Weight-loss maintenance is accompanied by interconnected alterations in circulating FGF21-adiponectin-leptin and bioactive sphingolipids.

Weight loss is often followed by weight regain. Characterizing endocrine alterations accompanying weight reduction and regain may disentangle the complex biology of weight-loss maintenance. Here, we profile energy-balance-regulating metabokines and sphingolipids in adults with obesity undergoing an initial low-calorie diet-induced weight loss and a subsequent weight-loss maintenance phase with exercise, glucagon-like peptide-1 (GLP-1) analog therapy, both combined, or placebo. We show that circulating growth differentiation factor 15 (GDF15) and C16:0-C18:0 ceramides transiently increase upon initial diet-induced weight loss. Conversely, circulating fibroblast growth factor 21 (FGF21) is downregulated following weight-loss maintenance with combined exercise and GLP-1 analog therapy, coinciding with increased adiponectin, decreased leptin, and overall decrements in ceramide and sphingosine-1-phosphate levels. Subgroup analyses reveal differential alterations in FGF21-adiponectin-leptin-sphingolipids between weight maintainers and regainers. Clinically, cardiometabolic health outcomes associate with selective metabokine-sphingolipid remodeling signatures. Collectively, our findings indicate distinct FGF21, GDF15, and ceramide responses to diverse phases of weight change and suggest that weight-loss maintenance involves alterations within the metabokine-sphingolipid axis.

Urine Ammonium Concentrations and Cardiovascular and Kidney Outcomes in SPRINT Participants with Chronic Kidney Disease.

BACKGROUND: Impaired urine ammonium excretion is common in chronic kidney disease (CKD) and may identify risk of metabolic acidosis earlier than reductions in serum bicarbonate or pH, and thus may have associations with cardiovascular disease (CVD) outcomes. We evaluated the association of urine ammonium with CVD and kidney outcomes among persons with hypertension and non-diabetic CKD enrolled in a trial of blood pressure reduction. METHODS: We measured urine ammonium concentration in spot urine specimens collected at baseline among 2092 participants of the Systolic Blood Pressure Intervention Trial (SPRINT) with an eGFR <60 ml/min/1.73m2. We used multivariable-adjusted Cox models to evaluate associations of urine ammonium concentration with the SPRINT primary CVD composite outcome (myocardial infarction, acute coronary syndrome, stroke, heart failure, or CVD death), all-cause mortality, the SPRINT kidney composite outcome (50% kidney function decline, end stage kidney disease, or transplant), and acute kidney injury (AKI). RESULTS: At baseline, the mean (SD) age was 73 (9) years; 40% were female; and 25% were Black participants. The mean (SD) serum bicarbonate was 25.6 (2.8) mmol/L, median (interquartile range, IQR) urine ammonium concentration was 14.4 (9.5, 23.1) mmol/L, and median (IQR) eGFR was 49 (39,55) ml/min/1.73m2. There were 255 CVD composite events, 143 deaths, 63 kidney composite events, and 146 AKI events during a median follow-up of 3.8 years. In multivariable models, each 2-fold higher urinary ammonium concentration was associated with a 26% (95% CI 1.05, 1.52) higher risk of the CVD composite, whereas there was no association with all-cause mortality, the SPRINT kidney composite outcome, or AKI. CONCLUSION: Among non-diabetic individuals with hypertension and CKD, higher urine ammonium concentration is associated with higher risk of CVD. Further studies are needed to evaluate this association in other cohorts.

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results.

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as default or visual. Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

High-Sensitivity Troponin T, NT-proBNP, and Cognitive Outcomes in SPRINT.

BACKGROUND: Hs-cTnT (cardiac troponin T measured with a highly sensitive assay) and NT-proBNP (N-terminal pro-B-type natriuretic peptide) may identify adults with hypertension who derive greater cognitive benefits from lower systolic blood pressure targets. METHODS: In the SPRINT (Systolic Blood Pressure Intervention Trial) MIND study, participants were categorized as having both hs-cTnT and NT-proBNP in the lower 2 tertiles (n=4226), one in the highest tertile (n=2379), and both in the highest tertile (n=1506). We assessed the effect of intensive versus standard treatment on the composite of mild cognitive impairment (MCI) or probable dementia (PD) across biomarker categories. RESULTS: Over a median follow-up of 5.1 years, 830 of 8111 participants (10.2%) developed MCI or PD. Participants in the highest biomarker category were at higher risk of MCI or PD compared with those in the lowest category (hazard ratio, 1.34 [95% CI, 1.00-1.56]). The effect of intensive treatment on reducing the risk of MCI or PD was greater among participants in the lowest biomarker category (hazard ratio, 0.64 [95% CI, 0.50-0.81]) than those in the intermediate (hazard ratio, 1.01 [95% CI, 0.80-1.28]) or highest categories (hazard ratio, 0.90 [95% CI, 0.72-1.13]; Pinteraction=0.02). The 5-year absolute risk differences in MCI or PD with intensive treatment were -2.9% (-4.4%, -1.3%), -0.2% (-3.0%, 2.6%), and -1.9% (-6.2%, 2.4%) in the lowest, intermediate, and highest biomarker categories, respectively. CONCLUSIONS: In SPRINT, the relative effect of intensive systolic blood pressure lowering on preventing cognitive impairment appears to be stronger among participants with lower compared with higher cardiac biomarker levels, though the absolute risk reductions were similar.

Modeling Ligand Exchange Kinetics in Iridium Complexes Catalyzing SABRE Nuclear Spin Hyperpolarization.

Large signal enhancements can be obtained for NMR analytes using the process of nuclear spin hyperpolarization. Organometallic complexes that bind parahydrogen can themselves become hyperpolarized. Moreover, if parahydrogen and a to-be-hyperpolarized analyte undergo chemical exchange with the organometallic complex it is possible to catalytically sensitize the detection of the analyte via hyperpolarization transfer through spin-spin coupling in this organometallic complex. This process is called Signal Amplification By Reversible Exchange (SABRE). Signal intensity gains of several orders of magnitude can thus be created for various compounds in seconds. The chemical exchange processes play a defining role in controlling the efficiency of SABRE because the lifetime of the complex must match the spin-spin couplings. Here, we show how analyte dissociation rates in the key model substrates pyridine (the simplest six-membered heterocycle), 4-aminopyridine (a drug), and nicotinamide (an essential vitamin biomolecule) can be examined. This is achieved for the most widely employed SABRE motif that is based on IrIMes-derived catalysts by 1H 1D and 2D exchange NMR spectroscopy techniques. Several kinetic models are evaluated for their accuracy and simplicity. By incorporating variable temperature analysis, the data yields key enthalpies and entropies of activation that are critical for understanding the underlying SABRE catalyst properties and subsequently optimizing behavior through rational chemical design. While several studies of chemical exchange in SABRE have been reported, this work also aims to establish a toolkit on how to quantify chemical exchange in SABRE and ensure that data can be compared reliably.

Does Spinal Cord Stimulation Improve Sleep Disturbances Independently of Pain Relief in Patients With Chronic Pain? An Explorative, Observational Study.

OBJECTIVES: Chronic pain poses a significant health challenge worldwide and is associated with both disability and reduced quality of life. Sleep disturbances are reported in 67% to 88% of patients with chronic pain. Pain and sleep affect each other reciprocally; we aimed to study this bidirectional relationship in patients treated with spinal cord stimulation (SCS) for chronic pain. Specifically, we investigated whether sleep improves after treatment with SCS and whether this improvement may be mediated by pain reduction. MATERIALS AND METHODS: An observational cohort study was conducted in patients with chronic neuropathic pain treated with SCS at a single neurosurgical department in Denmark. Outcomes were assessed preoperatively and at three, six, and 12 months postoperatively, and thereafter annually. Primary outcomes were pain intensity (numeric rating scale) and insomnia at first follow-up (Insomnia Severity Index). The association between sleep and pain was investigated using linear regression and mediation analysis. RESULTS: Forty-three patients were included in the study. The mean insomnia score was reduced by 25% from 18.1 (SD 6.0) to 13.5 (SD 6.6) (p = 0.0001). Pain intensity was reduced 38% from 7.4 (SD 1.6) to 4.6 (SD 2.1) at the first follow-up (p ≤ 0.0001). Changes in pain and changes in insomnia scores were significantly but weakly associated (regression coefficient = 1.3, 95% CI [0.3; 2.2], p = 0.008, r2 = 15.7%); and changes in pain score were not found to mediate changes in sleep score (ß = -0.02, 95% CI [-0.15; 0.11], p = 0.76). CONCLUSIONS: We found that patients treated with SCS showed significant improvements in both insomnia and pain intensity at first follow-up. Improvements in insomnia and pain intensity were significantly but weakly associated, and improvements in pain intensity score did not mediate improvements in insomnia score. Thus, improvements in self-reported insomnia in patients treated with SCS for chronic pain may predominantly be caused by other factors than reduced pain intensity.

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling.

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

Connectome predictive modeling of trait mindfulness.

Introduction: Trait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. Methods: To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites. Results: In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging . Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. Conclusions: We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations.

Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.

Evidence for Bidirectional, Cross-Lagged Associations Between Alliance and Psychological Distress in an Unguided Mobile-Health Intervention.

Bidirectional associations between changes in symptoms and alliance are established for in-person psychotherapy. Alliance may play an important role in promoting engagement and effectiveness within unguided mobile health (mHealth) interventions. Using models disaggregating alliance and psychological distress into within- and between-person components (random intercept cross-lagged panel model), we report bidirectional associations between alliance and distress over the course of a 4-week smartphone-based meditation intervention (n=302, 80.0% elevated depression/anxiety). Associations were stable across time with effect sizes similar to those observed for psychotherapy (ßs=-.13 to -.14 and -.09 to -.10, for distress to alliance and alliance to distress, respectively). Alliance may be worth measuring to improve the acceptability and effectiveness of mHealth tools. Further empirical and theoretical work characterizing the role and meaning of alliance in unguided mHealth is warranted.

Impact of data processing varieties on DCM estimates of effective connectivity from task-fMRI.

Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.

Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.

In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.

The interplay between insomnia symptoms and Alzheimer's Disease across three main brain networks.

STUDY OBJECTIVES: Insomnia symptoms are prevalent along the trajectory of Alzheimer's disease (AD), but the neurobiological underpinning of their interaction is poorly understood. Here, we assessed structural and functional brain measures within and between the default mode network (DMN), salience network (SN), and central executive network (CEN). METHODS: We selected 320 subjects from the ADNI database and divided by their diagnosis: cognitively normal (CN), Mild Cognitive Impairment (MCI), and AD, with and without self-reported insomnia symptoms. We measured the gray matter volume (GMV), structural covariance (SC), degrees centrality (DC), and functional connectivity (FC), testing the effect and interaction of insomnia symptoms and diagnosis on each index. Subsequently, we performed a within-group linear regression across each network and ROI. Finally, we correlated observed abnormalities with changes in cognitive and affective scores. RESULTS: Insomnia symptoms were associated with FC alterations across all groups. The AD group also demonstrated an interaction between insomnia and diagnosis. Within-group analyses revealed that in CN and MCI, insomnia symptoms were characterised by within-network hyperconnectivity, while in AD, within- and between-network hypoconnectivity was ubiquitous. SC and GMV alterations were non-significant in the presence of insomnia symptoms, and DC indices only showed network-level alterations in the CEN of AD individuals. Abnormal FC within and between DMN and CEN hubs was additionally associated with reduced cognitive function across all groups, and increased depressive symptoms in AD. CONCLUSIONS: We conclude that patients with clinical AD present with a unique pattern of insomnia-related functional alterations, highlighting the profound interaction between both conditions.

Network and state specificity in connectivity-based predictions of individual behavior.

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Adolescents do not benefit from universal school-based mindfulness interventions: a reanalysis of Dunning et al. (2022).

Are universal school-based mindfulness interventions an effective way to reduce risk for mental disorders and improve adolescents' lives? To answer this question, we reanalyzed data from Dunning et al.'s (2022) meta-analysis of randomized controlled trials of mindfulness interventions delivered to children and adolescents. Though Dunning et al. (2022) reported some benefits of universal mindfulness interventions, their analysis did not examine adolescents separately from children. Consequently, their conclusions may not entirely reflect the effectiveness of universal mindfulness interventions specifically for adolescents, a developmental period when mental disorders are known to increase. Using their open-access data tables, we tested impacts of 22 randomized controlled trials (N = 16,558) on eight outcome categories-anxiety/stress, attention, depression, executive functioning, mindfulness, negative behavior, social behavior, and wellbeing-at immediate post-test and longest follow-up. Our reanalysis shows that when compared to passive controls, mindfulness interventions significantly reduced trait mindfulness (d = -0.10). When compared to active controls, mindfulness interventions significantly improved anxiety/stress (d = 0.17) and wellbeing (d = 0.10). When compared to all controls combined, mindfulness interventions did not significantly improve any outcome (ds = 0.01 to 0.26). No effects of mindfulness interventions were observed at follow-up assessment. Overall, results of our analysis cast doubt about the value of existing school-based mindfulness interventions as a universal prevention strategy for adolescents.

Mapping the interplay of atrial fibrillation, brain structure, and cognitive dysfunction.

INTRODUCTION: Atrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs. METHODS AND RESULTS: We examined 1335 stroke-free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free-water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance. DISCUSSION: Integrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies. Highlights We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls. Our analysis revealed AF-associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning. Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free-water content as well as widespread differences of white matter integrity. Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF-related cognitive decline.

P(V)-bis(amidophenolate) ligand cooperation: stoichiometric CO-bond cleavage in aldehydes and ketones.

The cooperation between a geometrically constrained, highly electrophilic phosphorus(V) center, and an electronically rich tetradentate bis(amidophenolate) ligand enables the cleavage of the CO bond from typical aldehydes and ketones delivering iminio phosphoramidate species. The amphiphilic nature of these products, which is demonstrated through their reaction with typical Lewis acids and bases, enables their use as a mild source of silylium cations from silanes, allowing the selective reductive coupling of aldehydes to ethers under catalytic conditions.

Common neural dysfunction of economic decision-making across psychiatric conditions.

Adaptive decision-making, which is often impaired in various psychiatric conditions, is essential for well-being. Recent evidence has indicated that decision-making capacity in multiple tasks could be accounted for by latent dimensions, enlightening the question of whether there is a common disruption of brain networks in economic decision-making across psychiatric conditions. Here, we addressed the issue by combining activation/lesion network mapping analyses with a transdiagnostic brain imaging meta-analysis. Our findings indicate that there were transdiagnostic alterations in the thalamus and ventral striatum during the decision or outcome stage of decision-making. The identified regions represent key nodes in a large-scale network, which is composed of multiple heterogeneous brain regions and plays a causal role in motivational functioning. The findings suggest that disturbances in the network associated with emotion- and reward-related processing play a key role in dysfunctions of decision-making observed in various psychiatric conditions. This study provides the first meta-analytic evidence of common neural alterations linked to deficits in economic decision-making.

Interactions between pili affect the outcome of bacterial competition driven by the type VI secretion system.

The bacterial type VI secretion system (T6SS) is a widespread, kin-discriminatory weapon capable of shaping microbial communities. Due to the system's dependency on contact, cellular interactions can lead to either competition or kin protection. Cell-to-cell contact is often accomplished via surface-exposed type IV pili (T4Ps). In Vibrio cholerae, these T4Ps facilitate specific interactions when the bacteria colonize natural chitinous surfaces. However, it has remained unclear whether and, if so, how these interactions affect the bacterium's T6SS-mediated killing. In this study, we demonstrate that pilus-mediated interactions can be harnessed by T6SS-equipped V. cholerae to kill non-kin cells under liquid growth conditions. We also show that the naturally occurring diversity of pili determines the likelihood of cell-to-cell contact and, consequently, the extent of T6SS-mediated competition. To determine the factors that enable or hinder the T6SS's targeted reduction of competitors carrying pili, we developed a physics-grounded computational model for autoaggregation. Collectively, our research demonstrates that T4Ps involved in cell-to-cell contact can impose a selective burden when V. cholerae encounters non-kin cells that possess an active T6SS. Additionally, our study underscores the significance of T4P diversity in protecting closely related individuals from T6SS attacks through autoaggregation and spatial segregation.

Noninvasive brain stimulation in autism: review and outlook for personalized interventions in adult patients.

Noninvasive brain stimulation (NIBS) has been increasingly investigated during the last decade as a treatment option for persons with autism spectrum disorder (ASD). Yet, previous studies did not reach a consensus on a superior treatment protocol or stimulation target. Persons with ASD often suffer from social isolation and high rates of unemployment, arising from difficulties in social interaction. ASD involves multiple neural systems involved in perception, language, and cognition, and the underlying brain networks of these functional domains have been well documented. Aiming to provide an overview of NIBS effects when targeting these neural systems in late adolescent and adult ASD, we conducted a systematic search of the literature starting at 631 non-duplicate publications, leading to six studies corresponding with inclusion and exclusion criteria. We discuss these studies regarding their treatment rationale and the accordingly chosen methodological setup. The results of these studies vary, while methodological advances may allow to explain some of the variability. Based on these insights, we discuss strategies for future clinical trials to personalize the selection of brain stimulation targets taking into account intersubject variability of brain anatomy as well as function.