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ObjectiveSchizophrenia is associated with widespread brain-morphological alterations, believed to be shaped by the underlying connectome architecture. This study tests whether large-scale structural reorganization in schizophrenia relates to normative network architecture, in particular regional centrality/hubness and connectivity patterns. We examine network effects in schizophrenia across different disease stages, and transdiagnostically explore consistency of such relationships in patients with bipolar and major depressive disorder.MethodsWe studied anatomical MRI scans from 2,439 adults with schizophrenia and 2,867 healthy controls from 26 ENIGMA sites. Case-control patterns of structural alterations were evaluated against two network susceptibility models: 1) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; 2) epicenter models, which identify regions whose typical connectivity profile most closely resembles the disease-related morphological alteration patterns. Both susceptibility models were tested across schizophrenia disease stages and compared to meta-analytic bipolar and major depressive disorder case-control maps.ResultsIn schizophrenia, regional gray matter reductions co-localized with interconnected hubs, in both the functional (r=0.58, pspinspin=0.01). Epicenters were identified in temporo-paralimbic regions, extending to frontal areas. We found unique epicenters for first-episode and early stages, and a shift from occipital to temporal-frontal epicenters in chronic stages. Transdiagnostic comparisons revealed shared epicenters in schizophrenia and bipolar, but not major depressive disorders.ConclusionsCortical reorganization over the course of schizophrenia closely reflects brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters. The observed overlapping epicenters for schizophrenia and bipolar disorder furthermore suggest shared pathophysiological processes within the schizophrenia-bipolar-spectrum.

Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that aberrant prediction errors lead to a brittle model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for a more unstable model of the world and investigate the computational mechanisms underlying emerging paranoia.We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high-risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task, designed to probe learning about others’ changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility.There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between shifts in perceived volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically.Our results suggest that FEP are characterised by a different computational mechanism – perceiving the environment as increasingly volatile – in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.

Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences.TeaserWe present a global effort to devise harmonization procedures necessary to meaningfully leverage big data.

BackgroundFatigue has an adaptive function and serves as a temporary signal to rest and save energy often in response to immune activation. It may, however, also persist in a pathological condition incurring significant burden. While subjective symptoms and scientific consensus indicate that both physical and mental determinants of motivated behavior are affected in fatigue, the underlying processes are rarely examined using objective, task-based indicators.MethodsIn three consecutive studies, including validation (N = 48) and reliability assessments (N = 27), we use an experimental task to jointly objectify reward learning and effort execution as two determinants of behavioral motivation. In addition, we tested how fatigue and its acute perturbation in response to immune activation after SARS-CoV-2 vaccination are linked to these task-based indicators of motivation in a longitudinal cross-over design (N = 55).ResultsThe validation study showed the utility of the experimental task for simultaneously assessing learning, effort exertion, and its regulation based on subjective confidence. The reliability assessment over a one-week period indicated that symptoms of fatigue and task behavior are highly reliable and that repetition effects have little impact on motivated behavior. Finally, in the vaccination trial, we found significant links between fatigue and task behavior. Baseline levels of fatigue predicted how effort is gauged in dependence of current confidence about reward outcomes, and state perturbations of fatigue in the context of the SARS-CoV-2 vaccination reduced confidence during learning. Importantly, task success was significantly lower in subjects who reported high fatigue at baseline and who additionally experienced stronger increase in fatigue in response to vaccination.DiscussionOur results demonstrate that the experimental task allows to jointly assess determinants of motivated behavior, and to link its constituent processes to subjective fatigue. This suggests that our understanding of fatigue and its perturbation due to acute immune activation can benefit from objective, task-based indicators of the underlying motivational mechanisms. Future studies could build on these findings to further deepen the understanding of neurobehavioral mechanisms underlying fatigue in the context of immune activation.

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.64 years (95% CI: 3.01, 4.26; I2 = 55.28%) compared to controls, after adjusting for age and sex (Cohen’s d = 0.50). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

Caffeine elicits widespread effects in the central nervous system and is the most frequently consumed psychostimulant worldwide. First evidence indicates that, during daily intake, the elimination of caffeine may slow down and the primary metabolite, paraxanthine, may accumulate. The neural impact of such adaptions is virtually unexplored. In this report, we leveraged the data of a laboratory study with N= 20 participants and three within-subject conditions: caffeine (150 mg caffeine x 3/day x 10 days), placebo (150 mg mannitol x 3/day x 10 days), and withdrawal (caffeine x 9 days, afterwards placebo x 1 day). Using liquid chromatography–mass spectrometry coupled with tandem mass spectrometry, we determined the course of salivary caffeine and paraxanthine, measured regularly at day 10. We assessed grey matter (GM) intensity and cerebral blood flow (CBF) in the withdrawal condition as compared to their changes in caffeine in our previous report. The results indicate high remaining levels of paraxanthine and of caffeine carried overnight during daily intake, and the levels of paraxanthine remained higher than in placebo during withdrawal. After 36 h of withdrawal, the previously reported caffeine-induced GM reduction was partially mitigated, while CBF was elevated compared to placebo. Our findings unveil that conventional daily caffeine intake does not provide sufficient time to clear up psychoactive compounds and restore cerebral responses, even after 36 hours of abstinence. They also suggest investigating consequences of a paraxanthine accumulation during daily caffeine intake.

Neuroprotective effects of caffeine have been frequently reported in the context of disease and cognitive dysfunction as well as in epidemiological studies in humans. However, evidence on caffeine effects on neural and memory functions during daily intake in a healthy cognitive state remains scarce. This randomized double-blind placebo-controlled crossover study investigated working memory functions by N-back tasks and functional magnetic resonance imaging (fMRI) after daily caffeine intake compared to a placebo baseline and to acute caffeine withdrawal in 20 young healthy volunteers. Each volunteer was given 3 times 150 mg caffeine for 10 days in the daily caffeine condition, 3 times 150 mg mannitol for 10 days in the placebo condition, and 9-day caffeine plus 1-day mannitol in the acute withdrawal condition. During the 10th day, participants performed 4 N-back sessions (two loads each: 0- and 3-back) under controlled laboratory conditions. During the 4th session of N-Back (i.e. at 5.5 h, 36.5 h and > 10 days after the last caffeine intake in the caffeine, withdrawal, and placebo condition, respectively) we assessed blood-oxygen-level-dependent (BOLD) activity. During the entire 10th day, in 0-back tasks, we observed longer reaction times (RTs) in the withdrawal compared to the placebo (Cohen’s d = 0.7) and caffeine condition (Cohen’s d = 0.6), but no significant effects of conditions on error rates. In contrast, in 3-back tasks (controlled for 0-back), the RTs in the caffeine condition were longer compared to placebo (Cohen’s d = 0.6) and withdrawal (Cohen’s d = 0.5). Error rates were higher during both caffeine and withdrawal conditions compared to placebo (Cohen’s d of both contrasts = 0.4). Whole-brain analyses on fMRI data did not reveal significant condition-dependent differences in activities between task loads. Across task loads, however, we observed a reduced hippocampal activation (Cohen’s d = −1.3) during the caffeine condition compared to placebo, while no significant difference in brain activities between withdrawal and placebo conditions. Taken together, the worse working memory function and the hippocampal hypoactivation implicate a potential detrimental effect of daily caffeine intake on neurocognitive functions of healthy adults. Moreover, they echo the hippocampal volumetric reduction reported previously in the same volunteers. Lastly, acute withdrawal from daily caffeine intake impairs both low-order cognitive processes and working memory performance. Taking earlier studies on acute caffeine effects into account, our findings indicate that daily caffeine intake elicits a dynamic change in cerebral activities during the course of repeated consumption, with unknown consequences in the long run.

Acute caffeine intake can attenuate homeostatic sleep pressure and worsen sleep quality. Besides, caffeine intake – particularly in high doses and close to bedtime – may also affect circadian-regulated REM sleep promotion, an important determinant of subjective sleep quality. However, it is not known whether such changes persist under chronic caffeine consumption during daytime. Twenty male caffeine consumers (26.4 ± 4 years old, habitual caffeine intake 478.1 ± 102.8 mg/day) participated in a double-blind crossover study. Each volunteer completed a caffeine (3 × 150 mg caffeine daily), a withdrawal (3 × 150 mg caffeine for eight days then placebo), and a placebo condition. After ten days of controlled intake and a fixed sleep-wake cycle, we recorded 8 h of electroencephalography starting 5 h after habitual bedtime (i.e., start on average at 04:22 am which is around the peak of circadian REM sleep promotion). A 60 min evening nap preceded each sleep episode and reduced high sleep pressure levels. While total sleep time and sleep architecture did not significantly differ between the three conditions, REM latency was longer after daily caffeine intake compared to both placebo and withdrawal. Moreover, the accumulation of REM sleep proportion was slower, and volunteers reported more difficulties at awakening after sleep and feeling more tired upon wake-up in the caffeine condition compared to placebo. Our data indicate that besides acute also regular daytime caffeine intake affects REM sleep regulation in men. We have evidence that regular caffeine intake during daytime weakens circadian sleep promotion when compared to placebo. Moreover, the observed caffeine-induced deterioration in the quality of awakening may suggest a potential motive to reinstate caffeine intake after sleep.

Healthy sleep, positive general affect, and the ability to regulate emotional experiences are fundamental for well-being. In contrast, various mental disorders are associated with altered rapid eye movement (REM) sleep, negative affect, and diminished emotion regulation abilities. However, the neural processes mediating the relationship between these different phenomena are still not fully understood. In the present study of 42 healthy volunteers, we investigated the effects of selective REM sleep suppression (REMS) on general affect, as well as on feelings of social exclusion, emotion regulation, and their neural underpinnings. Using functional magnetic resonance imaging we show that REMS increases amygdala responses to experimental social exclusion, as well as negative affect on the morning following sleep deprivation. There was no evidence that emotional responses to experimentally induced social exclusion or their regulation using cognitive reappraisal were impacted by diminished REM sleep. Our findings indicate that general affect and amygdala activity depend on REM sleep, while specific emotional experiences possibly rely on additional psychological processes and neural systems that are less readily influenced by REMS.

Acute caffeine intake can delay sleep initiation and reduce sleep intensity, particularly when consumed in the evening. However, it is not clear whether these sleep disturbances disappear when caffeine is continuously consumed during daytime, which is common for most coffee drinkers. To address this question, we investigated the sleep of twenty male young habitual caffeine consumers during a double-blind, randomized, crossover study including three 10-day conditions: caffeine (3 x 150 mg caffeine daily), withdrawal (3 x 150 mg caffeine for eight days, then switch to placebo), and placebo (3 x placebo daily). After nine days of continuous treatment, electroencephalographically (EEG)-derived sleep structure and intensity were recorded during a scheduled 8-h nighttime sleep episode starting 8 (caffeine condition) and 15 h (withdrawal condition) after the last caffeine intake. Upon scheduled wake up time, subjective sleep quality and caffeine withdrawal symptoms were assessed. Unexpectedly, neither polysomnography-derived total sleep time, sleep latency, sleep architecture, nor subjective sleep quality differed among placebo, caffeine, and withdrawal conditions. Nevertheless, EEG power density in the sigma frequencies (12-16 Hz) during non-rapid eye movement (NREM) sleep was reduced in both caffeine and withdrawal conditions when compared to placebo. These results indicate that daily caffeine intake in the morning and afternoon hours does not strongly impair nighttime sleep structure or subjective sleep quality in healthy good sleepers who regularly consume caffeine. The reduced EEG power density in the sigma range might represent early signs of overnight withdrawal from the continuous presence of the stimulant during the day.Statement of SignificanceCaffeine consumption is highly prevalent worldwide and has been repeatedly shown to acutely disrupt sleep, particularly when consumed after several days of abstinence or close to bedtime. However, commonly, caffeine is consumed daily and during daytime. Our well-controlled laboratory study revealed that this common pattern of intake affects nighttime sleep differently: While slow-wave sleep duration or slow-wave activity were rather similar compared to placebo, caffeine intake surprisingly reduced EEG power density in the sigma frequencies. In the light of earlier studies, this might present early signs of caffeine withdrawal which occurs due to overnight caffeine abstinence. The present findings provide novel insights into the impact of daily presence and nightly abstinence of caffeine on nighttime sleep in regular consumers.

Delineating age-related cortical trajectories in healthy individuals is critical given the association of cortical thickness with cognition and behaviour. Previous research has shown that deriving robust estimates of age-related brain morphometric changes requires large-scale studies. In response, we conducted a large-scale analysis of cortical thickness in 17,075 individuals aged 3-90 years by pooling data through the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium. We used fractional polynomial (FP) regression to characterize age-related trajectories in cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma (LMS) method. Inter-individual variability was estimated using meta-analysis and one-way analysis of variance. Overall, cortical thickness peaked in childhood and had a steep decrease during the first 2-3 decades of life; thereafter, it showed a gradual monotonic decrease which was steeper in men than in women particularly in middle-life. Notable exceptions to this general pattern were entorhinal, temporopolar and anterior cingulate cortices. Inter-individual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results reconcile uncertainties about age-related trajectories of cortical thickness; the centile values provide estimates of normative variance in cortical thickness, and may assist in detecting abnormal deviations in cortical thickness, and associated behavioural, cognitive and clinical outcomes.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalised on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine the age-related morphometric trajectories of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum early in life; the volume of the basal ganglia showed a gradual monotonic decline thereafter while the volumes of the thalamus, amygdala and the hippocampus remained largely stable (with some degree of decline in thalamus) until the sixth decade of life followed by a steep decline thereafter. The lateral ventricles showed a trajectory of continuous enlargement throughout the lifespan. Significant age-related increase in inter-individual variability was found for the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to derive risk predictions for the early identification of diverse clinical phenotypes.

BackgroundA growing body of neuroimaging research has revealed a relationship between blunted activation of the ventral striatum (VS) and apathy in schizophrenia. In contrast, the association between reduced striatal volume and apathy is less well established, while the relationship between VS function and structure in patients with schizophrenia remains an open question. Here, we aimed to replicate previous structural findings in a larger independent sample and to investigate the relationship between VS hypoactivation and VS volume.MethodsWe included brain structural magnetic resonance imaging (MRI) data from 60 patients with schizophrenia (SZ) that had shown an association of VS hypoactivation with apathy during reward anticipation and 58 healthy controls (HC). To improve replicability, we applied analytical methods developed in two previously published studies: Voxel-based morphometry and the Multiple Automatically Generated Templates (MAGeT) algorithm. VS and dorsal striatum (DS) volume were correlated with apathy correcting for age, gender and total brain volume. Additionally, left VS activity was correlated with left VS volume.ResultsWe failed to replicate the association between apathy and reduced VS volume and did not find a correlation with DS volume. Functional and structural left VS measures exhibited a trend-level correlation (rs=0.248, p=0.067, r2=0.06).ConclusionsOur present data suggests that functional and structural striatal neuroimaging correlates of apathy can occur independently. Replication of previous findings may have been limited by other factors (medication, illness duration, age) potentially related to striatal volume changes in SZ. Finally, associations between reward-related VS function and structure should be further explored.

Background Circadian and sleep-homeostatic mechanisms regulate timing and quality of wakefulness. To enhance wakefulness, daily consumption of caffeine in the morning and afternoon is highly common. However, the effects of such a regular intake pattern on circadian sleep-wake regulation are unknown. Thus, we investigated if daily daytime caffeine intake and caffeine withdrawal affect circadian rhythms and wake-promotion in habitual consumers. Methods Twenty male young volunteers participated in a randomised, double-blind, within-subject study with three conditions: i) caffeine (150 mg 3 x daily for 10 days), ii) placebo (3 x daily for 10 days) and iii) withdrawal (150 mg caffeine 3 x daily for eight days, followed by a switch to placebo for two days). Starting on day nine of treatment, salivary melatonin and cortisol, evening nap sleep as well as sleepiness and vigilance performance throughout day and night were quantified during 43 h in an in-laboratory, light and posture-controlled protocol. Results Neither the time course of melatonin (i.e. onset, amplitude or area under the curve) nor the time course of cortisol was significantly affected by caffeine or withdrawal. During withdrawal, however, volunteers reported increased sleepiness, showed more attentional lapses as well as polysomnography-derived markers of elevated sleep propensity in the late evening compared to both the placebo and caffeine condition. Conclusions The typical pattern of caffeine intake with consumption in both the morning and afternoon hours may not necessarily result in a circadian phase shift in the evening nor lead to clear-cut benefits in alertness. The time-of-day independent effects of caffeine withdrawal on evening nap sleep, sleepiness and performance suggest an adaptation to the substance, presumably in the homeostatic aspect of sleep-wake regulation.

We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks.

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.