Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

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.

A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity.

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.

A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations.

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.

Volumetric study on sheep brain using stereology technique.

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 mm3. 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.

Imaging lifespan brain structural growth: From region, to connectome, to gradient.

Throughout human life, the brain undergoes intricate structural changes that support cognition. A study in PLOS Biology introduces new avenues for depicting the trajectory of the brain morphometric connectome and its underlying genetic and molecular mechanisms.

Construction and validation of a brain magnetic resonance imaging template for normal older Koreans.

BACKGROUND: Spatial normalization to a standardized brain template is a crucial step in magnetic resonance imaging (MRI) studies. Brain templates made from sufficient sample size have low brain variability, improving the accuracy of spatial normalization. Using population-specific template improves accuracy of spatial normalization because brain morphology varies according to ethnicity and age. METHODS: We constructed a brain template of normal Korean elderly (KNE200) using MRI scans 100 male and 100 female aged over 60 years old with normal cognition. We compared the deformation after spatial normalization of the KNE200 template to that of the KNE96, constructed from 96 cognitively normal elderly Koreans and to that of the brain template (OCF), constructed from 434 non-demented older Caucasians to examine the effect of sample size and ethnicity on the accuracy of brain template, respectively. We spatially normalized the MRI scans of elderly Koreans and quantified the amount of deformations associated with spatial normalization using the magnitude of displacement and volumetric changes of voxels. RESULTS: The KNE200 yielded significantly less displacement and volumetric change in the parahippocampal gyrus, medial and posterior orbital gyrus, fusiform gyrus, gyrus rectus, cerebellum and vermis than the KNE96. The KNE200 also yielded much less displacement in the cerebellum, vermis, hippocampus, parahippocampal gyrus and thalamus and much less volumetric change in the cerebellum, vermis, hippocampus and parahippocampal gyrus than the OCF. CONCLUSION: KNE200 had the better accuracy than the KNE96 due to the larger sample size and was far accurate than the template constructed from elderly Caucasians in elderly Koreans.

Neuroanatomical correlates of musicianship in left-handers.

BACKGROUND: Left-handedness is a condition that reverses the typical left cerebral dominance of motor control to an atypical right dominance. The impact of this distinct control - and its associated neuroanatomical peculiarities - on other cognitive functions such as music processing or playing a musical instrument remains unexplored. Previous studies in right-handed population have linked musicianship to a larger volume in the (right) auditory cortex and a larger volume in the (right) arcuate fasciculus. RESULTS: In our study, we reveal that left-handed musicians (n = 55), in comparison to left-handed non-musicians (n = 75), exhibit a larger gray matter volume in both the left and right Heschl's gyrus, critical for auditory processing. They also present a higher number of streamlines across the anterior segment of the right arcuate fasciculus. Importantly, atypical hemispheric lateralization of speech (notably prevalent among left-handers) was associated to a rightward asymmetry of the AF, in contrast to the leftward asymmetry exhibited by the typically lateralized. CONCLUSIONS: These findings suggest that left-handed musicians share similar neuroanatomical characteristics with their right-handed counterparts. However, atypical lateralization of speech might potentiate the right audiomotor pathway, which has been associated with musicianship and better musical skills. This may help explain why musicians are more prevalent among left-handers and shed light on their cognitive advantages.

Insights from the past: the work of Hans von Alten on the evolution of brain structure, ecological adaptation, and cognition in hymenopteran species.

In his treatise on arthropod brains, Hans von Alten (1910) focuses on a specific functional group of insects-the flying Hymenoptera-which exhibit a spectrum of lifestyles ranging from solitary to social. His work presents a distinctive comparative neuro-anatomical approach rooted in an eco-evolutionary and eco-behavioral background. We regard his publication as an exceptionally valuable source of information and seek to inspire the research community dedicated to the study of the insect brain to explore its insights further, even after more than 110 years. We have translated and annotated his work, expecting it to engage researchers not just with its remarkable drawings but also with its substantive content and exemplary research strategy. The present text is designed to complement von Alten's publication, situating it within the temporal context of nineteenth-century and early twentieth-century studies, and to draw connections to contemporary perspectives, especially concerning a central brain structure: the mushroom body.

A hierarchy index for networks in the brain reveals a complex entangled organizational structure.

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.

Detailed mapping of the complex fiber structure and white matter pathways of the chimpanzee brain.

Long-standing questions about human brain evolution may only be resolved through comparisons with close living evolutionary relatives, such as chimpanzees. This applies in particular to structural white matter (WM) connectivity, which continuously expanded throughout evolution. However, due to legal restrictions on chimpanzee research, neuroscience research currently relies largely on data with limited detail or on comparisons with evolutionarily distant monkeys. Here, we present a detailed magnetic resonance imaging resource to study structural WM connectivity in the chimpanzee. This open-access resource contains (1) WM reconstructions of a postmortem chimpanzee brain, using the highest-quality diffusion magnetic resonance imaging data yet acquired from great apes; (2) an optimized and validated method for high-quality fiber orientation reconstructions; and (3) major fiber tract segmentations for cross-species morphological comparisons. This dataset enabled us to identify phylogenetically relevant details of the chimpanzee connectome, and we anticipate that it will substantially contribute to understanding human brain evolution.

Personal Income Performance Correlates with Brain Structural Network Modularity but Not Intelligence Quotient.

Introduction: This study aims to use diffusion tensor imaging (DTI) in conjunction with brain graph techniques to define brain structural connectivity and investigate its association with personal income (PI) in individuals of various ages and intelligence quotients (IQ). Methods: MRI examinations were performed on 55 male subjects (mean age: 40.1 ± 9.4 years). Graph data and metrics were generated, and DTI images were analyzed using tract-based spatial statistics (TBSS). All subjects underwent the Wechsler Adult Intelligence Scale for a reliable estimation of the full-scale IQ (FSIQ), which includes verbal comprehension index, perceptual reasoning index, working memory index, and processing speed index. The performance score was defined as the monthly PI normalized by the age of the subject. Results: The analysis of global graph metrics showed that modularity correlated positively with performance score (p = 0.003) and negatively with FSIQ (p = 0.04) and processing speed index (p = 0.005). No significant correlations were found between IQ indices and performance scores. Regional analysis of graph metrics showed modularity differences between right and left networks in sub-cortical (p = 0.001) and frontal (p = 0.044) networks. TBSS analysis showed greater axial and mean diffusivities in the high-performance group in correlation with their modular brain organization. Conclusion: This study showed that PI performance is strongly correlated with a modular organization of brain structural connectivity, which implies short and rapid networks, providing automatic and unconscious brain processing. Additionally, the lack of correlation between performance and IQ suggests a reduced role of academic reasoning skills in performance to the advantage of high uncertainty decision-making networks.

Soft-tissue fossilization illuminates the stepwise evolution of the ray-finned fish brain.

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.

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency.

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re-identification protection but can significantly impact post-processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post-processing results by the price of higher re-identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1-weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re-identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post-processing results after defacing.

Choir singing is associated with enhanced structural connectivity across the adult lifespan.

The global ageing of populations calls for effective, ecologically valid methods to support brain health across adult life. Previous evidence suggests that music can promote white matter (WM) microstructure and grey matter (GM) volume while supporting auditory and cognitive functioning and emotional well-being as well as counteracting age-related cognitive decline. Adding a social component to music training, choir singing is a popular leisure activity among older adults, but a systematic account of its potential to support healthy brain structure, especially with regard to ageing, is currently missing. The present study used quantitative anisotropy (QA)-based diffusion MRI connectometry and voxel-based morphometry to explore the relationship of lifetime choir singing experience and brain structure at the whole-brain level. Cross-sectional multiple regression analyses were carried out in a large, balanced sample (N = 95; age range 21-88) of healthy adults with varying levels of choir singing experience across the whole age range and within subgroups defined by age (young, middle-aged, and older adults). Independent of age, choir singing experience was associated with extensive increases in WM QA in commissural, association, and projection tracts across the brain. Corroborating previous work, these overlapped with language and limbic networks. Enhanced corpus callosum microstructure was associated with choir singing experience across all subgroups. In addition, choir singing experience was selectively associated with enhanced QA in the fornix in older participants. No associations between GM volume and choir singing were found. The present study offers the first systematic account of amateur-level choir singing on brain structure. While no evidence for counteracting GM atrophy was found, the present evidence of enhanced structural connectivity coheres well with age-typical structural changes. Corroborating previous behavioural studies, the present results suggest that regular choir singing holds great promise for supporting brain health across the adult life span.

Paleoneurology of stem palaeognaths clarifies the plesiomorphic condition of the crown bird central nervous system.

Lithornithidae, an assemblage of volant Palaeogene fossil birds, provide our clearest insights into the early evolutionary history of Palaeognathae, the clade that today includes the flightless ratites and volant tinamous. The neotype specimen of Lithornis vulturinus, from the early Eocene (approximately 53 million years ago) of Europe, includes a partial neurocranium that has never been thoroughly investigated. Here, we describe these cranial remains including the nearly complete digital endocasts of the brain and bony labyrinth. The telencephalon of Lithornis is expanded and its optic lobes are ventrally shifted, as is typical for crown birds. The foramen magnum is positioned caudally, rather than flexed ventrally as in some crown birds, with the optic lobes, cerebellum, and foramen magnum shifted further ventrally. The overall brain shape is similar to that of tinamous, the only extant clade of flying palaeognaths, suggesting that several aspects of tinamou neuroanatomy may have been evolutionarily conserved since at least the early Cenozoic. The estimated ratio of the optic lobe's surface area relative to the total brain suggests a diurnal ecology. Lithornis may provide the clearest insights to date into the neuroanatomy of the ancestral crown bird, combining an ancestrally unflexed brain with a caudally oriented connection with the spinal cord, a moderately enlarged telencephalon, and ventrally shifted, enlarged optic lobes.

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure.

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22-37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92-0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

Disproportionate neuroanatomical effects of DCC haploinsufficiency in adolescence compared with adulthood: links to dopamine, connectivity, covariance, and gene expression brain maps in mice.

BACKGROUND: Critical adolescent neural refinement is controlled by the DCC (deleted in colorectal cancer) protein, a receptor for the netrin-1 guidance cue. We sought to describe the effects of reduced DCC on neuroanatomy in the adolescent and adult mouse brain. METHODS: We examined neuronal connectivity, structural covariance, and molecular processes in a DCC-haploinsufficient mouse model, compared with wild-type mice, using new, custom analytical tools designed to leverage publicly available databases from the Allen Institute. RESULTS: We included 11 DCC-haploinsufficient mice and 16 wild-type littermates. Neuroanatomical effects of DCC haploinsufficiency were more severe in adolescence than adulthood and were largely restricted to the mesocorticolimbic dopamine system. The latter finding was consistent whether we identified the regions of the mesocorticolimbic dopamine system a priori or used connectivity data from the Allen Brain Atlas to determine de novo where these dopamine axons terminated. Covariance analyses found that DCC haploinsufficiency disrupted the coordinated development of the brain regions that make up the mesocorticolimbic dopamine system. Gene expression maps pointed to molecular processes involving the expression of DCC, UNC5C (encoding DCC's co-receptor), and NTN1 (encoding its ligand, netrin-1) as underlying our structural findings. LIMITATIONS: Our study involved a single sex (males) at only 2 ages. CONCLUSION: The neuroanatomical phenotype of DCC haploinsufficiency described in mice parallels that observed in DCC-haploinsufficient humans. It is critical to understand the DCC-haploinsufficient mouse as a clinically relevant model system.

The heritability and structural correlates of resting-state fMRI complexity.

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.

Association of past and current sleep duration with structural brain differences: A large population-based study from the UK Biobank.

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 ß [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 ß: -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 ß: -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 Pinteraction <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.