Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex.

Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.

A Comparative Perspective on the Cerebello-Cerebral System and Its Link to Cognition.

The longstanding idea that the cerebral cortex is the main neural correlate of human cognition can be elaborated by comparative analyses along the vertebrate phylogenetic tree that support the view that the cerebello-cerebral system is suited to support non-motor functions more generally. In humans, diverse accounts have illustrated cerebellar involvement in cognitive functions. Although the neocortex, and its transmodal association cortices such as the prefrontal cortex, have become disproportionately large over primate evolution specifically, human neocortical volume does not appear to be exceptional relative to the variability within primates. Rather, several lines of evidence indicate that the exceptional volumetric increase of the lateral cerebellum in conjunction with its connectivity with the cerebral cortical system may be linked to non-motor functions and mental operation in primates. This idea is supported by diverging cerebello-cerebral adaptations that potentially coevolve with cognitive abilities across other vertebrates such as dolphins, parrots, and elephants. Modular adaptations upon the vertebrate cerebello-cerebral system may thus help better understand the neuroevolutionary trajectory of the primate brain and its relation to cognition in humans. Lateral cerebellar lobules crura I-II and their reciprocal connections to the cerebral cortical association areas appear to have substantially expanded in great apes, and humans. This, along with the notable increase in the ventral portions of the dentate nucleus and a shift to increased relative prefrontal-cerebellar connectivity, suggests that modular cerebellar adaptations support cognitive functions in humans. In sum, we show how comparative neuroscience provides new avenues to broaden our understanding of cerebellar and cerebello-cerebral functions in the context of cognition.

Comparing brain asymmetries independently of brain size.

Studies examining cerebral asymmetries typically divide the l-R Measure (e.g., Left-Right Volume) by the L + R Measure to obtain an Asymmetry Index (AI). However, contrary to widespread belief, such a division fails to render the AI independent from the L + R Measure and/or from total brain size. As a result, variations in brain size may bias correlation estimates with the AI or group differences in AI. We investigated how to analyze brain asymmetries in to distinguish global from regional effects, and report unbiased group differences in cerebral asymmetries in the UK Biobank (N = 40, 028). We used 306 global and regional brain measures provided by the UK Biobank. Global gray and white matter volumes were taken from Freesurfer ASEG, subcortical gray matter volumes from Freesurfer ASEG and subsegmentation, cortical gray matter volumes, mean thicknesses, and surface areas from the Destrieux atlas applied on T1-and T2-weighted images, cerebellar gray matter volumes from FAST FSL, and regional white matter volumes from Freesurfer ASEG. We analyzed the extent to which the L + R Measure, Total Cerebral Measure (TCM, e.g., Total Brain Volume), and l-R TCM predict regional asymmetries. As a case study, we assessed the consequences of omitting each of these predictors on the magnitude and significance of sex differences in asymmetries. We found that the L + R Measure, the TCM, and the l-R TCM predicted the AI of more than 89% of regions and that their relationships were generally linear. Removing any of these predictors changed the significance of sex differences in 33% of regions and the magnitude of sex differences across 13-42% of regions. Although we generally report similar sex and age effects on cerebral asymmetries to those of previous large-scale studies, properly adjusting for regional and global brain size revealed additional sex and age effects on brain asymmetry.

Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery.

MRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 - far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.

The meaning of significant mean group differences for biomarker discovery.

Over the past decade, biomarker discovery has become a key goal in psychiatry to aid in the more reliable diagnosis and prognosis of heterogeneous psychiatric conditions and the development of tailored therapies. Nevertheless, the prevailing statistical approach is still the mean group comparison between "cases" and "controls," which tends to ignore within-group variability. In this educational article, we used empirical data simulations to investigate how effect size, sample size, and the shape of distributions impact the interpretation of mean group differences for biomarker discovery. We then applied these statistical criteria to evaluate biomarker discovery in one area of psychiatric research-autism research. Across the most influential areas of autism research, effect size estimates ranged from small (d = 0.21, anatomical structure) to medium (d = 0.36 electrophysiology, d = 0.5, eye-tracking) to large (d = 1.1 theory of mind). We show that in normal distributions, this translates to approximately 45% to 63% of cases performing within 1 standard deviation (SD) of the typical range, i.e., they do not have a deficit/atypicality in a statistical sense. For a measure to have diagnostic utility as defined by 80% sensitivity and 80% specificity, Cohen's d of 1.66 is required, with still 40% of cases falling within 1 SD. However, in both normal and nonnormal distributions, 1 (skewness) or 2 (platykurtic, bimodal) biologically plausible subgroups may exist despite small or even nonsignificant mean group differences. This conclusion drastically contrasts the way mean group differences are frequently reported. Over 95% of studies omitted the "on average" when summarising their findings in their abstracts ("autistic people have deficits in X"), which can be misleading as it implies that the group-level difference applies to all individuals in that group. We outline practical approaches and steps for researchers to explore mean group comparisons for the discovery of stratification biomarkers.

Imaging evolution of the primate brain: the next frontier?

Evolution, as we currently understand it, strikes a delicate balance between animals' ancestral history and adaptations to their current niche. Similarities between species are generally considered inherited from a common ancestor whereas observed differences are considered as more recent evolution. Hence comparing species can provide insights into the evolutionary history. Comparative neuroimaging has recently emerged as a novel subdiscipline, which uses magnetic resonance imaging (MRI) to identify similarities and differences in brain structure and function across species. Whereas invasive histological and molecular techniques are superior in spatial resolution, they are laborious, post-mortem, and oftentimes limited to specific species. Neuroimaging, by comparison, has the advantages of being applicable across species and allows for fast, whole-brain, repeatable, and multi-modal measurements of the structure and function in living brains and post-mortem tissue. In this review, we summarise the current state of the art in comparative anatomy and function of the brain and gather together the main scientific questions to be explored in the future of the fascinating new field of brain evolution derived from comparative neuroimaging.

A collaborative resource platform for non-human primate neuroimaging.

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field .

Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age.

Few neuroimaging studies are sufficiently large to adequately describe population-wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40,028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry-the nonlinear scaling relationship between a region and brain size (e.g., total brain volume)-across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |ß| = .13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large-scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.

Common genetic variants influence human subcortical brain structures.

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Polygenic Architecture of Human Neuroanatomical Diversity.

We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured

Neuroanatomy of dyslexia: An allometric approach.

Despite evidence for a difference in total brain volume between dyslexic and good readers, no previous neuroimaging study examined differences in allometric scaling (i.e. differences in the relationship between regional and total brain volumes) between dyslexic and good readers. The present study aims to fill this gap by testing differences in allometric scaling and regional brain volume differences in dyslexic and good readers. Object-based morphometry analysis was used to determine grey and white matter volumes of the four lobes, the cerebellum and limbic structures in 130 dyslexic and 106 good readers aged 8-14 years. Data were collected across three countries (France, Poland and Germany). Three methodological approaches were used as follows: principal component analysis (PCA), linear regression and multiple-group confirmatory factor analysis (MGCFA). Difference in total brain volume between good and dyslexic readers was Cohen's d = 0.39. We found no difference in allometric scaling, nor in regional brain volume between dyslexic and good readers. Results of our three methodological approaches (PCA, linear regression and MGCFA) were consistent. This study provides evidence for total brain volume differences between dyslexic and control children, but no evidence for differences in the volumes of the four lobes, the cerebellum or limbic structures, once allometry is taken into account. It also finds no evidence for a difference in allometric relationships between the groups. We highlight the methodological interest of the MGCFA approach to investigate such research issues.

Adjusting for allometric scaling in ABIDE I challenges subcortical volume differences in autism spectrum disorder.

Inconsistencies across studies investigating subcortical correlates of autism spectrum disorder (ASD) may stem from small sample size, sample heterogeneity, and omitting or linearly adjusting for total brain volume (TBV). To properly adjust for TBV, brain allometry-the nonlinear scaling relationship between regional volumes and TBV-was considered when examining subcortical volumetric differences between typically developing (TD) and ASD individuals. Autism Brain Imaging Data Exchange I (ABIDE I; N = 654) data was analyzed with two methodological approaches: univariate linear mixed effects models and multivariate multiple group confirmatory factor analyses. Analyses were conducted on the entire sample and in subsamples based on age, sex, and full scale intelligence quotient (FSIQ). A similar ABIDE I study was replicated and the impact of different TBV adjustments on neuroanatomical group differences was investigated. No robust subcortical allometric or volumetric group differences were observed in the entire sample across methods. Exploratory analyses suggested that allometric scaling and volume group differences may exist in certain subgroups defined by age, sex, and/or FSIQ. The type of TBV adjustment influenced some reported volumetric and scaling group differences. This study supports the absence of robust volumetric differences between ASD and TD individuals in the investigated volumes when adjusting for brain allometry, expands the literature by finding no group difference in allometric scaling, and further suggests that differing TBV adjustments contribute to the variability of reported neuroanatomical differences in ASD.

Sex Differences Along the Autism Continuum: A Twin Study of Brain Structure.

Females might possess protective mechanisms regarding autism spectrum disorder (ASD) and require a higher detrimental load, including structural brain alterations, before developing clinically relevant levels of autistic traits. This study examines sex differences in structural brain morphology in autism and autistic traits using a within-twin pair approach. Twin design inherently controls for shared confounders and enables the study of gene-independent neuroanatomical variation. N = 148 twins (62 females) from 49 monozygotic and 25 dizygotic same-sex pairs were included. Participants were distributed along the whole continuum of autism including twin pairs discordant and concordant for clinical ASD. Regional brain volume, surface area, and cortical thickness were computed. Within-twin pair increases in autistic traits were related to decreases in cortical volume and surface area of temporal and frontal regions specifically in female twin pairs, in particular regions involved in social communication, while only two regions were associated with autistic traits in males. The same pattern was detected in the monozygotic twin pairs only. Thus, non-shared environmental factors seem to impact female more than male cerebral architecture associated with autistic traits. Our results are in line with the hypothesis of a female protective effect in autism and highlights the need to study ASD in females separately from males.

Autistic-like behaviours and hyperactivity in mice lacking ProSAP1/Shank2.

Autism spectrum disorders comprise a range of neurodevelopmental disorders characterized by deficits in social interaction and communication, and by repetitive behaviour. Mutations in synaptic proteins such as neuroligins, neurexins, GKAPs/SAPAPs and ProSAPs/Shanks were identified in patients with autism spectrum disorder, but the causative mechanisms remain largely unknown. ProSAPs/Shanks build large homo- and heteromeric protein complexes at excitatory synapses and organize the complex protein machinery of the postsynaptic density in a laminar fashion. Here we demonstrate that genetic deletion of ProSAP1/Shank2 results in an early, brain-region-specific upregulation of ionotropic glutamate receptors at the synapse and increased levels of ProSAP2/Shank3. Moreover, ProSAP1/Shank2(-/-) mutants exhibit fewer dendritic spines and show reduced basal synaptic transmission, a reduced frequency of miniature excitatory postsynaptic currents and enhanced N-methyl-d-aspartate receptor-mediated excitatory currents at the physiological level. Mutants are extremely hyperactive and display profound autistic-like behavioural alterations including repetitive grooming as well as abnormalities in vocal and social behaviours. By comparing the data on ProSAP1/Shank2(-/-) mutants with ProSAP2/Shank3αß(-/-) mice, we show that different abnormalities in synaptic glutamate receptor expression can cause alterations in social interactions and communication. Accordingly, we propose that appropriate therapies for autism spectrum disorders are to be carefully matched to the underlying synaptopathic phenotype.

Global genetic variations predict brain response to faces.

Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼ 500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40-50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R(2) = 0.38, p<0.001). Furthermore, this variability showed an inverted U relationship with both the number of observed connections (R2 = 0.48, p<0.001) and the magnitude of brain response (R(2) = 0.32, p<0.001). Thus, a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network. These regions show the highest inter-individual variability in the number of connections with other network nodes, suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network.

Meta-analysis of SHANK Mutations in Autism Spectrum Disorders: a gradient of severity in cognitive impairments.

SHANK genes code for scaffold proteins located at the post-synaptic density of glutamatergic synapses. In neurons, SHANK2 and SHANK3 have a positive effect on the induction and maturation of dendritic spines, whereas SHANK1 induces the enlargement of spine heads. Mutations in SHANK genes have been associated with autism spectrum disorders (ASD), but their prevalence and clinical relevance remain to be determined. Here, we performed a new screen and a meta-analysis of SHANK copy-number and coding-sequence variants in ASD. Copy-number variants were analyzed in 5,657 patients and 19,163 controls, coding-sequence variants were ascertained in 760 to 2,147 patients and 492 to 1,090 controls (depending on the gene), and, individuals carrying de novo or truncating SHANK mutations underwent an extensive clinical investigation. Copy-number variants and truncating mutations in SHANK genes were present in ∼1% of patients with ASD: mutations in SHANK1 were rare (0.04%) and present in males with normal IQ and autism; mutations in SHANK2 were present in 0.17% of patients with ASD and mild intellectual disability; mutations in SHANK3 were present in 0.69% of patients with ASD and up to 2.12% of the cases with moderate to profound intellectual disability. In summary, mutations of the SHANK genes were detected in the whole spectrum of autism with a gradient of severity in cognitive impairment. Given the rare frequency of SHANK1 and SHANK2 deleterious mutations, the clinical relevance of these genes remains to be ascertained. In contrast, the frequency and the penetrance of SHANK3 mutations in individuals with ASD and intellectual disability-more than 1 in 50-warrant its consideration for mutation screening in clinical practice.

11q24.2-25 micro-rearrangements in autism spectrum disorders: Relation to brain structures.

Jacobsen syndrome (JS) is characterized by intellectual disability and higher risk for autism spectrum disorders (ASD). All patients with JS are carriers of contiguous de novo deletions of 11q24.2-25, but the causative genes remain unknown. Within the critical interval, we hypothesized that haploinsufficiency of the neuronal cell adhesion molecule Neurotrimin (NTM) might increase the risk for ASD and could affect brain structure volumes. We searched for deleterious mutations affecting NTM in 1256 ASD patients and 1287 controls, using SNP arrays, and by direct sequencing of 250 ASD patients and 180 controls. We compared our results to those obtained from independent cohorts of ASD patients and controls. We identified two patients with Copy Number Variants (CNV) encompassing NTM, one with a large de novo deletion, and a clinical phenotype of JS (including macrocephaly), and a second with a paternally inherited duplication, not consistent with JS. Interestingly, no similar CNVs were observed in controls. We did not observe enrichment for deleterious NTM mutations in our cohort. We then explored if the macrocephaly in the patient with JS was associated with a homogeneous increase of brain structures volumes using automatic segmentation. Compared to subjects without NTM micro-rearrangements (n=188), the patient had an increased volume of the sub-cortical structures but a decrease of the occipital gray matter. Finally our explorations could not incriminate NTM as a susceptibility gene for ASD, but provides new information on the impact of the 11q24.2-25 deletion on brain anatomy.

Genetic and functional analyses of SHANK2 mutations suggest a multiple hit model of autism spectrum disorders.

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders with a complex inheritance pattern. While many rare variants in synaptic proteins have been identified in patients with ASD, little is known about their effects at the synapse and their interactions with other genetic variations. Here, following the discovery of two de novo SHANK2 deletions by the Autism Genome Project, we identified a novel 421 kb de novo SHANK2 deletion in a patient with autism. We then sequenced SHANK2 in 455 patients with ASD and 431 controls and integrated these results with those reported by Berkel et al. 2010 (n = 396 patients and n = 659 controls). We observed a significant enrichment of variants affecting conserved amino acids in 29 of 851 (3.4%) patients and in 16 of 1,090 (1.5%) controls (P = 0.004, OR = 2.37, 95% CI = 1.23-4.70). In neuronal cell cultures, the variants identified in patients were associated with a reduced synaptic density at dendrites compared to the variants only detected in controls (P = 0.0013). Interestingly, the three patients with de novo SHANK2 deletions also carried inherited CNVs at 15q11-q13 previously associated with neuropsychiatric disorders. In two cases, the nicotinic receptor CHRNA7 was duplicated and in one case the synaptic translation repressor CYFIP1 was deleted. These results strengthen the role of synaptic gene dysfunction in ASD but also highlight the presence of putative modifier genes, which is in keeping with the "multiple hit model" for ASD. A better knowledge of these genetic interactions will be necessary to understand the complex inheritance pattern of ASD.

The Roots of Autism and ADHD Twin Study in Sweden (RATSS).

Neurodevelopmental disorders affect a substantial minority of the general population. Their origins are still largely unknown, but a complex interplay of genetic and environmental factors causing disturbances of the central nervous system's maturation and a variety of higher cognitive skills is presumed. Only limited research of rather small sample size and narrow scope has been conducted in neurodevelopmental disorders using a twin-differences design. The Roots of Autism and ADHD Twin Study in Sweden (RATSS) is an ongoing project targeting monozygotic twins discordant for categorical or dimensional autistic and inattentive/hyperactive-impulsive phenotypes as well as other neurodevelopmental disorders, and typically developing twin controls. Included pairs are 9 years of age or older, and comprehensively assessed for psychopathology, medical history, neuropsychology, and dysmorphology, as well as structural, functional, and molecular brain imaging. Specimens are collected for induced pluripotent (iPS) and neuroepithelial stem cells, genetic, gut bacteria, protein-/monoamine, and electron microscopy analyses. RATSS's objective is to generate a launch pad for novel surveys to understand the complexity of genotype-environment-phenotype interactions in autism spectrum disorder and attention-deficit hyperactivity disorder (ADHD). By October 2013, RATSS had collected data from 55 twin pairs, among them 10 monozygotic pairs discordant for autism spectrum disorder, seven for ADHD, and four for other neurodevelopmental disorders. This article describes the design, recruitment, data collection, measures, collected pairs' characteristics, as well as ongoing and planned analyses in RATSS. Potential gains of the study comprise the identification of environmentally mediated biomarkers, the emergence of candidates for drug development, translational modeling, and new leads for prevention of incapacitating outcomes.