Development and aging of cortical thickness correspond to genetic organization patterns

Changes in cortical thickness and volume after cranial radiation treatment: A systematic review

Cognitive decline has a clear impact on quality of life in patients who have received cranial radiation treatment. The pathophysiological process is most likely multifactorial, with a possible role for decreased cortical thickness and volume. As radiotherapy treatment systems are becoming more sophisticated, precise sparing of vulnerable regions and tissue is possible. This allows radiation oncologists to make treatment more patient-tailored. A systematic search was performed to collect and review all available evidence regarding the effect of cranial radiation treatment on cortical thickness and volume. We searched the Pubmed, Embase and Cochrane databases, with an additional reference check in the Scopus database. Studies that examined cortical changes on MRI within patients as well as between treated and non-treated patients were included. The quality of the studies was assessed with a checklist specially designed for this review. No meta-analysis was performed due to the lack of randomised trials. Out of 1915 publications twenty-one papers were selected, of which fifteen observed cortical changes after radiation therapy. Two papers reported radiation-dependent decrease in cortical thickness within patients one year after radiation treatment, suggesting a clear relation between the two. However, study quality was considered mostly suboptimal, and there was great inhomogeneity between the included studies. This means that, although there has been increasing interest in the effects of radiation treatment on cortex morphology, no reliable conclusion can be drawn based on the currently available evidence. This calls for more research, preferably with a sufficiently large patient population, and adequate methodology.

Assessment of hip fracture risk by cross-sectional strain-energy derived from image-based beam model

BACKGROUND

Clinicians have been looking for a simple and effective biomechanical tool for the assessment of hip fracture risk. Dual-energy X-ray absorptiometry (DXA) is currently the primary bone imaging modality in clinic, and the engineering beam is the simplest model for a mechanical analysis. Therefore, we developed a DXA-based beam model for the above purpose.

METHODS

A beam model of the proximal femur was constructed from the subject's hip DXA image and denoted DXA-beam. Femur stiffness was calculated at cross-sections of interest using areal bone-mineral-density profile. Impact force induced in a sideways fall was applied as a critical loading. Fracture risk index at a cross-section was defined as the ratio of strain-energy induced by the impact force to the allowable strain-energy. A clinic cohort was used to study the discriminability of DXA-beam, which was measured by the area under the curve and odds ratio, both with 95% confidential interval.

FINDINGS

Fracture risk measured by DXA-beam model at the femoral neck [odds ratio 2.23, 95% confidence interval (1.83, 2.57)], inter-trochanter [2.49, (2.14, 3.25)] and sub-trochanter [2.82, (2.38, 3.51)] were strongly associated with hip fracture. The area under the curve by DXA-beam at the femoral neck [0.74, 95% confidence interval (0.70, 0.76)], inter-trochanter [0.77, (0.75, 0.82)] and sub-trochanter [0.76, (0.74, 0.81)] were higher than that by femoral neck bone mineral density [0.71, (0.65, 0.78)].

INTERPRETATION

The DXA-beam model is a simple and yet effective mechanical model. It had promising performance in discrimination of fracture cases from controls.

Genetic Complexity of Cortical Structure: Differences in Genetic and Environmental Factors Influencing Cortical Surface Area and Thickness

Quantifying the genetic architecture of the cerebral cortex is necessary for understanding disease and changes to the brain across the lifespan. Prior work shows that both surface area (SA) and cortical thickness (CT) are heritable. However, we do not yet understand the extent to which region-specific genetic factors (i.e., independent of global effects) play a dominant role in the regional patterning or inter-regional associations across the cortex. Using a population sample of young adult twins (N = 923), we show that the heritability of SA and CT varies widely across regions, generally independent of measurement error. When global effects are controlled for, we detected a complex pattern of genetically mediated clusters of inter-regional associations, which varied between hemispheres. There were generally weak associations between the SA of different regions, except within the occipital lobe, whereas CT was positively correlated within lobar divisions and negatively correlated across lobes, mostly due to genetic covariation. These findings were replicated in an independent sample of twins and siblings (N = 698) from the Human Connectome Project. The different genetic contributions to SA and CT across regions reveal the value of quantifying sources of covariation to appreciate the genetic complexity of cortical structures.

Generalizing age effects on brain structure and cognition: A two-study comparison approach

Normal aging is accompanied by an interindividually variable decline in cognitive abilities and brain structure. This variability, in combination with methodical differences and differences in sample characteristics across studies, pose a major challenge for generalizability of results from different studies. Therefore, the current study aimed at cross-validating age-related differences in cognitive abilities and brain structure (measured using cortical thickness [CT]) in two large independent samples, each consisting of 228 healthy older adults aged between 65 and 85 years: the Longitudinal Healthy Aging Brain (LHAB) database (University of Zurich, Switzerland) and the 1000BRAINS (Research Centre Jülich, Germany). Participants from LHAB showed significantly higher education, physical well-being, and cognitive abilities (processing speed, concept shifting, reasoning, semantic verbal fluency, and vocabulary). In contrast, CT values were larger for participants of 1000BRAINS. Though, both samples showed highly similar age-related differences in both, cognitive abilities and CT. These effects were in accordance with functional aging theories, for example, posterior to anterior shift in aging as was shown for the default mode network. Thus, the current two-study approach provides evidence that independently on heterogeneous metrics of brain structure or cognition across studies, age-related effects on cognitive ability and brain structure can be generalized over different samples, assuming the same methodology is used.

Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study

The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen's d effect size (ES) 0.06-0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08-0.1) and fluid intelligence (ES: 0.04-0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.

Functional Parcellation of the Cerebral Cortex Across the Human Adult Lifespan

Adult aging is associated with differences in structure, function, and connectivity of brain areas. Age-based brain comparisons have typically rested on the assumption that brain areas exhibit a similar spatial organization across age; we evaluate this hypothesis directly. Area parcellation methods that identify locations where resting-state functional correlations (RSFC) exhibit abrupt transitions (boundary-mapping) are used to define cortical areas in cohorts of individuals sampled across a large range of the human adult lifespan (20-93 years). Most of the strongest areal boundaries are spatially consistent across age. Differences in parcellation boundaries are largely explained by differences in cortical thickness and anatomical alignment in older relative to younger adults. Despite the parcellation similarities, age-specific parcellations exhibit better internal validity relative to a young-adult parcellation applied to older adults' data, and age-specific parcels are better able to capture variability in task-evoked functional activity. Incorporating age-specific parcels as nodes in RSFC network analysis reveals that the spatial topography of the brain's large-scale system organization is comparable throughout aging, but confirms that the segregation of systems declines with increasing age. These observations demonstrate that many features of areal organization are consistent across adulthood, and reveal sources of age-related brain variation that contribute to the differences.

Cigarette smoking is associated with cortical thinning in anterior frontal regions, insula and regions showing atrophy in early Alzheimer's Disease

BACKGROUND

Magnetic resonance imaging studies of cigarette smoking-related effects on human brain structure primarily focused on cortical volumes. Much less is known about the effects of smoking on cortical thickness. Smokers and Non-smokers were compared on regional cortical thickness. We predicted smokers would demonstrate greater age-related thinning localized to anterior frontal regions that serve as nodes for the executive, salience, and emotional regulation networks (ESER regions) and those demonstrating significant atrophy in early Alzheimer's Disease (AD regions).

METHODS

Non-smokers (n = 41) and smokers (n = 41), 22-70 years of age, completed a 4 T MRI study. Regional cortical thickness was quantitated via FreeSurfer. In smokers, associations between smoking severity, decision-making, impulsivity, and regional cortical thickness were examined.

RESULTS

Smokers demonstrated cortical thinning in the medial and lateral OFC, insula, entorhinal, fusiform, middle temporal, and Composite AD regions. In Smokers, greater pack-years were associated with thinner lateral OFC, middle temporal, inferior parietal, fusiform, precuneus, and Composite AD regions. In Smokers, poorer decision-making/greater risk taking was related to thinner cortices in caudal ACC, rostral middle frontal and superior frontal gyri, and Composite ESER. Higher self-reported impulsivity was associated with thinner rostral and caudal ACC.

CONCLUSIONS

This study provides additional evidence that cigarette smoking is associated with thinner cortices in regions implicated in the development and maintenance of substance use disorders and in regions demonstrating significant atrophy in early AD. The novel structure-function relationships in Smokers further our understanding of the neurobiological substrates potentially underlying the neuropsychological abnormalities documented in smokers.

The cortical structure of functional networks associated with age-related cognitive abilities in older adults

Age and cortical structure are both associated with cognition, but characterizing this relationship remains a challenge. A popular approach is to use functional network organization of the cortex as an organizing principle for post-hoc interpretations of structural results. In the current study, we introduce two complimentary approaches to structural analyses that are guided by a-priori functional network maps. Specifically, we systematically investigated the relationship of cortical structure (thickness and surface area) of distinct functional networks to two cognitive domains sensitive to age-related decline thought to rely on both common and distinct processes (executive function and episodic memory) in older adults. We quantified the cortical structure of individual functional network's predictive ability and spatial extent (i.e., number of significant regions) with cognition and its mediating role in the age-cognition relationship. We found that cortical thickness, rather than surface area, predicted cognition across the majority of functional networks. The default mode and somatomotor network emerged as particularly important as they appeared to be the only two networks to mediate the age-cognition relationship for both cognitive domains. In contrast, thickness of the salience network predicted executive function and mediated the age-cognition relationship for executive function. These relationships remained significant even after accounting for global cortical thickness. Quantifying the number of regions related to cognition and mediating the age-cognition relationship yielded similar patterns of results. This study provides a potential approach to organize and describe the apparent widespread regional cortical structural relationships with cognition and age in older adults.

Brain structure mediates the association between height and cognitive ability

Height and general cognitive ability are positively associated, but the underlying mechanisms of this relationship are not well understood. Both height and general cognitive ability are positively associated with brain size. Still, the neural substrate of the height-cognitive ability association is unclear. We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to investigate whether the association between height and cognitive ability is mediated by cortical size. In addition to cortical volume, we used genetically, ontogenetically and phylogenetically distinct cortical metrics of total cortical surface area and mean cortical thickness. Height was positively associated with general cognitive ability and total cortical volume and cortical surface area, but not with mean cortical thickness. Mediation models indicated that the well-replicated height-general cognitive ability association is accounted for by individual differences in total cortical volume and cortical surface area (highly heritable metrics related to global brain size), and that the genetic association between cortical surface area and general cognitive ability underlies the phenotypic height-general cognitive ability relationship.

Neurobehavioral correlates of obesity are largely heritable

Obesity is a widespread heritable health condition. Evidence from psychology, cognitive neuroscience, and genetics has proposed links between obesity and the brain. The current study tested whether the heritable variance in body mass index (BMI) is explained by brain and behavioral factors in a large brain imaging cohort that included multiple related individuals. We found that the heritable variance in BMI had genetic correlations 0.25–0.45 with cognitive tests, cortical thickness, and regional brain volume. In particular, BMI was associated with frontal lobe asymmetry and differences in temporal-parietal perceptual systems. Further, we found genetic overlap between certain brain and behavioral factors. In summary, the genetic vulnerability to BMI is expressed in the brain. This may inform intervention strategies.

Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioral factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioral factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (n = 895 siblings). Phenotypically, cortical thickness findings supported the “right brain hypothesis” for obesity. Namely, increased BMI is associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures and increased thickness in parietal-occipital structures in participants with higher BMI supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined a negative association between BMI and visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality–BMI correlations were inconsistent. We then aggregated the effects for each neurobehavioral factor for a behavioral genetics analysis and estimated each factor’s genetic overlap with BMI. Cognitive test scores and brain morphometry had 0.25–0.45 genetic correlations with BMI, and the phenotypic correlations with BMI were 77–89% explained by genetic factors. Neurobehavioral factors also had some genetic overlap with each other. In summary, obesity as measured by BMI has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function and may inform intervention strategies.

Cortical thickness development of human primary visual cortex related to the age of blindness onset

Blindness primarily induces structural alteration in the primary visual cortex (V1). Some studies have found that the early blind subjects had a thicker V1 compared to sighted controls, whereas late blind subjects showed no significant differences in the V1. This implies that the age of blindness onset may exert significant effects on the development of cortical thickness of the V1. However, no previous research used a trajectory of the age of blindness onset-related changes to investigate these effects. Here we explored this issue by mapping the cortical thickness trajectory of the V1 against the age of blindness onset using data from 99 blind individuals whose age of blindness onset ranged from birth to 34 years. We found that the cortical thickness of the V1 could be fitted well with a quadratic curve in both the left (F = 11.59, P = 3 × 10^-5) and right hemispheres (F = 6.54, P = 2 × 10^-3). Specifically, the cortical thickness of the V1 thinned rapidly during childhood and adolescence and did not change significantly thereafter. This trend was not observed in the primary auditory cortex (A1), primary motor cortex (M1), or primary somatosensory cortex (S1). These results provide evidence that an onset of blindness before adulthood significantly affects the cortical thickness of the V1 and suggest a critical period for cortical development of the human V1.

Trajectories of imaging markers in brain aging: the Rotterdam Study

With aging, the brain undergoes several structural changes. These changes reflect the normal aging process and are therefore not necessarily pathologic. In fact, better understanding of these normal changes is an important cornerstone to also disentangle pathologic changes. Several studies have investigated normal brain aging, both cross-sectional and longitudinal, and focused on a broad range of magnetic resonance imaging (MRI) markers. This study aims to comprise the different aspects in brain aging, by performing a comprehensive longitudinal assessment of brain aging, providing trajectories of volumetric (global and lobar; subcortical and cortical), microstructural, and focal (presence of microbleeds, lacunar or cortical infarcts) brain imaging markers in aging and the sequence in which these markers change in aging. Trajectories were calculated on 10,755 MRI scans that were acquired between 2005 and 2016 among 5286 persons aged 45 years and older from the population-based Rotterdam Study. The average number of MRI scans per participant was 2 scans (ranging from 1 to 4 scans), with a mean interval between MRI scans of 3.3 years (ranging from 0.2 to 9.5 years) and an average follow-up time of 5.2 years (ranging from 0.3 to 9.8 years). We found that trajectories of the different volumetric, microstructural, and focal markers show nonlinear curves, with accelerating change with advancing age. We found earlier acceleration of change in global and lobar volumetric and microstructural markers in men compared with women. For subcortical and cortical volumes, results show a mix of more linear and nonlinear trajectories, either increasing, decreasing, or stable over age for the subcortical and cortical volume and thickness. Differences between men and women are visible in several parcellations; however, the direction of these differences is mixed. The presence of focal markers show a nonlinear increase with age, with men having a higher probability for cortical or lacunar infarcts. The data presented in this study provide insight into the normal aging process in the brain, and its variability.

Dynamic Patterns of Brain Structure-Behavior Correlation Across the Lifespan

Although the brain/behavior correlation is one of the premises of cognitive neuroscience, there is still no consensus about the relationship between brain measures and cognitive function, and only little is known about the effect of age on this relationship. We investigated the age-associated variations on the spatial patterns of cortical thickness correlates of four cognitive domains. We showed that the spatial distribution of the cortical thickness correlates of each cognitive domain is distinctive and depicts varying age-association differences across the adult lifespan. Specifically, the present study provides evidence that distinct cognitive domains are associated with unique structural patterns in three adulthood periods: Early, middle, and late adulthood. These findings suggest a dynamic interaction between multiple neural substrates supporting each cognitive domain across the adult lifespan.

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks.

Neuroanatomical characteristics of youths with prenatal opioid and poly-drug exposure

Neuroanatomical and cognitive differences have been documented during childhood between children with prenatal opioid- and poly-drug exposure and controls in small samples. We investigated whether these differences persisted in larger samples of youth at older ages. Quantitative MRI and cognitive data were compared between 38 youths in the risk group and 44 youths in the non-exposed group (aged 17 to 22 years) who had been followed prospectively since birth. Most drug-exposed youths (84%) moved to permanent foster or adoptive homes before one year of age. The drug-exposed group displayed smaller neuroanatomical volumes (0.70 SD difference in total brain volume, p = 0.001), smaller cortical surface areas and thinner cortices than the comparison group. The birth weight accounted for some of the intergroup differences. Neuroanatomical characteristics partially mediated group differences in cognitive function. The present study cannot differentiate between causal factors but indicates persistent neurocognitive differences associated with prenatal opioid or poly-drug exposure.

Automated Segmentation of Head Computed Tomography Images Using FSL

OBJECTIVE

The aim of this study was to investigate the use of one magnetic resonance image-processing tool, FSL, in its ability to perform automated segmentation of computed tomographic images of the brain.

METHODS

Head computed tomography (CT) images were brain extracted and segmented using the FSL tools BET and FAST, respectively. The products of segmentation were analyzed by histogram. The impact of image intensity inhomogeneity correction was investigated using simulated bias fields, 14 routine head CT scans, and selected illustrative clinical cases.

RESULTS

FSL FAST performs direct segmentation of head CT images, permitting quantitation of gray and white matter densities and volumes, achieving a more complete segmentation than masking methods. "Bias field correction" reduced the covariance of image signal intensities of the total brain and gray matter images (P < 0.01). Correction is larger when the effects of beam hardening and radiation scatter are larger, resulting in improved segmentation.

CONCLUSIONS

FSL FAST enables direct segmentation of head CT images.

Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults

Despite extensive research in the field of aging neuroscience, it still remains unclear whether age related cortical changes can be detected in different functional networks of younger adults and whether these networks respond identically to healthy aging. We collected high-resolution brain anatomical data from 56 young healthy adults (mean age = 30.8 ± 8.1 years, 29 males). We performed whole brain parcellation into seven functional networks, including visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal and default mode networks. We estimated intracranial volume (ICV) and averaged cortical thickness (CT), cortical surface area (CSA) and cortical volume (CV) over each hemisphere as well as for each network. Averaged cortical measures over each hemisphere, especially CT and CV, were significantly lower in older individuals compared to younger ones (one-way ANOVA, p < 0.05, corrected for multiple comparisons). There were negative correlations between age and averaged CT and CV over each hemisphere (p < 0.05, corrected for multiple comparisons) as well as between age and ICV (p = 0.05). Network level analysis showed that age was negatively correlated with CT for all functional networks (p < 0.05, corrected for multiple comparisons), apart from the limbic network. While age was unrelated to CSA, it was negatively correlated with CV across several functional networks (p < 0.05, corrected for multiple comparisons). We also showed positive associations between CV and CT and between CV and CSA for all networks (p < 0.05, corrected for multiple comparisons). We interpret the lack of association between age and CT of the limbic network as evidence that the limbic system may be particularly resistant to age-related declines during this period of life, whereas the significant age-related declines in averaged CT over each hemisphere as well as in all other six networks suggests that CT may serve as a reliable biomarker to capture the effect of normal aging. Due to the simultaneous dependence of CV on CT and CSA, CV was unable to identify such effects of normal aging consistently for the other six networks, but there were negative associations observed between age and averaged CV over each hemisphere as well as between age and ICV. Our findings suggest that the identification of early cortical changes within various functional networks during normal aging might be useful for predicting the effect of aging on the efficiency of functional performance even during early adulthood.

Structural brain development: A review of methodological approaches and best practices

Continued advances in neuroimaging technologies and statistical modelling capabilities have improved our knowledge of structural brain development in children and adolescents. While this has provided an increasingly nuanced understanding of brain development, the field is still plagued by inconsistent findings. This review highlights the methodological diversity in existing longitudinal magnetic resonance imaging (MRI) studies on structural brain development during childhood and adolescence, and addresses how such variation might contribute to inconsistencies in the literature. We discuss the impact of method choices at multiple decision points across the research process, from study design and sample selection, to image processing and statistical analysis. We also highlight the extent to which different methodological considerations have been empirically examined, drawing attention to specific areas that would benefit from future investigation. Where appropriate, we recommend certain best practices that would be beneficial for the field to adopt, including greater completeness and transparency in reporting methods, in order to ultimately develop an accurate and detailed understanding of normative child and adolescent brain development.

Greater body mass index is associated with reduced frontal cortical volumes among adolescents with bipolar disorder

BACKGROUND

Higher body mass index (BMI) and obesity is common among youth with bipolar disorder (BD) and is associated with greater psychiatric illness severity, including suicidality. Obesity has been associated with frontal, temporal and subcortical volumetric reductions in adults with BD. We examined the neurostructural correlates of BMI in adolescents early in their course of BD.

METHODS

We processed T1-weighted images of adolescents with BD and psychiatrically healthy controls using FreeSurfer to derive a priori region of interest (ROI) volumes/cortical thickness for the frontal lobe (FL), prefrontal cortex (PFC) and orbitofrontal cortex (OFC) as well as volumes for the amygdala and hippocampus. General linear models assessed the association between BMI and the ROIs, controlling for age, sex and intracranial volume. We also conducted exploratory within-BD group and whole brain vertex-wise analyses.

RESULTS

We included 40 adolescents with BD and 48 controls in our analyses. In addition to a main effect of BMI on the ROIs, there were significant diagnosis × BMI interaction effects on FL volumes. In the BD group only, BMI was negatively associated with FL, OFC and PFC cortical thickness. Whole brain analysis of BMI-volume correlations revealed 2 significant interaction clusters: 1 in the medial OFC and 1 in the caudal anterior cingulate cortex, with BD showing a stronger negative correlation.

LIMITATIONS

Reliance on BMI rather than a more nuanced measure of obesity may have influenced the findings.

CONCLUSION

Our results suggest that elevated BMI among adolescents with BD is associated with frontal neurostructural differences that are not observed in controls. Prospective studies examining the direction of the observed associations and the effect of BMI optimization on brain structure in adolescents with BD are warranted.

Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI

The human brain can be modeled as multiple interrelated shapes (or a multishape), each for characterizing one aspect of the brain, such as the cortex and white matter pathways. Predicting the developing multishape is a very challenging task due to the contrasting nature of the developmental trajectories of the constituent shapes: smooth for the cortical surface and non-smooth for white matter tracts due to changes such as bifurcation. We recently addressed this problem and proposed an approach for predicting the multishape developmental spatiotemporal trajectories of infant brains based only on neonatal MRI data using a set of geometric, dynamic, and fiber-to-surface connectivity features. In this paper, we propose two key innovations to further improve the prediction of multishape evolution. First, for a more accurate cortical surface prediction, instead of simply relying on one neonatal atlas to guide the prediction of the multishape, we propose to use multiple neonatal atlases to build a spatially heterogeneous atlas using the multidirectional varifold representation. This individualizes the atlas by locally maximizing its similarity to the testing baseline cortical shape for each cortical region, thereby better representing the baseline testing cortical surface, which founds the multishape prediction process. Second, for temporally consistent fiber prediction, we propose to reliably estimate spatiotemporal connectivity features using low-rank tensor completion, thereby capturing the variability and richness of the temporal development of fibers. Experimental results confirm that the proposed variants significantly improve the prediction performance of our original multishape prediction framework for both cortical surfaces and fiber tracts shape at 3, 6, and 9 months of age. Our pioneering model will pave the way for learning how to predict the evolution of anatomical shapes with abnormal changes. Ultimately, devising accurate shape evolution prediction models that can help quantify and predict the severity of a brain disorder as it progresses will be of great aid in individualized treatment planning.

Prefrontal gray matter volume mediates genetic risks for obesity

Genetic and neuroimaging research has identified neurobiological correlates of obesity. However, evidence for an integrated model of genetic risk and brain structural alterations in the pathophysiology of obesity is still absent. Here we investigated the relationship between polygenic risk for obesity, gray matter structure and body mass index (BMI) by the use of univariate and multivariate analyses in two large, independent cohorts (n=330 and n=347). Higher BMI and higher polygenic risk for obesity were significantly associated with medial prefrontal gray matter decrease, and prefrontal gray matter was further shown to significantly mediate the effect of polygenic risk for obesity on BMI in both samples. Building on this, the successful individualized prediction of BMI by means of multivariate pattern classification algorithms trained on whole-brain imaging data and external validations in the second cohort points to potential clinical applications of this imaging trait marker.

A peripheral epigenetic signature of immune system genes is linked to neocortical thickness and memory

Increasing age is tightly linked to decreased thickness of the human neocortex. The biological mechanisms that mediate this effect are hitherto unknown. The DNA methylome, as part of the epigenome, contributes significantly to age-related phenotypic changes. Here, we identify an epigenetic signature that is associated with cortical thickness (P=3.86 × 10⁻⁸) and memory performance in 533 healthy young adults. The epigenetic effect on cortical thickness was replicated in a sample comprising 596 participants with major depressive disorder and healthy controls. The epigenetic signature mediates partially the effect of age on cortical thickness (P<0.001). A multilocus genetic score reflecting genetic variability of this signature is associated with memory performance (P=0.0003) in 3,346 young and elderly healthy adults. The genomic location of the contributing methylation sites points to the involvement of specific immune system genes. The decomposition of blood methylome-wide patterns bears considerable potential for the study of brain-related traits.

Cortical thickness has high heritability estimates and is known to be influenced by genetic factors. Here, Freytag and colleagues show that DNA methylation patterns of peripheral blood monocytes are also correlated with cortical thickness and memory performance in human.

Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion

During adolescence, the human cortex undergoes substantial remodeling to support a rapid expansion of behavioral repertoire. Accurately quantifying these changes is a prerequisite for understanding normal brain development, as well as the neuropsychiatric disorders that emerge in this vulnerable period. Past accounts have demonstrated substantial regional heterogeneity in patterns of brain development, but frequently have been limited by small samples and analytics that do not evaluate complex multivariate imaging patterns. Capitalizing on recent advances in multivariate analysis methods, we used nonnegative matrix factorization (NMF) to uncover coordinated patterns of cortical development in a sample of 934 youths ages 8-20, who completed structural neuroimaging as part of the Philadelphia Neurodevelopmental Cohort. Patterns of structural covariance (PSCs) derived by NMF were highly reproducible over a range of resolutions, and differed markedly from common gyral-based structural atlases. Moreover, PSCs were largely symmetric and showed correspondence to specific large-scale functional networks. The level of correspondence was ordered according to their functional role and position in the evolutionary hierarchy, being high in lower-order visual and somatomotor networks and diminishing in higher-order association cortex. Furthermore, PSCs showed divergent developmental associations, with PSCs in higher-order association cortex networks showing greater changes with age than primary somatomotor and visual networks. Critically, such developmental changes within PSCs were significantly associated with the degree of evolutionary cortical expansion. Together, our findings delineate a set of structural brain networks that undergo coordinated cortical thinning during adolescence, which is in part governed by evolutionary novelty and functional specialization.

Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion

During adolescence, the human cortex undergoes substantial remodeling to support a rapid expansion of behavioral repertoire. Accurately quantifying these changes is a prerequisite for understanding normal brain development, as well as the neuropsychiatric disorders that emerge in this vulnerable period. Past accounts have demonstrated substantial regional heterogeneity in patterns of brain development, but frequently have been limited by small samples and analytics that do not evaluate complex multivariate imaging patterns. Capitalizing on recent advances in multivariate analysis methods, we used nonnegative matrix factorization (NMF) to uncover coordinated patterns of cortical development in a sample of 934 youths ages 8-20, who completed structural neuroimaging as part of the Philadelphia Neurodevelopmental Cohort. Patterns of structural covariance (PSCs) derived by NMF were highly reproducible over a range of resolutions, and differed markedly from common gyral-based structural atlases. Moreover, PSCs were largely symmetric and showed correspondence to specific large-scale functional networks. The level of correspondence was ordered according to their functional role and position in the evolutionary hierarchy, being high in lower-order visual and somatomotor networks and diminishing in higher-order association cortex. Furthermore, PSCs showed divergent developmental associations, with PSCs in higher-order association cortex networks showing greater changes with age than primary somatomotor and visual networks. Critically, such developmental changes within PSCs were significantly associated with the degree of evolutionary cortical expansion. Together, our findings delineate a set of structural brain networks that undergo coordinated cortical thinning during adolescence, which is in part governed by evolutionary novelty and functional specialization.

Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness

Before we can assess and interpret how developmental changes in human brain structure relate to cognition, affect, and motivation, and how these processes are perturbed in clinical or at-risk populations, we must first precisely understand typical brain development and how changes in different structural components relate to each other. We conducted a multisample magnetic resonance imaging study to investigate the development of cortical volume, surface area, and thickness, as well as their inter-relationships, from late childhood to early adulthood (7-29 years) using four separate longitudinal samples including 388 participants and 854 total scans. These independent datasets were processed and quality-controlled using the same methods, but analyzed separately to study the replicability of the results across sample and image-acquisition characteristics. The results consistently showed widespread and regionally variable nonlinear decreases in cortical volume and thickness and comparably smaller steady decreases in surface area. Further, the dominant contributor to cortical volume reductions during adolescence was thinning. Finally, complex regional and topological patterns of associations between changes in surface area and thickness were observed. Positive relationships were seen in sulcal regions in prefrontal and temporal cortices, while negative relationships were seen mainly in gyral regions in more posterior cortices. Collectively, these results help resolve previous inconsistencies regarding the structural development of the cerebral cortex from childhood to adulthood, and provide novel insight into how changes in the different dimensions of the cortex in this period of life are inter-related.SIGNIFICANCE STATEMENT Different measures of brain anatomy develop differently across adolescence. Their precise trajectories and how they relate to each other throughout development are important to know if we are to fully understand both typical development and disorders involving aberrant brain development. However, our understanding of such trajectories and relationships is still incomplete. To provide accurate characterizations of how different measures of cortical structure develop, we performed an MRI investigation across four independent datasets. The most profound anatomical change in the cortex during adolescence was thinning, with the largest decreases observed in the parietal lobe. There were complex regional patterns of associations between changes in surface area and thickness, with positive relationships seen in sulcal regions in prefrontal and temporal cortices, and negative relationships seen mainly in gyral regions in more posterior cortices.

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.

Quality Control of Structural MRI Images Applied Using FreeSurfer-A Hands-On Workflow to Rate Motion Artifacts

In structural magnetic resonance imaging motion artifacts are common, especially when not scanning healthy young adults. It has been shown that motion affects the analysis with automated image-processing techniques (e.g., FreeSurfer). This can bias results. Several developmental and adult studies have found reduced volume and thickness of gray matter due to motion artifacts. Thus, quality control is necessary in order to ensure an acceptable level of quality and to define exclusion criteria of images (i.e., determine participants with most severe artifacts). However, information about the quality control workflow and image exclusion procedure is largely lacking in the current literature and the existing rating systems differ. Here, we propose a stringent workflow of quality control steps during and after acquisition of T1-weighted images, which enables researchers dealing with populations that are typically affected by motion artifacts to enhance data quality and maximize sample sizes. As an underlying aim we established a thorough quality control rating system for T1-weighted images and applied it to the analysis of developmental clinical data using the automated processing pipeline FreeSurfer. This hands-on workflow and quality control rating system will aid researchers in minimizing motion artifacts in the final data set, and therefore enhance the quality of structural magnetic resonance imaging studies.

Human Connectomics across the Life Span

Connectomics has enhanced our understanding of neurocognitive development and decline by the integration of network sciences into studies across different stages of the human life span. However, these studies commonly occurred independently, missing the opportunity to test integrated models of the dynamical brain organization across the entire life span. In this review article, we survey empirical findings in life-span connectomics and propose a generative framework for computationally modeling the connectome over the human life span. This framework highlights initial findings that across the life span, the human connectome gradually shifts from an 'anatomically driven' organization to one that is more 'topological'. Finally, we consider recent advances that are promising to provide an integrative and systems perspective of human brain plasticity as well as underscore the pitfalls and challenges.

Distinct Genetic Influences on Cortical and Subcortical Brain Structures

This study examined the heritability of brain grey matter structures in a subsample of older adult twins (93 MZ and 68 DZ twin pairs; mean age 70 years) from the Older Australian Twins Study. The heritability estimates of subcortical regions ranged from 0.41 (amygdala) to 0.73 (hippocampus), and of cortical regions, from 0.55 (parietal lobe) to 0.78 (frontal lobe). Corresponding structures in the two hemispheres were influenced by the same genetic factors and high genetic correlations were observed between the two hemispheric regions. There were three genetically correlated clusters, comprising (i) the cortical lobes (frontal, temporal, parietal and occipital lobes); (ii) the basal ganglia (caudate, putamen and pallidum) with weak genetic correlations with cortical lobes, and (iii) the amygdala, hippocampus, thalamus and nucleus accumbens grouped together, which genetically correlated with both basal ganglia and cortical lobes, albeit relatively weakly. Our study demonstrates a complex but patterned and clustered genetic architecture of the human brain, with divergent genetic determinants of cortical and subcortical structures, in particular the basal ganglia.

Global Visual Motion Sensitivity: Associations with Parietal Area and Children's Mathematical Cognition

Sensitivity to global visual motion has been proposed as a signature of brain development, related to the dorsal rather than ventral cortical stream. Thresholds for global motion have been found to be elevated more than for global static form in many developmental disorders, leading to the idea of "dorsal stream vulnerability." Here we explore the association of global motion thresholds with individual differences in children's brain development, in a group of typically developing 5- to 12-year-olds. Good performance was associated with a relative increase in parietal lobe surface area, most strongly around the intraparietal sulcus and decrease in occipital area. In line with the involvement of intraparietal sulcus, areas in visuospatial and numerical cognition, we also found that global motion performance was correlated with tests of visuomotor integration and numerical skills. Individual differences in global form detection showed none of these anatomical or cognitive correlations. This suggests that the correlations with motion sensitivity are unlikely to reflect general perceptual or attentional abilities required for both form and motion. We conclude that individual developmental variations in global motion processing are not linked to greater area in the extrastriate visual areas, which initially process such motion, but in the parietal systems that make decisions based on this information. The overlap with visuospatial and numerical abilities may indicate the anatomical substrate of the "dorsal stream vulnerability" proposed as characterizing neurodevelopmental disorders.

Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome

How does human brain structure mature during adolescence? We used MRI to measure cortical thickness and intracortical myelination in 297 population volunteers aged 14-24 y old. We found and replicated that association cortical areas were thicker and less myelinated than primary cortical areas at 14 y. However, association cortex had faster rates of shrinkage and myelination over the course of adolescence. Age-related increases in cortical myelination were maximized approximately at the internal layer of projection neurons. Adolescent cortical myelination and shrinkage were coupled and specifically associated with a dorsoventrally patterned gene expression profile enriched for synaptic, oligodendroglial- and schizophrenia-related genes. Topologically efficient and biologically expensive hubs of the brain anatomical network had greater rates of shrinkage/myelination and were associated with overexpression of the same transcriptional profile as cortical consolidation. We conclude that normative human brain maturation involves a genetically patterned process of consolidating anatomical network hubs. We argue that developmental variation of this consolidation process may be relevant both to normal cognitive and behavioral changes and the high incidence of schizophrenia during human brain adolescence.

Neurodevelopmental origins of lifespan changes in brain and cognition

Neurodevelopmental origins of functional variation in older age are increasingly being acknowledged, but identification of how early factors impact human brain and cognition throughout life has remained challenging. Much focus has been on age-specific mechanisms affecting neural foundations of cognition and their change. In contrast to this approach, we tested whether cerebral correlates of general cognitive ability (GCA) in development could be extended to the rest of the lifespan, and whether early factors traceable to prenatal stages, such as birth weight and parental education, may exert continuous influences. We measured the area of the cerebral cortex in a longitudinal sample of 974 individuals aged 4-88 y (1,633 observations). An extensive cortical region was identified wherein area related positively to GCA in development. By tracking area of the cortical region identified in the child sample throughout the lifespan, we showed that the cortical change trajectories of higher and lower GCA groups were parallel through life, suggesting continued influences of early life factors. Birth weight and parental education obtained from the Norwegian Mother-Child Cohort study were identified as such early factors of possible life-long influence. Support for a genetic component was obtained in a separate twin sample (Vietnam Era Twin Study of Aging), but birth weight in the child sample had an effect on cortical area also when controlling for possible genetic differences in terms of parental height. Our results provide novel evidence for stability in brain-cognition relationships throughout life, and indicate that early life factors impact brain and cognition for the entire life course.

Trajectories of adolescent conduct problems in relation to cortical thickness development: a longitudinal MRI study

Multiple cross-sectional imaging studies have identified structural abnormalities in prefrontal, temporal and limbic regions related to conduct problems (CPs). However, the relationship between development of such neurobiological deficits and developmental pathways of CPs has remained unclear. The current study investigated distinct trajectories of CP and related trajectories of cortical thickness within a community-based sample of adolescents (n=239), age range 12-19, to address this gap. Three trajectory classes were revealed using latent class growth analyses (LCGAs), comprising a 'desisting' CP group, an 'intermediate' CP group and a 'stable low' CP group. Structural magnetic resonance imaging (MRI) scans were collected with a subgroup of 171 adolescents at three waves throughout adolescence (ages 12, 16 and 19). Generalized estimating equation (GEE) analysis-comparing longitudinal changes in cortical thickness and subcortical volume between CP groups for several regions of interest (ROIs)-showed that these CP groups had differential trajectories of cortical thickness in the dorsolateral prefrontal cortex (dl-PFC), and the anterior cingulate cortex (ACC), and volume of the hippocampus. Adolescents in the desisting CP group showed an attenuation of the typical pattern of cortical thinning as present in the intermediate and stable low CP groups, in addition to an exaggeration of the typical pattern of hippocampal volume increase. These findings suggest that a deviant cortical thickness trajectory was related to a desisting CP pathway across adolescence. Such deviant neurodevelopmental growth trajectories may act as an underlying mechanism for developmental CP pathways, and possibly distinguish desisting antisocial adolescents.