John W. Harwell

An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex

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.

A multi-modal parcellation of human cerebral cortex

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.

Similar patterns of cortical expansion during human development and evolution

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.

A Surface-Based Analysis of Hemispheric Asymmetries and Folding of Cerebral Cortex in Term-Born Human Infants

We have established a population average surface-based atlas of human cerebral cortex at term gestation and used it to compare infant and adult cortical shape characteristics. Accurate cortical surface reconstructions for each hemisphere of 12 healthy term gestation infants were generated from structural magnetic resonance imaging data using a novel segmentation algorithm. Each surface was inflated, flattened, mapped to a standard spherical configuration, and registered to a target atlas sphere that reflected shape characteristics of all 24 contributing hemispheres using landmark constrained surface registration. Population average maps of sulcal depth, depth variability, three-dimensional positional variability, and hemispheric depth asymmetry were generated and compared with previously established maps of adult cortex. We found that cortical structure in term infants is similar to the adult in many respects, including the pattern of individual variability and the presence of statistically significant structural asymmetries in lateral temporal cortex, including the planum temporale and superior temporal sulcus. These results indicate that several features of cortical shape are minimally influenced by the postnatal environment.

Informatics and Data Mining Tools and Strategies for the Human Connectome Project

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.

Cortical Parcellations of the Macaque Monkey Analyzed on Surface-Based Atlases

Surface-based atlases provide a valuable way to analyze and visualize the functional organization of cerebral cortex. Surface-based registration (SBR) is a primary method for aligning individual hemispheres to a surface-based atlas. We used landmark-constrained SBR to register many published parcellation schemes to the macaque F99 surface-based atlas. This enables objective comparison of both similarities and differences across parcellations. Cortical areas in the macaque vary in surface area by more than 2 orders of magnitude. Based on a composite parcellation derived from 3 major sources, the total number of macaque neocortical and transitional cortical areas is estimated to be about 130-140 in each hemisphere.

Human Connectome Project informatics: quality control, database services, and data visualization.

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 HCPs database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.

Analysis of Cortical Shape in Children with Simplex Autism

We used surface-based morphometry to test for differences in cortical shape between children with simplex autism (n = 34, mean age 11.4 years) and typical children (n = 32, mean age 11.3 years). This entailed testing for group differences in sulcal depth and in 3D coordinates after registering cortical midthickness surfaces to an atlas target using 2 independent registration methods. We identified bilateral differences in sulcal depth in restricted portions of the anterior-insula and frontal-operculum (aI/fO) and in the temporoparietal junction (TPJ). The aI/fO depth differences are associated with and likely to be caused by a shape difference in the inferior frontal gyrus in children with simplex autism. Comparisons of average midthickness surfaces of children with simplex autism and those of typical children suggest that the significant sulcal depth differences represent local peaks in a larger pattern of regional differences that are below statistical significance when using coordinate-based analysis methods. Cortical regions that are statistically significant before correction for multiple measures are peaks of more extended, albeit subtle regional differences that may guide hypothesis generation for studies using other imaging modalities.

Comparison of cortical folding measures for evaluation of developing human brain

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.

Automated landmark identification for human cortical surface-based registration

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 (<10 min 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.