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MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness. This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

Received 11 February 2022 Accepted 27 May 2022 Keywords: Cortical thickness Cortical surface area Juvenile offenders Childhood trauma Psychopathy ABSTRACT Objectives: This study contributes to the neuroscience of offending behavior by addressing two aims: a) to examine differences in the cortical features in a group of male serious juvenile offenders (21 OG), versus controls (28 CG), both ranging from 18 to 21 years old; and b) to determine to what extent the differential cortical features and the risk psychological profile discriminate between the two groups. Research on exposure to adverse childhood experiences has shown that the number, severity, and diversity of adverse experiences that children are exposed to have an impact on their future maladaptive behaviors, including depression (Allwood et al., 2011), anxiety (Tatar et al., 2012), aggressive behavior (Ford et al., 2012), delinquency in general (Baglivio et al., 2015), and offenses related to child-to-parent violence in particular (Nowakowski-Sims & Rowe, 2017; see reviews by Jaffee, 2017 or Teicher & Samson, 2013). The percentage of youth with mental health needs in the juvenile justice system is higher than in the community and seems to be increasing (Fazel et al., 2008). Early traumatic experiences promote, among other effects, irritability, impulsiveness, or substance use, which are risk factors for juvenile delinquency, characteristics that are currently used to assign diagnoses of mental disorders such as depression, traumatic stress, attention deficit disorder, or substance use disorders (Colins & Grisso, 2019).

Several reports have described cortical thickness (CTh) developmental trajectories, with conflicting results. Some studies have reported inverted-U shape curves with peaks of CTh in late childhood to adolescence, while others suggested predominant monotonic decline after age 6. In this study, we reviewed CTh developmental trajectories in the NIH MRI Study of Normal Brain Development, and in a second step, evaluated the impact of post-processing quality control (QC) procedures on identified trajectories. The quality-controlled sample included 384 individual subjects with repeated scanning (1–3 per subject, total scans n=753) from 4.9 to 22.3years of age. The best-fit model (cubic, quadratic, or first-order linear) was identified at each vertex using mixed-effects models. The majority of brain regions showed linear monotonic decline of CTh. There were few areas of cubic trajectories, mostly in bilateral temporo-parietal areas and the right prefrontal cortex, in which CTh peaks were at, or prior to, age 8. When controlling for total brain volume, CTh trajectories were even more uniformly linear. The only sex difference was faster thinning of occipital areas in boys compared to girls. The best-fit model for whole brain mean thickness was a monotonic decline of 0.027mm per year. QC procedures had a significant impact on identified trajectories, with a clear shift toward more complex trajectories (i.e., quadratic or cubic) when including all scans without QC (n=954). Trajectories were almost exclusively linear when using only scans that passed the most stringent QC (n=598). The impact of QC probably relates to decreasing the inclusion of scans with CTh underestimation secondary to movement artifacts, which are more common in younger subjects. In summary, our results suggest that CTh follows a simple linear decline in most cortical areas by age 5, and all areas by age 8. This study further supports the crucial importance of implementing post-processing QC in CTh studies of development, aging, and neuropsychiatric disorders. •Cortical thickness follows mostly a monotonic linear decline after 5years of age.•Areas with cubic developmental trajectories have peaks of cortical thickness prior to age 8.•The only sex difference in maturation is faster occipital thinning in males.•Mean cortical thickness follows a monotonic linear decline of 0.027mm per year.•Quality control processes have a significant impact on identified trajectories.•A post-processing quality control should be applied in all cortical thickness studies.

There is evidence that the timing of developmental changes in cortical volume and thickness varies across the brain, although the processes behind these differences are not well understood. In contrast to volume and thickness, the regional developmental trajectories of cortical surface area have not yet been described. The present study used a combined cross-sectional and longitudinal design with 201 MRI-scans (acquired at 1.5-T) from 135 typically developing children and adolescents. Scans were processed using FreeSurfer software and the Desikan–Killiany atlas. Developmental trajectories were estimated using mixed model regression analysis. Within most regions, cortical thickness showed linear decreases with age, whereas both cortical volume and surface area showed curvilinear trajectories. On average, maximum surface area occurred later in development than maximum volume. Global gender differences were more pronounced in cortical volume and surface area than in average thickness. Our findings suggest that developmental trajectories of surface area and thickness differ across the brain, both in their pattern and their timing, and that they also differ from the developmental trajectory of global cortical volume. Taken together, these findings indicate that the development of surface area and thickness is driven by different processes, at least in part. •We examined cortical change in a large cohort of typically developing children.•Cortical thickness and surface area development do not mirror volume development.•On average, maximum surface area occurred later than maximum volume development.•Timing of volume, thickness and surface area development varies by cortical region.•Cortical thickness and surface area develop independent of one another.

•Genetic variants affecting one cortical region often affect other cortical regions.•Standard mass-univariate methods ignore the distributed nature of genetic effects.•Multivariate MOSTest method exploits distributed effects boosting genetic discovery.•Considering fine-grained vertex-wise measures improves genetic discovery further.•Obtained increase in discovery does not come at a cost of poorer generalizability. Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

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 |β| = .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. 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). Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and we found sex differences in 67% of the investigated brain regions. 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.

Subject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy or indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to more directly measure image quality from T1-weighted images to understand if these measures of image quality: (1) relate to rigorous quality-control checks visually completed by human raters; (2) are associated with sociodemographic variables of interest; (3) influence regional estimates of cortical surface area, cortical thickness, and subcortical volumes from the commonly used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range ( N  =  388; ages 5–21 ). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical surface area, cortical thickness, and subcortical volumes from multiple regions across the brain (~ 23.4% of all areas investigated). We believe these results are important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.

Surface area of the human cerebral cortex expands extremely dynamically and regionally heterogeneously from the third trimester of pregnancy to 2 y of age, reflecting the spatial heterogeneity of the underlying microstructural and functional development of the cerebral cortex. However, little is known about the developmental patterns and regionalization of cortical surface area during this critical stage, due to the lack of high-quality imaging data and accurate computational tools for pediatric brain MRI data. To fill this critical knowledge gap, by leveraging 1,037 high-quality MRI scans with the age between 29 post-menstrual weeks and 24 mo from 735 pediatric subjects in two complementary datasets, i.e., the Baby Connectome Project (BCP) and the developing Human Connectome Project (dHCP), and state-of-the-art dedicated image-processing tools, we unprecedentedly parcellate the cerebral cortex into a set of distinct subdivisions purely according to the developmental patterns of the cortical surface. Our discovered developmentally distinct subdivisions correspond well to structurally and functionally meaningful regions and reveal spatially contiguous, hierarchical, and bilaterally symmetric patterns of early cortical surface expansion. We also show that highorder association subdivisions, where cortical folds emerge later during prenatal stages, undergo more dramatic cortical surface expansion during infancy, compared with the central regions, especially the sensorimotor and insula cortices, thus forming a distinct central-pole division in early cortical surface expansion. These results provide an important reference for exploring and understanding dynamic early brain development in health and disease.

Changes in cortical gray matter are a key feature of neurodegenerative diseases that have been linked with concussion and repetitive head impacts (RHIs). Prior evidence implicates prior concussion and RHI in reduced cortical thickness or volume in temporal and frontal regions, with results largely restricted to older retired contact sport athletes. Fewer studies have investigated similar associations in younger athletes or applied approaches to capture more subtle differences in gray matter earlier in the lifespan. The current study assessed the association of concussion and RHI with cortical macrostructure (cortical thickness, cortical surface area), and cortical microstructure (cortical mean diffusivity), the latter of which has been suggested to be an earlier marker of gray matter abnormalities in neurodegenerative diseases. A total of 207 otherwise healthy collegiate‐aged athletes completed semistructured interviews for concussion and sport participation history, as well as a magnetic resonance imaging session including anatomical and diffusion imaging (N = 205 with available diffusion data). Cortical surface area and cortical thickness were estimated using FreeSurfer; cortical mean diffusivity was calculated with correction for partial volume. Bayesian multilevel modeling was conducted on regions of interest derived from Desikan–Killiany Atlas parcellations to determine the association of the number of prior concussions and RHI (included in the same models) with each metric, controlling for sex, age, and intracranial volume (area only). There was strong evidence for a positive association between the number of prior concussions and cortical mean diffusivity throughout most of the cortex. In addition, there was strong evidence for a positive association of the number of prior concussions with cortical surface area across several regions. For cortical thickness, there was strong evidence of inverse associations between the number of prior concussions and anterior and medial temporal cortical regions only. In contrast, only weak to no evidence of associations between years of contact sport exposure, a proxy for RHI, and any cortical surface metric was observed. These results demonstrate that cortical diffusivity may represent a more sensitive metric of subtle, early structural changes associated with repetitive neurotrauma, and highlight the importance of efforts to reduce concussion risk in sport. A greater number of prior concussions was associated with widespread increases in cortical mean diffusivity, increased cortical surface area, and thinner anterior and medial temporal cortex in otherwise healthy collegiate athletes. Microscopic changes in gray matter associated with cumulative concussion may represent a vulnerability or antecedent to future degeneration.