Genetic and phenotypic architecture of the ontogenetic determinants of severe mental illness

• 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
• 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.

• Barth, Claudia; Galea, Liisa A.M.; Jacobs, Emily G.; Lee, Bonnie H; Westlye, Lars Tjelta & Lange, Ann-Marie Glasø de (2024). Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort. eLIFE. doi: 10.7554/eLife.99538.1. Full text in Research Archive Show summary

  Background and Objectives Menopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a comprehensive study of MHT use and brain characteristics in middle-to older aged females from the UK Biobank, assessing detailed MHT data, APOE ε4 genotype, and tissue-specific gray (GM) and white matter (WM) brain age gap (BAG), as well as hippocampal and white matter hyperintensity (WMH) volumes. Methods A total of 19,846 females with magnetic resonance imaging data were included (current-users = 1,153, 60.1 ± 6.8 years; past-users = 6,681, 67.5 ± 6.2 years; never-users = 12,012, mean age 61.6 ± 7.1 years). For a sub-sample (n = 538), MHT prescription data was extracted from primary care records. Brain measures were derived from T1-, T2- and diffusion-weighted images. We fitted regression models to test for associations between the brain measures and MHT variables including user status, age at initiation, dosage and duration, formulation, route of administration, and type (i.e., bioidentical vs synthetic), as well as active ingredient (e.g., estradiol hemihydrate). We further tested for differences in brain measures among MHT users with and without a history of hysterectomy ± bilateral oophorectomy and examined associations by APOE ε4 status. Results We found significantly higher GM and WM BAG (i.e., older brain age relative to chronological age) as well as smaller left and right hippocampus volumes in current MHT users, not past users, compared to never-users. Effects were modest, with the largest effect size indicating a group difference of 0.77 years (∼9 months) for GM BAG. Among MHT users, we found no significant associations between age at MHT initiation and brain measures. Longer duration of use and older age at last use post menopause was associated with higher GM and WM BAG, larger WMH volume, and smaller left and right hippocampal volumes. MHT users with a history of hysterectomy ± bilateral oophorectomy showed lower GM BAG relative to MHT users without such history. Although we found smaller hippocampus volumes in carriers of two APOE ε4 alleles compared to non-carriers, we found no interactions with MHT variables. In the sub-sample with prescription data, we found no significant associations between detailed MHT variables and brain measures after adjusting for multiple comparisons. Discussion Our results indicate that population-level associations between MHT use, and female brain health might vary depending on duration of use and past surgical history. Future research is crucial to establish causality, dissect interactions between menopause-related neurological changes and MHT use, and determine individual-level implications to advance precision medicine in female health care.

• Slapø, Nora Berz; Jørgensen, Kjetil Nordbø; Elvsåshagen, Torbjørn; Nerland, Stener; Roelfs, Daniel & Valstad, Mathias [Show all 18 contributors for this article] (2023). Relationship between function and structure in the visual cortex in healthy individuals and in patients with severe mental disorders. Psychiatry Research : Neuroimaging. ISSN 0925-4927. 332. doi: 10.1016/j.pscychresns.2023.111633. Full text in Research Archive Show summary

  Patients with schizophrenia spectrum disorders (SCZspect) and bipolar disorders (BD) show impaired function in the primary visual cortex (V1), indicated by altered visual evoked potential (VEP). While the neural substrate for altered VEP in these patients remains elusive, altered V1 structure may play a role. One previous study found a positive relationship between the amplitude of the P100 component of the VEP and V1 surface area, but not V1 thickness, in a small sample of healthy individuals. Here, we aimed to replicate these findings in a larger healthy control (HC) sample (n = 307) and to examine the same relationship in patients with SCZspect (n = 30) or BD (n = 45). We also compared the mean P100 amplitude, V1 surface area and V1 thickness between controls and patients and found no significant group differences. In HC only, we found a significant positive P100-V1 surface area association, while there were no significant P100-V1 thickness relationships in HC, SCZspect or BD. Together, our results confirm previous findings of a positive P100-V1 surface area association in HC, whereas larger patient samples are needed to further clarify the function-structure relationship in V1 in SCZspect and BD.

• Maximov, Ivan & Westlye, Lars Tjelta (2023). Comparison of different neurite density metrics with brain asymmetry evaluation. Zeitschrift für Medizinische Physik. ISSN 0939-3889. doi: 10.1016/j.zemedi.2023.07.003. Full text in Research Archive Show summary

  The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.

• Meer, Dennis van der; Gurholt, Tiril Pedersen; Sønderby, Ida Elken; Shadrin, Alexey; Hindley, Guy Frederick Lanyon & Rahman, Zillur [Show all 18 contributors for this article] (2022). The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition. Communications Biology. 5(1). doi: 10.1038/s42003-022-04237-4. Full text in Research Archive Show summary

  Abstract Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h 2  = .25 vs. .13, p  = 1.8x10 −7 ). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r g  = .49, p  = 2.7x10 −22 ). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.

• Tesli, Natalia; Bell, Christina; Hjell, Gabriela; Fischer-Vieler, Thomas; Maximov, Ivan & Richard, Geneviève [Show all 14 contributors for this article] (2022). The age of violence: Mapping brain age in psychosis and psychopathy. NeuroImage: Clinical. 36. doi: 10.1016/j.nicl.2022.103181. Full text in Research Archive Show summary

  Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen's d = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.

• Schindler, Louise S; Subramaniapillai, Sivaniya; Barth, Claudia; Meer, Dennis van der; Pedersen, Mads Lund & Kaufmann, Tobias [Show all 18 contributors for this article] (2022). Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women. NeuroImage: Clinical. 36. doi: 10.1016/j.nicl.2022.103239. Full text in Research Archive Show summary

  The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.

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