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Traits often do not evolve in isolation or vary independently of other traits. Instead, they can be affected by covariation, both within and across species. However, the importance of within-species trait covariation and, critically, the degree to which it varies between species has yet to be thoroughly studied. Brain morphology is a trait of great ecological and behavioral importance, with regions that are hypothesized to vary in size based on behavioral and cognitive demands. Sizes of brain regions have also been shown to covary with each other across various taxa. Here, we test the degree to which covariation in brain region sizes within species has been conserved across 10 teleost fish species. These 10 species span five orders, allowing us to examine how phylogenetic proximity influences similarities in intraspecific trait covariation. Our results showed a trend that similar patterns of brain region size covariation occur in more closely related species. Interestingly, there were certain brain region pairs that showed similar levels of covariation across all species regardless of phylogenetic distance, such as the telencephalon and optic tectum, while others, such as the olfactory bulb and the hypothalamus, varied more independently. Ultimately, the patterns of brain region covariation shown here suggest that evolutionary mechanisms or constraints can act on specific brain regions independently, and that these constraints can change over evolutionary time., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)

Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared male and female participants. Here we screen for sex differences in the common genetic architecture of over 1000 neuroanatomical phenotypes in the UK Biobank and establish a general concordance between male and female participants in heritability estimates, genetic correlations, and variant-level effects. Notable exceptions include higher mean heritability in the female group for regional volume and surface area phenotypes; between-sex genetic correlations that are significantly below 1 in the insula and parietal cortex; and a common variant with stronger effect in male participants mapping to RBFOX1 - a gene linked to multiple neuropsychiatric disorders more common in men. This work suggests that common variant influences on human brain anatomy are largely consistent between males and females, with a few exceptions that will guide future research in growing datasets., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Differences in brain size between the sexes are consistently reported. However, the consequences of this anatomical difference on sex differences in intrinsic brain function remain unclear. In the current study, we investigate whether sex differences in intrinsic cortical functional organization may be associated with differences in cortical morphometry, namely different measures of brain size, microstructure, and the geodesic distance of connectivity profiles. For this, we compute a low dimensional representation of functional cortical organization, the sensory-association axis, and identify widespread sex differences. Contrary to our expectations, sex differences in functional organization do not appear to be systematically associated with differences in total surface area, microstructural organization, or geodesic distance, despite these morphometric properties being per se associated with functional organization and differing between sexes. Instead, functional sex differences in the sensory-association axis are associated with differences in functional connectivity profiles and network topology. Collectively, our findings suggest that sex differences in functional cortical organization extend beyond sex differences in cortical morphometry., (© 2024. The Author(s).)

Significance: Mueller matrix imaging (MMI) is a comprehensive form of polarization imaging useful for assessing structural changes. However, there is limited literature on the polarimetric properties of brain specimens, especially with multispectral analysis., Aim: We aim to employ multispectral MMI for an exhaustive polarimetric analysis of brain structures, providing a reference dataset for future studies and enhancing the understanding of brain anatomy for clinicians and researchers., Approach: A multispectral wide-field MMI system was used to measure six fresh lamb brain specimens. Multiple decomposition methods (forward polar, symmetric, and differential) and polarization invariants (indices of polarimetric purity and anisotropy coefficients) have been calculated to obtain a complete polarimetric description of the samples. A total of 16 labels based on major brain structures, including grey matter (GM) and white matter (WM), were identified. K -nearest neighbors classification was used to distinguish between GM and WM and validate the feasibility of MMI for WM identification., Results: As the wavelength increases, both depolarization and retardance increase, suggesting enhanced tissue penetration into deeper layers. Moreover, utilizing multiple wavelengths allowed us to track dynamic shifts in the optical axis of retardance within the brain tissue, providing insights into morphological changes in WM beneath the cortical surface. The use of multispectral data for classification outperformed all results obtained with single-wavelength data and provided over 95% accuracy for the test dataset., Conclusions: The consistency of these observations highlights the potential of multispectral wide-field MMI as a non-invasive and effective technique for investigating the brain's architecture., (© 2024 The Authors.)

Despite their distinct embryonic origins, the skull and brain are highly integrated. Understanding the covariation between the skull and brain can shed light on anatomical, cognitive, and behavioral traits in extant and extinct species. Domestic dogs offer a unique opportunity to investigate skull-brain covariation due to their diverse skull morphologies and neural anatomy. To assess this question, we examined T2-weighted MRI studies of 62 dogs from 33 breeds, plus an additional 17 dogs of mixed or unknown breeds. Scans were opportunistically collected from a veterinary teaching hospital of dogs that were referred for neurological examination but did not have grossly observable structural brain abnormalities. As the neurocrania of dogs become broader and shorter, there is a significant decrease in the gray matter volume of the right olfactory bulb, frontal cortex, marginal gyrus, and cerebellum. On the other hand, as the neurocrania of dogs become narrower and longer, there is a significant decrease in the gray matter volume of the olfactory bulb, frontal cortex, temporal cortex, amygdala, hypothalamus, hippocampus, periaqueductal gray, cerebellum, and brainstem. Selective breeding for specific skull shapes may impact canine brain anatomy and function., (© 2024 Wiley Periodicals LLC.)

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool's generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Behavioral differences between men and women have been studied extensively, as have differences in brain anatomy. However, most studies have focused on differences in gray matter, while white matter has been much less studied. We conducted a comprehensive study of 77 deep white matter tracts to analyze their volumetric and microstructural variability between men and women in the full Human Connectome Project (HCP) cohort of 1065 healthy individuals aged 22-35 years. We found a significant difference in total brain volume between men and women (+ 12.6% in men), consistent with the literature. 16 tracts showed significant volumetric differences between men and women, one of which stood out due to a larger effect size: the corpus callosum genu, which was larger in women (+ 7.3% in women, p = 5.76 × 10 -19 ). In addition, we found several differences in microstructural parameters between men and women, both using standard Diffusion Tensor Imaging (DTI) parameters and more complex microstructural parameters from the Neurite Orientation Dispersion and Density Imaging (NODDI) model, with the tracts showing the greatest differences belonging to motor (cortico-spinal tracts, cortico-cerebellar tracts) or limbic (cingulum, fornix, thalamo-temporal radiations) systems. These microstructural differences may be related to known behavioral differences between the sexes in timed motor performance, aggressiveness/impulsivity, and social cognition., (© 2024. The Author(s).)

The field of aging biology, which aims to extend healthy lifespans and prevent age-related diseases, has turned its focus to the Callithrix jacchus (common marmoset) to understand the aging process better. This study utilized magnetic resonance imaging (MRI) to non-invasively analyze the brains of 216 marmosets, investigating age-related changes in brain structure; the relationship between body weight and brain volume; and potential differences between males and females. The key findings revealed that, similar to humans, Callithrix jacchus experiences a reduction in total intracranial volume, cortex, subcortex, thalamus, and cingulate volumes as they age, highlighting site-dependent changes in brain tissue. Notably, the study also uncovered sex differences in cerebellar volume. These insights into the structural connectivity and volumetric changes in the marmoset brain throughout aging contribute to accumulating valuable knowledge in the field, promising to inform future aging research and interventions for enhancing healthspan., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: To investigate potential effects of heading on the neurocognitive performance and the white matter (WM) of the brain in high-level adult male football players., Design: Prospective longitudinal., Methods: Football players engaging in the highest football leagues in Germany were included. Neurocognitive performance tests and diffusion tensor imaging (DTI) were executed before and after the observation period. Video recordings of each training session and each match play during the observation period were analyzed regarding heading exposure and characteristics. Four DTI measures from tract-based spatial statistics (fractional anisotropy, mean, axial, and radial diffusivity) were investigated. Associations between heading variables and DTI and neurocognitive parameters were tested subsequently., Results: 8052 headers of 22 players (19.9 ± 2.7 years) were documented in a median of 16.9 months. The individual total heading number ranged from 57 to 943 (median: 320.5). Header characteristics differed between training sessions and matches. Neurocognitive performance (n = 22) and DTI measures (n = 14) showed no significant differences from pre- to post-test. After correction for multiple comparisons, no significant correlations with the total heading number were found. However, the change in fractional anisotropy in the splenium of the corpus callosum correlated significantly with the total amount of long-distance headers (Pearson's r = -0.884; p < 0.0001)., Conclusions: Over the median observation period of 16.9 months, DTI measures and neurocognitive performance remained unchanged. To elucidate the meaning of the association between individual change in fractional anisotropy and long-distance headers further investigations with larger samples, longer observations, and various cohorts regarding age and level of play are required., Competing Interests: Declaration of interest statement CR receives scientific funding for projects on traumatic brain injuries and heading from the Federal Institute of Sports Sciences (Germany) and the Heinz Nixdorf Westfalian Foundation. He is a member of the medical committee of the German Football association (DFB) and provides counseling on the management of traumatic brain injury to the Union of European Football Associations (UEFA). All other authors declare no conflicting interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Examining underlying neurostructural correlates of specific cognitive abilities is practically and theoretically complicated by the existence of the positive manifold (all cognitive tests positively correlate): if a brain structure is associated with a cognitive task, how much of this is uniquely related to the cognitive domain, and how much is due to covariance with all other tests across domains (captured by general cognitive functioning, also known as general intelligence, or 'g')? We quantitatively address this question by examining associations between brain structural and diffusion MRI measures (global tissue volumes, white matter hyperintensities, global white matter diffusion fractional anisotropy and mean diffusivity, and FreeSurfer processed vertex-wise cortical volumes, smoothed at 20mm fwhm) with g and cognitive domains (processing speed, crystallised ability, memory, visuospatial ability). The cognitive domains were modelled using confirmatory factor analysis to derive both hierarchical and bifactor solutions using 13 cognitive tests in 697 participants from the Lothian Birth Cohort 1936 study (mean age 72.5 years; SD = .7). Associations between the extracted cognitive factor scores for each domain and g were computed for each brain measure covarying for age, sex and intracranial volume, and corrected for false discovery rate. There were a range of significant associations between cognitive domains and global MRI brain structural measures (r range .008 to .269, p < .05). Regions implicated by vertex-wise regional cortical volume included a widespread number of medial and lateral areas of the frontal, temporal and parietal lobes. However, at both global and regional level, much of the domain-MRI associations were shared (statistically accounted for by g). Removing g-related variance from cognitive domains attenuated association magnitudes with global brain MRI measures by 27.9-59.7% (M = 46.2%), with only processing speed retaining all significant associations. At the regional cortical level, g appeared to account for the majority (range 22.1-88.4%; M = 52.8% across cognitive domains) of regional domain-specific associations. Crystallised and memory domains had almost no unique cortical correlates, whereas processing speed and visuospatial ability retained limited cortical volumetric associations. The greatest spatial overlaps across cognitive domains (as denoted by g) were present in the medial and lateral temporal, lateral parietal and lateral frontal areas., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although the impact of occupation on cognitive skills has been extensively studied, there is limited research examining if genetically predicted cognitive score may influence occupation. We examined the association between Cognitive Polygenic Index (PGI) and occupation, including the role of brain measures. Participants were recruited for the Reference Ability Neural Network and the Cognitive Reserve studies. Occupational complexity ratings for Data, People, or Things came from the Dictionary of Occupational Titles. A previously-created Cognitive PGI and linear regression models were used for the analyses. Age, sex, education, and the first 20 genetic Principal Components (PCs) of the sample were covariates. Total cortical thickness and total gray matter volume were further covariates. We included 168 white-ethnicity participants, 20-80 years old. After initial adjustment, higher Cognitive PGI was associated with higher Data complexity (B=-0.526, SE = 0.227, Beta= -0.526 p = 0.022, R 2  = 0.259) (lower score implies higher complexity). Associations for People or Things were not significant. After adding brain measures, association for Data remained significant (B=-0.496, SE: 0.245, Beta= -0.422, p = 0.045, R 2  = 0.254). Similarly, for a further, fully-adjusted analysis including all the three occupational complexity measures (B=-0.568, SE = 0.237, Beta= -0.483, p = 0.018, R 2  = 0.327). Cognitive genes were associated with occupational complexity over and above brain morphometry. Working with Data occupational complexity probably acquires higher cognitive status, which can be significantly genetically predetermined., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Spatial interactions among anatomical elements help to identify topological factors behind morphological variation and can be investigated through network analysis. Here, a whole-brain network model of the chimpanzee (Pan troglodytes, Blumenbach 1776) is presented, based on macroanatomical divisions, and compared with a previous equivalent model of the human brain. The goal was to contrast which regions are essential in the geometric balance of the brains of the two species, to compare underlying phenotypic patterns of spatial variation, and to understand how these patterns might have influenced the evolution of human brain morphology. The human and chimpanzee brains share morphologically complex inferior-medial regions and a topological organization that matches the spatial constraints exerted by the surrounding braincase. These shared topological features are interesting because they can be traced back to the Chimpanzee-Human Last Common Ancestor, 7-10 million years ago. Nevertheless, some key differences are found in the human and chimpanzee brains. In humans, the temporal lobe, particularly its deep and medial limbic aspect (the parahippocampal gyrus), is a crucial node for topological complexity. Meanwhile, in chimpanzees, the cerebellum is, in this sense, more embedded in an intricate spatial position. This information helps to interpret brain macroanatomical change in fossil hominids., (© 2024 Wiley Periodicals LLC.)

Background and Purpose: Automated methods for quantifying brain tissue volumes have gained clinical interest for their objective assessment of neurological diseases. This study aimed to establish reference curves for brain volumes and fractions in the Indian population using Synthetic MRI (SyMRI), a quantitative imaging technique providing multiple contrast-weighted images through fast postprocessing., Methods: The study included a cohort of 314 healthy individuals aged 15-65 years from multiple hospitals/centers across India. The SyMRI-quantified brain volumes and fractions, including brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and myelin., Results: Normative age-stratified quantification curves were created based on the obtained data. The results showed significant differences in brain volumes between the sexes, but not after normalization by intracranial volume., Conclusion: The findings provide normative data for the Indian population and can be used for comparative analysis of brain structure values. Furthermore, our data indicate that the use of fractions rather than absolute volumes in normative curves, such as BPF, GMF, and WMF, can mitigate sex and population differences as they account for individual differences in head size or brain volume., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Zhejiang University of Technology. Published by Elsevier Inc. All rights reserved.)

Modern birds possess highly encephalized brains that evolved from non-avian dinosaurs. Evolutionary shifts in developmental timing, namely juvenilization of adult phenotypes, have been proposed as a driver of head evolution along the dinosaur-bird transition, including brain morphology. Testing this hypothesis requires a sufficient developmental sampling of brain morphology in non-avian dinosaurs. In this study, we harness brain endocasts of a postnatal growth series of the ornithischian dinosaur Psittacosaurus and several other immature and mature non-avian dinosaurs to investigate how evolutionary changes to brain development are implicated in the origin of the avian brain. Using three-dimensional characterization of neuroanatomical shape across archosaurian reptiles, we demonstrate that (i) the brain of non-avian dinosaurs underwent a distinct developmental trajectory compared to alligators and crown birds; (ii) ornithischian and non-avialan theropod dinosaurs shared a similar developmental trajectory, suggesting that their derived trajectory evolved in their common ancestor; and (iii) the evolutionary shift in developmental trajectories is partly consistent with paedomorphosis underlying overall brain shape evolution along the dinosaur-bird transition; however, the heterochronic signal is not uniform across time and neuroanatomical region suggesting a highly mosaic acquisition of the avian brain form., (© 2024. The Author(s).)

Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level., Competing Interests: Declaration of competing interest The authors declare there is no conflict of interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19-80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct the streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve the tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can facilitate the study of fetal brain white matter tracts with dMRI., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

While brain size in primates and their relatives within Euarchontoglires is well-studied, less research has examined brain shape, or the allometric trajectories that underlie the relationship between size and shape. Defining these patterns is key to understanding evolutionary trends. 3D geometric morphometric analyses of endocranial shape were performed on 140 species of extant euarchontoglirans using digital cranial endocasts. Principal component analyses on Procrustes shape variables show a clear phylogenetic pattern in endocranial shape, supported by an ANOVA which identified significant differences in shape among several groups (e.g., Platyrrhini, Strepsirrhini, Scandentia, Rodentia, and Lagomorpha). ANOVAs of shape and size also indicate that allometry has a small but significant impact on endocranial shape across Euarchontoglires, with homogeneity of slopes tests finding significant differences in the scaling relationship between shape and size among these same groups. While most of these clades possess a distinct endocranial morphotype, the highly derived platyrrhines display the strongest relationship between size and shape. Rodents show the most diversity in endocranial shape, potentially attributed to their comparatively weak relationship between shape and size. These results suggest fundamental differences in how shape and size covary among Euarchontoglires, which may have facilitated the adaptive radiations that characterize members of this group., (© 2024. The Author(s).)

Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The study provides useful insights and establishes connections between the methods, thereby facilitating a profound understanding of the methodological landscape. The methods considered in our study are extensively evaluated in T1w MRI images using traditional NIREP and Learn2Reg OASIS evaluation protocols with a focus on fairness, to establish equitable benchmarks and facilitate informed comparisons. Through a comprehensive analysis of the results, we address key questions, including the intricate relationship between accuracy and transformation quality in performance, the disentanglement of the influence of registration ingredients on performance, and the determination of benchmark methods and baselines. We offer valuable insights into the strengths and limitations of both traditional and deep-learning methods, shedding light on their comparative performance and guiding future advancements in the field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The human brain undergoes structural development from childhood to adolescence, with specific regions in the sensorimotor, social, and affective networks continuing to grow into adulthood. While genetic and environmental factors contribute to individual differences in these brain trajectories, the extent remains understudied. Our longitudinal study, utilizing up to three biennial MRI scans (n=485), aimed to assess the genetic and environmental effects on brain structure (age 7) and development (ages 7-14) in these regions. Heritability estimates varied across brain regions, with all regions showing genetic influence (ranging from 18 % to 59 %) with additional shared environmental factors affecting the primary motor cortex (30 %), somatosensory cortex (35 %), DLPFC (5 %), TPJ (17 %), STS (17 %), precuneus (10 %), hippocampus (22 %), amygdala (5 %), and nucleus accumbens (10 %). Surface area was more genetically driven (38 %) than cortical thickness (14 %). Longitudinal brain changes were primarily driven by genetics (ranging from 1 % to 29 %), though shared environment factors (additionally) influenced the somatosensory cortex (11 %), DLPFC (7 %), cerebellum (28 %), TPJ (16 %), STS (20 %), and hippocampus (17 %). These findings highlight the importance of further investigating brain-behavior associations and the influence of enriched and deprived environments from childhood to adolescence. Ultimately, our study can provide insights for interventions aimed at supporting children's development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Voay robustus, the extinct Malagasy "horned" crocodile, was originally considered to be the only crocodylian representative in Madagascar during most part of the Holocene. However, Malagasy crocodylian remains have had confused taxonomic attributions and recent studies have underlined that Crocodylus and Voay populations coexisted on the island for at least 7500 years. Here, we describe the inner braincase anatomy of Voay robustus using x-ray computed tomography on four specimens, to provide new anatomical information that distinguishes Voay from Crocodylus, especially features of the brain endocast and the paratympanic sinuses. Geometric morphometric analyses are performed on 3D models of the internal organs to compare statistically Voay with a subset of extant Crocodylidae. Following these comparisons, we build an endocranial morphological matrix to discuss the proposed phylogenetic affinities of Voay with Osteolaeminae from an endocranial point of view. Additionally, we discuss the use of internal characters in systematic studies and find that they can have a major impact on morphological analyses. Finally, new radiocarbon data on Voay and subfossil Crocodylus specimens are recovered between 2010 and 2750 cal BP, which confirm the cohabitation of the two species in the same area for a long period of time. We thus assess several extinction scenarios, and propose a slightly different ecology of Voay compared to Crocodylus, which could have allowed habitat partitioning on the island. Our approach complements information obtained from previous molecular and morphological phylogenies, as well as previous radiocarbon dating, together revealing past diversity and faunal turnovers in Madagascar., (© 2023 The Authors. The Anatomical Record published by Wiley Periodicals LLC on behalf of American Association for Anatomy.)

Alzheimer's disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood 1-3 . Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer's disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer's disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer's disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer's disease pathology., (© 2024. The Author(s).)

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Abdominal fat is increasingly linked to brain health. A total of 10,001 healthy participants were scanned on 1.5T MRI with a short whole-body MR imaging protocol. Deep learning with FastSurfer segmented 96 brain regions. Separate models segmented visceral and subcutaneous abdominal fat. Regression analyses of abdominal fat types and normalized brain volumes were evaluated, controlling for age and sex. Logistic regression models determined the risk of brain total gray and white matter volume loss from the highest quartile of visceral fat and lowest quartile of these brain volumes. This cohort had an average age of 52.9 ± 13.1 years with 52.8% men and 47.2% women. Segmented visceral abdominal fat predicted lower volumes in multiple regions including: total gray matter volume (r = -.44, p<.001), total white matter volume (r =-.41, p<.001), hippocampus (r = -.39, p< .001), frontal cortex (r = -.42, p<.001), temporal lobes (r = -.44, p<.001), parietal lobes (r = -.39, p<.001), occipital lobes (r =-.37, p<.001). Women showed lower brain volumes than men related to increased visceral fat. Visceral fat predicted increased risk for lower total gray matter (age 20-39: OR = 5.9; age 40-59, OR = 5.4; 60-80, OR = 5.1) and low white matter volume: (age 20-39: OR = 3.78; age 40-59, OR = 4.4; 60-80, OR = 5.1). Higher subcutaneous fat is related to brain volume loss. Elevated visceral and subcutaneous fat predicted lower brain volumes and may represent novel modifiable factors in determining brain health.

The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Domestication has long been considered the most powerful evolutionary engine behind dramatic reductions in brain size in several taxa, and the dog ( Canis familiaris ) is considered as a typical example that shows a substantial decrease in brain size relative to its ancestor, the grey wolf ( Canis lupus ). However, to make the case for exceptional evolution of reduced brain size under domestication requires an interspecific approach in a phylogenetic context that can quantify the extent by which domestication reduces brain size in comparison to closely related non-domesticated species responding to different selection factors in the wild. Here, we used a phylogenetic method to identify evolutionary singularities to test if the domesticated dog stands out in terms of relative brain size from other species of canids. We found that the dog does not present unambiguous signature of evolutionary singularity with regard to its small brain size, as the results were sensitive to the considerations about the ancestral trait values upon domestication. However, we obtained strong evidence for the hibernating common raccoon dog ( Nyctereutes procyonoides ) being an evolutionary outlier for its brain size. Therefore, domestication is not necessarily an exceptional case concerning evolutionary reductions in brain size in an interspecific perspective.

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research., (Copyright © 2024 Science China Press. Published by Elsevier B.V. All rights reserved.)

Glymphatic-lymphatic brain cleansing may reveal new therapeutic strategies.

Despite extensive studies published on the canine brain, inconsistencies and disagreements in the nomenclature and representation of various cerebral structures continue to exist. This study aimed to create a comprehensive mapping of the external architecture of the mesocephalic canine brain with a focus on the major gyri and sulci. Standardized dissection techniques were used on 20 ethically sourced brains obtained from 6 to 10-year-old dogs that were free of neurological disorders. Distinct gyri and sulci with unique locations and bordering structures were observed. Thus, it was possible to identify the often-ignored subprorean gyrus. In addition, this study was able to illustrate the unique locations and bordering structures of gyri and sulci. The findings can contribute to a consensus among researchers on the canine brain anatomy and assist in clarifying the inconsistencies in cerebral structure representation. Furthermore, the results of this study may hold significant implications for veterinary medicine and neuroscience and serve as a foundation for the development of diagnostic and therapeutic approaches for various neurological diseases in dogs. Our findings offer valuable insights into the unique evolutionary adaptations and specialized behaviors of the canine brain, thereby increasing awareness about the neural structures that enable dogs to demonstrate their unique traits., (© 2024. The Author(s).)

The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds., (© 2024. The Author(s).)

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation. Building on cutting-edge deep learning techniques by employing lifelong learning to flexibly integrate data from diverse NHP populations and innovatively constructing 3D U-NeXt architecture, nBEST can well handle structural NHP brain MR images from multi-species, multi-site, and multi-developmental-stage (from neonates to the elderly). We extensively validated nBEST based on, to our knowledge, the largest assemblage dataset in NHP brain studies, encompassing 1,469 scans with 11 species (e.g., rhesus macaques, cynomolgus macaques, chimpanzees, marmosets, squirrel monkeys, etc.) from 23 independent datasets. Compared to alternative tools, nBEST outperforms in precision, applicability, robustness, comprehensiveness, and generalizability, greatly benefiting downstream longitudinal, cross-sectional, and cross-species quantitative analyses. We have made nBEST an open-source toolbox (https://github.com/TaoZhong11/nBEST) and we are committed to its continual refinement through lifelong learning with incoming data to greatly contribute to the research field., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations., (© 2024. The Author(s).)

A complex brain is central to the success of backboned animals. However, direct evidence bearing on vertebrate brain evolution comes almost exclusively from extant species, leaving substantial knowledge gaps. Although rare, soft-tissue preservation in fossils can yield unique insights on patterns of neuroanatomical evolution. Paleontological evidence from an exceptionally preserved Pennsylvanian (∼318 Ma) actinopterygian, Coccocephalus, calls into question prior interpretations of ancestral actinopterygian brain conditions. However, the ordering and timing of major evolutionary innovations, such as an everted telencephalon, modified meningeal tissues, and hypothalamic inferior lobes, remain unclear. Here, we report two distinct actinopterygian morphotypes from the latest Carboniferous-earliest Permian (∼299 Ma) of Brazil that show extensive soft-tissue preservation of brains, cranial nerves, eyes, and potential cardiovascular tissues. These fossils corroborate inferences drawn from ✝Coccocephalus, while adding new information about neuroanatomical evolution. Skeletal features indicate that one of these Brazilian morphotypes is more closely related to living actinopterygians than the other, which is also reflected in soft-tissue features. Significantly, the more crownward morphotype shows a key neuroanatomical feature of extant actinopterygians-an everted telencephalon-that is absent in the other morphotype and ✝Coccocephalus. All preserved Paleozoic actinopterygian brains show broad similarities, including an invaginated cerebellum, hypothalamus inferior lobes, and a small forebrain. In each case, preserved brains are substantially smaller than the enclosing cranial chamber. The neuroanatomical similarities shared by this grade of Permo-Carboniferous actinopterygians reflect probable primitive conditions for actinopterygians, providing a revised model for interpreting brain evolution in a major branch of the vertebrate tree of life., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background and Purpose: The eye is a well-established model of brain structure and function, yet region-specific structural correlations between the retina and the brain remain underexplored. Therefore, we aim to explore and describe the relationships between the retinal layer thicknesses and brain magnetic resonance image (MRI)-derived phenotypes in UK Biobank., Methods: Participants with both quality-controlled optical coherence tomography (OCT) and brain MRI were included in this study. Retinal sublayer thicknesses and total macular thickness were derived from OCT scans. Brain image-derived phenotypes (IDPs) of 153 cortical and subcortical regions were processed from MRI scans. We utilized multivariable linear regression models to examine the association between retinal thickness and brain regional volumes. All analyses were corrected for multiple testing and adjusted for confounders., Results: Data from 6446 participants were included in this study. We identified significant associations between volumetric brain MRI measures of subregions in the occipital lobe (intracalcarine cortex), parietal lobe (postcentral gyrus), cerebellum (lobules VI, VIIb, VIIIa, VIIIb, and IX), and deep brain structures (thalamus, hippocampus, caudate, putamen, pallidum, and accumbens) and the thickness of the innermost retinal sublayers and total macular thickness (all p < 3.3 × 10 -5 ). We did not observe statistically significant associations between brain IDPs and the thickness of the outer retinal sublayers., Conclusions: Thinner inner and total retinal thicknesses are associated with smaller volumes of specific brain regions. Notably, these relationships extend beyond anatomically established retina-brain connections., (© 2024 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Brain size tripled in the human lineage over four million years, but why this occurred remains uncertain. Here, to study what caused this brain expansion, I mathematically model the evolutionary and developmental (evo-devo) dynamics of hominin brain size. The model recovers (1) the evolution of brain and body sizes of seven hominin species starting from brain and body sizes of the australopithecine scale, (2) the evolution of the hominin brain-body allometry and (3) major patterns of human development and evolution. I show that the brain expansion recovered is not caused by direct selection for brain size but by its genetic correlation with developmentally late preovulatory ovarian follicles. This correlation is generated over development if individuals experience a challenging ecology and seemingly cumulative culture, among other conditions. These findings show that the evolution of exceptionally adaptive traits may not be primarily caused by selection for them but by developmental constraints that divert selection., (© 2024. The Author(s).)

Objective: This study aimed to examine the association between past/current sleep duration and macro-/micro-structural brain outcomes and explore whether hypertension or social activity plays a role in such association., Methods: Within the UK Biobank, 40 436 dementia-free participants (age 40-70 years) underwent a baseline assessment followed by a brain magnetic resonance imaging (MRI) scan 9 years later. Past (baseline) and current (MRI scans) sleep duration (hours/day) were recorded and classified as short (≤5), intermediate (6-8), and long (≥9). Brain structural volumes and diffusion markers were assessed by MRI scans., Results: Compared with past intermediate sleep, past short sleep was related to smaller cortex volumes (standardized β [95 % CI]: -0.04 [-0.07, -0.02]) and lower regional fractional anisotropy (FA) (-0.08 [-0.13, -0.03]), while past long sleep was related to smaller regional subcortical volumes (standardized β: -0.04 to -0.07 for thalamus, accumbens, and hippocampus). Compared to current intermediate sleep, current short sleep was associated with smaller cortex volumes (-0.03 [-0.05, -0.01]), greater white matter hyperintensities (WMH) volumes (0.04 [0.01, 0.08]), and lower regional FA (-0.07 [-0.11, -0.02]). However, current long sleep was related to smaller total brain (-0.03 [-0.05, -0.02]), grey matter (-0.05 [-0.07, -0.03]), cortex (-0.05 [-0.07, -0.03]), regional subcortical volumes [standardized β: -0.05 to -0.09 for putamen, thalamus, hippocampus, and accumbens]), greater WMH volumes (0.06 [0.03, 0.09]), as well as lower regional FA (-0.05 [-0.09, -0.02]). The association between current long sleep duration and poor brain health was stronger among people with hypertension or low frequency of social activity (all P interaction <0.05)., Conclusions: Both past and current short/long sleep are associated with smaller brain volume and poorer white matter health in the brain, especially in individuals with hypertension and low frequency of social activity. Our findings highlight the need to maintain 6-8 h' sleep duration for healthy brain aging., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Photogrammetry scans has directed attention to the development of advanced camera systems to improve the creation of three-dimensional (3D) models, especially for educational and medical-related purposes. This could be a potential cost-effective method for neuroanatomy education, especially when access to laboratory-based learning is limited. The aim of this study was to describe a new photogrammetry system based on a 5 Digital Single-Lens Reflex (DSLR) cameras setup to optimize accuracy of neuroanatomical 3D models. One formalin-fixed brain and specimen and one dry skull were used for dissections and scanning using the photogrammetry technique. After each dissection, the specimens were placed inside a new MedCreator® scanner (MedReality, Thyng, Chicago, IL) to be scanned with the final 3D model being displayed on SketchFab® (Epic, Cary, NC) and MedReality® platforms. The scanner consisted of 5 cameras arranged vertically facing the specimen, which was positioned on a platform in the center of the scanner. The new multi-camera system contains automated software packages, which allowed for quick rendering and creation of a high-quality 3D models. Following uploading the 3D models to the SketchFab® and MedReality® platforms for display, the models can be freely manipulated in various angles and magnifications in any devices free of charge for users. Therefore, photogrammetry scans with this new multi-camera system have the potential to enhance the accuracy and resolution of the 3D models, along with shortening creation time of the models. This system can serve as an important tool to optimize neuroanatomy education and ultimately, improve patient outcomes., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

It is well-established that brain size is associated with intelligence. But the relationship between cortical morphometric measures and intelligence is unclear. Studies have produced conflicting results or no significant relations between intelligence and cortical morphometric measures such as cortical thickness and peri-cortical contrast. This discrepancy may be due to multicollinearity amongst the independent variables in a multivariate regression analysis, or a failure to fully account for the relationship between brain size and intelligence in some other way. Our study shows that neither cortical thickness nor peri-cortical contrast reliably improves IQ prediction accuracy beyond what is achieved with brain volume alone. We show this in multiple datasets, with child data, developmental data, and with adult data; we show this with data acquired either at multiple sites, or at a single site; we show this with data acquired with different MRI scanner manufacturers, or with all data acquired on a single scanner; and we show this with fluid intelligence, full-scale IQ, performance IQ, and verbal IQ. But our point is not really even about IQ; rather we proffer a methodological caveat and potential explanation of the discrepancies in previous results, and which applies broadly., (© 2024. The Author(s).)

Three-dimensional morphometric data better show the structural and functional characteristics of the brain. The objective of this study was to estimate the volume of the cerebral structures of the sheep using design-based stereology. The brains of five sheep were used, fixed in formalin 10% and embedded in agar 6%. An average of 10-12 slab was obtained from each brain. All slabs were stained using Mulligan's method and photographs were recorded. The volume of the brain and its structures were estimated using the Cavalieri's estimator and the point counting system. The total volume was 70604.8 ± 132.45 mm 3 . The volume fractions of the grey and white matters were calculated as 42.55 ± 0.21% and 24.23 ± 0.51% of the whole brain, respectively. The fractional volume of the caudate nucleus and claustrum were estimated at 2.39 ± 0.08% and at 1.008 ± 0.057% of total brain volume. The volumes of corpus callosum, internal capsule and external capsule were 1.24 ± 0.053%, 3.63 ± 0.22% and 0.698 ± 0.049% of total cerebral volume, respectively. These data could help improve the veterinary comparative neuroanatomy knowledge and development of experimental studies in the field., (© 2024 Wiley‐VCH GmbH. Published by John Wiley & Sons Ltd.)

Phenotypic plasticity is critical for organismal performance and can evolve in response to natural selection. Brain morphology is often developmentally plastic, affecting animal performance in a variety of contexts. However, the degree to which the plasticity of brain morphology evolves has rarely been explored. Here, we use Trinidadian guppies (Poecilia reticulata), which are known for their repeated adaptation to high-predation (HP) and low-predation (LP) environments, to examine the evolution and plasticity of brain morphology. We exposed second-generation offspring of individuals from HP and LP sites to 2 different treatments: predation cues and conspecific social environment. Results show that LP guppies had greater plasticity in brain morphology compared to their ancestral HP population, suggesting that plasticity can evolve in response to environmentally divergent habitats. We also show sexual dimorphism in the plasticity of brain morphology, highlighting the importance of considering sex-specific variation in adaptive diversification. Overall, these results may suggest the evolution of brain morphology plasticity as an important mechanism that allows for ecological diversification and adaptation to divergent habitats., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Society for the Study of Evolution (SSE). All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

The brain size of vertebrates represents a trade-off between natural selection for enhanced cognitive abilities and the energetic constraints of brain tissue production. Processing information efficiently can confer benefits, but it also entails time costs. Breeding strategies, encompassing timing of breeding onset and nest-site selection, may be related to brain size. In this study, we aim to elucidate the relationship between brain size, breeding timing, nest-site choice, and breeding success in the red-backed shrike Lanius collurio. Our findings revealed that the timing of the first egg-laying date was associated with female head size, with larger-headed females tending to lay eggs later in the breeding season. Additionally, we observed that breeding success was positively correlated with increased nest concealment. However, this relationship was stronger in males with smaller heads. In turn, nest concealment was not related to head size but primarily influenced breeding onset. These results suggest that the choice of breeding strategy may be moderated by brain size, with differences between sexes. Larger-headed females may invest more time in selecting nesting sites, leading to delayed breeding onset, while larger-headed males may compensate for suboptimal nest concealment. Our study sheds light on the intricate interplay between brain size, breeding timing, nest-site preferences, and breeding success in passerine birds, underscoring the potential role of cognitive capacity in shaping individual decision-making processes., (© 2024 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.)

The study of the brain by magnetic resonance imaging (MRI) allows to obtain detailed anatomical images, useful to describe specific encephalic structures and to analyze possible variabilities. It is widely used in clinical practice and is becoming increasingly used in veterinary medicine, even in exotic animals; however, despite its potential, its use in comparative neuroanatomy studies is still incipient. It is a technology that in recent years has significantly improved anatomical resolution, together with the fact that it is non-invasive and allows for systematic comparative analysis. All this makes it particularly interesting and useful in evolutionary neuroscience studies, since it allows for the analysis and comparison of brains of rare or otherwise inaccessible species. In the present study, we have analyzed the prosencephalon of three representative sauropsid species, the turtle Trachemys scripta (order Testudine), the lizard Pogona vitticeps (order Squamata) and the snake Python regius (order Squamata) by MRI. In addition, we used MRI sections to analyze the total brain volume and ventricular system of these species, employing volumetric and chemometric analyses together. The raw MRI data of the sauropsida models analyzed in the present study are available for viewing and downloading and have allowed us to produce an atlas of the forebrain of each of the species analyzed, with the main brain regions. In addition, our volumetric data showed that the three groups presented clear differences in terms of total and ventricular brain volumes, particularly the turtles, which in all cases presented distinctive characteristics compared to the lizards and snakes., (© 2024. The Author(s).)

Trust is a key component of human relationships. Sex differences in trust behavior have been elucidated by parental investment theory and social role theory, attributing men's higher trust propensity to their increased engagement in physically and socially risky activities aimed at securing additional resources. Although sex differences in trust behavior exist and the neuropsychological signatures of trust are known, the underlying anatomical structure of sex differences is still unexplored. Our study aimed to investigate the anatomical structure of sex differences in trust behavior toward strangers (i.e., trust propensity, TP) by employing voxel-based morphometry (VBM) in a sample of healthy young adults. We collected behavioral data for TP as measured with participants in the role of trustors completing the one-shot trust game (TG) with anonymous partners as trustees. We conducted primary region of interest (ROI) and exploratory whole-brain (WB) VBM analyses of high-resolution structural images to test for the association between TP and regional gray matter volume (GMV) associated with sex differences. Confirming previous studies, our behavioral results demonstrated that men trusted more than women during the one-shot TG. Our WB analysis showed a greater GMV related to TP in men than women in the precuneus (PreC), whereas our ROI analysis in regions of the default-mode network (dorsomedial prefrontal cortex [dmPFC], PreC, superior temporal gyrus) to simulate the partner's trustworthiness, central-executive network (ventrolateral PFC) to implement a calculus-based trust strategy, and action-perception network (precentral gyrus) to performance cost-benefit calculations, as proposed by a neuropsychoeconomic model of trust. Our findings advance the neuropsychological understanding of sex differences in TP, which has implications for interpersonal partnerships, financial transactions, and societal engagements., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Papo and Buldú [1] ask whether the brain truly acts as a network, or whether it is a convenient coincidence that it can be described with the tools of complex network theory, and the emerging field of network neuroscience. After a broad ranging discussion of networkness they explore some of the ways in which the combination of brain structure and dynamics can indeed better be understood as realising a complex network that subserves brain function. To complement and bolster this perspective, which is informed largely from a physics viewpoint, we direct the reader to additional tools, approaches and insights available from applied mathematics that may further help address some of the many remaining open challenges in this field., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2024. Published by Elsevier B.V. All rights reserved.)

Medical illustration, which involves the creation of visual representations of anatomy, has long been an essential tool for medical professionals and educators. The integration of AI and medical illustration has the potential to revolutionize the field of anatomy education, providing highly accurate, customizable images. The authors evaluated three AI-powered text-to-image generators in producing anatomical illustrations of the human skulls, heart, and brain. The generators were assessed for their accurate depiction of foramina, suture lines, coronary arteries, aortic and pulmonary trunk branching, gyri, sulci, and the relationship between the cerebellum and temporal lobes. None of the generators produced illustrations with comprehensive anatomical details. Foramina, such as the mental and supraorbital foramina, were frequently omitted, and suture lines were inaccurately represented. The illustrations of the heart failed to indicate proper coronary artery origins, and the branching of the aorta and pulmonary trunk was often incorrect. Brain illustrations lacked accurate gyri and sulci depiction, and the relationship between the cerebellum and temporal lobes remained unclear. Although AI generators tended toward esoteric imagery, they exhibited significant speed and cost advantages over human illustrators. However, improving their accuracy necessitates augmenting the training databases with anatomically correct images. The study emphasizes the ongoing role of human medical illustrators, especially in ensuring the provision of accurate and accessible illustrations., (© 2023 The Author. Anatomical Sciences Education published by Wiley Periodicals LLC on behalf of American Association for Anatomy.)