Patterns of coordinated anatomical change in human cortical development: a longitudinal neuroimaging study of maturational coupling

Sex-specific contribution of DHEA-cortisol ratio to prefrontal-hippocampal structural development, cognitive abilities and personality traits

Although dehydroepiandrosterone (DHEA) may exert neuroprotective effects in the developing brain, prolonged or excessive elevations in cortisol may exert neurotoxic effects. The ratio between DHEA and cortisol (DC ratio) has been linked to internalising and externalising disorders, as well as cognitive performance, supporting the clinical relevance of this hormonal ratio during development. However, the brain mechanisms by which these effects may be mediated have not yet been identified. Furthermore, although there is evidence that the effects of cortisol in the central nervous system may be sexually dimorphic in humans, the opposite is true for DHEA, with human studies showing no sex-specific associations in cortical thickness, cortico-amygdalar or cortico-hippocampal structural covariance. Therefore, it remains unclear whether sex moderates the developmental associations between DC ratio, brain structure, cognition and behaviour. In the present study, we examined the associations between DC ratio, structural covariance of the hippocampus with whole-brain cortical thickness, and measures of personality, behaviour and cognition in a longitudinal sample of typically developing children, adolescents and young adults aged 6-22 years (N = 225 participants [F = 128]; 355 scans [F = 208]), using mixed effects models that accounted for both within- and between-subject variances. We found sex-specific interactions between DC ratio and anterior cingulate cortex-hippocampal structural covariance, with higher DC ratios being associated with a more negative covariance between these structures in girls, and a more positive covariance in boys. Furthermore, the negative prefrontal-hippocampal structural covariance found in girls was associated with higher verbal memory and mathematical ability, whereas the positive covariance found in boys was associated with lower cooperativeness and reward dependence personality traits. These findings support the notion that the ratio between DHEA and cortisol levels may contribute, at least in part, to the development of sex differences in cognitive abilities, as well as risk for internalising/externalising disorders, via an alteration in prefrontal-hippocampal structure during the transition from childhood to adulthood.

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.

Individual variation in longitudinal postnatal development of the primate brain

Quantifying individual variation in postnatal brain development can provide insight into cognitive diversity within a population and the aetiology of common neuropsychiatric and neurodevelopmental disorders. Non-invasive studies of the non-human primate can aid understanding of human brain development, facilitating longitudinal analysis during early postnatal development when comparative human populations are difficult to sample. In this study, we perform analysis of a longitudinal MRI dataset of 32 macaques, each with up to five magnetic resonance imaging (MRI) scans acquired between 3 and 36 months of age. Using nonlinear mixed effects model we derive growth trajectories for whole brain, cortical and subcortical grey matter, cerebral white matter and cerebellar volume. We then test the association between individual variation in postnatal tissue volumes and birth weight. We report nonlinear growth models for all tissue compartments, as well as significant variation in total intracranial volume between individuals. We also demonstrate that regional subcortical grey matter varies both in total volume and rate of change between individuals and is associated with differences in birth weight. This supports evidence that birth weight may act as a marker of subsequent brain development and highlights the importance of longitudinal MRI analysis in developmental studies.

Role of DHEA and cortisol in prefrontal-amygdalar development and working memory

There is accumulating evidence that both dehydroepiandrosterone (DHEA) and cortisol play an important role in regulating physical maturation and brain development. High DHEA levels tend to be associated with neuroprotective and indirect anabolic effects, while high cortisol levels tend to be associated with catabolic and neurotoxic properties. Previous literature has linked the ratio between DHEA and cortisol levels (DC ratio) to disorders of attention, emotional regulation and conduct, but little is known as to the relationship between this ratio and brain development. Due to the extensive links between the amygdala and the cortex as well as the known amygdalar involvement in emotional regulation, we examined associations between DC ratio, structural covariance of the amygdala with whole-brain cortical thickness, and validated report-based measures of attention, working memory, internalizing and externalizing symptoms, in a longitudinal sample of typically developing children and adolescents 6-22 years of age. We found that DC ratio predicted covariance between amygdalar volume and the medial anterior cingulate cortex, particularly in the right hemisphere. DC ratio had a significant indirect effect on working memory through its impact on prefrontal-amygdalar covariance, with higher DC ratios associated with a prefrontal-amygdalar covariance pattern predictive of higher scores on a measure of working memory. Taken together, these findings support the notion, as suggested by animal and in vitro studies, that there are opposing effects of DHEA and cortisol on brain development in humans, and that these effects may especially target prefrontal-amygdalar development and working memory, in a lateralized fashion.

Progressive dissociation of cortical and subcortical network development in children with new-onset juvenile myoclonic epilepsy

OBJECTIVE

Structural and functional magnetic resonance imaging (MRI) studies have consistently documented cortical and subcortical abnormalities in patients with juvenile myoclonic epilepsy (JME). However, little is known about how these structural abnormalities emerge from the time of epilepsy onset and how network interactions between and within cortical and subcortical regions may diverge in youth with JME compared to typically developing children.

METHODS

We examined prospective covariations of volumetric differences derived from high-resolution structural MRI during the first 2 years of epilepsy diagnosis in a group of youth with JME (n = 21) compared to healthy controls (n = 22). We indexed developmental brain changes using graph theory by computing network metrics based on the correlation of the cortical and subcortical structural covariance near the time of epilepsy and 2 years later.

RESULTS

Over 2 years, normally developing children showed modular cortical development and network integration between cortical and subcortical regions. In contrast, children with JME developed a highly correlated and less modular cortical network, which was atypically dissociated from subcortical structures. Furthermore, the JME group also presented higher clustering and lower modularity indices than controls, indicating weaker modules or communities. The local efficiency in JME was higher than controls across the majority of cortical nodes. Regarding network hubs, controls presented a higher number than youth with JME that were spread across the brain with ample representation from the different modules. In contrast, children with JME showed a lower number of hubs that were mainly from one module and comprised mostly subcortical structures.

SIGNIFICANCE

Youth with JME prospectively developed a network of highly correlated cortical regions dissociated from subcortical structures during the first 2 years after epilepsy onset. The cortical-subcortical network dissociation provides converging insights into the disparate literature of cortical and subcortical abnormalities found in previous studies.

Adolescence as a neurobiological critical period for the development of higher-order cognition

The transition from adolescence to adulthood is characterized by improvements in higher-order cognitive abilities and corresponding refinements of the structure and function of the brain regions that support them. Whereas the neurobiological mechanisms that govern early development of sensory systems are well-understood, the mechanisms that drive developmental plasticity of association cortices, such as prefrontal cortex (PFC), during adolescence remain to be explained. In this review, we synthesize neurodevelopmental findings at the cellular, circuit, and systems levels in PFC and evaluate them through the lens of established critical period (CP) mechanisms that guide early sensory development. We find remarkable correspondence between these neurodevelopmental processes and the mechanisms driving CP plasticity, supporting the hypothesis that adolescent development is driven by CP mechanisms that guide the rapid development of neurobiology and cognitive ability during adolescence and their subsequent stability in adulthood. Critically, understanding adolescence as a CP not only provides a mechanism for normative adolescent development, it provides a framework for understanding the role of experience and neurobiology in the emergence of psychopathology that occurs during this developmental period.

A robust dissociation among the language, multiple demand, and default mode networks: Evidence from inter-region correlations in effect size

Complex cognitive processes, including language, rely on multiple mental operations that are carried out by several large-scale functional networks in the frontal, temporal, and parietal association cortices of the human brain. The central division of cognitive labor is between two fronto-parietal bilateral networks: (a) the multiple demand (MD) network, which supports executive processes, such as working memory and cognitive control, and is engaged by diverse task domains, including language, especially when comprehension gets difficult; and (b) the default mode network (DMN), which supports introspective processes, such as mind wandering, and is active when we are not engaged in processing external stimuli. These two networks are strongly dissociated in both their functional profiles and their patterns of activity fluctuations during naturalistic cognition. Here, we focus on the functional relationship between these two networks and a third network: (c) the fronto-temporal left-lateralized "core" language network, which is selectively recruited by linguistic processing. Is the language network distinct and dissociated from both the MD network and the DMN, or is it synchronized and integrated with one or both of them? Recent work has provided evidence for a dissociation between the language network and the MD network. However, the relationship between the language network and the DMN is less clear, with some evidence for coordinated activity patterns and similar response profiles, perhaps due to the role of both in semantic processing. Here we use a novel fMRI approach to examine the relationship among the three networks: we measure the strength of activations in different language, MD, and DMN regions to functional contrasts typically used to identify each network, and then test which regions co-vary in their contrast effect sizes across 60 individuals. We find that effect sizes correlate strongly within each network (e.g., one language region and another language region, or one DMN region and another DMN region), but show little or no correlation for region pairs across networks (e.g., a language region and a DMN region). Thus, using our novel method, we replicate the language/MD network dissociation discovered previously with other approaches, and also show that the language network is robustly dissociated from the DMN, overall suggesting that these three networks contribute to high-level cognition in different ways and, perhaps, support distinct computations. Inter-individual differences in effect sizes therefore do not simply reflect general differences in vascularization or attention, but exhibit sensitivity to the functional architecture of the brain. The strength of activation in each network can thus be probed separately in studies that attempt to link neural variability to behavioral or genetic variability.

A Network Neuroscience Approach to Typical and Atypical Brain Development

Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions.

Adolescent Tuning of Association Cortex in Human Structural Brain Networks

Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organization of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N = 297 healthy participants, aged 14–24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge

Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization by integrating information on both brain structure and function. In particular, incorporating anatomical knowledge leads to desirable outcomes such as increased accuracy in brain network estimates and greater reproducibility of topological features across scanning sessions. Despite the clear advantages, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures due to inherent deficiencies in existing imaging technology. This calls for the development of advanced network modeling tools that appropriately incorporate anatomical structure in constructing brain functional networks. We propose a hierarchical Bayesian Gaussian graphical modeling approach which models the brain functional networks via sparse precision matrices whose degree of edge specific shrinkage is a random variable that is modeled using both anatomical structure and an independent baseline component. The proposed approach adaptively shrinks functional connections and flexibly identifies functional connections supported by structural connectivity knowledge. This enables robust brain network estimation even in the presence of misspecified anatomical knowledge, while accommodating heterogeneity in the structure-function relationship. We implement the approach via an efficient optimization algorithm which yields maximum a posteriori estimates. Extensive numerical studies involving multiple functional network structures reveal the clear advantages of the proposed approach over competing methods in accurately estimating brain functional connectivity, even when the anatomical knowledge is misspecified up to a certain degree. An application of the approach to data from the Philadelphia Neurodevelopmental Cohort (PNC) study reveals gender based connectivity differences across multiple age groups, and higher reproducibility in the estimation of network metrics compared to alternative methods.

Structural brain anomalies in healthy adolescents in the NCANDA cohort: relation to neuropsychological test performance, sex, and ethnicity

Structural MRI of volunteers deemed "normal" following clinical interview provides a window into normal brain developmental morphology but also reveals unexpected dysmorphology, commonly known as "incidental findings." Although unanticipated, these anatomical findings raise questions regarding possible treatment that could even ultimately require neurosurgical intervention, which itself carries significant risk but may not be indicated if the anomaly is nonprogressive or of no functional consequence. Neuroradiological readings of 833 structural MRI from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) cohort found an 11.8 % incidence of brain structural anomalies, represented proportionately across the five collection sites and ethnic groups. Anomalies included 26 mega cisterna magna, 15 subarachnoid cysts, 12 pineal cysts, 12 white matter dysmorphologies, 5 tonsillar ectopias, 5 prominent perivascular spaces, 5 gray matter heterotopias, 4 pituitary masses, 4 excessively large or asymmetrical ventricles, 4 cavum septum pellucidum, 3 developmental venous anomalies, 1 exceptionally large midsagittal vein, and single cases requiring clinical followup: cranio-cervical junction stenosis, parietal cortical mass, and Chiari I malformation. A case of possible demyelinating disorder (e.g., neuromyelitis optica or multiple sclerosis) newly emerged at the 1-year NCANDA followup, requiring clinical referral. Comparing test performance of the 98 anomalous cases with 619 anomaly-free no-to-low alcohol consuming adolescents revealed significantly lower scores on speed measures of attention and motor functions; these differences were not attributed to any one anomaly subgroup. Further, we devised an automated approach for quantifying posterior fossa CSF volumes for detection of mega cisterna magna, which represented 26.5 % of clinically identified anomalies. Automated quantification fit a Gaussian distribution with a rightward skew. Using a 3SD cut-off, quantification identified 22 of the 26 clinically-identified cases, indicating that cases with percent of CSF in the posterior-inferior-middle aspect of the posterior fossa ≥3SD merit further review, and support complementing clinical readings with objective quantitative analysis. Discovery of asymptomatic brain structural anomalies, even when no clinical action is indicated, can be disconcerting to the individual and responsible family members, raising a disclosure dilemma: refrain from relating the incidental findings to avoid unnecessary alarm or anxiety; or alternatively, relate the neuroradiological findings as "normal variants" to the study volunteers and family, thereby equipping them with knowledge for the future should they have the occasion for a brain scan following an illness or accident that the incidental findings predated the later event.

On testing for spatial correspondence between maps of human brain structure and function

A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence between two or more brain maps. This "correspondence problem" affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis (https://www.github.com/spin-test). This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data.

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings. Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed. We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

Structural Covariance Networks in Children with Autism or ADHD

Background

While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics.

Method

Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69). Regional cortical thickness was used to construct the structural covariance networks. This was analysed in a theoretical framework examining potential differences in long and short-range connectivity, with a specific focus on relation between central graph measures and cortical thickness.

Results

We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance between centroids compared with a NT population. The 2 conditions also show divergence. Namely, there is less modular overlap between the 2 conditions than there is between each condition and the NT group. The ADHD group also showed reduced cortical thickness and lower degree in hub regions than the autism group. Lastly, the ADHD group also showed reduced wiring costs compared with the autism groups.

Conclusions

Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Furthermore, autism and ADHD both showed alterations in the relation between inter-regional covariance and centroid distance, where both groups show a steeper decline in covariance as a function of distance. The 2 groups also diverge on modular organization, cortical thickness of hub regions and wiring cost of the covariance network. Thus, on some network features the groups are distinct, yet on others there is convergence.

'Developmental Delay' Reconsidered: The Critical Role of Age-Dependent, Co-variant Development

In memory of Annette Karmiloff-Smith . This paper reviews recent neurobiological research reporting structural co-variance and temporal dependencies in age-dependent gene expression, parameters of cortical maturation, long range connectivity and interaction of the biological network with the environment. This research suggests that age by size trajectories of brain structures relate to functional properties more than absolute sizes. In line with these findings, recent behavioral studies of typically developing children whose language development was delayed reported long term consequences of such delays. As for neurodevelopmental disorders, disrupted developmental timing and slow acquisitional pace are hallmarks of these populations. It is argued that these behavioral and neuro-biological results highlight the need to commit to a developmental model which will reflect the fact that temporal dependencies overseeing structural co-variance among developmental components are major regulatory factors of typical development of the brain/mind network. Consequently, the concept of 'developmental delay' in developmental theorizing needs to be reconsidered.

Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking

OBJECTIVE

The authors sought evidence for altered adolescent brain growth trajectory associated with moderate and heavy alcohol use in a large national, multisite, prospective study of adolescents before and after initiation of appreciable alcohol use.

METHOD

This study examined 483 adolescents (ages 12-21) before initiation of drinking and 1 and 2 years later. At the 2-year assessment, 356 participants continued to meet the study's no/low alcohol consumption entry criteria, 65 had initiated moderate drinking, and 62 had initiated heavy drinking. MRI was used to quantify regional cortical and white matter volumes. Percent change per year (slopes) in adolescents who continued to meet no/low criteria served as developmental control trajectories against which to compare those who initiated moderate or heavy drinking.

RESULTS

In no/low drinkers, gray matter volume declined throughout adolescence and slowed in many regions in later adolescence. Complementing gray matter declines, white matter regions grew at faster rates at younger ages and slowed toward young adulthood. Youths who initiated heavy drinking exhibited an accelerated frontal cortical gray matter trajectory, divergent from the norm. Although significant effects on trajectories were not observed in moderate drinkers, their intermediate position between no/low and heavy drinkers suggests a dose effect. Neither marijuana co-use nor baseline volumes contributed significantly to the alcohol effect.

CONCLUSIONS

Initiation of drinking during adolescence, with or without marijuana co-use, disordered normal brain growth trajectories. Factors possibly contributing to abnormal cortical volume trajectories include peak consumption in the past year and family history of alcoholism.

Through Thick and Thin: a Need to Reconcile Contradictory Results on Trajectories in Human Cortical Development

Understanding how brain development normally proceeds is a premise of understanding neurodevelopmental disorders. This has sparked a wealth of magnetic resonance imaging (MRI) studies. Unfortunately, they are in marked disagreement on how the cerebral cortex matures. While cortical thickness increases for the first 8-9 years of life have repeatedly been reported, others find continuous cortical thinning from early childhood, at least from age 3 or 4 years. We review these inconsistencies, and discuss possible reasons, including the use of different scanners, recording parameters and analysis tools, and possible effects of variables such as head motion. When tested on the same subsample, 2 popular thickness estimation methods (CIVET and FreeSurfer) both yielded a continuous thickness decrease from 3 years. Importantly, MRI-derived measures of cortical development are merely our best current approximations, hence the term "apparent cortical thickness" may be preferable. We recommend strategies for reaching consensus in the field, including multimodal neuroimaging to measure phenomena using different techniques, for example, the use of T1/T2 ratio, and data sharing to allow replication across analysis methods. As neurodevelopmental origins of early- and late-onset disease are increasingly recognized, resolving inconsistencies in brain maturation trajectories is important.

Repairing the brain with physical exercise: Cortical thickness and brain volume increases in long-term pediatric brain tumor survivors in response to a structured exercise intervention

There is growing evidence that exercise induced experience dependent plasticity may foster structural and functional recovery following brain injury. We examined the efficacy of exercise training for neural and cognitive recovery in long-term pediatric brain tumor survivors treated with radiation.

We conducted a controlled clinical trial with crossover of exercise training (vs. no training) in a volunteer sample of 28 children treated with cranial radiation for brain tumors (mean age = 11.5 yrs.; mean time since diagnosis = 5.7 yrs). The endpoints were anatomical T1 MRI data and multiple behavioral outcomes presenting a broader analysis of structural MRI data across the entire brain. This included an analysis of changes in cortical thickness and brain volume using automated, user unbiased approaches. A series of general linear mixed effects models evaluating the effects of exercise training on cortical thickness were performed in a voxel and vertex-wise manner, as well as for specific regions of interest. In exploratory analyses, we evaluated the relationship between changes in cortical thickness after exercise with multiple behavioral outcomes, as well as the relation of these measures at baseline.

Exercise was associated with increases in cortical thickness within the right pre and postcentral gyri. Other notable areas of increased thickness related to training were present in the left pre and postcentral gyri, left temporal pole, left superior temporal gyrus, and left parahippocampal gyrus. Further, we observed that compared to a separate cohort of healthy children, participants displayed multiple areas with a significantly thinner cortex prior to training and fewer differences following training, indicating amelioration of anatomical deficits. Partial least squares analysis (PLS) revealed specific patterns of relations between cortical thickness and various behavioral outcomes both after training and at baseline.

Overall, our results indicate that exercise training in pediatric brain tumor patients treated with radiation has a beneficial impact on brain structure. We argue that exercise training should be incorporated into the development of neuro-rehabilitative treatments for long-term pediatric brain tumor survivors and other populations with acquired brain injury. (ClinicalTrials.gov, NCT01944761)

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Exercise training in pediatric brain tumor patients treated with radiation results in changes in brain structure

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Exercise was associated with increased cortical thickness in several areas including motor and somatosensory cortex

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Fewer differences between patients and healthy controls in cortical thickness were seen following exercise training

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Specific patterns of relations between cortical thickness and behavior at a baseline and after exercise training were seen

Modular Development of Cortical Gray Matter Across Childhood and Adolescence

Brain maturation across childhood and adolescence is characterized by cortical thickness (CT) and volume contraction, and early expansion of surface area (SA). These processes occur asynchronously across the cortical surface, with functional, topographic, and network-based organizing principles proposed to account for developmental patterns. Characterizing regions undergoing synchronized development can help determine whether "maturational networks" overlap with well-described functional networks, and whether they are targeted by neurodevelopmental and psychiatric disorders. In the present study, we modeled changes with age in CT, SA, and volume from 335 typically developing subjects in the NIH MRI study of normal brain development, with 262 followed longitudinally for a total of 724 scans. Vertices showing similar maturation between 5 and 22 years were grouped together using data-driven clustering. Patterns of CT development distinguished sensory and motor regions from association regions, and were vastly different from SA patterns, which separated anterior from posterior regions. Developmental modules showed little similarity to networks derived from resting-state functional connectivity. Our findings present a novel perspective on maturational changes across the cortex, showing that several proposed organizing principles of cortical development co-exist, albeit in different structural parameters, and enable visualization of developmental trends occurring in parallel at remote cortical sites.

Structural Covariance Network of Cortical Gyrification in Benign Childhood Epilepsy with Centrotemporal Spikes

Benign childhood epilepsy with centrotemporal spikes (BECTS) is associated with cognitive and language problems. According to recent studies, disruptions in brain structure and function in children with BECTS are beyond a Rolandic focus, suggesting atypical cortical development. However, previous studies utilizing surface-based metrics (e.g., cortical gyrification) and their structural covariance networks at high resolution in children with BECTS are limited. Twenty-six children with BECTS (15 males/11 females; 10.35 ± 2.91 years) and 26 demographically matched controls (15 males/11 females; 11.35 ± 2.51 years) were included in this study and subjected to high-resolution structural brain MRI scans. The gyrification index was calculated, and structural brain networks were reconstructed based on the covariance of the cortical folding. In the BECTS group, significantly increased gyrification was observed in the bilateral Sylvain fissures and the left pars triangularis, temporal, rostral middle frontal, lateral orbitofrontal, and supramarginal areas (cluster-corrected p < 0.05). Global brain network measures were not significantly different between the groups; however, the nodal alterations were most pronounced in the insular, frontal, temporal, and occipital lobes (FDR corrected, p < 0.05). In children with BECTS, brain hubs increased in number and tended to shift to sensorimotor and temporal areas. Furthermore, we observed significantly positive relationships between the gyrification index and age (vertex p < 0.001, cluster-level correction) as well as duration of epilepsy (vertex p < 0.001, cluster-level correction). Our results suggest that BECTS may be a condition that features abnormal over-folding of the Sylvian fissures and uncoordinated development of structural wiring, disrupted nodal profiles of centrality, and shifted hub distribution, which potentially represents a neuroanatomical hallmark of BECTS in the developing brain.

Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia

BACKGROUND

There is evidence suggesting neuropsychiatric disorders share genomic, cognitive and clinical features. Here, we ask if autism-spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD) and schizophrenia share neuroanatomical variations.

METHODS

First, we used measures of cortical anatomy to estimate spatial overlap of neuroanatomical variation using univariate methods. Next, we developed a novel methodology to determine whether cortical deficits specifically target or are "enriched" within functional resting-state networks.

RESULTS

We found cortical anomalies were preferentially enriched across functional networks rather than clustering spatially. Specifically, cortical thickness showed significant enrichment between patients with ASD and those with ADHD in the default mode network, between patients with ASD and those with schizophrenia in the frontoparietal and limbic networks, and between patients with ADHD and those with schizophrenia in the ventral attention network. Networks enriched in cortical thickness anomalies were also strongly represented in functional MRI results (Neurosynth; r = 0.64, p = 0.032).

LIMITATIONS

We did not account for variable symptom dimensions and severity in patient populations, and our cross-sectional design prevented longitudinal analyses of developmental trajectories.

CONCLUSION

These findings suggest that common deficits across neuropsychiatric disorders cannot simply be characterized as arising out of local changes in cortical grey matter, but rather as entities of both local and systemic alterations targeting brain networks.

Associations between cortical thickness, structural connectivity and severity of dimensional bulimia nervosa symptomatology

Bulimia nervosa (BN) is a psychiatric illness defined by preoccupation with body image (cognitive 'symptoms'), binge eating and compensatory behaviors. Although diagnosed BN has been related to grey matter alterations, characterization of brain structure in women with a range of BN symptoms has not been made. This study examined whether cortical thickness (CT) values scaled with severity of BN cognitions in 33 women with variable BN pathology. We then assessed global structural connectivity (SC) of CT to determine if individual differences in global SC relate to BN symptom severity. We used the Eating Disorder Examination Questionnaire (EDE-Q) as a continuous measure of BN symptom severity. EDE-Q score was negatively related to global CT and local CT in the left middle frontal gyrus, right superior frontal gyrus and bilateral orbitofrontal cortex (OFC) and temporoparietal regions. Moreover, cortical thinning was most pronounced in regions with high global connectivity. Finally, individual contributions to global SC at the group level related to EDE-Q score, where increased EDE-Q score correlated with reduced connectivity of the left OFC and middle temporal cortex and increased connectivity of the right superior parietal lobule. Findings represent the first evidence of cortical thinning that relates to cognitive BN symptoms.

Structural Covariance of Gray Matter Volume in HIV Vertically Infected Adolescents

Human immunodeficiency virus (HIV) infection significantly affect neurodevelopmental and behavioral outcomes. We investigated whether alterations of gray matter organization and structural covariance networks with vertical HIV infection adolescents exist, by using the GAT toolbox. MRI data were analysed from 25 HIV vertically infected adolescents and 33 HIV-exposed-uninfected control participants. The gray matter volume (GMV) was calculated, and structural brain networks were reconstructed from gray matter co-variance. Gray matter losses were pronounced in anterior cingulate cortex (ACC), right pallidum, right occipital lobe, inferior parietal lobe, and bilateral cerebellum crus. The global brain network measures were not significantly different between the groups; however, the nodal alterations were most pronounced in frontal, temporal, basal ganglia, cerebellum, and temporal lobes. Brain hubs in the HIV-infected subjects increased in number and tended to shift to sensorimotor and temporal areas. In the HIV-infected subjects, decreased GMVs in ACC and bilateral cerebellum were related to lower Mini-Mental State Examination scores; the CD4 counts were positively related to the GMVs in ACC and sensorimotor areas. These findings suggest that focally reduced gray matter, disrupted nodal profiles of structural wirings, and a shift in hub distribution may represent neuroanatomical biomarkers of HIV infection on the developing brain.

Dehydroepiandrosterone impacts working memory by shaping cortico-hippocampal structural covariance during development

Existing studies suggest that dehydroepiandrosterone (DHEA) may be important for human brain development and cognition. For example, molecular studies have hinted at the critical role of DHEA in enhancing brain plasticity. Studies of human brain development also support the notion that DHEA is involved in preserving cortical plasticity. Further, some, though not all, studies show that DHEA administration may lead to improvements in working memory in adults. Yet these findings remain limited by an incomplete understanding of the specific neuroanatomical mechanisms through which DHEA may impact the CNS during development. Here we examined associations between DHEA, cortico-hippocampal structural covariance, and working memory (216 participants [female=123], age range 6-22 years old, mean age: 13.6 +/-3.6 years, each followed for a maximum of 3 visits over the course of 4 years). In addition to administering performance-based, spatial working memory tests to these children, we also collected ecological, parent ratings of working memory in everyday situations. We found that increasingly higher DHEA levels were associated with a shift toward positive insular-hippocampal and occipito-hippocampal structural covariance. In turn, DHEA-related insular-hippocampal covariance was associated with lower spatial working memory but higher overall working memory as measured by the ecological parent ratings. Taken together with previous research, these results support the hypothesis that DHEA may optimize cortical functions related to general attentional and working memory processes, but impair the development of bottom-up, hippocampal-to-cortical connections, resulting in impaired encoding of spatial cues.

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.

Mapping structural covariance networks of facial emotion recognition in early psychosis: A pilot study

People with psychosis show deficits recognizing facial emotions and disrupted activation in the underlying neural circuitry. We evaluated associations between facial emotion recognition and cortical thickness using a correlation-based approach to map structural covariance networks across the brain. Fifteen people with an early psychosis provided magnetic resonance scans and completed the Penn Emotion Recognition and Differentiation tasks. Fifteen historical controls provided magnetic resonance scans. Cortical thickness was computed using CIVET and analyzed with linear models. Seed-based structural covariance analysis was done using the mapping anatomical correlations across the cerebral cortex methodology. To map structural covariance networks involved in facial emotion recognition, the right somatosensory cortex and bilateral fusiform face areas were selected as seeds. Statistics were run in SurfStat. Findings showed increased cortical covariance between the right fusiform face region seed and right orbitofrontal cortex in controls than early psychosis subjects. Facial emotion recognition scores were not significantly associated with thickness in any region. A negative effect of Penn Differentiation scores on cortical covariance was seen between the left fusiform face area seed and right superior parietal lobule in early psychosis subjects. Results suggest that facial emotion recognition ability is related to covariance in a temporal-parietal network in early psychosis.

The Default Mode Network in Autism

Autism spectrum disorder (ASD) is characterized by deficits in social communication and interaction. Since its discovery as a major functional brain system, the default mode network (DMN) has been implicated in a number of psychiatric disorders, including ASD. Here we review converging multimodal evidence for DMN dysfunction in the context of specific components of social cognitive dysfunction in ASD: 'self-referential processing' - the ability to process social information relative to oneself and 'theory of mind' or 'mentalizing' - the ability to infer the mental states such as beliefs, intentions, and emotions of others. We show that altered functional and structural organization of the DMN, and its atypical developmental trajectory, are prominent neurobiological features of ASD. We integrate findings on atypical cytoarchitectonic organization and imbalance in excitatory-inhibitory circuits, which alter local and global brain signaling, to scrutinize putative mechanisms underlying DMN dysfunction in ASD. Our synthesis of the extant literature suggests that aberrancies in key nodes of the DMN and their dynamic functional interactions contribute to atypical integration of information about the self in relation to 'other', as well as impairments in the ability to flexibly attend to socially relevant stimuli. We conclude by highlighting open questions for future research.

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging and disease. Developments in MRI acquisition, image processing and data modeling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and for inferring microstructural properties; we also describe key artifacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, although methods need to improve and caution is required in interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works.

Cortico-amygdalar maturational coupling is associated with depressive symptom trajectories during adolescence

BACKGROUND

Adolescence is characterized by increasing prevalence of depressive symptomatology, along with significant structural brain development. While much research has examined focal abnormalities in gray matter structure underlying depression, we employed a structural coupling approach to examine whether longitudinal associations between amygdala and cortical development (referred to as maturational coupling) was related to concurrent changes in depressive symptomatology during adolescence.

METHOD

166 participants underwent up to three MRI scans (367 scans) between 11 and 20 years of age. Depressive symptoms were measured at three coinciding time points using the Center for Epidemiological Studies-Depression scale. Linear mixed models were employed to identify whether change in amygdala volume was related to development of cortical thickness, and if maturational coupling of these regions was related to changes in depressive symptomatology.

RESULTS

Positive maturational coupling was identified between the right amygdala and (predominantly anterior) prefrontal cortex, as well as parts of the temporal cortices. Greater positive coupling of these regions was associated with reductions in depressive symptoms over time.

CONCLUSIONS

Findings highlight significant associations between cortico-amygdalar maturational coupling and the emergence of depressive symptoms during adolescence, suggesting that synchronous development of these regions might support more adaptive affect regulation and functioning.

Morphological connectivity correlates with trait impulsivity in healthy adults

Background

Impulsivity is one crucial personality trait associated with various maladaptive behavior and many mental disorders. In the study reported here, we investigated the relationship between impulsivity and morphological connectivity (MC) between human brain regions, a newly proposed measure for brain coordination through the development and learning.

Method

Twenty-four participants’ T1-weighted magnetic resonance imaging (MRI) images and their self-reported impulsivity scores, measured by the Barratt impulsiveness scale (BIS), were retrieved from the OpenfMRI project. First, we assessed the MC by quantifying the similarity of probability density function of local morphological features between the anterior cingulate cortex (ACC), one of the most crucial hubs in the neural network modulating cognitive control, and other association cortices in each participant. Then, we correlated the MC to impulsivity scores across participants.

Results

The BIS total score was found to correlate with the MCs between the ACC and two other brain regions in the right hemisphere: the inferior frontal gyrus (IFG), a well-established structure for inhibition control; the inferior temporal gyrus (ITG), which has been previously shown to be associated with hyperactive/impulsivity symptoms. Furthermore, the ACC-IFG MC was mainly correlated with motor impulsivity, and the ACC-ITG MC was mainly correlated with attentional impulsivity.

Discussion

Together, these findings provide evidence that the ACC, IFG, and ITG in the right hemisphere are involved neural networks modulating impulsivity. Also, the current findings highlight the utility of MC analyses in facilitating our understanding of neural correlates of behavioral and personality traits.

Adolescent maturation of the relationship between cortical gyrification and cognitive ability

There are changes to the degree of cortical folding from gestation through adolescence into young adulthood. Recent evidence suggests that degree of cortical folding is linked to individual differences in general cognitive ability in healthy adults. However, it is not yet known whether age-related cortical folding changes are related to maturation of specific cognitive abilities in adolescence. To address this, we examined the relationship between frontoparietal cortical folding as measured by a Freesurfer-derived local gyrification index (lGI) and performance on subtests from the Wechsler Abbreviated Scale of Intelligence and scores from Conner's Continuous Performance Test-II in 241 healthy adolescents (ages 12-25 years). We hypothesized that age-related lGI changes in the frontoparietal cortex would contribute to cognitive development. A secondary goal was to explore if any gyrification-cognition relationships were either test-specific or sex-specific. Consistent with previous studies, our results showed a reduction of frontoparietal local gyrification with age. Also, as predicted, all cognitive test scores (i.e., Vocabulary, Matrix Reasoning, the CPT-II Commission, Omission, Variabiltiy, d') showed age × cognitive ability interaction effects in frontoparietal and temporoparietal brain regions. Mediation analyses confirmed a causal role of age-related cortical folding changes only for CPT-II Commission errors. Taken together, the results support the functional significance of cortical folding, as well as provide the first evidence that cortical folding maturational changes play a role in cognitive development.

Altered Network Topology in Patients with Primary Brain Tumors After Fractionated Radiotherapy

Radiation therapy (RT) is a critical treatment modality for patients with brain tumors, although it can cause adverse effects. Recent data suggest that brain RT is associated with dose-dependent cortical atrophy, which could disrupt neocortical networks. This study examines whether brain RT affects structural network properties in brain tumor patients. We applied graph theory to MRI-derived cortical thickness estimates of 54 brain tumor patients before and after RT. Cortical surfaces were parcellated into 68 regions and correlation matrices were created for patients pre- and post-RT. Significant changes in graph network properties were tested using nonparametric permutation tests. Linear regressions were conducted to measure the association between dose and changes in nodal network connectivity. Increases in transitivity, modularity, and global efficiency (n = 54, p < 0.0001) were all observed in patients post-RT. Decreases in local efficiency (n = 54, p = 0.007) and clustering coefficient (n = 54, p = 0.005) were seen in regions receiving higher RT doses, including the inferior parietal lobule and rostral anterior cingulate. These findings demonstrate alterations in global and local network topology following RT, characterized by increased segregation of brain regions critical to cognition. These pathological network changes may contribute to the late delayed cognitive impairments observed in many patients following brain RT.

Network component analysis reveals developmental trajectories of structural connectivity and specific alterations in autism spectrum disorder

The structural organization of the brain can be characterized as a hierarchical ensemble of segregated modules linked by densely interconnected hub regions that facilitate distributed functional interactions. Disturbances to this network may be an important marker of abnormal development. Recently, several neurodevelopmental disorders, including autism spectrum disorder (ASD), have been framed as disorders of connectivity but the full nature and timing of these disturbances remain unclear. In this study, we use non-negative matrix factorization, a data-driven, multivariate approach, to model the structural network architecture of the brain as a set of superposed subnetworks, or network components. In an openly available dataset of 196 subjects scanned between 5 and 85 years we identify a set of robust and reliable subnetworks that develop in tandem with age and reflect both anatomically local and long-range, network hub connections. In a second experiment, we compare network components in a cohort of 51 high-functioning ASD adolescents to a group of age-matched controls. We identify a specific subnetwork representing an increase in local connection strength in the cingulate cortex in ASD (t = 3.44, P < 0.001). This work highlights possible long-term implications of alterations to the developmental trajectories of specific cortical subnetworks. Hum Brain Mapp 38:4169-4184, 2017. © 2017 Wiley Periodicals, Inc.

Exploring the role of white matter connectivity in cortex maturation

The maturation of the cortical gray matter (GM) and white matter (WM) are described as sequential processes following multiple, but distinct rules. However, neither the mechanisms driving brain maturation processes, nor the relationship between GM and WM maturation are well understood. Here we use connectomics and two MRI measures reflecting maturation related changes in cerebral microstructure, namely the Apparent Diffusion Coefficient (ADC) and the T1 relaxation time (T1), to study brain development. We report that the advancement of GM and WM maturation are inter-related and depend on the underlying brain connectivity architecture. Particularly, GM regions and their incident WM connections show corresponding maturation levels, which is also observed for GM regions connected through a WM tract. Based on these observations, we propose a simple computational model supporting a key role for the connectome in propagating maturation signals sequentially from external stimuli, through primary sensory structures to higher order functional cortices.

Language Ability Predicts Cortical Structure and Covariance in Boys with Autism Spectrum Disorder

There is significant clinical heterogeneity in language and communication abilities of individuals with Autism Spectrum Disorders (ASD). However, no consistent pathology regarding the relationship of these abilities to brain structure has emerged. Recent developments in anatomical correlation-based approaches to map structural covariance networks (SCNs), combined with detailed behavioral characterization, offer an alternative for studying these relationships. In this study, such an approach was used to study the integrity of SCNs of cortical thickness and surface area associated with language and communication, in 46 high-functioning, school-age children with ASD compared with 50 matched, typically developing controls (all males) with IQ > 75. Findings showed that there was alteration of cortical structure and disruption of fronto-temporal cortical covariance in ASD compared with controls. Furthermore, in an analysis of a subset of ASD participants, alterations in both cortical structure and covariance were modulated by structural language ability of the participants, but not communicative function. These findings indicate that structural language abilities are related to altered fronto-temporal cortical covariance in ASD, much more than symptom severity or cognitive ability. They also support the importance of better characterizing ASD samples while studying brain structure and for better understanding individual differences in language and communication abilities in ASD.

Structural and Maturational Covariance in Early Childhood Brain Development

Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development.

Intelligence as a Developing Function: A Neuroconstructivist Approach

The concept of intelligence encompasses the mental abilities necessary to survival and advancement in any environmental context. Attempts to grasp this multifaceted concept through a relatively simple operationalization have fostered the notion that individual differences in intelligence can often be expressed by a single score. This predominant position has contributed to expect intelligence profiles to remain substantially stable over the course of ontogenetic development and, more generally, across the life-span. These tendencies, however, are biased by the still limited number of empirical reports taking a developmental perspective on intelligence. Viewing intelligence as a dynamic concept, indeed, implies the need to identify full developmental trajectories, to assess how genes, brain, cognition, and environment interact with each other. In the present paper, we describe how a neuroconstructivist approach better explains why intelligence can rise or fall over development, as a result of a fluctuating interaction between the developing system itself and the environmental factors involved at different times across ontogenesis.

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.

Activation in Context: Differential Conclusions Drawn from Cross-Sectional and Longitudinal Analyses of Adolescents' Cognitive Control-Related Neural Activity

Although immature cognitive control, subserved by late-developing prefrontal regions, has been proposed to underlie increased risk taking during adolescence, it remains unclear what patterns of PFC activation represent mature brain states: more or less activation? One challenge to drawing cogent conclusions from extant work stems from its reliance on single-time point neuroimaging and cross-sectional comparisons, which are ill-suited for assessing the complex changes that characterize adolescence. This necessitates longitudinal fMRI work to track within-subject changes in PFC function and links to risk-taking behavior, which can serve as an external marker for maturation of neural systems involved in cognitive control. In the current study, 20 healthy adolescents (13 males) completed a go/nogo task during two fMRI scans, once at age 14 years and again at age 15 years. We found that the association between cognitive control-related VLPFC activation and risk-taking behavior reversed when examining wave 1 (W1) versus longitudinal change (W2 > W1) and wave 2 (W2) in neural activation, such that increased VLPFC activation at W1 was associated with lower risk taking, whereas longitudinal increases in cognitive control-related VLPFC activation as well as heightened VLPFC activation at W2 were associated with greater risk taking. Several steps were taken to disentangle potential alternative accounts that might explain these disparate results across time. Findings highlight the necessity of considering brain-behavior relationships in the context of ongoing developmental changes and suggests that using neuroimaging data at a single time point to predict behavioral changes can introduce interpretation errors when failing to account for changes in neural trajectories.

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.

Multivariate dynamical modelling of structural change during development

Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e.g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development. In particular, the inputs to the system are specified to account for known or latent developmental growth/decline factors, e.g. due to effects of growth hormones, puberty, or sudden behavioural changes etc. Because effects of developmental factors might be region-specific, the sensitivity of each ROI to contributions of each factor is explicitly modelled. In addition to the external effects of developmental factors on regional change, the framework enables modelling and inference about directed (potentially reciprocal) interactions between brain regions, due to competition for space, or structural connectivity, and suchlike. This approach accounts for repeated measures in typical MRI studies of development and aging. Model inversion and posterior distributions are obtained using earlier established variational methods enabling Bayesian evidence-based comparisons between various models of structural change. Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289 individuals. In particular, we model volumetric changes in 26 bilateral ROIs, which cover large portions of cortical and subcortical gray matter. We account for (1) puberty-related effects on gray matter regions; (2) effects of an early transient growth process with additional time-lag parameter; (3) sexual dimorphism by modelling parameter differences between boys and girls. There is evidence that the regional pattern of sensitivity to dynamic hidden growth factors in late childhood is similar across genders and shows a consistent anterior-posterior gradient with strongest impact to prefrontal cortex (PFC) brain changes. Finally, we demonstrate the potential of the framework to explore the coupling of structural changes across a priori defined subnetworks using an example of previously established resting state functional connectivity.

Age-associated patterns in gray matter volume, cerebral perfusion and BOLD oscillations in children and adolescents

Healthy brain development involves changes in brain structure and function that are believed to support cognitive maturation. However, understanding how structural changes such as grey matter thinning relate to functional changes is challenging. To gain insight into structure-function relationships in development, the present study took a data driven approach to define age-related patterns of variation in gray matter volume (GMV), cerebral blood flow (CBF) and blood-oxygen level dependent (BOLD) signal variation (fractional amplitude of low-frequency fluctuations; fALFF) in 59 healthy children aged 7-18 years, and examined relationships between modalities. Principal components analysis (PCA) was applied to each modality in parallel, and participant scores for the top components were assessed for age associations. We found that decompositions of CBF, GMV and fALFF all included components for which scores were significantly associated with age. The dominant patterns in GMV and CBF showed significant (GMV) or trend level (CBF) associations with age and a strong spatial overlap, driven by increased signal intensity in default mode network (DMN) regions. GMV, CBF and fALFF additionally showed components accounting for 3-5% of variability with significant age associations. However, these patterns were relatively spatially independent, with small-to-moderate overlap between modalities. Independence of age effects was further demonstrated by correlating individual subject maps between modalities: CBF was significantly less correlated with GMV and fALFF in older children relative to younger. These spatially independent effects of age suggest that the parallel decline observed in global GMV and CBF may not reflect spatially synchronized processes. Hum Brain Mapp 38:2398-2407, 2017. © 2017 Wiley Periodicals, Inc.

Globally Efficient Brain Organization and Treatment Response in Psychosis: A Connectomic Study of Gyrification

BACKGROUND

Converging evidence suggests that patients with first-episode psychosis who show a poor treatment response may have a higher degree of neurodevelopmental abnormalities than good Responders. Characterizing the disturbances in the relationship among brain regions (covariance) can provide more information on neurodevelopmental integrity than searching for localized changes in the brain. Graph-based connectomic approach can measure structural covariance thus providing information on the maturational processes. We quantified the structural covariance of cortical folding using graph theory in first-episode psychosis, to investigate if this systems-level approach would improve our understanding of the biological determinants of outcome in psychosis.

METHODS

Magnetic Resonance Imaging data were acquired in 80 first-episode psychosis patients and 46 healthy controls. Response to treatment was assessed after 12 weeks of naturalistic follow-up. Gyrification-based connectomes were constructed to study the maturational organization of cortical folding.

RESULTS

Nonresponders showed a reduction in the distributed relationship among brain regions (high segregation, poor integration) when compared to Responders and controls, indicating a higher burden of aberrant neurodevelopment. They also showed reduced centrality of key regions (left insula and anterior cingulate cortex) indicating a marked reconfiguration of gyrification. Nonresponders showed a vulnerable pattern of covariance that disintegrated when simulated lesions removed high-degree hubs, indicating an abnormal dependence on highly central hub regions in Nonresponders.

CONCLUSIONS

These findings suggest that a perturbed maturational relationship among brain regions underlies poor treatment response in first-episode psychosis. The information obtained from gyrification-based connectomes can be harnessed for prospectively predicting treatment response and prognosis in psychosis.

The Structural and Functional Organization of Cognition

This article proposes that what have been historically and contemporarily defined as different domains of human cognition are served by one of four functionally- and structurally-distinct areas of the prefrontal cortex (PFC). Their contributions to human intelligence are as follows: (a) BA9, enables our emotional intelligence, engaging the psychosocial domain; (b) BA47, enables our practical intelligence, engaging the material domain; (c) BA46 (or BA46-9/46), enables our abstract intelligence, engaging the hypothetical domain; and (d) BA10, enables our temporal intelligence, engaging in planning within any of the other three domains. Given their unique contribution to human cognition, it is proposed that these areas be called the, social (BA9), material (BA47), abstract (BA46-9/46) and temporal (BA10) mind. The evidence that BA47 participates strongly in verbal and gestural communication suggests that language evolved primarily as a consequence of the extreme selective pressure for practicality; an observation supported by the functional connectivity between BA47 and orbital areas that negatively reinforce lying. It is further proposed that the abstract mind (BA46-9/46) is the primary seat of metacognition charged with creating adaptive behavioral strategies by generating higher-order concepts (hypotheses) from lower-order concepts originating from the other three domains of cognition.

Adolescent Development of Cortical and White Matter Structure in the NCANDA Sample: Role of Sex, Ethnicity, Puberty, and Alcohol Drinking

Brain structural development continues throughout adolescence, when experimentation with alcohol is often initiated. To parse contributions from biological and environmental factors on neurodevelopment, this study used baseline National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) magnetic resonance imaging (MRI) data, acquired in 674 adolescents meeting no/low alcohol or drug use criteria and 134 adolescents exceeding criteria. Spatial integrity of images across the 5 recruitment sites was assured by morphological scaling using Alzheimer's disease neuroimaging initiative phantom-derived volume scalar metrics. Clinical MRI readings identified structural anomalies in 11.4%. Cortical volume and thickness were smaller and white matter volumes were larger in older than in younger adolescents. Effects of sex (male > female) and ethnicity (majority > minority) were significant for volume and surface but minimal for cortical thickness. Adjusting volume and area for supratentorial volume attenuated or removed sex and ethnicity effects. That cortical thickness showed age-related decline and was unrelated to supratentorial volume is consistent with the radial unit hypothesis, suggesting a universal neural development characteristic robust to sex and ethnicity. Comparison of NCANDA with PING data revealed similar but flatter, age-related declines in cortical volumes and thickness. Smaller, thinner frontal, and temporal cortices in the exceeds-criteria than no/low-drinking group suggested untoward effects of excessive alcohol consumption on brain structural development.

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.