Under the influence of genes and a varying environment, human brain structure changes throughout the lifespan. Even in adulthood, when the brain seems relatively stable, individuals differ in the profile and rate of brain changes 1 . Longitudinal studies are crucial to identify genetic and environmental factors that influence the rate of these brain changes throughout development 2 and aging 3 . Inter-individual differences in brain development are associated with general cognitive function 4,5 and risk for psychiatric disorders 6,7 and neurological diseases 8,9 . Genetic factors involved in brain development and aging overlap with those for cognition 10 and risk for neuropsychiatric disorders 11 . A recent cross-sectional study showed brain age to be advanced in several brain disorders. Brain age is an estimate of biological age based on brain structure, which can deviate from chronological age. Several shared loci were found between the genome-wide association study (GWAS) summary statistics for advanced brain age and psychiatric disorders 12 . However, information is still lacking on which genetic variants influence an individual's brain changes throughout life, because this requires longitudinal data. Discovering genetic factors that explain variation between individuals in brain structural changes may reveal key biological pathways that drive normal development and aging and may contribute to identifying disease risk and resilience-a crucial goal given the urgent need for new treatments for aberrant brain development and aging worldwide.As part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium 13 , the ENIGMA Plasticity Working Group quantified the overall genetic contribution to longitudinal brain changes by combining evidence from multiple twin cohorts across the world 14 . Most global and subcortical brain measures showed genetic influences on change over time, with a higher genetic contribution in the elderly (heritability, 16-42%). Genetic factors that influence longitudinal changes were partially independent of those that influence baseline volumes of brain structures, suggesting that there might be genetic variants that specifically affect the rate of development or aging. However, the genes involved in these processes are still not known, with only a single, small-scale GWAS performed for longitudinal volume change in gray and white matter of the cerebrum, basal ganglia and cerebellum 15 . In this study, we set out to find genetic variants that may influence rates of brain changes over time, using genome-wide analysis in individuals scanned with magnetic resonance imaging (MRI) on more than one occasion. We also aimed to identify references

Assessing stability and change of children’s psychopathology symptoms can help elucidate whether specific behaviors are transient developmental variations or indicate persistent psychopathology. This study included 6930 children across early childhood (T1), late childhood (T2) and early adolescence (T3), from the general population. Latent profile analysis identified psychopathology subgroups and latent transition analysis quantified the probability that children remained within, or transitioned across psychopathology subgroups. We identified four psychopathology subgroups; no problems (T1: 85.9%, T2: 79.0%, T3: 78.0%), internalizing (T1: 5.1%, T2: 9.2%, T3: 9.0%), externalizing (T1: 7.3%, T2: 8.3%, T3: 10.2%) and the dysregulation profile (DP) (T1: 1.7%, T2: 3.5%, T3: 2.8%). From T1 to T2, 44.7% of the children remained in the DP. Between T2 and T3, 33.6% remained in the DP; however, 91.4% were classified in one of the psychopathology subgroups. Our findings suggest that for many children, internalizing or externalizing symptoms encompass a transient phase within development. Contrary, the DP resembles a severe at-risk state in which the predictive value for being in one of the psychopathology subgroups increases over time.

Background: Multiple sclerosis (MS) patients are protected from relapses during pregnancy and have an increased relapse risk after delivery. It is unknown how pregnancy controls disease-contributing CD4+ T helper (Th) cells and whether this differs in MS patients who experience a postpartum relapse. Here, we studied the effector phenotype of Th cells in relation to pregnancy and postpartum relapse occurrence in MS.Methods: Memory skewing and activation of effector Th subsets were analyzed in paired third trimester and postpartum blood of 19 MS patients with and without a postpartum relapse and 12 healthy controls. Ex vivo results were associated with circulating levels of pregnancy-induced hormones and mirrored in vitro by exposing proliferating Th cells to corresponding serum samples.Results: Based on HSNE-guided analyses, we found that effector memory proportions of Th cells were increased in postpartum vs. third trimester samples from MS patients without a postpartum relapse. This was not seen for relapsing patients or healthy controls. CXCR3 was upregulated on postpartum memory Th cells, except for relapsing patients. These changes were verified by adding sera from the same individuals to proliferating Th cells, but did not associate with third trimester cortisol, estradiol or progesterone levels. For relapsing patients, activated memory Th cells of both third trimester and postpartum samples produced higher levels of pro-inflammatory cytokines.Conclusion: Effector Th cells are differentially regulated during pregnancy in MS patients, likely via serum-related factors beyond the studied hormones. The pro-inflammatory state of memory Th cells during pregnancy may predict a postpartum relapse.

We identified common genetic variants associated with the rate of brain development and aging, in longitudinal MRI scans worldwide. AbstractHuman brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. While heritable, specific loci in the genome that influence these rates are largely unknown. Here, we sought to find common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association analysis of longitudinal changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 10,163 individuals aged 4 to 99 years, on average 3.5 years apart, were used to compute rates of morphological change for 15 brain structures. We discovered 5 genome-wide significant loci and 15 genes associated with brain structural changes. Most individual variants exerted age-dependent effects. All identified genes are expressed in fetal and adult brain tissue, and some exhibit developmentally regulated expression across the lifespan. We demonstrate genetic overlap with depression, schizophrenia, cognitive functioning, height, body mass index and smoking. Several of the discovered loci are implicated in early brain development and point to involvement of metabolic processes. Gene-set findings also implicate immune processes in the rates of brain changes. Taken together, in the world's largest longitudinal imaging genetics dataset we identified genetic variants that alter agedependent brain growth and atrophy throughout our lives. a Position based on build hg19. Study-wide significant hits are displayed in bold. *This gene also showed a genome-wide significant quadratic age effect. The most parsimonious model is listed in this table.Genome-wide significant gene sets based on gene ontology. Study-wide significant gene sets are displayed in bold. a See Supplementary Table S9 for genes included in the gene set. Genes included in GO_INTERLEUKIN_1_RECEPTOR_ACTIVITY and GO_RESPONSE_TO_INTERLEUKIN_2 do not overlap.