Human Brain Volume Research Articles

Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference

3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods able to perform fast and accurate cell counting and localization. Recent advances in deep learning have enabled the development of various tools for cell segmentation. However, accurate quantification of neurons in the human brain presents specific challenges, such as high pixel intensity variability, autofluorescence, non-specific fluorescence and very large size of data. In this paper, we provide a thorough empirical evaluation of three techniques based on deep learning (StarDist, CellPose and BCFind-v2, an updated version of BCFind) using a recently introduced three-dimensional stereological design as a reference for large-scale insights. As a representative problem in human brain analysis, we focus on a 4-cm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4~\ ext {-cm}^3$$\\end{document} portion of the Broca’s area. We aim at helping users in selecting appropriate techniques depending on their research objectives. To this end, we compare methods along various dimensions of analysis, including correctness of the predicted density and localization, computational efficiency, and human annotation effort. Our results suggest that deep learning approaches are very effective, have a high throughput providing each cell 3D location, and obtain results comparable to the estimates of the adopted stereological design.

Photoacoustic digital brain and deep-learning-assisted image reconstruction

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals’ distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull’s acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull.

Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG)

Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings. Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding. Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only. Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.

Oligodendrocytes in the aging brain.

More than half of the human brain volume is made up of white matter: regions where axons are coated in myelin, which primarily functions to increase the conduction speed of axon potentials. White matter volume significantly decreases with age, correlating with cognitive decline. Much research in the field of non-pathological brain aging mechanisms has taken a neuron-centric approach, with relatively little attention paid to other neural cells. This review discusses white matter changes, with focus on oligodendrocyte lineage cells and their ability to produce and maintain myelin to support normal brain homoeostasis. Improved understanding of intrinsic cellular changes, general senescence mechanisms, intercellular interactions and alterations in extracellular environment which occur with aging and impact oligodendrocyte cells is paramount. This may lead to strategies to support oligodendrocytes in aging, for example by supporting myelin synthesis, protecting against oxidative stress and promoting the rejuvenation of the intrinsic regenerative potential of progenitor cells. Ultimately, this will enable the protection of white matter integrity thus protecting cognitive function into the later years of life.

Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method.

Regional human brain volumes including total area, average thickness, and total volume are heritable and associated with neurological disorders. However, the genetic architecture of brain structure and function is still largely unknown and worthy of exploring. The Pediatric Imaging, Neurocognition, and Genetics (PING) data set provides an excellent resource with genome-wide genetic data and related neuroimaging data. In this study, we perform genome-wide association studies (GWAS) of 315 brain volumetric phenotypes from the PING data set including 1036 samples with 539,865 single-nucleotide polymorphisms (SNPs). We introduce a nonparametric test based on K-sample Ball Divergence (KBD) to identify genetic risk variants that influence regional brain volumes. We carry out simulations to demonstrate that KBD is a powerful test for identifying significant SNPs associated with multivariate phenotypes although controlling the type I error rate. We successfully identify nine SNPs below a significance level of 5 × 10-5 for the PING data. Among the nine identified genetic variants, two SNPs rs486179 and rs562110 are located in the ADRA1A gene that is a well-known risk factor of mental illness, such as schizophrenia and attention deficit hyperactivity disorder. Our study suggests that the nonparametric test KBD is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS of multiple phenotypes.

Effects of weather and season on human brain volume.

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p = 0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right- cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen’s d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments.

Synthesis of 3D MRI Brain Images With Shape and Texture Generative Adversarial Deep Neural Networks

Generative Adversarial Networks (GAN) are emerging as an exciting training paradigm which promises a step improvement to the impressive feature learning capabilities of deep neural networks. Unlike supervised learning approaches, GAN learns generalizable features without requiring labeled images to achieve new capabilities like distinguishing previously unseen anomalies, creating novel instances of data and factorizing learned features into explainable dimensions in fully unsupervised fashion. The advanced feature learning property of GAN will enable the next generation of computational image understanding tasks. However, GAN models are difficult to train to converge towards good models, especially for high resolution and high dimensional datasets like image volumes. We develop a GAN approach to learn a generative model of T1-contrast 3D MRI image volumes of the healthy human brain by training on 1112 MRI images from the Human Connectome Project. Our method utilizes a first unconditional Super-Resolution GAN, dubbed the shape network, to learn the 3D shape variations in adult brains and a second conditional pix2pix GAN, dubbed the texture network, to upgrade image slices with realistic local contrast patterns. Novel 3D MRI images are synthesized by first applying the 3D voxel-wise deformation map which is generated from the shape network to deform the Montreal Neurological Institute (MNI) brain template and subsequently performing style transfer on axial-wise slices using the texture network. The Maximum Mean Discrepancy (MMD) and Multi-scale Structural Similarity Index Measure (MS-SSIM) scores of MRI image volumes synthesized using our GAN approach are competitive with state-of-art GAN methods. Our work establishes the feasibility of an alternative approach to high-dimensional GAN learning - splitting the type of information content learned among several GANs can be an effective form of regularization and complementary to latent code shaping or super-resolution approaches in state-of-the-art methods.

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (rg) between BV and intelligence to be 0.24. We show that the rg is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence.

Cross-Sectional Volumes and Trajectories of the Human Brain, Gray Matter, White Matter and Cerebrospinal Fluid in 9473 Typically Aging Adults.

Accurate knowledge of adult human brain volume (BV) is critical for studies of aging- and disease-related brain alterations, and for monitoring the trajectories of neural and cognitive functions in conditions like Alzheimer's disease and traumatic brain injury. This scoping meta-analysis aggregates normative reference values for BV and three related volumetrics-gray matter volume (GMV), white matter volume (WMV) and cerebrospinal fluid volume (CSFV)-from typically-aging adults studied cross-sectionally using magnetic resonance imaging (MRI). Drawing from an aggregate sample of 9473 adults, this study provides (A) regression coefficients β describing the age-dependent trajectories of volumetric measures by sex within the range from 20 to 70years based on both linear and quadratic models, and (B) average values for BV, GMV, WMV and CSFV at the representative ages of 20 (young age), 45 (middle age) and 70 (old age). The results provided synthesize ~20years of brain volumetrics research and allow one to estimate BV at any age between 20 and 70. Importantly, however, such estimates should be used and interpreted with caution because they depend on MRI hardware specifications (e.g. scanner manufacturer, magnetic field strength), data acquisition parameters (e.g. spatial resolution, weighting), and brain segmentation algorithms. Guidelines are proposed to facilitate future meta- and mega-analyses of brain volumetrics.

Identification of genes involved in the evolution of human intelligence through combination of inter-species and intra-species genetic variations.

Understanding the evolution of human intelligence is an important undertaking in the science of human genetics. A great deal of biological research has been conducted to search for genes which are related to the significant increase in human brain volume and cerebral cortex complexity during hominid evolution. However, genetic changes affecting intelligence in hominid evolution have remained elusive. We supposed that a subset of intelligence-related genes, which harbored intra-species variations in human populations, may also be evolution-related genes which harbored inter-species variations between humans (Homo sapiens) and great apes (including Pan troglodytes and Pongo abelii). Here we combined inter-species and intra-species genetic variations to discover genes involved in the evolution of human intelligence. Information was collected from published GWAS works on intelligence and a total of 549 genes located within the intelligence-associated loci were identified. The intelligence-related genes containing human-specific variations were detected based on the latest high-quality genome assemblies of three human’s closest species. Finally, we identified 40 strong candidates involved in human intelligence evolution. Expression analysis using RNA-Seq data revealed that most of the genes displayed a relatively high expression in the cerebral cortex. For these genes, there is a distinct expression pattern between humans and other species, especially in neocortex tissues. Our work provided a list of strong candidates for the evolution of human intelligence, and also implied that some intelligence-related genes may undergo inter-species evolution and contain intra-species variation.

Influence of Severity of Type and Timing of Retrospectively Reported Childhood Maltreatment on Female Amygdala and Hippocampal Volume

Deleterious effects of adverse childhood experiences (ACE) on human brain volume are widely reported. First evidence points to differential effects of ACE on brain volume in terms of timing of ACE. Upcoming studies additionally point towards the impact of different types (i.e., neglect and abuse) of ACE in terms of timing. The current study aimed to investigate the correlation between retrospectively reported severity of type (i.e., the extent to which subjects were exposed to abuse and/or neglect, respectively) and timing of ACE on female brain volume in a sample of prolonged traumatized subjects. A female sample with ACE (N = 68) underwent structural magnetic resonance imaging and a structured interview exploring the severity of ACE from age 3 up to 17 using the “Maltreatment and Abuse Chronology of Exposure” (MACE). Random forest regression with conditional interference trees was applied to assess the impact of ACE severity as well as the severity of ACE type, (i.e. to what extent individuals were exposed to neglect and/or abuse) at certain ages on pre-defined regions of interest such as the amygdala, hippocampus, and anterior cingulate (ACC) volume. Analyses revealed differential type and timing-specific effects of ACE on stress sensitive brain structures: Amygdala and hippocampal volume were affected by ACE severity during a period covering preadolescence and early adolescence. Crucially, this effect was driven by the severity of neglect.

An 8-channel receive array for improved 31 P MRSI of the whole brain at 3T.

PurposeTo demonstrate a 1H/31P whole human brain volume coil configuration for 3 Tesla with separate 31P transmit and receive components that maintains 1H MRS performance and delivers optimal 31P MRSI with 1H decoupling.MethodsWe developed an 8‐channel 31P receive array coil covering the head to be used as an insert for a commercial double‐tuned 1H/31P birdcage transmit‐receive coil. This retains the possibility of using low‐power rectangular pulses for 1H‐decoupled 3D 31P MRSI (nominal resolution 17.6 cm3; acquisition duration 13 min) but increases the SNR with the receive sensitivity of 31P surface coils. The performance of the combined coil setup was evaluated by measuring 1H and 31P SNR with and without the 31P receive array and by assessing the effect of the receive array on the transmit efficiencies of the birdcage coil.ResultsCompared to the birdcage coil alone, the 31P insert in combination with the birdcage achieved an average 31P SNR gain of 1.4 ± 0.4 in a center partition of the brain. The insert did not cause losses in 1H MRS performance and transmit efficiency, whereas for 31P approximately 20% more power was needed to achieve the same γB1.ConclusionThe new coil configuration allows 1H MRSI and optimal 1H‐decoupled 3D 31P MRSI, with increased SNR of the human brain without patient repositioning, for clinical and research purposes at 3 Tesla.

Differential expression of synaptic and interneuron genes in the aging human prefrontal cortex.

Altered inhibition-excitation balance is implicated in brain aging. We hypothesized that expression of 14 genes encoding proteins localized to synapses or interneurons would show age-related changes relative to 1 another in postmortem tissue from the prefrontal cortex of 37 individuals (18-78 years) and that synaptic or interneuron markers would be differentially correlated with human brain volumes across aging. The majority of genes examined were differentially expressed with age, most being downregulated. Expression of 3 interneuron-related genes was significantly negatively associated with age (calbindin, somatostatin, cholecystokinin), whereas 3 synapse-related genes showed significant age-related expression change (PSD95, GAP43, VGLUT1). On covarying for 2 glial markers (GFAP, IBA1), all 3 interneuron genes and 1 synaptic gene (Growth-associated protein 43) remained significant. Two genes were significantly associated with total brain volume (calbindin, complexin 2) and a marker of synaptic density (synaptophysin) was significantly associated with cortical gray matter volume. Age-related change in expression of genes involved in maintenance of inhibition-excitation balance and regulation of prefrontocortical network dynamics suggests these pathways may contribute to brain aging.

Normative human brain volume growth.

OBJECTIVE While there is a long history of interest in measuring brain growth, as of yet there is no definitive model for normative human brain volume growth. The goal of this study was to analyze a variety of candidate models for such growth and select the model that provides the most statistically applicable fit. The authors sought to optimize clinically applicable growth charts that would facilitate improved treatment and predictive management for conditions such as hydrocephalus. METHODS The Weibull, two-term power law, West ontogenic, and Gompertz models were chosen as potential models. Normative brain volume data were compiled from the NIH MRI repository, and the data were fit using a nonlinear least squares regression algorithm. Appropriate statistical measures were analyzed for each model, and the best model was characterized with prediction bound curves to provide percentile estimates for clinical use. RESULTS Each model curve fit and the corresponding statistics were presented and analyzed. The Weibull fit had the best statistical results for both males and females, while the two-term power law generated the worst scores. The statistical measures and goodness of fit parameters for each model were provided to assure reproducibility. CONCLUSIONS The authors identified the Weibull model as the most effective growth curve fit for both males and females. Clinically usable growth charts were developed and provided to facilitate further clinical study of brain volume growth in conditions such as hydrocephalus. The authors note that the homogenous population from which the normative MRI data were compiled limits the study. Gaining a better understanding of the dynamics that underlie childhood brain growth would yield more predictive growth curves and improved neurosurgical management of hydrocephalus.

Variation in a range of mTOR-related genes associates with intracranial volume and intellectual disability

De novo mutations in specific mTOR pathway genes cause brain overgrowth in the context of intellectual disability (ID). By analyzing 101 mMTOR-related genes in a large ID patient cohort and two independent population cohorts, we show that these genes modulate brain growth in health and disease. We report the mTOR activator gene RHEB as an ID gene that is associated with megalencephaly when mutated. Functional testing of mutant RHEB in vertebrate animal models indicates pathway hyperactivation with a concomitant increase in cell and head size, aberrant neuronal migration, and induction of seizures, concordant with the human phenotype. This study reveals that tight control of brain volume is exerted through a large community of mTOR-related genes. Human brain volume can be altered, by either rare disruptive events causing hyperactivation of the pathway, or through the collective effects of common alleles.

The VEGF gene polymorphism impacts brain volume and arterial blood volume.

Vascular endothelial growth factor (VEGF) plays a critical role in the angiogenesis and proliferation of various types of cells such as neurons, astroglia, and endothelial cells in the brain. A common polymorphism in the VEGF gene (-2578 C/A) is associated with circulating VEGF levels, cancers and Alzheimer's disease. Nonetheless, the effects of this polymorphism on normal human brain volume, arterial blood volume, and blood supply remain unclear. In this study, the effects of this polymorphism on the total gray matter volume (TGMV) and total white matter volume (TWMV) using T1-weighted structural images and the total arterial blood volume (TABV) and mean cerebral blood flow (mCBF) during rest using arterial spin labeling (ASL) in 765 young adult humans were investigated. Voxel-by-voxel whole-brain analyses of these measures were also performed. Multiple regression analyses with age and sex as covariates revealed that the VEGF genotype (number of C alleles) was significantly and positively correlated with TGMV, TWMV, and TABV as well as with regional gray and white matter volumes in widespread areas and regional arterial blood volume in some areas with high arterial blood volume. However, these regional associations were not seen when the corresponding global signal was included as a covariate in the multiple regression analyses, indicating that we failed to obtain evidence of region-specific associations between these brain measures and the genotype. The results suggest that the VEGF-2578C allele, is associated with changes in the vascular system that lead to increased blood volume and larger brain volume. Hum Brain Mapp 38:3516-3526, 2017. © 2017 Wiley Periodicals, Inc.

Adaptive evolution of interleukin-3 (IL3), a gene associated with brain volume variation in general human populations.

Greatly expanded brain volume is one of the most characteristic traits that distinguish humans from other primates. Recent studies have revealed genes responsible for the dramatically enlarged human brain size (i.e., the microcephaly genes), and it has been well documented that many microcephaly genes have undergone accelerated evolution along the human lineage. In addition to being far larger than other primates, human brain volume is also highly variable in general populations. However, the genetic basis underlying human brain volume variation remains elusive and it is not known whether genes regulating human brain volume variation also have experienced positive selection. We have previously shown that genetic variants (near the IL3 gene) on 5q33 were significantly associated with brain volume in Chinese population. Here, we provide further evidence that support the significant association of genetic variants on 5q33 with brain volume. Bioinformatic analyses suggested that rs31480 is likely to be the causal variant among the studied SNPs. Molecular evolutionary analyses suggested that IL3 might have undergone positive selection in primates and humans. Neutrality tests further revealed signatures of positive selection of IL3 in Han Chinese and Europeans. Finally, extended haplotype homozygosity (EHH) and relative EHH analyses showed that the C allele of SNP rs31480 might have experienced recent positive selection in Han Chinese. Our results suggest that IL3 is an important genetic regulator for human brain volume variation and implied that IL3 might have experienced weak or modest positive selection in the evolutionary history of humans, which may be due to its contribution to human brain volume.

CRISPR/Cas9-mediated heterozygous knockout of the autism gene CHD8 and characterization of its transcriptional networks in neurodevelopment.

BackgroundDisruptive mutation in the CHD8 gene is one of the top genetic risk factors in autism spectrum disorders (ASDs). Previous analyses of genome-wide CHD8 occupancy and reduced expression of CHD8 by shRNA knockdown in committed neural cells showed that CHD8 regulates multiple cell processes critical for neural functions, and its targets are enriched with ASD-associated genes.MethodsTo further understand the molecular links between CHD8 functions and ASD, we have applied the CRISPR/Cas9 technology to knockout one copy of CHD8 in induced pluripotent stem cells (iPSCs) to better mimic the loss-of-function status that would exist in the developing human embryo prior to neuronal differentiation. We then carried out transcriptomic and bioinformatic analyses of neural progenitors and neurons derived from the CHD8 mutant iPSCs.ResultsTranscriptome profiling revealed that CHD8 hemizygosity (CHD8+/−) affected the expression of several thousands of genes in neural progenitors and early differentiating neurons. The differentially expressed genes were enriched for functions of neural development, β-catenin/Wnt signaling, extracellular matrix, and skeletal system development. They also exhibited significant overlap with genes previously associated with autism and schizophrenia, as well as the downstream transcriptional targets of multiple genes implicated in autism. Providing important insight into how CHD8 mutations might give rise to macrocephaly, we found that seven of the twelve genes associated with human brain volume or head size by genome-wide association studies (e.g., HGMA2) were dysregulated in CHD8+/− neural progenitors or neurons.ConclusionsWe have established a renewable source of CHD8+/− iPSC lines that would be valuable for investigating the molecular and cellular functions of CHD8. Transcriptomic profiling showed that CHD8 regulates multiple genes implicated in ASD pathogenesis and genes associated with brain volume.Electronic supplementary materialThe online version of this article (doi:10.1186/s13229-015-0048-6) contains supplementary material, which is available to authorized users.

生物进化过程中人类脑容量的演变

在生物进化的过程中，动物的神经系统从低级到高级，不断地由简单到复杂进行过渡，到脊椎动物最终形成了高级、复杂的大脑。而脑容量的演变是随着动物神经系统的进化而逐渐发展的，动物从低等到高等，脑容量逐渐增大。人类脑容量的演变趋势总体呈现为“两头缓慢，中间迅速”的“S”型曲线式发展；从“能人”时期开始，人类的脑容量迅速扩张；而经“智人”后，人类脑容量的发展渐趋平缓；脑容量的扩增在一定程度上可能促进了人类智力和认知能力的提升。脑容量的变化受到了包括基因和行为等多种因素的共同调节；就人类而言，诸如工具的制造和使用，劳动的日益增加，语言文字的形成，食性由素食向肉食转变等等，这些都对脑容量的进化起到了至关重要的推动作用。 In the process of biological evolution, the animal’s nervous system has experienced a process from simple to complex, the advanced, sophisticated brain eventually formed until to the appearance of the vertebrate. And with the evolution of the animal’s nervous system, the brain volume increases gradually. As to the human beings, the overall development trend of brain volume evolution ap-pears as a “S” curve type; from the “Homo habilis” period, the brain volume starts expansion rapidly; and after the period of “Homo sapiens”, the development of human brain volume becomes stable; the distensible brain volume may cause the improvement of human’s intelligence and cognitive abilities. Brain volume is regulated by many factors, including gene and behavior; In humans, the things such as the manufacture and the use of tools, the increasing of labor, the formation of language, changing of feeding from vegetarian to carnivorous all play a crucial role in promoting the brain volume evolution.

Heritability of brain volume change and its relation to intelligence

Human brain volumes change throughout life, are highly heritable, and have been associated with general cognitive functioning. Cross-sectionally, this association between volume and cognition can largely be attributed to the same genes influencing both traits. We address the question whether longitudinal changes in brain volume or in surface area in young adults are under genetic control and whether these changes are also related to general cognitive functioning. We measured change in brain volume and surface area over a 5-year interval in 176 monozygotic and dizygotic twins and their non-twin siblings aged 19 to 56, using magnetic resonance imaging. Results show that changes in volumes of total brain (mean=−6.4ml; 0.5% loss), cerebellum (1.4ml, 1.0% increase), cerebral white matter (4.4ml, 0.9% increase), lateral ventricles (0.6ml; 4.8% increase) and in surface area (−19.7cm2,1.1% contraction) are heritable (h2=43%; 52%; 29%; 31%; and 33%, respectively). An association between IQ (available for 91 participants) and brain volume change was observed, which was attributed to genes involved in both the variation in change in brain volume and in intelligence. Thus, dynamic changes in brain structure are heritable and may have cognitive significance in adulthood.