Cortical thinning in young psychosis and bipolar patients correlate with common neurocognitive deficits

Background: People in midlife with established psychosis or bipolar disorder exhibit patterns of cortical thinning across several brain regions. It is unclear whether these patterns are indicative of a continuously active pathological process, residual effects of an earlier illness phase or pre-illness onset developmental risk factors. Here, we investigated whether cortical thinning is evident in younger patients in the early phase of psychosis or bipolar disorder and the relationship between cortical thinning and neurocognitive performance in young people. Methods: Magnetic resonance imaging was obtained from a sample of young patients with psychosis (n = 40; mean age 23.5 years), bipolar disorder (n = 73; mean age 21.9 years) or controls (n = 49; mean age 24.2 years). Group differences in cortical thickness were assessed using statistical difference maps, and regions of cortical thinning were correlated with medication dosage and performance on neurocognitive tasks. As initial comparisons using multiple corrections found no differences between the groups, follow-up analysis with a significance threshold of p < 0.001 was performed. Results and discussion: As distinct from reported findings in older subjects, young patients with psychosis have less extensive thinning in parietal-temporal areas and do not demonstrate significant thinning in the insula or dorsal lateral prefrontal cortex. Young patients with bipolar disorder exhibit cortical thinning in regions more consistent with those previously reported in paediatric bipolar patients. Although there were some differences in the regions of cortical thinning between the two groups, the shared regions of cortical thinning were correlated with neurocognitive deficits in visual sustained attention, semantic verbal fluency and verbal learning and memory that are commonly reported in young people with either psychosis or bipolar disorder

Underdiagnosis of mild cognitive impairment: A consequence of ignoring practice effects

INTRODUCTION: Longitudinal testing is necessary to accurately measure cognitive change. However, repeated testing is susceptible to practice effects, which may obscure true cognitive decline and delay detection of mild cognitive impairment (MCI). METHODS: We retested 995 late-middle-aged men in a ∼6-year follow-up of the Vietnam Era Twin Study of Aging. In addition, 170 age-matched replacements were tested for the first time at study wave 2. Group differences were used to calculate practice effects after controlling for attrition effects. MCI diagnoses were generated from practice-adjusted scores. RESULTS: There were significant practice effects on most cognitive domains. Conversion to MCI doubled after correcting for practice effects, from 4.5% to 9%. Importantly, practice effects were present although there were declines in uncorrected scores. DISCUSSION: Accounting for practice effects is critical to early detection of MCI. Declines, when lower than expected, can still indicate practice effects. Replacement participants are needed for accurately assessing disease progression.Published versio

Mediators of the Effect of Childhood Socioeconomic Status on Late Midlife Cognitive Abilities : A Four Decade Longitudinal Study

Peer reviewe

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Dynamic configuration of the CMS Data Acquisition cluster

The CMS Data Acquisition cluster, which runs around 10000 applications, is configured dynamically at run time. XML configuration documents determine what applications are executed on each node and over what networks these applications communicate. Through this mechanism the DAQ System may be adapted to the required performance, partitioned in order to perform (test-) runs in parallel, or re-structured in case of hardware faults. This paper presents the CMS DAQ Configurator tool, which is used to generate comprehensive configurations of the CMS DAQ system based on a high-level description given by the user. Using a database of configuration templates and a database containing a detailed model of hardware modules, data and control links, nodes and the network topology, the tool automatically determines which applications are needed, on which nodes they should run, and over which networks the event traffic will flow. The tool computes application parameters and generates the XML configuration documents as well as the configuration of the run-control system. The performance of the tool and operational experience during CMS commissioning and the first LHC runs are discussed

Network-based atrophy modelling in the common epilepsies: a worldwide ENIGMA study

SUMMARY Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1,021 adults with epilepsy compared to 1,564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy co-localized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Indices of progressive atrophy further revealed a strong influence of connectome architecture on disease progression in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients, and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided novel insights into the macroscale features that shape the pathophysiology of common epilepsies

Brain-age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.</p

MicroRNAs Dynamically Remodel Gastrointestinal Smooth Muscle Cells

Smooth muscle cells (SMCs) express a unique set of microRNAs (miRNAs) which regulate and maintain the differentiation state of SMCs. The goal of this study was to investigate the role of miRNAs during the development of gastrointestinal (GI) SMCs in a transgenic animal model. We generated SMC-specific Dicer null animals that express the reporter, green fluorescence protein, in a SMC-specific manner. SMC-specific knockout of Dicer prevented SMC miRNA biogenesis, causing dramatic changes in phenotype, function, and global gene expression in SMCs: the mutant mice developed severe dilation of the intestinal tract associated with the thinning and destruction of the smooth muscle (SM) layers; contractile motility in the mutant intestine was dramatically decreased; and SM contractile genes and transcriptional regulators were extensively down-regulated in the mutant SMCs. Profiling and bioinformatic analyses showed that SMC phenotype is regulated by a complex network of positive and negative feedback by SMC miRNAs, serum response factor (SRF), and other transcriptional factors. Taken together, our data suggest that SMC miRNAs are required for the development and survival of SMCs in the GI tract

The ENIGMA-Epilepsy working group: Mapping disease from large data sets

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller‐scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well‐established by the ENIGMA Consortium, ENIGMA‐Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event‐based modeling analysis. We explore age of onset‐ and duration‐related features, as well as phenomena‐specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA‐Epilepsy