The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.

How does human brain structure mature during adolescence? We used MRI to measure cortical thickness and intracortical myelination in 297 population volunteers aged 14-24 y old. We found and replicated that association cortical areas were thicker and less myelinated than primary cortical areas at 14 y. However, association cortex had faster rates of shrinkage and myelination over the course of adolescence. Age-related increases in cortical myelination were maximized approximately at the internal layer of projection neurons. Adolescent cortical myelination and shrinkage were coupled and specifically associated with a dorsoventrally patterned gene expression profile enriched for synaptic, oligodendroglial-and schizophrenia-related genes. Topologically efficient and biologically expensive hubs of the brain anatomical network had greater rates of shrinkage/myelination and were associated with overexpression of the same transcriptional profile as cortical consolidation. We conclude that normative human brain maturation involves a genetically patterned process of consolidating anatomical network hubs. We argue that developmental variation of this consolidation process may be relevant both to normal cognitive and behavioral changes and the high incidence of schizophrenia during human brain adolescence. A dolescence is associated with major behavioral, social, and sexual changes as well as increased risk for many psychiatric disorders (1). However, human brain maturation during adolescence is not yet so well understood. Historically, pioneering studies used histological techniques to show that distinct areas of cortex were differentially myelinated in postmortem examination of perinatal tissue, suggesting "myelinogenesis" as an important process in human brain development (2, 3). MRI can measure human brain development more comprehensively and over a wider age range than is possible for postmortem anatomists. The thickness of human cortex can be reliably and replicably measured by MRI (4), and longitudinal studies have shown that cortical thickness (CT; millimeters) monotonically shrinks over the course of postnatal development, with variable shrinkage rates estimated for different age ranges (5-11; review in ref. 12). CT typically shrinks from about 3.5 mm at age 13 y old (9) to about 2.2 mm at age 75 y old (10, 11). Rates of cortical shrinkage are faster during adolescence (approximately −0.05 mm/y) than in later adulthood or earlier childhood (9).What does this MRI phenomenon of cortical shrinkage represent at a cellular level? There are broadly two tenable models: pruning and myelination. Basic physical principles of MRI predict that shorter longitudinal (T1) relaxation times reflect either a reduction in the fraction of "watery" cytoplasmic material, like cell bodies, synapses, or extracellular fluid, or an increase in the fraction of "fatty" myelinated material, like axons. Pruning models propose that cortical shrinkage in adolescence represents loss or remodeling of synapses, dendrites, or cell bodies (13). Myelin...

Human brain functional networks are embedded in anatomical space and have topological properties-small-worldness, modularity, fat-tailed degree distributions-that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.neuroimaging | graph theory | systems | trade-off T he human brain is a large and complex network, operating over several decades of scale in space and time (1, 2). Its organization defies complete description at a cellular scale (3); but neuroimaging techniques for whole-brain scanning have been used to describe network organization, or connectomics, at anatomical scales on the order of millimeters and centimeters (2). In the language of graph theory, these large-scale human brain networks have already been shown consistently to demonstrate a number of key topological properties in common with other complex networks. For example, human brain networks have the smallworld property of high clustering and high efficiency (or short path length) (4); they are also modular (5) and comprise a number of highly connected hub nodes in a fat-tailed degree distribution (6).It is well known that the brain overall is expensive, in the sense of having high metabolic cost relative to its mass (7), and that cost control or cost minimization is likely to have been an important selection criterion for the evolution of the nervous system (8-11). One measure of cost in a spatially embedded network like the brain is the physical distance of connections between nodes: generally, connection costs will increase with distance (8, 12). In the nervous system of the nematode worm Caenorhabditis elegans, which has been mapped completely at the cellular level of synaptic conn...

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.