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Pu, Y., Francks, C., & Kong, X. (2025). Global brain asymmetry. Trends in Cognitive Sciences, 29(2), 114-117. doi:10.1016/j.tics.2024.10.008.
Abstract
Lateralization is a defining characteristic of the human brain, often studied through localized approaches that focus on interhemispheric differences between homologous pairs of regions. It is also important to emphasize an integrative perspective of global brain asymmetry, in which hemispheric differences are understood through global patterns across the entire brain. -
Korbmacher, M., Tranfa, M., Pontillo, G., Van der Meer, D., Wang, M.-Y., Andreassen, O. A., Westlye, L. T., & Maximov, I. I. (2025). White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life. NeuroImage, 310: 121132. doi:10.1016/j.neuroimage.2025.121132.
Abstract
Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organisation and microstructure in vivo. Various dMRI models with different theoretical and practical assumptions have been developed, representing partly overlapping characteristics of the underlying brain biology with potentially complementary value in the cognitive and clinical neurosciences. To which degree the different dMRI metrics relate to clinically relevant geno- and phenotypes is still debated. Hence, we investigate how tract-based and whole WM skeleton parameters from different dMRI approaches associate with clinically relevant and white matter-related phenotypes (sex, age, pulse pressure (PP), body-mass-index (BMI), brain asymmetry) and genetic markers in the UK Biobank (UKB, n=52,140) and the Adolescent Brain Cognitive Development (ABCD) Study (n=5,844). In general, none of the imaging approaches could explain all examined phenotypes, though the approaches were overall similar in explaining variability of the examined phenotypes. Nevertheless, particular diffusion parameters of the used dMRI approaches stood out in explaining some important phenotypes known to correlate with general human health outcomes. A multi-compartment Bayesian dMRI approach provided the strongest WM associations with age, and together with diffusion tensor imaging, the largest accuracy for sex-classifications. We find a similar pattern of metric and tract-dependent asymmetries across datasets, with stronger asymmetries in ABCD data. The magnitude of WM associations with polygenic scores as well as PP depended more on the sample, and likely age, than dMRI metrics. However, kurtosis was most indicative of BMI and potentially of bipolar disorder polygenic scores. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life. -
Korbmacher, M., Vidal‐Pineiro, D., Wang, M.-Y., Van der Meer, D., Wolfers, T., Nakua, H., Eikefjord, E., Andreassen, O. A., Westlye, L. T., & Maximov, I. I. (2025). Cross‐sectional brain age assessments are limited in predicting future brain change. Human Brain Mapping, 46(6): e70203. doi:10.1002/hbm.70203.
Abstract
The concept of brain age (BA) describes an integrative imaging marker of brain health, often suggested to reflect aging processes. However, the degree to which cross-sectional MRI features, including BA, reflect past, ongoing, and future brain changes across different tissue types from macro- to microstructure remains controversial. Here, we use multimodal imaging data of 39,325 UK Biobank participants, aged 44–82 years at baseline and 2,520 follow-ups within 1.12–6.90 years to examine BA changes and their relationship to anatomical brain changes. We find insufficient evidence to conclude that BA reflects the rate of brain aging. However, modality-specific differences in brain ages reflect the state of the brain, highlighting diffusion and multimodal MRI brain age as potentially useful cross-sectional markers. -
Rivera-Olvera, A., Houwing, D. J., Ellegood, J., Masifi, S., Martina, S., Silberfeld, A., Pourquie, O., Lerch, J. P., Francks, C., Homberg, J. R., Van Heukelum, S., & Grandjean, J. (2025). The universe is asymmetric, the mouse brain too. Molecular Psychiatry, 30, 489-496. doi:10.1038/s41380-024-02687-2.
Abstract
Hemispheric brain asymmetry is a basic organizational principle of the human brain and has been implicated in various psychiatric conditions, including autism spectrum disorder. Brain asymmetry is not a uniquely human feature and is observed in other species such as the mouse. Yet, asymmetry patterns are generally nuanced, and substantial sample sizes are required to detect these patterns. In this pre-registered study, we use a mouse dataset from the Province of Ontario Neurodevelopmental Network, which comprises structural MRI data from over 2000 mice, including genetic models for autism spectrum disorder, to reveal the scope and magnitude of hemispheric asymmetry in the mouse. Our findings demonstrate the presence of robust hemispheric asymmetry in the mouse brain, such as larger right hemispheric volumes towards the anterior pole and larger left hemispheric volumes toward the posterior pole, opposite to what has been shown in humans. This suggests the existence of species-specific traits. Further clustering analysis identified distinct asymmetry patterns in autism spectrum disorder models, a phenomenon that is also seen in atypically developing participants. Our study shows potential for the use of mouse models in studying the biological bases of typical and atypical brain asymmetry but also warrants caution as asymmetry patterns seem to differ between humans and mice. -
Sha, Z., & Francks, C. (2025). Large-scale genetic mapping for human brain asymmetry. In C. Papagno, & P. Corballis (
Eds. ), Handbook of Clinical Neurology: Cerebral Asymmetries (pp. 241-254). Amsterdam: Elsevier.Abstract
Left-right asymmetry is an important aspect of human brain organization for functions including language and hand motor control, which can be altered in some psychiatric traits. The last five years have seen rapid advances in the identification of specific genes linked to variation in asymmetry of the human brain and/or handedness. These advances have been driven by a new generation of large-scale genome-wide association studies, carried out in samples ranging from roughly 16,000 to over 1.5 million participants. The implicated genes tend to be most active in the embryonic and fetal brain, consistent with early developmental patterning of brain asymmetry. Several of the genes encode components of microtubules, or other microtubule-associated proteins. Microtubules are key elements of the internal cellular skeleton (cytoskeleton). A major challenge remains to understand how these genes affect, or even induce, the brain’s left-right axis. Several of the implicated genes have also been associated with psychiatric or neurological disorders, and polygenic dispositions to autism and schizophrenia have been associated with structural brain asymmetry. Knowledge of developmental mechanisms that lead to hemispheric specialization may ultimately help to define etiologic subtypes of brain disorders. -
Gialluisi, A., Dediu, D., Francks, C., & Fisher, S. E. (2013). Persistence and transmission of recessive deafness and sign language: New insights from village sign languages. European Journal of Human Genetics, 21, 894-896. doi:10.1038/ejhg.2012.292.
Abstract
First paragraph: The study of the transmission of sign languages can give novel insights into the transmission of spoken languages1 and, more generally, into gene–culture coevolution. Over the years, several papers related to the persistence of sign language have been
reported.2–6 All of these studies have emphasized the role of assortative (non-random) mating by deafness state (ie, a tendency for deaf individuals to partner together) for increasing the frequency of recessive deafness, and hence for the persistence of sign language in a population. -
Stephens, S., Hartz, S., Hoft, N., Saccone, N., Corley, R., Hewitt, J., Hopfer, C., Breslau, N., Coon, H., Chen, X., Ducci, F., Dueker, N., Franceschini, N., Frank, J., Han, Y., Hansel, N., Jiang, C., Korhonen, T., Lind, P., Liu, J. and 105 moreStephens, S., Hartz, S., Hoft, N., Saccone, N., Corley, R., Hewitt, J., Hopfer, C., Breslau, N., Coon, H., Chen, X., Ducci, F., Dueker, N., Franceschini, N., Frank, J., Han, Y., Hansel, N., Jiang, C., Korhonen, T., Lind, P., Liu, J., Michel, M., Lyytikäinen, L.-P., Shaffer, J., Short, S., Sun, J., Teumer, A., Thompson, J., Vogelzangs, N., Vink, J., Wenzlaff, A., Wheeler, W., Yang, B.-Z., Aggen, S., Balmforth, A., Baumesiter, S., Beaty, T., Benjamin, D., Bergen, A., Broms, U., Cesarini, D., Chatterjee, N., Chen, J., Cheng, Y.-C., Cichon, S., Couper, D., Cucca, F., Dick, D., Foround, T., Furberg, H., Giegling, I., Gillespie, N., Gu, F.,.Hall, A., Hällfors, J., Han, S., Hartmann, A., Heikkilä, K., Hickie, I., Hottenga, J., Jousilahti, P., Kaakinen, M., Kähönen, M., Koellinger, P., Kittner, S., Konte, B., Landi, M.-T., Laatikainen, T., Leppert, M., Levy, S., Mathias, R., McNeil, D., Medlund, S., Montgomery, G., Murray, T., Nauck, M., North, K., Paré, P., Pergadia, M., Ruczinski, I., Salomaa, V., Viikari, J., Willemsen, G., Barnes, K., Boerwinkle, E., Boomsma, D., Caporaso, N., Edenberg, H., Francks, C., Gelernter, J., Grabe, H., Hops, H., Jarvelin, M.-R., Johannesson, M., Kendler, K., Lehtimäki, T., Magnusson, P., Marazita, M., Marchini, J., Mitchell, B., Nöthen, M., Penninx, B., Raitakari, O., Rietschel, M., Rujescu, D., Samani, N., Schwartz, A., Shete, S., Spitz, M., Swan, G., Völzke, H., Veijola, J., Wei, Q., Amos, C., Canon, D., Grucza, R., Hatsukami, D., Heath, A., Johnson, E., Kaprio, J., Madden, P., Martin, N., Stevens, V., Weiss, R., Kraft, P., Bierut, L., & Ehringer, M. (2013). Distinct Loci in the CHRNA5/CHRNA3/CHRNB4 Gene Cluster are Associated with Onset of Regular Smoking. Genetic Epidemiology, 37, 846-859. doi:10.1002/gepi.21760.
Abstract
Neuronal nicotinic acetylcholine receptor (nAChR) genes (CHRNA5/CHRNA3/CHRNB4) have been reproducibly associated with nicotine dependence, smoking behaviors, and lung cancer risk. Of the few reports that have focused on early smoking behaviors, association results have been mixed. This meta-analysis examines early smoking phenotypes and SNPs in the gene cluster to determine: (1) whether the most robust association signal in this region (rs16969968) for other smoking behaviors is also associated with early behaviors, and/or (2) if additional statistically independent signals are important in early smoking. We focused on two phenotypes: age of tobacco initiation (AOI) and age of first regular tobacco use (AOS). This study included 56,034 subjects (41 groups) spanning nine countries and evaluated five SNPs including rs1948, rs16969968, rs578776, rs588765, and rs684513. Each dataset was analyzed using a centrally generated script. Meta-analyses were conducted from summary statistics. AOS yielded significant associations with SNPs rs578776 (beta = 0.02, P = 0.004), rs1948 (beta = 0.023, P = 0.018), and rs684513 (beta = 0.032, P = 0.017), indicating protective effects. There were no significant associations for the AOI phenotype. Importantly, rs16969968, the most replicated signal in this region for nicotine dependence, cigarettes per day, and cotinine levels, was not associated with AOI (P = 0.59) or AOS (P = 0.92). These results provide important insight into the complexity of smoking behavior phenotypes, and suggest that association signals in the CHRNA5/A3/B4 gene cluster affecting early smoking behaviors may be different from those affecting the mature nicotine dependence phenotypeAdditional information
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