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The cerebral cortex of the human infant at term is complexly folded in a similar fashion to adult cortex but has only one third the total surface area. By comparing 12 healthy infants born at term with 12 healthy young adults, we demonstrate that postnatal cortical expansion is strikingly nonuniform: regions of lateral temporal, parietal, and frontal cortex expand nearly twice as much as other regions in the insular and medial occipital cortex. This differential postnatal expansion may reflect regional differences in the maturity of dendritic and synaptic architecture at birth and/or in the complexity of dendritic and synaptic architecture in adults. This expression may also be associated with differential sensitivity of cortical circuits to childhood experience and insults. By comparing human and macaque monkey cerebral cortex, we infer that the pattern of human evolutionary expansion is remarkably similar to the pattern of human postnatal expansion. To account for this correspondence, we hypothesize that it is beneficial for regions of recent evolutionary expansion to remain less mature at birth, perhaps to increase the influence of postnatal experience on the development of these regions or to focus prenatal resources on regions most important for early survival.

The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study. •HCP quality control procedures have enabled high throughout data acquisition.•HCP database services enable operations and open access data sharing.•Connectome Workbench enables cross-modal data visualization and exploration.

We report on a new neuroimaging database, BALSA, that is a repository for extensively analyzed neuroimaging datasets from humans and nonhuman primates. BALSA is organized into two distinct sections. BALSA Reference is a curated repository of reference data accurately mapped to brain atlas surfaces and volumes, including various types of anatomically and functionally derived spatial maps as well as brain connectivity. BALSA Studies is a repository of extensively analyzed neuroimaging and neuroanatomical datasets associated with specific published studies, as voluntarily submitted by authors. It is particularly well suited for sharing of neuroimaging data as displayed in published figures. Uploading and downloading of data to BALSA involves ‘scene’ files that replicate how datasets appear in Connectome Workbench visualization software. Altogether, BALSA offers efficient access to richly informative datasets that are related to but transcend the images available in scientific publications. •BALSA is a new database for sharing extensively analyzed neuroimaging data.•BALSA reference includes data mapped to human and nonhuman primate atlases.•BALSA Studies includes datasets associated with specific published studies.•BALSA offers efficient access to richly informative neuroimaging data.

We evaluated 22 measures of cortical folding, 20 derived from local curvature (curvature-based measures) and two based on other features (sulcal depth and gyrification index), for their capacity to distinguish between normal and aberrant cortical development. Cortical surfaces were reconstructed from 12 term-born control and 63 prematurely-born infants. Preterm infants underwent 2–4 MR imaging sessions between 27 and 42weeks postmenstrual age (PMA). Term infants underwent a single MR imaging session during the first postnatal week. Preterm infants were divided into two groups. One group (38 infants) had no/minimal abnormalities on qualitative assessment of conventional MR images. The second group (25 infants) consisted of infants with injury on conventional MRI at term equivalent PMA. For both preterm infant groups, all folding measures increased or decreased monotonically with increasing PMA, but only sulcal depth and gyrification index differentiated preterm infants with brain injury from those without. We also compared scans obtained at term equivalent PMA (36–42weeks) for all three groups. No curvature-based measured distinguished between the groups, whereas sulcal depth distinguished term control from injured preterm infants and gyrification index distinguished all three groups. When incorporating total cerebral volume into the statistical model, sulcal depth no longer distinguished between the groups, though gyrification index distinguished between all three groups and positive shape index distinguished between the term control and uninjured preterm groups. We also analyzed folding measures averaged over brain lobes separately. These results demonstrated similar patterns to those obtained from the whole brain analyses. Overall, though the curvature-based measures changed during this period of rapid cerebral development, they were not sensitive for detecting the differences in folding associated with brain injury and/or preterm birth. In contrast, gyrification index was effective in differentiating these groups. •We compared 20 measures of cortical curvature in term and prematurely born infants•Data were collected throughout the neonatal intensive care unit stay•All of the measures changed markedly with brain development•No curvature-based measure distinguished injured from uninjured premature infants•Gyrification index, a non-curvature based measure, consistently differentiated groups

The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community, enabling discoveries of how the brain is wired and how it functions in different individuals. To fulfill its potential, the HCP consortium is developing an informatics platform that will handle: (1) storage of primary and processed data, (2) systematic processing and analysis of the data, (3) open-access data-sharing, and (4) mining and exploration of the data. This informatics platform will include two primary components. ConnectomeDB will provide database services for storing and distributing the data, as well as data analysis pipelines. Connectome Workbench will provide visualization and exploration capabilities. The platform will be based on standard data formats and provide an open set of application programming interfaces (APIs) that will facilitate broad utilization of the data and integration of HCP services into a variety of external applications. Primary and processed data generated by the HCP will be openly shared with the scientific community, and the informatics platform will be available under an open source license. This paper describes the HCP informatics platform as currently envisioned and places it into the context of the overall HCP vision and agenda.

Volume-based registration (VBR) is the predominant method used in human neuroimaging to compensate for individual variability. However, surface-based registration (SBR) techniques have an inherent advantage over VBR because they respect the topology of the convoluted cortical sheet. There is evidence that existing SBR methods indeed confer a registration advantage over affine VBR. Landmark-SBR constrains registration using explicit landmarks to represent corresponding geographical locations on individual and atlas surfaces. The need for manual landmark identification has been an impediment to the widespread adoption of Landmark-SBR. To circumvent this obstacle, we have implemented and evaluated an automated landmark identification (ALI) algorithm for registration to the human PALS-B12 atlas. We compared ALI performance with that from two trained human raters and one expert anatomical rater (ENR). We employed both quantitative and qualitative quality assurance metrics, including a biologically meaningful analysis of hemispheric asymmetry. ALI performed well across all quality assurance tests, indicating that it yields robust and largely accurate results that require only modest manual correction (<10min per subject). ALI largely circumvents human error and bias and enables high throughput analysis of large neuroimaging datasets for inter-subject registration to an atlas. ► We evaluate automated landmark identification (ALI) for surface-based registration. ► We compare ALI reliability along a number of quantitative metrics to human raters. ► Results suggest ALI is rapid and reliable as compared to human raters. ► ALI largely circumvents human error and bias. ► ALI enables high throughput analysis of large neuroimaging datasets.

There is a large class of real-world problems, such as warehouse transport, at different scales, swarm densities, etc., that can be characterized as Central Place Foraging Problems (CPFPs). We contribute to swarm engineering by designing an Ordinary Differential Equation (ODE) model that strives to capture the underlying behavioral dynamics of the CPFP in these application areas. Our simulation results show that a hybrid ODE modeling approach combining analytic parameter calculations and post-hoc (i.e., after running experiments) parameter fitting can be just as effective as a purely post-hoc approach to computing parameters via simulations, while requiring less tuning and iterative refinement. This makes it easier to design systems with provable bounds on behavior. Additionally, the resulting model parameters are more understandable because their values can be traced back to problem features, such as system size, robot control algorithm, etc. Finally, we perform real-robot experiments to further understand the limits of our model from an engineering standpoint.

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease. A detailed parcellation (map) of the human cerebral cortex has been obtained by integrating multi-modal imaging data, including functional magnetic resonance imaging (fMRI), and the resulting freely available resources will enable detailed comparative studies of the human brain in health, ageing and disease. A modern map of the brain For more than a century, neuroscientists have sought to subdivide the human cerebral cortex into a patchwork of anatomically and functionally distinct areas. Until now such maps have relied largely on only a single property such as micro-architecture or functional imaging, have been based on a relatively small number of individuals, and have usually been blurry due to misalignment of brain areas from person to person. Matthew Glasser, David Van Essen and colleagues have tackled these deficiencies in a new more 'universal' map of the human cerebral cortex by integrating multi-modal imaging data obtained from 210 healthy subjects and validated on 210 other individuals. The authors propose a total of 180 areas per cerebral hemisphere (97 of them previously unknown) and apply a machine-learning classifier to automatically identify these areas in new subjects, even in individuals with atypical parcellations. This freely available resource will enhance the anatomical accuracy and interpretability of future structural and functional studies of the human brain in health and disease.

This thesis develops new mathematical tools to aid in the design of robot swarms consisting of large numbers of simple robots. It develops new ways of measuring these systems, mechanisms to understand how these “simple” systems can nonetheless act intelligently, and models for predicting their behavior under different conditions. Using this tools, future robotic systems will be are more understandable and have better guarantees of individual and collective behavior. The contributions of this thesis are fourfold. First, metrics for quantifying the observable swarm properties of self organization, scalability, flexibility to changing external environments, and robustness to internal system stimuli, such as sensor and actuator noise and robot failures, are derived. Researcher intuitions about comparative algorithm performance are shown to be well supported by the quantitative results obtained using the derived metrics. Second, the origin of emergent intelligence in task allocating swarms is investigated. Task allocation within the context of relational task graphs with different average node centralities is used to compare an optimal (under constraints) greedy method, which disregards task dependencies, with a non-optimal dependency-aware method which emphasizes collective learning of graph structure. Results show that swarm emergent intelligence is (a) positively correlated with average node centrality and performance, and (b) arises out of learning and exploitation of graph connectivity, rather than content. Third, we determine that the underlying collective dynamics of object gathering in robot swarms can (sometimes) be captured using Poisson-based modeling even when the phenomena modeled are not Poisson distributed, thereby establishing better limits on when Poisson-based modeling can be applied to swarm behaviors. Fourth, we develop some initial properties for graphs representing 3D structures as a partial solution to the parallel bricklayer problem. With these properties, we prove the existence of an appropriate algorithm with which a swarm of N robots can provably construct a 3D structure starting from an empty state.

The authors describe and illustrate an integrated trio of software programs for carrying out surface-based analyses of cerebral cortex. The first component of this trio, SureFit (Surface Reconstruction by Filtering and Intensity Transformations), is used primarily for cortical segmentation, volume visualization, surface generation, and the mapping of functional neuroimaging data onto surfaces. The second component, Caret (Computerized Anatomical Reconstruction and Editing Tool Kit), provides a wide range of surface visualization and analysis options as well as capabilities for surface flattening, surface-based deformation, and other surface manipulations. The third component, SuMS (Surface Management System), is a database and associated user interface for surface-related data. It provides for efficient insertion, searching, and extraction of surface and volume data from the database.