The heritability of volumes of brain structures and its relationship to age: a review of twin and family studies

Shared and unique heritability of hippocampal subregion volumes in children and adults

Behavioral genetic analyses have not demonstrated robust, unique, genetic correlates of hippocampal subregion volume. Genetic differentiation of hippocampal longitudinal axis subregion volume has not yet been investigated in population-based samples, although this has been demonstrated in rodent and post-mortem human tissue work. The following study is the first population-based investigation of genetic factors that contribute to gray matter volume along the hippocampal longitudinal axis. Twin-based biometric analyses demonstrated that longitudinal axis subregions are associated with significant, unique, genetic variance, and that longitudinal axis subregions are also associated with significant shared, hippocampus-general, genetic factors. Our study's findings suggest that genetic differences in hippocampal longitudinal axis structure can be detected in individual differences in gray matter volume in population-level research designs.

Brain structure, phenotypic and genetic correlates of reading performance

Reading is an evolutionarily recent development that recruits and tunes brain circuitry connecting primary- and language-processing regions. We investigated whether metrics of the brain's physical structure correlate with reading performance and whether genetic variants affect this relationship. To this aim, we used the Adolescent Brain Cognitive Development dataset (n = 9,013) of 9-10-year-olds and focused on 150 measures of cortical surface area (CSA) and thickness. Our results reveal that reading performance is associated with nine measures of brain structure including relevant regions of the reading network. Furthermore, we show that this relationship is partially mediated by genetic factors for two of these measures: the CSA of the entire left hemisphere and, specifically, of the left superior temporal gyrus CSA. These effects emphasize the complex and subtle interplay between genes, brain and reading, which is a partly heritable polygenic skill that relies on a distributed network.

No signs of neurodegenerative effects in 15q11.2 BP1-BP2 copy number variant carriers in the UK Biobank

The 15q11.2 BP1-BP2 copy number variant (CNV) is associated with altered brain morphology and risk for atypical development, including increased risk for schizophrenia and learning difficulties for the deletion. However, it is still unclear whether differences in brain morphology are associated with neurodevelopmental or neurodegenerative processes. This study derived morphological brain MRI measures in 15q11.2 BP1-BP2 deletion (n = 124) and duplication carriers (n = 142), and matched deletion-controls (n = 496) and duplication-controls (n = 568) from the UK Biobank study to investigate the association with brain morphology and estimates of brain ageing. Further, we examined the ageing trajectory of age-affected measures (i.e., cortical thickness, surface area, subcortical volume, reaction time, hand grip strength, lung function, and blood pressure) in 15q11.2 BP1-BP2 CNV carriers compared to non-carriers. In this ageing population, the results from the machine learning models showed that the estimated brain age gaps did not differ between the 15q11.2 BP1-BP2 CNV carriers and non-carriers, despite deletion carriers displaying thicker cortex and lower subcortical volume compared to the deletion-controls and duplication carriers, and lower surface area compared to the deletion-controls. Likewise, the 15q11.2 BP1-BP2 CNV carriers did not deviate from the ageing trajectory on any of the age-affected measures examined compared to non-carriers. Despite altered brain morphology in 15q11.2 BP1-BP2 CNV carriers, the results did not show any clear signs of apparent altered ageing in brain structure, nor in motor, lung or heart function. The results do not indicate neurodegenerative effects in 15q11.2 BP1-BP2 CNV carriers.

Heritability of Subcortical Grey Matter Structures

Background and Objectives: Subcortical grey matter structures play essential roles in cognitive, affective, social, and motoric functions in humans. Their volume changes with age, and decreased volumes have been linked with many neuropsychiatric disorders. The aim of our study was to examine the heritability of six subcortical brain volumes (the amygdala, caudate nucleus, pallidum, putamen, thalamus, and nucleus accumbens) and four general brain volumes (the total intra-cranial volume and the grey matter, white matter, and cerebrospinal fluid (CSF) volume) in twins. Materials and Methods: A total of 118 healthy adult twins from the Hungarian Twin Registry (86 monozygotic and 32 dizygotic; median age 50 ± 27 years) underwent brain magnetic resonance imaging. Two automated volumetry pipelines, Computational Anatomy Toolbox 12 (CAT12) and volBrain, were used to calculate the subcortical and general brain volumes from three-dimensional T1-weighted images. Age- and sex-adjusted monozygotic and dizygotic intra-pair correlations were calculated, and the univariate ACE model was applied. Pearson's correlation test was used to compare the results obtained by the two pipelines. Results: The age- and sex-adjusted heritability estimates, using CAT12 for the amygdala, caudate nucleus, pallidum, putamen, and nucleus accumbens, were between 0.75 and 0.95. The thalamus volume was more strongly influenced by common environmental factors (C = 0.45-0.73). The heritability estimates, using volBrain, were between 0.69 and 0.92 for the nucleus accumbens, pallidum, putamen, right amygdala, and caudate nucleus. The left amygdala and thalamus were more strongly influenced by common environmental factors (C = 0.72-0.85). A strong correlation between CAT12 and volBrain (r = 0.74-0.94) was obtained for all volumes. Conclusions: The majority of examined subcortical volumes appeared to be strongly heritable. The thalamus was more strongly influenced by common environmental factors when investigated with both segmentation methods. Our results underline the importance of identifying the relevant genes responsible for variations in the subcortical structure volume and associated diseases.

Brain Volumes and Abnormalities in Adults Born Preterm at Very Low Birth Weight

OBJECTIVES

To assess radiographic brain abnormalities and investigate volumetric differences in adults born preterm at very low birth weight (<1500 g), using siblings as controls.

STUDY DESIGN

We recruited 79 adult same-sex sibling pairs with one born preterm at very low birth weight and the sibling at term. We acquired 3-T brain magnetic resonance imaging from 78 preterm participants and 72 siblings. A neuroradiologist, masked to participants' prematurity status, reviewed the images for parenchymal and structural abnormalities, and FreeSurfer software 6.0 was used to conduct volumetric analyses. Data were analyzed by linear mixed models.

RESULTS

We found more structural abnormalities in very low birth weight participants than in siblings (37% vs 13%). The most common finding was periventricular leukomalacia, present in 15% of very low birth weight participants and in 3% of siblings. The very low birth weight group had smaller absolute brain volumes (-0.4 SD) and, after adjusting for estimated intracranial volume, less gray matter (-0.2 SD), larger ventricles (1.5 SD), smaller thalami (-0.6 SD), caudate nuclei (-0.4 SD), right hippocampus (-0.4 SD), and left pallidum (-0.3 SD). We saw no volume differences in total white matter (-0.04 SD; 95% CI, -0.13 to 0.09).

CONCLUSIONS

Preterm very low birth weight adults had a higher prevalence of brain abnormalities than their term-born siblings. They also had smaller absolute brain volumes, less gray but not white matter, and smaller volumes in several gray matter structures.

Genomic Studies Across the Lifespan Point to Early Mechanisms Determining Subcortical Volumes

BACKGROUND

Subcortical brain structures play a key role in pathological processes of age-related neurodegenerative disorders. Mounting evidence also suggests that early-life factors may have an impact on the development of common late-life neurological diseases, including genetic factors that can influence both brain maturation and neurodegeneration.

METHODS

Using large population-based brain imaging datasets across the lifespan (N ≤ 40,628), we aimed to 1) estimate the heritability of subcortical volumes in young (18-35 years), middle (35-65 years), and older (65+ years) age, and their genetic correlation across age groups; 2) identify whether genetic loci associated with subcortical volumes in older persons also show associations in early adulthood, and explore underlying genes using transcriptome-wide association studies; and 3) explore their association with neurological phenotypes.

RESULTS

Heritability of subcortical volumes consistently decreased with increasing age. Genetic risk scores for smaller caudate nucleus, putamen, and hippocampus volume in older adults were associated with smaller volumes in young adults. Individually, 10 loci associated with subcortical volumes in older adults also showed associations in young adults. Within these loci, transcriptome-wide association studies showed that expression of several genes in brain tissues (especially MYLK2 and TUFM) was associated with subcortical volumes in both age groups. One risk variant for smaller caudate nucleus volume (TUFM locus) was associated with lower cognitive performance. Genetically predicted Alzheimer's disease was associated with smaller subcortical volumes in middle and older age.

CONCLUSIONS

Our findings provide novel insights into the genetic determinants of subcortical volumes across the lifespan. More studies are needed to decipher the underlying biology and clinical impact.

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics

Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients (N = 97) and healthy controls (N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out-of-sample prediction errors were assessed via five-fold cross-validation. Unimodal classifiers achieved a classification accuracy of 56.35-61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85-66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of ALS.

Aging Brain and Hearing: A Mini-Review

Brain reserve is a topic of great interest to researchers in aging medicine field. Some individuals retain well-preserved cognitive function until they fulfill their lives despite significant brain pathology. One concept that explains this paradox is the reserve hypothesis, including brain reserve that assumes a virtual ability to mitigate the effects of neuropathological changes and reduce the effects on clinical symptoms flexibly and efficiently by making complete use of the cognitive and compensatory processes. One of the surrogate measures of reserve capacity is brain volume. Evidence that dementia and hearing loss are interrelated has been steadily accumulating, and age-related hearing loss is one of the most promising modifiable risk factors of dementia. Research focused on the imaging analysis of the aged brain relative to auditory function has been gradually increasing. Several morphological studies have been conducted to understand the relationship between hearing loss and brain volume. In this mini review, we provide a brief overview of the concept of brain reserve, followed by a small review of studies addressing brain morphology and hearing loss/hearing compensation, including the findings obtained from our previous study that hearing loss after middle age could affect hippocampal and primary auditory cortex atrophy.

Along-tract analysis of the white matter is more informative about brain ageing, compared to whole-tract analysis

Diffusion Tensor Imaging (DTI) enabled the investigation of brain White Matter (WM), both qualitatively to study the macrostructure, and quantitatively to study the microstructure. The quantitative analyses are mostly performed at the whole-tract level, i.e., providing one measure of interest per tract; however, along-tract approaches may provide finer details of the quality of the WM tracts. In this study, using the DWI data collected from 40 young and 40 old individuals, we compared the DTI measures of FA, MD, AD, and RD, estimated by both whole-tract and along-tract approaches in 18 WM bundles, between the two groups. The results of the whole-tract quantitative analysis showed a statistically significant (p-FWER < 0.05) difference between the old and young groups in 6 tracts for FA, 8 tracts for MD, 1 tract for AD, and 7 tracts for RD. On the contrary, the along-tract approach showed differences between the two groups in 10 tracts for FA, 14 tracts for MD, 8 tracts for AD, and 11 tracts for RD. All the differences between the along-tract measures of the two groups had a large effect size (Cohen'd > 0.80). This study showed that the along-tract approach for the analysis of brain WM reveals changes in some WM tracts which had not shown any changes in the whole-tract approach, and therefore this finding emphasizes the utilization of the along-tract approach along with the whole-tract method for a more accurate study of the brain WM.

Rapid mosaic brain evolution under artificial selection for relative telencephalon size in the guppy (Poecilia reticulata)

The mosaic brain evolution hypothesis, stating that brain regions can evolve relatively independently during cognitive evolution, is an important idea to understand how brains evolve with potential implications even for human brain evolution. Here, we provide the first experimental evidence for this hypothesis through an artificial selection experiment in the guppy (Poecilia reticulata). After four generations of selection on relative telencephalon volume (relative to brain size), we found substantial changes in telencephalon size but no changes in other regions. Further comparisons revealed that up-selected lines had larger telencephalon, while down-selected lines had smaller telencephalon than wild Trinidadian populations. Our results support that independent evolutionary changes in specific brain regions through mosaic brain evolution can be important facilitators of cognitive evolution.

Genetic scores for adult subcortical volumes associate with subcortical volumes during infancy and childhood

Individual differences in subcortical brain volumes are highly heritable. Previous studies have identified genetic variants that underlie variation in subcortical volumes in adults. We tested whether those previously identified variants also affect subcortical regions during infancy and early childhood. The study was performed within the Generation R study, a prospective birth cohort. We calculated polygenic scores based on reported GWAS for volumes of the accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen, and thalamus. Participants underwent cranial ultrasound around 7 weeks of age (range: 3-20), and we obtained metrics for the gangliothalamic ovoid, a predecessor of the basal ganglia. Furthermore, the children participated in a magnetic resonance imaging (MRI) study around the age of 10 years (range: 9-12). A total of 340 children had complete data at both examinations. Polygenic scores primarily associated with their corresponding volumes at 10 years of age. The scores also moderately related to the diameter of the gangliothalamic ovoid on cranial ultrasound. Mediation analysis showed that the genetic influence on subcortical volumes at 10 years was only mediated for 16.5-17.6% of the total effect through the gangliothalamic ovoid diameter at 7 weeks of age. Combined, these findings suggest that previously identified genetic variants in adults are relevant for subcortical volumes during early life, and that they affect both prenatal and postnatal development of the subcortical regions.

Iranian Brain Imaging Database: A Neuropsychiatric Database of Healthy Brain

INTRODUCTION

The Iranian Brain Imaging Database (IBID) was initiated in 2017, with 5 major goals: provide researchers easy access to a neuroimaging database, provide normative quantitative measures of the brain for clinical research purposes, study the aging profile of the brain, examine the association of brain structure and function, and join the ENIGMA consortium. Many prestigious databases with similar goals are available. However, they were not done on an Iranian population, and the battery of their tests (e.g. cognitive tests) is selected based on their specific questions and needs.

METHODS

The IBID will include 300 participants (50% female) in the age range of 20 to 70 years old, with an equal number of participants (#60) in each age decade. It comprises a battery of cognitive, lifestyle, medical, and mental health tests, in addition to several Magnetic Resonance Imaging (MRI) protocols. Each participant completes the assessments on two referral days.

RESULTS

The study currently has a cross-sectional design, but longitudinal assessments are considered for the future phases of the study. Here, details of the methodology and the initial results of assessing the first 152 participants of the study are provided.

CONCLUSION

IBID is established to enable research into human brain function, to aid clinicians in disease diagnosis research, and also to unite the Iranian researchers with interests in the brain.

Larger Volume and Different Activation of the Brain in Response to Threat in Military Officers

Introduction:

Military missions involve stressful and life-threatening situations; however, soldiers should have a healthy cognition on the battlefield despite their high-stress levels. This is an ability that should be gained during prior military training. Successful and influential training is suggested to be associated with structural and functional improvements of the brain.

Methods:

This study investigated the pattern of brain activation while observing videos relevant to life-threatening situations, in addition to brain structure. Accordingly, the obtained data were compared between 20 military members and 26 healthy controls. The study participants were all male, aged between 19 to 24 years, right-handed, studying BSc, and from the same socioeconomic status.

Results:

The obtained data presented a larger volume in a total number of 1103 voxels of the brain (in 5 brain areas) in the military group. Furthermore, the military group suggested higher brain activation in the visual processing areas of the brain when observing real combat videos; however, this increment was mostly in the areas associated with motor processing and executive functions in the controls.

Conclusion:

This study indicated that military training is associated with positive structural changes in the brain. Besides, it provided a different brain activation in response to stressful situations. These findings highlighted the importance of qualified military training.

Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study

Normal brain-aging occurs at all structural levels. Excessive pathophysiological changes in the brain, beyond the normal one, are implicated in the etiology of brain disorders such as severe forms of the schizophrenia spectrum and dementia. To account for brain-aging in health and disease, it is critical to study the age-dependent trajectories of brain biomarkers at various levels and among different age groups. The intracranial volume (ICV) is a key biological marker, and changes in the ICV during the lifespan can teach us about the biology of development, aging, and gene X environment interactions. However, whether ICV changes with age in adulthood is not resolved. Applying a semi-automatic in-house-built algorithm for ICV extraction on T1w MR brain scans in the Dutch longitudinal cohort (GROUP), we measured ICV changes. Individuals between the ages of 16 and 55 years were scanned up to three consecutive times with 3.32±0.32 years between consecutive scans (N = 482, 359, 302). Using the extracted ICVs, we calculated ICV longitudinal aging-trajectories based on three analysis methods; direct calculation of ICV differences between the first and the last scan, fitting all ICV measurements of individuals to a straight line, and applying a global linear mixed model fitting. We report statistically significant increase in the ICV in adulthood until the fourth decade of life (average change +0.03%/y, or about 0.5 ml/y, at age 20), and decrease in the ICV afterward (-0.09%/y, or about -1.2 ml/y, at age 55). To account for previous cross-sectional reports of ICV changes, we analyzed the same data using a cross-sectional approach. Our cross-sectional analysis detected ICV changes consistent with the previously reported cross-sectional effect. However, the reported amount of cross-sectional changes within this age range was significantly larger than the longitudinal changes. We attribute the cross-sectional results to a generational effect. In conclusion, the human intracranial volume does not stay constant during adulthood but instead shows a small increase during young adulthood and a decrease thereafter from the fourth decade of life. The age-related changes in the longitudinalmeasure are smaller than those reported using cross-sectional approaches and unlikely to affect structural brain imaging studies correcting for intracranial volume considerably. As to the possible mechanisms involved, this awaits further study, although thickening of the meninges and skull bones have been proposed, as well as a smaller amount of brain fluids addition above the overall loss of brain tissue.

The Genetic Contributions to Maturational Coupling in the Human Cerebrum: A Longitudinal Pediatric Twin Imaging Study

Although prior studies have demonstrated that genetic factors play the dominant role in the patterning of the pediatric brain, it remains unclear how these patterns change over time. Using 1748 longitudinal anatomic MRI scans from 792 healthy twins and siblings, we quantified how genetically mediated inter-regional associations change over time via multivariate longitudinal structural equation modeling. These analyses found that genetic correlations for both lobar volumes and cortical thickness are dynamic, with relatively static effects on surface area. While genetic correlations for lobar volumes decrease over childhood and adolescence, in general they increase for cortical thickness in the second decade of life. Quantification of how genetic factors influence maturational coupling improves our understanding of typical neurodevelopment and informs future molecular genetic analyses.

A multifactorial approach of nutritional, intellectual, brain development, cardiovascular risk, socio-economic, demographic and educational variables affecting the scholastic achievement in Chilean students: An eight- year follow-up study

The aim of this study was to quantitate the relative impact of nutritional, intellectual, brain development, cardiovascular risk, socio-economic, demographic and educational variables on the results of the 2009 Quality Education Measurement System (SIMCE) tests of language and mathematics for scholastic achievement (SA) applying a multifactorial approach, in school-age children of the 2010 5th elementary school grade (5ESG) and of the 1st grade of high school (1HSG). The purposes were: i) to test the hypothesis that intellectual ability, the level of SA of the educational establishments in the 2009 SIMCE tests, sex, parental schooling levels, and head circumference-for-age Z-score are the most relevant parameters associated with 2009 SIMCE outcomes; ii) to determine the predictive ability of the 2009 SIMCE results in determining the 2013 SIMCE outcomes for the 2010 5ESG cohort (when they graduated from elementary school, 8th grade) and for determining the 2013 University Selection Test (PSU) outcomes for the 2010 1HSG group (for university admission, when they graduated from high school, 4th grade); iii) to determine the association between the 2009 SIMCE results with the 2017 PSU outcomes for the 2010 5ESG group (for university admission, when they graduated from high school, 4th grade). A representative, proportional and stratified sample of 33 schools of the Metropolitan Region of Chile was randomly chosen. In these schools, 1,353 school-age children of both sexes, of the 2010 5ESG (n = 682; mean age = 10.8 years, SD = 0.6) and of the 2010 1HSG (n = 671; mean age = 14.8 years, SD = 0.6) participated. In both grades and tests, the findings confirm the hypotheses formulated. 2009 SIMCE outcomes were positively and significantly associated with 2013 SIMCE and with 2017 PSU and, with 2013 PSU outcomes in school-age children from 2010 5ESG and 1HSG, respectively. These findings may be useful for educational and health planning in Chile and countries in a comparable stage of development.

Brain age and other bodily 'ages': implications for neuropsychiatry

As our brains age, we tend to experience cognitive decline and are at greater risk of neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases are also exacerbated during ageing. However, the ageing process does not affect people uniformly; nor, in fact, does the ageing process appear to be uniform even within an individual. Here, we outline recent neuroimaging research into brain ageing and the use of other bodily ageing biomarkers, including telomere length, the epigenetic clock, and grip strength. Some of these techniques, using statistical approaches, have the ability to predict chronological age in healthy people. Moreover, they are now being applied to neurological and psychiatric disease groups to provide insights into how these diseases interact with the ageing process and to deliver individualised predictions about future brain and body health. We discuss the importance of integrating different types of biological measurements, from both the brain and the rest of the body, to build more comprehensive models of the biological ageing process. Finally, we propose seven steps for the field of brain-ageing research to take in coming years. This will help us reach the long-term goal of developing clinically applicable statistical models of biological processes to measure, track and predict brain and body health in ageing and disease.

Heritability of neural reactions to social exclusion and prosocial compensation in middle childhood

Experiencing and observing social exclusion and inclusion, as well as prosocial behavior, are important aspects of social relationships in childhood. However, it is currently unknown to what extent these processes and their neural correlates differ in heritability. We investigated influences of genetics and environment on experiencing social exclusion and compensating for social exclusion of others with the Prosocial Cyberball Game using fMRI in a twin sample (aged 7-9; N = 500). Neuroimaging analyses (N = 283) revealed that experiencing possible self-exclusion resulted in activity in inferior frontal gyrus and medial prefrontal cortex, which was influenced by genetics and unique environment. Experiencing self-inclusion was associated with activity in anterior cingulate cortex, insula and striatum, but this was not significantly explained by genetics or shared environment. We found that children show prosocial compensating behavior when observing social exclusion. Prosocial compensating behavior was associated with activity in posterior cingulate cortex/precuneus, and showed unique environmental effects or measurement error at both behavioral and neural level. Together, these findings show that in children neural activation for experiencing possible self-exclusion and self-inclusion, and for displaying prosocial compensating behavior, is accounted for by unique environmental factors and measurement error, with a small genetic effect on possible self-exclusion.

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

The impact of schizophrenia and intelligence on the relationship between age and brain volume

Age has been shown to have an impact on both grey (GM) and white matter (WM) volume, with a steeper slope of age-related decline in schizophrenia compared to healthy controls. In schizophrenia, the relation between age and brain volume is further complicated by factors such as lower intelligence, antipsychotic medication, and cannabis use, all of which have been shown to have independent effects on brain volume.

In a study of first-episode, antipsychotic-naïve schizophrenia patients (N = 54) and healthy controls (N = 56), we examined the effects of age on whole brain measures of GM and WM volume, and whether these relationships were moderated by schizophrenia and intelligence (IQ). Secondarily, we examined lifetime cannabis use as a moderator of the relationship between age and brain volume.

Schizophrenia patients had lower GM volumes than healthy controls but did not differ on WM volume. We found an age effect on GM indicating that increasing age was associated with lower GM volumes, which did not differ between groups. IQ did not have a direct effect on GM, but showed a trend-level interaction with age, suggesting a greater impact of age with lower IQ. There were no age effects on WM volume, but a direct effect of IQ, with higher IQ showing an association with larger WM volume. Lifetime cannabis use did not alter these findings significantly.

This study points to effects of schizophrenia on GM early in the illness, before antipsychotic treatment is initiated, suggesting that WM changes may occur later in the disease process.

Alterations in resting state connectivity along the autism trait continuum: a twin study

Autism spectrum disorder (ASD) has been found to be associated with alterations in resting state (RS) functional connectivity, including areas forming the default mode network (DMN) and salience network (SN). However, insufficient control for confounding genetic and environmental influences and other methodological issues limit the generalizability of previous findings. Moreover, it has been hypothesized that ASD might be marked by early hyper-connectivity followed by later hypo-connectivity. To date, only a few studies have explicitly tested age-related influences on RS connectivity alterations in ASD. Using a within-twin pair design (N=150 twins; 8-23 years), we examined altered RS connectivity between core regions of the DMN and SN in relation to autistic trait severity and age in a sample of monozygotic (MZ) and dizygotic (DZ) twins showing typical development, ASD or other neurodevelopmental conditions. Connectivity between core regions of the SN was stronger in twins with higher autistic traits compared to their co-twins. This effect was significant both in the total sample and in MZ twins alone, highlighting the effect of non-shared environmental factors on the link between SN-connectivity and autistic traits. While this link was strongest in children, we did not identify differences between age groups for the SN. In contrast, connectivity between core hubs of the DMN was negatively correlated with autistic traits in adolescents and showed a similar trend in adults but not in children. The results support hypotheses of age-dependent altered RS connectivity in ASD, making altered SN and DMN connectivity promising candidate biomarkers for ASD.

Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases

The lack of specificity in neuroimaging studies of neurological and psychiatric diseases suggests that these different diseases have more in common than is generally considered. Potentially, features that are secondary effects of different pathological processes may share common neurobiological underpinnings. Intriguingly, many of these mechanisms are also observed in studies of normal (i.e., non-pathological) brain ageing. Different brain diseases may be causing premature or accelerated ageing to the brain, an idea that is supported by a line of "brain ageing" research that combines neuroimaging data with machine learning analysis. In reviewing this field, I conclude that such observations could have important implications, suggesting that we should shift experimental paradigm: away from characterizing the average case-control brain differences resulting from a disease toward methods that place individuals in their age-appropriate context. This will also lead naturally to clinical applications, whereby neuroimaging can contribute to a personalized-medicine approach to improve brain health.

Heritability of the human connectome: A connectotyping study

Recent progress in resting-state neuroimaging demonstrates that the brain exhibits highly individualized patterns of functional connectivity-a "connectotype." How these individualized patterns may be constrained by environment and genetics is unknown. Here we ask whether the connectotype is familial and heritable. Using a novel approach to estimate familiality via a machine-learning framework, we analyzed resting-state fMRI scans from two well-characterized samples of child and adult siblings. First we show that individual connectotypes were reliably identified even several years after the initial scanning timepoint. Familial relationships between participants, such as siblings versus those who are unrelated, were also accurately characterized. The connectotype demonstrated substantial heritability driven by high-order systems including the fronto-parietal, dorsal attention, ventral attention, cingulo-opercular, and default systems. This work suggests that shared genetics and environment contribute toward producing complex, individualized patterns of distributed brain activity, rather than constraining local aspects of function. These insights offer new strategies for characterizing individual aberrations in brain function and evaluating heritability of brain networks.

Reduced frontal grey matter, life history of aggression, and underlying genetic influence

Physically healthy, adult, same-sexed twins (n = 287) from a population-based twin cohort underwent high-resolution magnetic resonance imaging (MRI) to identify fronto-limbic brain regions significantly associated with lifetime history of aggression. MRI scans used a 3D magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) sequence, for voxel-based morphometry (VBM) and history of aggressive behavior was assessed using the Life History of Aggression measure. Aggression had modest, inverse associations with grey matter volume (GMV) in medial prefrontal cortex (mPFC, b = -0.20, se = 0.05, p < 0.001) and lateral prefrontal cortex (lPFC, b = -0.23, se = 0.06, p < 0.001). These associations were not confounded by other demographic, psychiatric, or personality factors. Biometrical twin analyses revealed significant heritabilities of 0.57 for GMV in the mPFC cluster and 0.36 for GMV in the lPFC cluster. Genetic factors accounted for the majority of the phenotypic correlations between aggression and mPFC GMV (85.3%) and between aggression and lPFC GMV (63.7%). Reduced GMV of prefrontal brain regions may be a neuronal characteristic of individuals with substantial histories of aggressive behavior regardless of psychiatric diagnosis. As such, these data suggest an anatomical correlate, with a possible genetic etiology, associated with functional deficits in social-emotional information processing.

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h² ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.

Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: Results of the ENIGMA plasticity working group

Structural brain changes that occur during development and ageing are related to mental health and general cognitive functioning. Individuals differ in the extent to which their brain volumes change over time, but whether these differences can be attributed to differences in their genotypes has not been widely studied. Here we estimate heritability (h² ) of changes in global and subcortical brain volumes in five longitudinal twin cohorts from across the world and in different stages of the lifespan (N = 861). Heritability estimates of brain changes were significant and ranged from 16% (caudate) to 42% (cerebellar gray matter) for all global and most subcortical volumes (with the exception of thalamus and pallidum). Heritability estimates of change rates were generally higher in adults than in children suggesting an increasing influence of genetic factors explaining individual differences in brain structural changes with age. In children, environmental influences in part explained individual differences in developmental changes in brain structure. Multivariate genetic modeling showed that genetic influences of change rates and baseline volume significantly overlapped for many structures. The genetic influences explaining individual differences in the change rate for cerebellum, cerebellar gray matter and lateral ventricles were independent of the genetic influences explaining differences in their baseline volumes. These results imply the existence of genetic variants that are specific for brain plasticity, rather than brain volume itself. Identifying these genes may increase our understanding of brain development and ageing and possibly have implications for diseases that are characterized by deviant developmental trajectories of brain structure. Hum Brain Mapp 38:4444-4458, 2017. © 2017 Wiley Periodicals, Inc.

Heritability of brain volume on MRI in middle to advanced age: A twin study of Japanese adults

Brain atrophy is part of the aging process and accelerated by neurodegenerative diseases, so an understanding of the background heritability of brain volume is essential. The purpose of this study was to determine the heritability of brain volume in middle to advanced age East Asian adults, an age group less studied and an ethnicity not previously studied. 3T magnetic resonance images were obtained and volumetric analyses conducted for a total of 74 individuals, 20 monozygotic twin pairs (mean age 61y min 41y max 75y) and 17 dizygotic twin pairs (mean age 64y min 41y max 85y). Total brain volume and a further seven regions were assessed, including lobar volumes, lateral divisions, and separated grey and white matter. Additive genetics and unique environment (AE) models for global brain volumes including total brain (90%), grey matter (91%) and white matter (84%) and many lobar volumes demonstrated high heritability in our study population. Our results present the heritability of brain volume in middle to advanced age as possibly higher in East Asian adults.

Parental family history of dementia in relation to subclinical brain disease and dementia risk

Objective:

To determine the association of parental family history with risk of dementia by age at onset and sex of affected parent in a population-based cohort.

Methods:

From 2000 to 2002, we assessed parental history of dementia in participants without dementia of the Rotterdam Study. We investigated associations of parental history with risk of dementia until 2015, adjusting for demographics, cardiovascular risk factors, and known genetic risk variants. Furthermore, we determined the association between parental history and markers of neurodegeneration and vascular disease on MRI.

Results:

Of 2,087 participants (mean age 64 years, 55% female), 407 (19.6%) reported a history of dementia in either parent (mean age at diagnosis 79 years). During a mean follow-up of 12.2 years, 142 participants developed dementia. Parental history was associated with risk of dementia independently of known genetic risk factors (hazard ratio [HR] 1.67, 95% confidence interval [CI] 1.12–2.48), in particular when parents were diagnosed at younger age (<80 years: HR 2.58, 95% CI 1.61–4.15; ≥80 years: HR 1.01, 95% CI 0.58–1.77). Accordingly, age at diagnosis in probands was highly correlated with age at diagnosis in their parents <80 years (r = 0.57, p = 0.001) but not thereafter (r = 0.17, p = 0.55). Among 1,161 participants without dementia with brain MRI, parental history was related to lower cerebral perfusion and higher burden of white matter lesions and microbleeds. Dementia risk and MRI markers were similar for paternal and maternal history.

Conclusions:

Parental history of dementia increases risk of dementia, primarily when age at parental diagnosis is <80 years. Unexplained heredity may be attributed in part to cerebral hypoperfusion and small vessel disease. We found no evidence of preferential maternal compared to paternal transmission.

Gray matter heritability in family-based and population-based studies using voxel-based morphometry

BACKGROUND

The combination of genetics and imaging has improved their understanding of the brain through studies of aggregate measures obtained from high-resolution structural imaging. Voxel-wise analyses have the potential to provide more detailed information of genetic influences on the brain. Here they report a large-scale study of the heritability of gray matter at voxel resolution (1 × 1 × 1 mm).

METHODS

Validated voxel-based morphometry (VBM) protocols were applied to process magnetic resonance imaging data of 3,239 unrelated subjects from a population-based study and 491 subjects from two family-based studies. Genome-wide genetic data was used to estimate voxel-wise gray matter heritability of the unrelated subjects and pedigree-structure was used to estimate heritability in families. They subsequently associated two genetic variants, known to be linked with subcortical brain volume, with most heritable voxels to determine if this would enhance their association signals.

RESULTS

Voxels significantly heritable in both estimates mapped to subcortical structures, but also voxels in the language areas of the left hemisphere were found significantly heritable. When comparing regional patterns of heritability, family-based estimates were higher than population-based estimates. However, regional consistency of the heritability measures across study designs was high (Pearson's correlation coefficient = 0.73, P = 2.6 × 10^-13 ). They further show enhancement of the association signal of two previously discovered genetic loci with subcortical volume by using only the most heritable voxels.

CONCLUSION

Gray matter voxel-wise heritability can be reliably estimated with different methods. Combining heritability estimates from multiple studies is feasible to construct reliable heritability maps of gray matter voxels. Hum Brain Mapp 38:2408-2423, 2017. © 2017 Wiley Periodicals, Inc.

Relaxed genetic control of cortical organization in human brains compared with chimpanzees

The study of hominin brain evolution has focused largely on the neocortical expansion and reorganization undergone by humans as inferred from the endocranial fossil record. Comparisons of modern human brains with those of chimpanzees provide an additional line of evidence to define key neural traits that have emerged in human evolution and that underlie our unique behavioral specializations. In an attempt to identify fundamental developmental differences, we have estimated the genetic bases of brain size and cortical organization in chimpanzees and humans by studying phenotypic similarities between individuals with known kinship relationships. We show that, although heritability for brain size and cortical organization is high in chimpanzees, cerebral cortical anatomy is substantially less genetically heritable than brain size in humans, indicating greater plasticity and increased environmental influence on neurodevelopment in our species. This relaxed genetic control on cortical organization is especially marked in association areas and likely is related to underlying microstructural changes in neural circuitry. A major result of increased plasticity is that the development of neural circuits that underlie behavior is shaped by the environmental, social, and cultural context more intensively in humans than in other primate species, thus providing an anatomical basis for behavioral and cognitive evolution.

Premises of plasticity - And the loneliness of the medial temporal lobe

In this perspective paper, we examine possible premises of plasticity in the neural substrates underlying cognitive change. We take the special role of the medial temporal lobe as an anchoring point, but also investigate characteristics throughout the cortex. Specifically, we examine the dimensions of evolutionary expansion, heritability, variability of morphometric change, and inter-individual variance in myelination with respect to the plastic potential of different brain regions. We argue that areas showing less evolutionary expansion, lower heritability, greater variability of cortical thickness change through the lifespan, and greater inter-individual differences in intracortical myelin content have a great extent of plasticity. While different regions of the brain show these features to varying extent, analyses converge on the medial temporal lobe including the hippocampi as the target of all these premises. We discuss implications for effects of training on brain structures, and conditions under which plasticity may be evoked.

Investigating the influence of KIBRA and CLSTN2 genetic polymorphisms on cross-sectional and longitudinal measures of memory performance and hippocampal volume in older individuals

The variability of episodic memory decline and hippocampal atrophy observed with increasing age may partly be explained by genetic factors. KIBRA (kidney and brain expressed protein) and CLSTN2 (calsyntenin 2) are two candidate genes previously linked to episodic memory performance and volume of the hippocampus, a key memory structure. However, whether polymorphisms in these two genes also influence age-related longitudinal memory decline and hippocampal atrophy is still unknown. Using data from two independent cohorts, the Sydney Memory and Ageing Study and the Older Australian Twins Study, we investigated whether the KIBRA and CLSTN2 genetic polymorphisms (rs17070145 and rs6439886) are associated with episodic memory performance and hippocampal volume in older adults (65-90 years at baseline). We were able to examine these polymorphisms in relation to memory and hippocampal volume using cross-sectional data and, more importantly, also using longitudinal data (2 years between testing occasions). Overall we did not find support for an association of KIBRA either alone or in combination with CLSTN2 with memory performance or hippocampal volume, nor did variation in these genes influence longitudinal memory decline or hippocampal atrophy in two cohorts of older adults.

Aging-related magnification of genetic effects on cognitive and brain integrity

Heritability studies document substantial genetic influences on cognitive performance and decline in old age. Increasing evidence shows that effects of genetic variations on cognition, brain structure, and brain function become stronger as people age. Disproportionate impairments are typically observed for older individuals carrying disadvantageous genotypes of different candidate genes. These data support the resource-modulation hypothesis, which states that genetic effects are magnified in persons with constrained neural resources, such as older adults. However, given that findings are not unequivocal, we discuss the need to address several factors that may resolve inconsistencies in the extant literature (gene-gene and gene-environment interactions, study populations, gene-environment correlations, and epigenetic mechanisms).

Genetic underpinnings of white matter 'connectivity': heritability, risk, and heterogeneity in schizophrenia

Schizophrenia is a highly heritable disorder. Thus, the combination of genetics and brain imaging may be a useful strategy to investigate the effects of risk genes on anatomical connectivity, and for gene discovery, i.e. discovering the genetic correlates of white matter phenotypes. Following a database search, I review evidence for heritability of white matter phenotypes. I also review candidate gene investigations, examining association of putative risk variants with white matter phenotypes, as well as the recent flurry of research exploring relationships of genome-wide significant risk loci with white matter phenotypes. Finally, I review multivariate and polygene approaches, which constitute a new wave of imaging-genetics research, including large collaborative initiatives aiming to discover new genes that may predict aspects of white matter microstructure. The literature supports the heritability of white matter phenotypes. Loci in genes intimately implicated in oligodendrocyte and myelin development, growth and maintenance, and neurotrophic systems are associated with white matter microstructure. GWAS variants have not yet sufficiently been explored using DTI-based evaluation of white matter to draw conclusions, although micro-RNA 137 is promising due to its potential regulation of other GWAS schizophrenia genes. Many imaging-genetic studies only include healthy participants, which, while helping control for certain confounds, cannot address questions related to disease heterogeneity or symptom expression, and thus more studies should include participants with schizophrenia. With sufficiently large sample sizes, the future of this field lies in polygene strategies aimed at risk prediction and heterogeneity dissection of schizophrenia that can translate to personalized interventions.

Indestructible plastic: the neuroscience of the new aging brain

In recent years, research on experience-dependent plasticity has provided valuable insight on adaptation to environmental input across the lifespan, and advances in understanding the minute cellular changes underlying the brain's capacity for self-reorganization have opened exciting new possibilities for treating illness and injury. Ongoing work in this line of inquiry has also come to deeply influence another field: cognitive neuroscience of the normal aging. This complex process, once considered inevitable or beyond the reach of treatment, has been transformed into an arena of intense investigation and strategic intervention. However, important questions remain about this characterization of the aging brain, and the assumptions it makes about the social, cultural, and biological space occupied by cognition in the older individual and body. The following paper will provide a critical examination of the move from basic experiments on the neurophysiology of experience-dependent plasticity to the growing market for (and public conception of) cognitive aging as a medicalized space for intervention by neuroscience-backed technologies. Entangled with changing concepts of normality, pathology, and self-preservation, we will argue that this new understanding, led by personalized cognitive training strategies, is approaching a point where interdisciplinary research is crucial to provide a holistic and nuanced understanding of the aging process. This new outlook will allow us to move forward in a space where our knowledge, like our new conception of the brain, is never static.