission of Covid-19 infection. The objective of this research was to identify places Nigerians visited during the lockdown to help prepare for a response towards future infectious diseases of public health importance similar to Covid-19. Methods: This was a secondary analysis of unconventional data collected using Google Forms and online social media platforms during the COVID-19 lockdown between April and June 2020 in Nigeria. Two datasets from: i) partnership for evidence-based response to COVID-19 (PERC) wave-1 and ii) College of Medicine, University of Lagos perception of and compliance with physical distancing survey (PCSH) were used. Data on places that people visited during the lockdown were extracted and compared with the sociodemographic characteristics of the respondents. Descriptive statistics were calculated for all independent variables and focused on frequencies and percentages. Chi-squared test was used to determine the significance between sociodemographic variables and places visited during the lockdown. Statistical significance was determined by P0.05. All statistical analyses were carried out using SPSS version 22. Results: There were 1304 and 879 participants in the PERC wave-1 and PCSH datasets, respectively. The mean age of PERC wave-1 and PCSH survey respondents was 31.8 [standard deviation (SD)=8.5] and 33.1 (SD=8.3) years, respectively. In the PCSH survey, 55.9% and 44.1% of respondents lived in locations with partial and complete covid-19 lockdowns, respectively. Irrespective of the type of lockdown, the most common place visited during the lockdown was the market (shopping); reported by 73% of respondents in states with partial lockdown and by 68% of respondents in states with the complete lockdown. Visits to families and friends happened more in states with complete (16.1%) than in states with partial (8.4%) lockdowns. Conclusions: Markets (shopping) were the main places visited during the lockdown compared to visiting friends/family, places of worship, gyms, and workplaces. It is important in the future for the Government to plan how citizens can safely access markets and get other household items during lockdowns for better adherence to stay-at-home directives for future infectious disease epidemics.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption.In this study, we introduce a novel index which we call an “AD-NeuroScore,” that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1–91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses.Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics.In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.

S'il ne fait aucun doute que la biodiversité décline à l'échelle mondiale, il est surprenant de constater que les dynamiques locales peuvent être contrastées. Nous possédons une compréhension encore incomplète de la façon dont les écosystèmes évoluent sous l'effet du changement global. Ici, nous avons cherché à caractériser la dynamique temporelle récente de la biodiversité des rivières en France, d'un point de vue taxonomique et fonctionnel, concernant différents organismes clés (diatomées, macro-invertébrés et poissons), à partir d’un large panel de mesures de la biodiversité et sur une période multi-décennale. Les résultats convergent vers la mise en évidence d'un retour significatif des taxons sensibles à l'eutrophisation, signe d'amélioration de la qualité des cours d’eau au cours de la période d’étude du point de vue de l’enrichissement trophique. D'autre part, la richesse en diatomées et, dans une moindre mesure, en poissons, a diminué au fil du temps, tandis que la richesse taxonomique des macro-invertébrés a augmenté. Enfin, l'étude a révélé des réarrangements cohérents au sein des communautés des cours d'eau, avec le passage d’une production primaire planctonique à une production primaire benthique, entraînant des modifications fonctionnelles des communautés de macro-invertébrés et de poissons.

Abstract The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Tourette syndrome (TS) is characterized by multiple motor and vocal tics, and high-comorbidity rates with other neuropsychiatric disorders. Obsessive compulsive disorder (OCD), attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), major depressive disorder (MDD), and anxiety disorders (AXDs) are among the most prevalent TS comorbidities. To date, studies on TS brain structure and function have been limited in size with efforts mostly fragmented. This leads to low-statistical power, discordant results due to differences in approaches, and hinders the ability to stratify patients according to clinical parameters and investigate comorbidity patterns. Here, we present the scientific premise, perspectives, and key goals that have motivated the establishment of the Enhancing Neuroimaging Genetics through Meta-Analysis for TS (ENIGMA-TS) working group. The ENIGMA-TS working group is an international collaborative effort bringing together a large network of investigators who aim to understand brain structure and function in TS and dissect the underlying neurobiology that leads to observed comorbidity patterns and clinical heterogeneity. Previously collected TS neuroimaging data will be analyzed jointly and integrated with TS genomic data, as well as equivalently large and already existing studies of highly comorbid OCD, ADHD, ASD, MDD, and AXD. Our work highlights the power of collaborative efforts and transdiagnostic approaches, and points to the existence of different TS subtypes. ENIGMA-TS will offer large-scale, high-powered studies that will lead to important insights toward understanding brain structure and function and genetic effects in TS and related disorders, and the identification of biomarkers that could help inform improved clinical practice.

Abstract Background Over one-quarter of all smokers in the United States identify as non-daily smokers and this number is projected to rise. Unlike daily smokers who typically maintain consistent levels of nicotine exposure with regular smoking, non-daily smokers have variable patterns of smoking that likely result in high intraindividual variability in nicotine intake. The current study aimed to characterize the weekly intraindividual variability in cotinine and identify smoking-related predictors in nondaily smokers. Methods An ecological momentary assessment of 60 non-daily smokers ages 24–57 years was conducted over a consecutive 7-day at-home protocol to log each smoking session, assessments of mood and social activity during smoking, and collection of daily saliva samples in a convenience sample from Pennsylvania, USA. Hierarchical linear regression analyses were conducted to determine the effects of smoking characteristics on total cotinine exposure measured by pharmacokinetic area under the curve and the range, maximum, and minimum cotinine values during the week controlling for demographic variables. Results The mean daily cotinine level was 119.2 ng/ml (SD = 168.9) with individual values that ranged from nondetectable to 949.6 ng/ml. Menthol predicted increased total cotinine levels (P

Abstract Exogenous electric fields are currently used in human therapy in a number of contexts. Interestingly, electric fields have also been shown to alter migration and function of immune cells, suggesting the potential for electric field-based immune therapy. Little is known as to the effect of electric field treatment (EFT) on the lung. To determine if EFT associates with changes in lung immune cell infiltration, we used a mouse model with varying methods of EFT application and measured cells and soluble mediators using flow cytometry and cytokine/chemokine multiplex. EFT was associated with a transient increase in lung neutrophils and decrease in eosinophils in naïve mice within 2 h of treatment, accompanied by an increase in IL-6 levels. In order to test whether EFT could alter eosinophil/neutrophil recruitment in a relevant disease model, a mouse model of allergic airway inflammation was used. Four EFT doses in allergen-sensitized mice resulted in increased neutrophil and reduced eosinophil infiltrates following allergen challenge, suggesting a durable change in inflammation by EFT. Mice with allergic inflammation were analyzed by flexiVent for measures of lung function. EFT-treated mice had increased inspiratory capacity and other measures of lung function were not diminished. These data suggest EFT may be used to manipulate immune cell infiltration in the lung without affecting lung function.

BackgroundMultiple lines of evidence show that the human amygdala is part of a neural network important for perception of emotion from environmental stimuli, including for processing of intrinsic attractiveness/“goodness” or averseness/“badness,” i.e., affective valence.Objective/HypothesisWith this in mind, we investigated the effect of electrical brain stimulation of the human amygdala on perception of affective valence of images taken from the International Affective Picture Set (IAPS).MethodsUsing intracranial electrodes in patients with epilepsy, we first obtained event-related potentials (ERPs) in eight patients as they viewed IAPS images of varying affective valence. Next, in a further cohort of 10 patients (five female and five male), we measured the effect of 50 Hz electrical stimulation of the left amygdala on perception of affective valence from IAPS images.ResultsWe recorded distinct ERPs from the left amygdala and found significant differences in the responses between positively and negatively valenced stimuli (p = 0.002), and between neutral and negatively valenced stimuli (p = 0.017) 300–500 ms after stimulus onset. Next, we found that amygdala stimulation did not significantly affect how patients perceived valence for neutral images (p = 0.58), whereas stimulation induced patients to report both positively (p = 0.05) and negatively (< 0.01) valenced images as more neutral.ConclusionThese results render further evidence that the left amygdala participates in a neural network for perception of emotion from environmental stimuli. These findings support the idea that electrical stimulation disrupts this network and leads to partial disruption of perception of emotion. Harnessing this effect may have clinical implications in treatment of certain neuropsychiatric disorders using deep brain stimulation (DBS) and neuromodulation.

BackgroundThe U.S. Food and Drug Administration and the government of New Zealand have proposed a reduction of the nicotine content in cigarettes to very low levels. This study examined the potential effects of this regulation in smokers with affective disorders.MethodsIn a randomized controlled parallel group trial conducted at two sites in the USA (Penn State University, Hershey, PA and Massachusetts General Hospital, Boston, MA) 188 adult smokers with a current (n = 118) or lifetime (n = 70) anxiety or unipolar mood disorder, not planning to quit in the next 6 months, were randomly assigned (1:1) to smoke either Usual Nicotine Content (UNC) (11.6 mg nicotine/cigarette) research cigarettes, or Reduced Nicotine Content (RNC) research cigarettes where the nicotine content per cigarette was progressively reduced to 0.2 mg in five steps over 18 weeks. Participants were then offered the choice to either receive assistance to quit smoking, receive free research cigarettes, or resume using their own cigarette brand during a 12-week follow-up period. Main outcomes were biomarkers of nicotine and toxicant exposure, smoking behavior and dependence and severity of psychiatric symptoms. The pre-registered primary outcome was plasma cotinine.ResultsA total of 143 (76.1%) randomized participants completed the randomized phase of the trial, 69 (73.4%) in the RNC group and 74 (78.8%) in the UNC group. After switching to the lowest nicotine content cigarettes, compared to smokers in the UNC group, at the last randomized visit the RNC group had significantly lower plasma cotinine (metabolite of nicotine): difference between groups, -175.7, 95% CI [-218.3, -133.1] ng/ml. Urine NNAL (metabolite of NNK, a lung carcinogen), exhaled carbon-monoxide, cigarette consumption, and cigarette dependence were also significantly lower in the RNC group than the UNC group. No between-group differences were found on a range of other biomarkers (e.g. 8-isoprostanes) or health indicators (e.g. blood pressure), or on 5 different psychiatric questionnaires, including the Kessler K6 measure of psychological distress. At the end of the subsequent 12-week treatment choice phase, those randomized to the RNC group were more likely to have quit smoking, based on initial intent-to-treat sample, n = 188 (18.1% RNC v 4.3% UNC, p = 0.004).ConclusionReducing nicotine content in cigarettes to very low levels reduces some toxicant exposures and cigarette addiction and increases smoking cessation in smokers with mood and/or anxiety disorders, without worsening mental health.Trial registrationTRN: NCT01928758, registered August 21, 2013.

Novel mRNA vaccines have been developed and were first distributed to high-risk individuals (including smokers) in the United States starting in December 2020 to combat the coronavirus (COVID-19) pandemic. Over one-half of the U.S. adult population has received at least 1 dose of a COVID-19 vaccine, but many others have reported hesitation about becoming vaccinated. We examined COVID-19 vaccine uptake and hesitancy from a convenience sample of Pennsylvanian adult smokers in April 2021, approximately 3 months after tobacco users were eligible to receive vaccination in the state. Participants (n = 231) were 23.4% male, 90.5% white, and had a mean age of 48.1 (SD = 11.9) years. All participants were current tobacco users, with the majority reporting current cigarette smoking (90.9%) with an average of 16 (SD = 8.1) cigarettes smoked per day. Nearly 60% (n = 137) reported receiving at least 1 dose of the vaccine and of those who did not (n = 94), 84% (n = 79) said they were somewhat or very unlikely to get a vaccine. Those who were unvaccinated were more likely to not consume news about COVID-19 (chi-square P -value < .01) and less likely to believe government news sources as reliable information for COVID-19 (chi-square P -value < .01). Qualitative responses among those who were vaccine hesitant expressed concerns about the lack of research on the vaccine, distrust of the safety of the vaccine, and fears about side effects. Understanding vaccine hesitancy among tobacco users can help develop targeted communication strategies and directly address concerns to promote vaccination among this population who may be at an increased risk of severe complications from COVID-19.

Bioassessment is widely used to measure ecological integrity of natural habitats following anthropogenic disturbances and modifications. Traditionally, bioassessment has been based exclusively on species-environment interactions, i.e. niche processes. However, dispersal processes, and in particular mass effect, could mask the influence of niche processes and lead to erroneous conclusions about ecosystem health. To circumvent this problem, we identified 40 diatom species with distributions driven primarily by mass effect and propose an alternative version of the Biological Diatom Index (IBD2007) excluding these species. We tested the environmental responses of both the original IBD (IBD2007) and the modified IBD (IBDmod) with a benthic diatom dataset from France, collected between 2007 and 2013 and including 9487 samples from 3913 spatially distinct localities. Our results indicate a better relationship between the IBDmod scores and environmental conditions, compared to the IBD2007 scores, leading to a more accurate determination of river ecological status, especially in conditions of moderate nutrient enrichment. This study supports the idea that mass effect may result in biased evaluation of water quality. It is advocated that this process is considered in other diatom-based indices, and by extension, in any biotic index.

Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed to quantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer’s disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19,393; 9138 M/10,255F; age = 64.8 ± 7.4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2,248; 805 M/1443F; age = 63.4 ± 7.4) had significantly higher RVI-MDD values (t = 5.6, p = 1·10−8), but showed no detectable difference in RVI-AD (t = 2.0, p = 0.10). Subjects with dementia (N = 7; 4 M/3F; age = 68.6 ± 8.6 years) showed significant elevation in RVI-AD (t = 4.2, p = 3·10−5) but not RVI-MDD (t = 2.1, p = 0.10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson’s disease (N = 37) showed elevation in RVI-AD (t = 2.4, p = 0.01) while subjects with stroke (N = 247) showed no such elevation (t = 1.1, p = 0.3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Airway epithelial barrier dysfunction is increasingly recognized as a key feature of asthma and other lung diseases. Respiratory viruses are responsible for a large fraction of asthma exacerbations, and are particularly potent at disrupting epithelial barrier function through pattern recognition receptor engagement leading to tight junction dysfunction. Although different mechanisms of barrier dysfunction have been described, relatively little is known about whether barrier integrity can be promoted to limit disease. Here, we tested three classes of drugs commonly prescribed to treat asthma for their ability to promote barrier function using a cell culture model of virus-induced airway epithelial barrier disruption. Specifically, we studied the corticosteroid budesonide, the long acting beta-agonist formoterol, and the leukotriene receptor antagonist montelukast for their ability to promote barrier integrity of a monolayer of human bronchial epithelial cells (16HBE) before exposure to the viral mimetic double-stranded RNA. Of the three, only budesonide treatment limited transepithelial electrical resistance and small molecule permeability (4 kDa FITC-dextran flux). Next, we used a mouse model of acute dsRNA challenge that induces transient epithelial barrier disruption in vivo, and studied the effects budesonide when administered prophylactically or therapeutically. We found that budesonide similarly protected against dsRNA-induced airway barrier disruption in the lung, independently of its effects on airway inflammation. Taken together, these data suggest that an under-appreciated effect of inhaled budesonide is to maintain or promote airway epithelial barrier integrity during respiratory viral infections.

RationaleProtein kinase D (PKD) is a serine/threonine kinase family that is involved in a wide array of signaling pathways. Although PKD has been implicated in immune responses, relatively little is known about the function of PKD in the lung or during viral infections.ObjectivesWe investigated the hypothesis that PKD is involved in multiple aspects of host response to viral infection.MethodsThe selective PKD inhibitor CRT0010166 was administered to C57BL/6 mice prior to and during challenge with either inhaled double-stranded RNA or Influenza A Virus. PKD signaling pathways were investigated in human bronchial epithelial cells treated with CRT0010166, double-stranded RNA, and/or infected with Influenza A Virus.MeasurementsTotal protein and albumin accumulation in the bronchoalveolar fluid was used to asses inside/out leak. Clearance of inhaled FITC-dextran out of the airspace was used to assess outside/in leak. Cytokines and neutrophils in bronchoalveolar lavage were assayed with ELISAs and cytospins respectively. Viral RNA level was assessed with RT-PCR and protein level assessed by ELISA.Main ResultsPKD inhibition prevented airway barrier dysfunction and pro-inflammatory cytokine release. Epithelial cells express PKD3, and PKD3 siRNA knock-down inhibited polyI:C induced cytokine production. Lung epithelial-specific deletion of PKD3 (CC10-Cre x PKD3-floxed mice) partially attenuated polyI:C-induced barrier disruption in vivo. Mechanistically, we found that PKD promoted cytokine mRNA transcription, not secretion, likely through activating the transcription factor Sp1. Finally, prophylactic CRT treatment of mice promoted barrier integrity during influenza virus infection and reduced viral burden.ConclusionsInhibiting PKD promotes barrier integrity, limit pathogenic cytokine levels, and restrict Influenza A Virus infection. Therefore, PKD is an attractive target for novel antiviral therapeutics.

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR

Abstract Background The Family Smoking Prevention and Tobacco Control Act gave the Food and Drug Administration jurisdiction over the regulation of all tobacco products, including their nicotine content. Under this act, a major strategy to reduce harm from cigarette tobacco is lowering the nicotine content without causing unintended adverse consequences. Initial research on reduced nicotine content (RNC) cigarettes has shown that smokers of these cigarettes gradually decrease their smoking frequency and biomarkers of exposure. The effectiveness of this strategy needs to be demonstrated in different populations whose response to RNC cigarettes might be substantially mediated by personal or environmental factors, such as low socioeconomic status (SES) populations. This study aims to evaluate the response to a reduced nicotine intervention in low SES smokers, as defined here as those with less than 16 years of education, by switching smokers from high nicotine commercial cigarettes to RNC cigarettes. Methods/design Adults (N = 280) who have smoked five cigarettes or more per day for the past year, have not made a quit attempt in the prior month, are not planning to quit, and have less than 16 years of education are recruited into a two-arm, double-blinded randomized controlled trial. First, participants smoke their usual brand of cigarettes for 1 week and SPECTRUM research cigarettes containing a usual amount of nicotine for 2 weeks. During the experimental phase, participants are randomized to continue smoking SPECTRUM research cigarettes that contain either (1) usual nicotine content (UNC) (11.6 mg/cigarette) or (2) RNC (11.6 to 0.2 mg/cigarette) over 18 weeks. During the final phase of the study, all participants are offered the choice to quit smoking with nicotine replacement therapy, continue smoking the research cigarettes, or return to their usual brand of cigarettes. The primary outcomes of the study include retention rates and compliance with using only research cigarettes and no use of other nicotine-containing products. Secondary outcomes are tobacco smoke biomarkers, nicotine dependence measures, smoking topography, stress levels, and adverse health consequences. Discussion Results from this study will provide information on whether low SES smokers can maintain a course of progressive nicotine reduction without increases in incidence of adverse effects. Trial registration ClinicalTrials.gov, NCT01928719. Registered on 21 August 2013.

Abstract Background The U.S. Food and Drug Administration can set standards for cigarettes that could include reducing their nicotine content. Such a standard should improve public health without causing unintended serious consequences for sub-populations. This study evaluates the effect of progressive nicotine reduction in cigarettes on smoking behavior, toxicant exposure, and psychiatric symptoms in smokers with comorbid mood and/or anxiety disorders using a two-site, two-arm, double-blind, parallel group, randomized controlled trial (RCT) in four phases over 34 weeks. Methods Adult smokers (N = 200) of 5 or more cigarettes per day will be randomized across two sites (Penn State and Massachusetts General). Participants must have not had a quit attempt in the prior month, nor be planning to quit in the next 6 months, meet criteria for a current or lifetime unipolar mood and/or anxiety disorder based on the structured Mini-International Neuropsychiatric Interview, and must not have an unstable medical or psychiatric condition. After a week of smoking their own cigarettes, participants receive two weeks of Spectrum research cigarettes with usual nicotine content (11.6 mg). After this baseline period, participants will be randomly assigned to continue smoking Spectrum research cigarettes that contain either (a) Usual Nicotine Content (11.6 mg); or (b) Reduced Nicotine Content: the nicotine content per cigarette is progressively reduced from approximately 11.6 mg to 0.2 mg in five steps over 18 weeks. At the end of the randomization phase, participants will be offered the choice to either (a) quit smoking with assistance, (b) continue smoking free research cigarettes, or (c) return to purchasing their own cigarettes, for the final 12 weeks of the study. The primary outcome measure is blood cotinine; key secondary outcomes are: exhaled carbon monoxide, urinary total NNAL- 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and 1-hydroxypyrene, oxidative stress biomarkers including 8-isoprostanes, measures of psychiatric symptoms (e.g., depression, anxiety), smoking behavior and dependence (e.g., cigarette consumption, quit attempts), and health effects (e.g., blood pressure, respiratory symptoms). Discussion Results from this study will inform FDA on the potential effects of regulating the nicotine content of cigarettes and help determine whether smokers with mood and/or anxiety disorders can safely transition to significantly reduced nicotine content cigarettes. Trial registration TRN: NCT01928758 , registered August 21, 2013.

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

The airway epithelial barrier is critical for preventing pathogen invasion and translocation of inhaled particles into the lung. Epithelial cells also serve an important sentinel role after infection and release various pro-inflammatory mediators that recruit and activate immune cells. Airway epithelial barrier disruption has been implicated in a growing number of respiratory diseases including viral infections. It is thought that when a pathogen breaks the barrier and gains access to the host tissue, pro-inflammatory mediators increase, which further disrupts the barrier and initiates a vicious cycle of leak. However, it is difficult to study airway barrier integrity in vivo, and little is known about relationship between epithelial barrier function and airway inflammation. Current assays of pulmonary barrier integrity quantify the leak of macromolecules from the vasculature into the airspaces (or "inside/out" leak). However, it is also important to measure the ease with which inhaled particles, allergens, or pathogens can enter the subepithelial tissues (or "outside/in" leak). We challenged mice with inhaled double stranded RNA (dsRNA) and explored the relationship between inside/out and outside/in barrier function and airway inflammation. Using wild-type and gene-targeted mice, we studied the roles of the dsRNA sensors Toll Like Receptor 3 (TLR3) and Melanoma Differentiation-Associated protein 5 (MDA5). Here we report that after acute challenge with inhaled dsRNA, airway barrier dysfunction occurs in a TLR3-dependent manner, whereas leukocyte accumulation is largely MDA5-dependent. We conclude that airway barrier dysfunction and inflammation are regulated by different mechanisms at early time points after exposure to inhaled dsRNA.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

Iridates are known to exhibit a range of exotic electronic and magnetic behaviours but it is difficult to prepare isolated [IrO6]8− species via soft chemical routes. Here, the authors isolate the isoelectronic [IrF6]2−complex, and assess it as a model and for iridate analogues.

A South African classic, Lauretta Ngcobo’s novel, And they didn’t die, explores the nexus of hegemony, culture, and transformation under apartheid rule. Edward Said’s thesis on the hierarchy of culture provides the conceptual framework for an analysis of politico-cultural agency in Ngcobo’s novel. Three characters in the narrative—Siyalo, Jezile, and Lungu—are used to comment on the cultural dynamics inherent in three different but interlocking sites: the nine-to-five working environment, rural Bantu culture, and the ambiguous location of “African Whiteness.” The paper establishes that the life of each character betrays culture’s negative discriminating power and the amplification of negativity by State policy

Correction to: Molecular Psychiatry, published online 2 May 2023 In this article Honami Arai, Irene Bollettini, Rosa Calvo Escalona, Ana Coelho, Federica Colombo, Leila Darwich, Martine Fontaine, Toshikazu Ikuta, Jonathan C. Ipser, Asier Juaneda-Seguí, Hitomi Kitagawa, Gerd Kvale, Mafalda Machado-Sousa, Astrid Morer, Takashi Nakamae, Jin Narumoto, Joseph O’Neill, Sho Okawa, Eva Real, Veit Roessner, Joao R. Sato, Cinto Segalàs, Roseli G. Shavitt, Dick J. Veltman, Kei Yamada were missing from the author list indexed under the ENIGMA-OCD Working Group. Additionally, there was an error regarding Tokiko Yoshida’s name, where the first name and last name were written in the wrong order. The original article has been corrected.

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.