Understanding human cortical maturation is a central goal for developmental neuroscience. Significant advances towards this goal have come from two recent strands of in-vivo structural magnetic resonance imaging (sMRI) research: (i) longitudinal study designs have revealed that factors such as sex, cognitive ability and disease are often better related to variations in the tempo of anatomical change than to variations in anatomy at any one time-point, and (ii) largely cross-sectional applications of new “surface-based morphometry” (SBM) methods have shown how the traditional focus on cortical volume (CV) can obscure information about the two evolutionarily and genetically distinct determinants of CV - cortical thickness (CT) and surface area (SA). Here, by combining these two strategies for the first time, and applying SBM in over 1,250 longitudinally acquired brain scans from 647 healthy individuals aged 3 to 30 years, we deconstruct cortical development to reveal that distinct trajectories of anatomical change are “hidden” within, and give rise to, a curvilinear pattern of CV maturation. Developmental changes in CV emerge through the sexually dimorphic and age-dependent changes in CT and SA. Moreover, SA change itself actually reflects complex interactions between brain size-related changes in exposed cortical “convex hull” area (CHA), and changes in the degree of cortical gyrification, which again vary by age and sex. Knowing of these developmental dissociations, and further specifying their timing and sex-biases provides potent new research targets for basic and clinical neuroscience.

The areas of the brain that mediate knowledge about objects were investigated by measuring changes in regional cerebral blood flow (rCBF) using positron emission tomography (PET). Subjects generated words denoting colors and actions associated with static, achromatic line drawings of objects in one experiment, and with the written names of objects in a second experiment. In both studies, generation of color words selectively activated a region in the ventral temporal lobe just anterior to the area involved in the perception of color, whereas generation of action words activated a region in the middle temporal gyrus just anterior to the area involved in the perception of motion. These data suggest that object knowledge is organized as a distributed system in which the attributes of an object are stored close to the regions of the cortex that mediate perception of those attributes.

Modularity is a fundamental concept in systems neuroscience, referring to the formation of local cliques or modules of densely intra-connected nodes that are sparsely inter-connected with nodes in other modules. Topological modularity of brain functional networks can quantify theoretically anticipated abnormality of brain network community structure – so-called dysmodularity – in developmental disorders such as childhood-onset schizophrenia (COS). We used graph theory to investigate topology of networks derived from resting-state fMRI data on 13 COS patients and 19 healthy volunteers. We measured functional connectivity between each pair of 100 regional nodes, focusing on wavelet correlation in the frequency interval 0.05–0.1 Hz, then applied global and local thresholding rules to construct graphs from each individual association matrix over the full range of possible connection densities. We show how local thresholding based on the minimum spanning tree facilitates group comparisons of networks by forcing the connectedness of sparse graphs. Threshold-dependent graph theoretical results are compatible with the results of a k-means unsupervised learning algorithm and a multi-resolution (spin glass) approach to modularity, both of which also find community structure but do not require thresholding of the association matrix. In general modularity of brain functional networks was significantly reduced in COS, due to a relatively reduced density of intra-modular connections between neighboring regions. Other network measures of local organization such as clustering were also decreased, while complementary measures of global efficiency and robustness were increased, in the COS group. The group differences in complex network properties were mirrored by differences in simpler statistical properties of the data, such as the variability of the global time series and the internal homogeneity of the time series within anatomical regions of interest.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Imaging has become an essential component in many jields of' medical and laboratory research and clinical practice. Biologists s t t i 4 cells and generate 3 0 confocal microscopy da fasets, virologists generate 3 0 reconstructions of' viruses jrom micrographs, radiologists idcnt{b cind qimntib tumors ,finm MRI ond CT scans, rind nenroscientists detect regioncrl metabolic brain activiy jrom PET andjirnctionnl MRI scans. Analysis ojthese diverse image types requires sophisticated computerized qiiantifica tion and visiializntion tools. Until recently, three-dimensional visircrlizrition o j images and quantitative analysis coiild only be perjbrmed using espensive UNIX workstations and ciistomized sojiware. Toda,v. much ofthe visualization and analysis can be petjbrmed on an inespensive desktop cornpiiter equipped with the appropriate graphics hardware und sojiware. This paper introduces an e.utensible plalfi,rmindependent, general-purpose image processing and visimlizotion program specifcal[v designed to meet the needs of'a Internet-linked medical research community. The application nnmed MIPA V (Medical Image Processing Analji.yis and Visiiolization) enables clinical and qrrantitative analysis of medical images over the Internet. Using MIPA V's standard tiserintetjface and analysis tools, researchers and clinicians at remote sites can easily share research data and analyses, thereby enhancing their ability to study. diagnose. monitor. and treat medical disorders.

Studies in experimental animals and humans have stressed the role of the cerebellum in motor skill learning. Yet, the relative importance of the cerebellar cortex and deep nuclei, as well as the nature of the dynamic functional changes occurring between these and other motor-related structures during learning, remains in dispute. Using functional magnetic resonance imaging and a motor sequence learning paradigm in humans, we found evidence of an experience-dependent shift of activation from the cerebellar cortex to the dentate nucleus during early learning, and from a cerebellar-cortical to a striatal-cortical network with extended practice. The results indicate that intrinsic modulation within the cerebellum, in concert with activation of motor-related cortical regions, serves to set up a procedurally acquired sequence of movements that is then maintained elsewhere in the brain.

Social cognition provides humans with the necessary skills to understand and interact with one another. One aspect of social cognition, mentalizing, is associated with a network of brain regions often referred to as the 'social brain.' These consist of medial prefrontal cortex [medial Brodmann Area 10 (mBA10)], temporoparietal junction (TPJ), posterior superior temporal sulcus (pSTS) and anterior temporal cortex (ATC). How these specific regions develop structurally across late childhood and adolescence is not well established. This study examined the structural developmental trajectories of social brain regions in the longest ongoing longitudinal neuroimaging study of human brain maturation. Structural trajectories of grey matter volume, cortical thickness and surface area were analyzed using surface-based cortical reconstruction software and mixed modeling in a longitudinal sample of 288 participants (ages 7-30 years, 857 total scans). Grey matter volume and cortical thickness in mBA10, TPJ and pSTS decreased from childhood into the early twenties. The ATC increased in grey matter volume until adolescence and in cortical thickness until early adulthood. Surface area for each region followed a cubic trajectory, peaking in early or pre-adolescence before decreasing into the early twenties. These results are discussed in the context of developmental changes in social cognition across adolescence.

The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive "pruning" of short-distance functional connections in schizophrenia.