Parsing the developmental and genetic architecture of risk and resilience in the adolescent brain

• Corfield, Elizabeth Claire; Shadrin, Alexey; Frei, Oleksandr; Rahman, Mohammad Zillur; Akdeniz, Bayram Cevdet & Filiz, Tahir Tekin [Show all 29 contributors for this article] (2026). Family genetic designs in MoBa provide insights into health and functioning. Nature. ISSN 0028-0836. doi: 10.1038/s41586-026-10926-5. Full text in Research Archive Show summary

  Genome-wide association studies using large, population-based samples of unrelated individuals have discovered thousands of genetic associations with health and disease1. These studies can help explain genetic and environmental risks. However, increasing evidence suggests that population-based estimates, while precise, can also reflect confounding that affects their use and interpretation. This confounding can be overcome using data from genotyped family members, such as nuclear mother–father–child trios2,3. However, samples of genotyped families are rare4,5,6,7,8,9,10,11. Here we illustrate some of the advantages of familial data using the Norwegian Mother, Father and Child Cohort Study (MoBa), a population-based cohort of parents and offspring with extensive genotype data (n ≈ 230,000) (ref. 3), along with broad and longitudinal phenotyping of health and functioning. We provide an overview of MoBa and describe the quality control of genotype data tailored to this extensively related sample. We then use trio data to illustrate how family-based genomic designs can identify distinct direct and indirect sources of genetic influence and structural confounding. As examples, we analyse children’s height, educational achievement, depressive symptoms and sleep duration. These demonstrations highlight MoBa as a broadly valuable resource for advancing understanding of health and functioning across the lifecourse and generations.

• Silva, Ana I.; Sønderby, Ida Elken; Kirov, George; Abdellaoui, Abdel; Agartz, Ingrid & Ames, David [Show all 106 contributors for this article] (2025). Penetrance of Neurodevelopmental Copy Number Variants Is Associated With Variations in Cortical Morphology. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. ISSN 2451-9022. 10(10), p. 1093–1106. doi: 10.1016/j.bpsc.2025.05.010. Full text in Research Archive Show summary

  Background: Copy number variants (CNVs) may increase the risk for neurodevelopmental conditions. The neurobiological mechanisms that link these high-risk genetic variants to clinical phenotypes are largely unknown. An important question is whether brain abnormalities in individuals who carry CNVs are associated with their degree of penetrance. Methods: We investigated whether increased CNV penetrance for schizophrenia and other developmental disorders was associated with variations in cortical and subcortical morphology. We pooled T1-weighted brain magnetic resonance imaging and genetic data from 22 cohorts from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-CNV consortium. In the main analyses, we included 9268 individuals (aged 7–90 years, 54% female), from which we identified 398 carriers of 36 neurodevelopmental CNVs at 20 distinct loci. A secondary analysis was performed including additional neuroimaging data from the ENIGMA-22q consortium, including 274 carriers of the 22q11.2 deletion and 291 noncarriers. CNV penetrance was estimated through penetrance scores that were previously generated from large cohorts of patients and controls. These scores represent the probability risk of developing either schizophrenia or other developmental disorders (including developmental delay, autism spectrum disorder, and congenital malformations). Results: For both schizophrenia and developmental disorders, increased penetrance scores were associated with lower surface area in the cerebral cortex and lower intracranial volume. For both conditions, associations between CNV-penetrance scores and cortical surface area were strongest in regions of the occipital lobes, specifically in the cuneus and lingual gyrus. Conclusions: Our findings link global and regional cortical morphometric features with CNV penetrance, providing new insights into neurobiological mechanisms of genetic risk for schizophrenia and other developmental disorders.

• Frogner, Erik R.; Dahl, Andreas; Kjelkenes, Rikka; Moberget, Torgeir; Collins, Anne G.E. & Westlye, Lars Tjelta [Show all 7 contributors for this article] (2025). Linking reinforcement learning, working memory, and choice dynamics to age and symptoms of anxiety and depression in adolescence. Developmental Cognitive Neuroscience. ISSN 1878-9293. 76. doi: 10.1016/j.dcn.2025.101626. Full text in Research Archive
• MacSweeney, Niamh; Beck, Dani; Whitmore, Lucy; Mills, Kathryn L.; Westlye, Lars Tjelta & Soest, Tilmann Martin von [Show all 8 contributors for this article] (2025). Multimodal Brain Age Indicators of Internalizing Problems in Early Adolescence: A Longitudinal Investigation. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. ISSN 2451-9022. doi: 10.1016/j.bpsc.2024.11.003. Full text in Research Archive
• Beck, Dani; Whitmore, Lucy; MacSweeney, Niamh; Brieant, Alexis; Karl, Valerie & Lange, Ann-Marie Glasø de [Show all 9 contributors for this article] (2024). Dimensions of Early-Life Adversity Are Differentially Associated With Patterns of Delayed and Accelerated Brain Maturation. Biological Psychiatry. ISSN 0006-3223. 97(1), p. 64–72. doi: 10.1016/j.biopsych.2024.07.019. Full text in Research Archive
• Beck, Dani; Lange, Ann-Marie Glasø de; Gurholt, Tiril Pedersen; Voldsbekk, Irene; Maximov, Ivan & Subramaniapillai, Sivaniya [Show all 19 contributors for this article] (2024). Dissecting unique and common variance across body and brain health indicators using age prediction. Human Brain Mapping. ISSN 1065-9471. 45(6). doi: 10.1002/hbm.26685. Full text in Research Archive Show summary

  Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6–82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict ‘body age’. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

• Dahl, Andreas; Eilertsen, Espen Moen; Cabello, Sara Fernandez Rodriguez; Norbom, Linn B.; Tandberg, Anneli Desire & Leonardsen, Esten [Show all 11 contributors for this article] (2024). Genetic and brain similarity independently predict childhood anthropometrics and neighborhood socioeconomic conditions. Developmental Cognitive Neuroscience. ISSN 1878-9293. 65. doi: 10.1016/j.dcn.2023.101339. Full text in Research Archive Show summary

  Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ an integrative approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, associations between cortical thickness similarity and traits were limited to anthropometrics such as height, weight, and birth weight, as well as a marker of neighborhood socioeconomic conditions. Common genetic variants explained significant proportions of variance across nearly all included outcomes, although estimates were somewhat lower than previous reports. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as neighborhood socioeconomic conditions and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample.

• Korbmacher, Max; Lange, Ann-Marie Glasø de; Meer, Dennis van der; Beck, Dani; Eikefjord, Eli Nina & Lundervold, Arvid [Show all 9 contributors for this article] (2023). Brain‐wide associations between white matter and age highlight the role of fornix microstructure in brain ageing. Human Brain Mapping. ISSN 1065-9471. 44, p. 4101–4119. doi: 10.1002/hbm.26333. Full text in Research Archive Show summary

  Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6–82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

• Korbmacher, Max; Wang, Mengyun; Eikeland, Rune; Buchert, Ralph; Andreassen, Ole & Espeseth, Thomas [Show all 10 contributors for this article] (2023). Considerations on brain age predictions from repeatedly sampled data across time. Brain and Behavior. doi: 10.1002/brb3.3219. Full text in Research Archive Show summary

  Introduction Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls. Methods We used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model. Results We found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality. Conclusion The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.

• Wiker, Thea; Norbom, Linn B.; Beck, Dani; Agartz, Ingrid; Andreassen, Ole A. & Alnæs, Dag [Show all 14 contributors for this article] (2023). Reaction Time Variability in Children Is Specifically Associated With Attention Problems and Regional White Matter Microstructure. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. ISSN 2451-9022. 8(8), p. 832–840. doi: 10.1016/j.bpsc.2023.03.010. Full text in Research Archive Show summary

  Background Increased intraindividual variability (IIV) in reaction times (RTs) has been suggested as a key cognitive and behavioral marker of attention problems, but findings for other dimensions of psychopathology are less consistent. Moreover, while studies have linked IIV to brain white matter microstructure, large studies testing the robustness of these associations are needed. Methods We used data from the Adolescent Brain Cognitive Development (ABCD) Study baseline assessment to test the associations between IIV and psychopathology (n = 8622, age = 8.9–11.1 years) and IIV and white matter microstructure (n = 7958, age = 8.9–11.1 years). IIV was investigated using an ex-Gaussian distribution analysis of RTs in correct response go trials in the stop signal task. Psychopathology was measured by the Child Behavior Checklist and a bifactor structural equation model was performed to extract a general p factor and specific factors reflecting internalizing, externalizing, and attention problems. To investigate white matter microstructure, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were examined in 23 atlas-based tracts. Results Increased IIV in both short and long RTs was positively associated with the specific attention problems factor (Cohen’s d = 0.13 and d = 0.15, respectively). Increased IIV in long RTs was also positively associated with radial diffusivity in the left and right corticospinal tract (both tracts, d = 0.12). Conclusions Using a large sample and a data-driven dimensional approach to psychopathology, the results provide novel evidence for a small but specific association between IIV and attention problems in children and support previous findings on the relevance of white matter microstructure for IIV.

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