Pediatric neuroimaging studies 1-5 , up to now exclusively cross sectional, identify linear decreases in cortical gray matter and increases in white matter across ages 4 to 20. In this large-scale longitudinal pediatric neuroimaging study, we confirmed linear increases in white matter, but demonstrated nonlinear changes in cortical gray matter, with a preadolescent increase followed by a postadolescent decrease. These changes in cortical gray matter were regionally specific, with developmental curves for the frontal and parietal lobe peaking at about age 12 and for the temporal lobe at about age 16, whereas cortical gray matter continued to increase in the occipital lobe through age 20.The subjects for this study were healthy boys and girls participating in an ongoing longitudinal pediatric brain-MRI project at the Child Psychiatry Branch at the National Institute of Mental Health. Subjects were recruited from the community as previously described, using phone screening, questionnaires mailed to parents and teachers and face-to-face physical and psychological testing; approximately one in six volunteers were accepted 5 . At least 1 scan was obtained from each of 145 healthy subjects (89 male). Of these, 65 had at least 2 scans, 30 had at least 3 scans, 2 had at least 4 scans and 1 had 5 scans, acquired at approximately two-year intervals. The age range was from 4.2 to 21.6 years. There were no significant sex differences for age, Tanner stage, ethnicity, socioeconomic status, height, weight or handedness.All subjects were scanned on the same GE 1.5 Tesla Signa scanner using the same three-dimensional, spoiled-gradient, recalled echo in the steady state (3D SPGR) imaging protocol, with an axial-slice thickness of 1.5 mm, a time-to-echo of 5 ms, a repetition time of 24 ms, flip angle of 45°, a 192 ( 256 acquisition matrix, 1 excitation and a field of view of 24 cm. A clinical neuroradiologist evaluated all scans; no gross abnormalities were reported.Volumes of white and cortical gray matter were quantitatively analyzed by combining a technique using an artificial neural network to classify tissues based on voxel intensity with non-linear registration to a template brain for which these tissue regions had been manually defined 7 . This technique supplemented MRI signal-intensity information with predetermined brain anatomy and provides lobar (frontal, parietal, temporal and occipital) parcellation of cortical gray-and white-matter volumes.We used previously described statistical analysis techniques that combine cross-sectional and longitudinal data 8 . These longitudinal methods are more sensitive to detecting individual growth patterns, even in the presence of large interindividual variation 9 . We assessed if there was significant change with age, if developmental curves differed by sex and/or region and whether the developmental curves were linear or quadratic.The volume of white matter increased linearly with age ( Fig. 1; Table 1), increasing less in females than in males. The net increase across ages 4 to 22 w...

Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/ motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.key assumption in neuroscience is that the topographical structure of the cerebral cortex provides an organizing principle that constrains its cognitive processes. Recent advances in the field of human connectomics have revealed multiple largescale networks (1-3), each characterized by distinct functional profiles (4). Some are related to basic primary functions, such as movement or perceiving sounds and images; some serve welldocumented, domain-general functions, such as attention or cognitive control (5-8); and some have functional characteristics that remain less well-understood, such as the default-mode network (DMN) (9, 10). Although the topography of these distinct distributed networks has been described using multiple methods (1-3), the reason for their particular spatial relationship and how this constrains their function remain unclear.Advances in mapping local processing streams have revealed spatial gradients that support increasingly abstract levels of representation, often extending along adjacent cortical regions in a stepwise manner (11). In the visual domain, for example, the ventral occipitotemporal object stream transforms simple visual features, coded by neurons in primary visual cortex, into more complex visual descriptions of objects in anterior inferior temporal cortical regions and ultimately, contributes to multimodal semantic representations in the middle temporal cortex and the most anterior temporal cortex that capture the meaning of what we see, hear, and do (12)(13)(14)(15). Similarly, in the prefrontal cortex, a rostral-caudal gradient has been proposed, whereby goals become increasingly abstract in anterior areas more distant from motor cortex...

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. Highthroughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.