Probabilistic approaches for atlasing normal and disease-specific brain variability

Neuromorphological Atlas of Human Prenatal Brain Development: White Paper

Recent morphological data on human brain development are quite fragmentary. However, they are highly requested for a number of medical practices, educational programs, and fundamental research in the fields of embryology, cytology and histology, neurology, physiology, path anatomy, neonatology, and others. This paper provides the initial information on the new online Human Prenatal Brain Development Atlas (HBDA). The Atlas will start with forebrain annotated hemisphere maps, based on human fetal brain serial sections at the different stages of prenatal ontogenesis. Spatiotemporal changes in the regional-specific immunophenotype profiles will also be demonstrated on virtual serial sections. The HBDA can serve as a reference database for the neurological research, which provides opportunity to compare the data obtained by noninvasive techniques, such as neurosonography, X-ray computed tomography and magnetic resonance imaging, functional magnetic resonance imaging, 3D high-resolution phase-contrast computed tomography visualization techniques, as well as spatial transcriptomics data. It could also become a database for the qualitative and quantitative analysis of individual variability in the human brain. Systemized data on the mechanisms and pathways of prenatal human glio- and neurogenesis could also contribute to the search for new therapy methods for a large spectrum of neurological pathologies, including neurodegenerative and cancer diseases. The preliminary data are now accessible on the special HBDA website.

An Iterative Centroid Approach for Diffeomorphic Online Atlasing

Online atlasing, i.e., incrementing an atlas with new images as they are acquired, is key when performing studies on very large, or still being gathered, databases. Regular approaches to atlasing however do not focus on this aspect and impose a complete reconstruction of the atlas when adding images. We propose instead a diffeomorphic online atlasing method that allows gradual updates to an atlas. In this iterative centroid approach, we integrate new subjects in the atlas in an iterative manner, gradually moving the centroid of the images towards its final position. This leads to a computationally cheap approach since it only necessitates one additional registration per new subject added. We validate our approach on several experiments with three main goals: 1- to evaluate atlas image quality of the obtained atlases with sharpness and overlap measures, 2- to assess the deviation in terms of transformations with respect to a conventional atlasing method and 3- to compare its computational time with regular approaches of the literature. We demonstrate that the transformations divergence with respect to a state-of-the-art atlas construction method is small and reaches a plateau, that the two construction methods have the same ability to map subject homologous regions onto a common space and produce images of equivalent quality. The computational time of our approach is also drastically reduced for regular updates. Finally, we also present a direct extension of our method to update spatio-temporal atlases, especially useful for developmental studies.

Comparison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation

INTRODUCTION AND GOAL TO BACKGROUND

Due to the importance of segmentation of MRI images in identifying brain tumors, various methods including deep learning have been introduced for automatic brain tumor segmentation. On the other hand, using a combination of methods can improve their performance. Among them is the use of wavelet transform as an auxiliary element in deep networks. The analysis of the requirements of such combinations has been addressed in this study.

METHOD

In this developmental study, different wavelet functions were used to compress brain MRI images and finally as an auxiliary element in improving the performance of the convolutional neural network in brain tumor segmentation.

RESULTS

Based on the results of the tests performed, the Daubechies1 function was most effective in enhancing network performance in segmenting MRI images and was able to balance the performance and computational overload.

CONCLUSION

Choosing the wavelet function to optimize the performance of a convolutional neural network should be based on the requirements of the problem, also taking into account some considerations such as computational load, processing time, and performance of the wavelet function in optimizing CNN output in the intended task.

Construction and validation of a cerebral white matter hyperintensity probability map of older Koreans

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We constructed WPM from healthy elderly Koreans.

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WPM may serve as a tool to study pathology and normal aging of distribution of WMH.

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WPM provides a prominent atlas of the age related distribution of WMH.

Background and purpose

Although two white matter hyperintensity (WMH) probability maps of healthy older adults already exist, they have several limitations in representing the distribution of WMH in healthy older adults, especially Asian older adults. We constructed and validated a WMH probability map (WPM) of healthy older Koreans and examined the age-associated differences of WMH.

Methods

We constructed WPM using development dataset that consisted of high-resolution 3D fluid-attenuated inversion recovery images of 5 age groups (60–64 years, 65–69 years, 70–74 years, 75–79 years, and 80+ years). Each age group included 30 age-matched men and women each. We tested the validity of the WPM by comparing WMH ages estimated by the WPM and the chronological ages of 30 healthy controls, 30 hypertension patients, and 30 S patients.

Results

Older age groups showed a higher volume of WMH in both hemispheres (p < 0.001). About 90% of the WMH were periventricular in all age groups. With advancing age, the peak of the distance histogram from the ventricular wall of the periventricular WMH shifted away from the ventricular wall, while that of deep WMH shifted toward the ventricular wall. The estimated WMH ages were comparable to the chronological ages in the healthy controls, while being higher than the chronological ages in hypertension and stroke patients.

Conclusions

This WPM may serve as a standard atlas in research on WMH of older adults, especially Asians.

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

The reliability of neuroanatomy as a predictor of eloquence: a review

The adjacency of intracranial pathology to canonical regions of eloquence has long been considered a significant source of potential morbidity in the neurosurgical care of patients. Yet, several reports exist of patients who undergo resection of gliomas or other intracranial pathology in eloquent regions without adverse effects. This raises the question of whether anatomical and intracranial location can or should be used as a means of estimating eloquence. In this review, the authors systematically evaluate the factors that are known to affect anatomical-functional relationships, including anatomical, functional, pathology-related, and modality-specific sources of variability. This review highlights the unpredictability of functional eloquence based on anatomical features alone and the fact that patients should not be considered ineligible for surgical intervention based on anatomical considerations alone. Rather, neurosurgeons need to take advantage of modern technology and mapping techniques to create individualized maps and management plans. An individualized approach allows one to expand the number of patients who are considered for and who potentially may benefit from surgical intervention. Perhaps most importantly, an individualized approach to mapping patients with brain tumors ensures that the risk of iatrogenic functional injury is minimized while maximizing the extent of resection.

Feature-based morphometry: discovering group-related anatomical patterns

This paper presents feature-based morphometry (FBM), a new fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1).

Is it time to re-prioritize neuroimaging databases and digital repositories?

The development of in vivo brain imaging has lead to the collection of large quantities of digital information. In any individual research article, several tens of gigabytes-worth of data may be represented-collected across normal and patient samples. With the ease of collecting such data, there is increased desire for brain imaging datasets to be openly shared through sophisticated databases. However, very often the raw and pre-processed versions of these data are not available to researchers outside of the team that collected them. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Though early sociological and technical concerns have been addressed, they have not been ameliorated altogether for many in the field. In this article, we review the progress made in neuroimaging databases, their role in data sharing, data management, potential for the construction of brain atlases, recording data provenance, and value for re-analysis, new publication, and training. We feature the LONI IDA as an example of an archive being used as a source for brain atlas workflow construction, list several instances of other successful uses of image databases, and comment on archive sustainability. Finally, we suggest that, given these developments, now is the time for the neuroimaging community to re-prioritize large-scale databases as a valuable component of brain imaging science.

Training-induced structural changes in the adult human brain

Structural and functional brain reorganisation can occur beyond the developmental maturation period and this was recently recognised as an intrinsic property of the human central nervous system. Brain injury or altered afferent input due to environmental changes, novel experience and learning new skills are known as modulators of brain function and underlying neuroanatomic circuitry. During the past decade invasive animal studies and in vivo imaging techniques have delineated the correlates of experience dependent reorganisation. The major future challenge is to understand the behavioural consequences and cellular mechanisms underlying training-induced neuroanatomic plasticity in order to adapt treatment strategies for patients with brain injury or neurodegenerative disorders.

Surface-based and probabilistic atlases of primate cerebral cortex

Brain atlases play an increasingly important role in neuroimaging, as they are invaluable for analysis, visualization, and comparison of results across studies. For both humans and macaque monkeys, digital brain atlases of many varieties are in widespread use, each having its own strengths and limitations. For studies of cerebral cortex there is particular utility in hybrid atlases that capitalize on the complementary nature of surface and volume representations, are based on a population average rather than an individual brain, and include measures of variation as well as averages. Linking different brain atlases to one another and to online databases containing a growing body of neuroimaging data will enable powerful forms of data mining that accelerate discovery and improve research efficiency.

Fast reproducible identification and large-scale databasing of individual functional cognitive networks

BACKGROUND

Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level.

RESULTS

81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects.

CONCLUSION

This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.

A statistical parts-based model of anatomical variability

In this paper, we present a statistical parts-based model (PBM) of appearance, applied to the problem of modeling intersubject anatomical variability in magnetic resonance (MR) brain images. In contrast to global image models such as the active appearance model (AAM), the PBM consists of a collection of localized image regions, referred to as parts, whose appearance, geometry and occurrence frequency are quantified statistically. The parts-based approach explicitly addresses the case where one-to-one correspondence does not exist between all subjects in a population due to anatomical differences, as model parts are not required to appear in all subjects. The model is constructed through a fully automatic machine learning algorithm, identifying image patterns that appear with statistical regularity in a large collection of subject images. Parts are represented by generic scale-invariant features, and the model can, therefore, be applied to a wide variety of image domains. Experimentation based on 2-D MR slices shows that a PBM learned from a set of 102 subjects can be robustly fit to 50 new subjects with accuracy comparable to 3 human raters. Additionally, it is shown that unlike global models such as the AAM, PBM fitting is stable in the presence of unexpected, local perturbation.

Direct cortical mapping via solving partial differential equations on implicit surfaces

In this paper, we propose a novel approach for cortical mapping that computes a direct map between two cortical surfaces while satisfying constraints on sulcal landmark curves. By computing the map directly, we can avoid conventional intermediate parameterizations and help simplify the cortical mapping process. The direct map in our method is formulated as the minimizer of a flexible variational energy under landmark constraints. The energy can include both a harmonic term to ensure smoothness of the map and general data terms for the matching of geometric features. Starting from a properly designed initial map, we compute the map iteratively by solving a partial differential equation (PDE) defined on the source cortical surface. For numerical implementation, a set of adaptive numerical schemes are developed to extend the technique of solving PDEs on implicit surfaces such that landmark constraints are enforced. In our experiments, we show the flexibility of the direct mapping approach by computing smooth maps following landmark constraints from two different energies. We also quantitatively compare the metric preserving property of the direct mapping method with a parametric mapping method on a group of 30 subjects. Finally, we demonstrate the direct mapping method in the brain mapping applications of atlas construction and variability analysis.

Expression mapping of tetracycline-responsive prion protein promoter: digital atlasing for generating cell-specific disease models

We present a digital atlas system that allows mapping of molecular expression patterns at cellular resolution through large series of histological sections. Using this system, we have mapped the distribution of a distinct marker, encoded by the LacZ reporter gene driven by the tetracycline-responsive prion protein promoter in double transgenic mice. The purpose is to evaluate the suitability of this promoter mouse line for targeting genes of interest to specific brain regions, essential for construction of inducible transgenic disease models. Following processing to visualize the promoter expression, sections were counterstained to simultaneously display cytoarchitectonics. High-resolution mosaic images covering entire coronal sections were collected through the mouse brain at intervals of 200 microm. A web-based application provides access to a customized virtual microscopy tool for viewing and navigation within and across the section images. For each section image, the nearest section in a standard atlas is defined, and annotations of key structures and regions inserted. Putative categorization of labeled cells was performed with use of distribution patterns, followed by cell size and shape, as parameters that were compared to legacy data. Among the ensuing results were expression of this promoter in putative glial cells in the cerebellum (and not in Purkinje cells), in putative glial cells in the substantia nigra, in pallidal glial cells or interneurons, and in distinct cell layers and regions of the hippocampus. The study serves as a precursor for a database resource allowing evaluation of the suitability of different promoter mouse lines for generating disease models.

Neural lineages of the Drosophila brain: a three-dimensional digital atlas of the pattern of lineage location and projection at the late larval stage

The late larval brain consists of embryonically produced primary neurons forming a deep core cortex, surrounded at the surface by approximately 100 secondary lineages. Each secondary lineage forms a tract (secondary lineage tract) with an invariant and characteristic trajectory. Within the neuropile, tracts of neighboring lineages bundle together to form secondary tract systems. In this paper, we visualized secondary lineages by the global marker BP106 (neurotactin), as well as green fluorescent protein-labeled clones and thereby establish a comprehensive digital atlas of secondary lineages. The information contained in this atlas is the location of the lineage within the cortex, the neuropile compartment contacted by the lineage tract, and the projection pattern of the lineage tract within the neuropile. We have digitally mapped the expression pattern of three genes, sine oculis, period, and engrailed into the lineage atlas. The atlas will enable us and others to analyze the phenotype of mutant clones in the larval brain. Mutant clones can only be interpreted if the corresponding wild-type clone is well characterized, and our lineage atlas, which visualizes all wild-type lineages, will provide this information. Secondly, secondary lineage tracts form a scaffold of connections in the neuropile that foreshadows adult nerve connections. Thus, starting from the larval atlas and proceeding forward through pupal development, one will be able to reconstruct adult brain connectivity at a high level of resolution. Third, the atlas can serve as a repository for genes expressed in lineage-specific patterns.

Mapping brain maturation

Human brain maturation is a complex, lifelong process that can now be examined in detail using neuroimaging techniques. Ongoing projects scan subjects longitudinally with structural magnetic resonance imaging (MRI), enabling the time-course and anatomical sequence of development to be reconstructed. Here, we review recent progress on imaging studies of development. We focus on cortical and subcortical changes observed in healthy children, and contrast them with abnormal developmental changes in early-onset schizophrenia, fetal alcohol syndrome, attention-deficit-hyperactivity disorder (ADHD) and Williams syndrome. We relate these structural changes to the cellular processes that underlie them, and to cognitive and behavioral changes occurring throughout childhood and adolescence.

Program for Assisted Labeling of Sulcal Regions (PALS): description and reliability

With the improvements in techniques for generating surface models from magnetic resonance (MR) images, it has recently become feasible to study the morphological characteristics of the human brain cortex in vivo. Studies of the entire surface are important for measuring global features, but analysis of specific cortical regions of interest provides a more detailed understanding of structure. We have previously developed a method for automatically segmenting regions of interest from the cortical surface using a watershed transform. Each segmented region corresponds to a cortical sulcus and is thus termed a "sulcal region." In this work, we describe two important augmentations of this methodology. First, we describe a user interface that allows for the efficient labeling of the segmented sulcal regions called the Program for Assisted Labeling of Sulcal Regions (PALS). An additional augmentation allows for even finer divisions on the cortex with a methodology that employs the fast marching technique to track a curve on the cortical surface that is then used to separate segmented regions. After regions of interest have been identified, we compute both the cortical surface area and gray matter volume. Reliability experiments are performed to assess both the long-term stability and short-term repeatability of the proposed techniques. These experiments indicate the proposed methodology gives both highly stable and repeatable results.

An MRI study of spatial probability brain map differences between first-episode schizophrenia and normal controls

We created a spatial probability atlas of schizophrenia to provide information about the neuroanatomic variability of brain regions of patients with the disorder. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects' magnetic resonance images (MRIs) and linearly transforming them into Talairach space using the Montreal Neurological Institute (MNI) template. ROIs included temporal, parietal, and prefrontal cortex subregions, with a principal focus on temporal lobe structures. Subject Ns ranged from 11 to 28 for the different ROIs. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. The superior temporal gyrus (STG) and fusiform gyrus (FG) had lower values for schizophrenic subjects than for normal controls, suggestive of greater spatial variability for these ROIs in schizophrenic subjects. For the computation of statistical significance of group differences in portions of the ROI, we used voxel-wise comparisons and Fisher's exact test. First-episode schizophrenic patients compared with controls showed lower probability (P < 0.05) at dorso-posterior areas of planum temporale and Heschl's gyrus, lateral and anterior regions in the left hippocampus (HIPP), and dorsolateral regions of fusiform gyrus. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls. Apparent group differences in activation may simply reflect a greater heterogeneity of spatial distribution in schizophrenia.

Sulcal pattern and morphology of the superior temporal sulcus

The superior temporal sulcus (STs) is the main sulcal landmark of the external temporal cortex and is very important for functional (posterior language areas on the left) mapping and surgery. The methodology we use is based on the extraction of the 3D shape of sulci and their separation into subunits called sulcal roots. Seventeen normal brains (male: 11, female: 6, age: 22-60) were systematically analyzed. Additionally, parameters generated by visual observation were recorded. Non-parametric statistics were performed to evaluate the variation of the STs and influence of side, handedness and sex. We found that the 3D architecture of the STs was consistent with our generic model in four sulcal roots and four "plis de passage" (PP) and significant differences between right and left hemispheres. These morphological differences may be related to the language-relevant cortical areas difference and are pertinent for defining the limits of morphometric variability of the STs in "normal humans".

Coordinate-based versus structural approaches to brain image analysis

A basic issue in neurosciences is to look for possible relationships between brain architecture and cognitive models. The lack of architectural information in magnetic resonance images, however, has led the neuroimaging community to develop brain mapping strategies based on various coordinate systems without accurate architectural content. Therefore, the relationships between architectural and functional brain organizations are difficult to study when analyzing neuroimaging experiments. This paper advocates that the design of new brain image analysis methods inspired by the structural strategies often used in computer vision may provide better ways to address these relationships. The key point underlying this new framework is the conversion of the raw images into structural representations before analysis. These representations are made up of data-driven elementary features like activated clusters, cortical folds or fiber bundles. Two classes of methods are introduced. Inference of structural models via matching across a set of individuals is described first. This inference problem is illustrated by the group analysis of functional statistical parametric maps (SPMs). Then, the matching of new individual data with a priori known structural models is described, using the recognition of the cortical sulci as a prototypical example.

Temporal dynamics of brain anatomy

The brain changes profoundly in structure and function during development and as a result of diseases such as the dementias, schizophrenia, multiple sclerosis, and tumor growth. Strategies to measure, map, and visualize these brain changes are of immense value in basic and clinical neuroscience. Algorithms that map brain change with sufficient spatial and temporal sensitivity can also assess drugs that aim to decelerate or arrest these changes. In neuroscience studies, these tools can reveal subtle brain changes in adolescence and old age and link these changes with measurable differences in brain function and cognition. Early detection of brain change in patients at risk for dementia; tumor recurrence; or relapsing-remitting conditions, such as multiple sclerosis, is also vital for optimizing therapy. We review a variety of mathematical and computational approaches to detect structural brain change with unprecedented sensitivity, both spatially and temporally. The resulting four-dimensional (4-D) maps of brain anatomy are warehoused in population-based brain atlases. Here, statistical tools compare brain changes across subjects and across populations, adjusting for complex differences in brain structure. Brain changes in an individual can be compared with a normative database comprised of subjects matched for age, gender, and other demographic factors. These dynamic brain maps offer key biological markers for understanding disease progression and testing therapeutic response. The early detection of disease-related brain changes is also critical for possible pre-emptive intervention before the ravages of disease have set in.

Zen and the art of medical image registration: correspondence, homology, and quality

Nonrigid registration (NRR) is routinely used in the study of neuroanatomy and function and is a standard component of analysis packages such as SPM. There remain many unresolved correspondence problems that arise from attempts to associate functional areas with specific neuroanatomy and to compare both function and anatomy across patient groups. Problems can result from ignorance of the underlying neurology which is then compounded by unjustified inferences drawn from the results of NRR. Usually the magnitude, distribution, and significance of errors in NRR are unknown so the errors in correspondences determined by NRR are also unknown and their effect on experimental results cannot easily be quantified. In this paper we review the principles by which the presumed correspondence and homology of structures is used to drive registration and identify the conceptual and algorithmic areas where current techniques are lacking. We suggest that for applications using NRR to be robust and achieve their potential, context-specific definitions of correspondence must be developed which properly characterise error. Prior knowledge of image content must be utilised to monitor and guide registration and gauge the degree of success. The use of NRR in voxel-based morphometry is examined from this context and found wanting. We conclude that a move away from increasingly sophisticated but context-free registration technology is required and that the veracity of studies that rely on NRR should be keenly questioned when the error distribution is unknown and the results are unsupported by other contextual information.

Three-dimensional reconstruction of a rhesus monkey brain from the Friedrich Sanides collection

For this study, serial sections of a rhesus monkey brain prepared by Friedrich Sanides (1914-1984) were used to examine the architectonic parcellation of the cerebral cortex. The sections through the anterior part of a rhesus monkey brain (250 sections stained for myeloarchitecture and 250 sections stained for cytoarchitecture) were digitally photographed, three-dimensionally aligned to each other, and three-dimensionally reconstructed. For three-dimensional registration of the sections rigid transformations were applied with the Euclidian distance as fit criterion. Afterwards, the derived volume was used for manual segmentation of several central structures of the monkey brain (caudate nucleus, putamen, pallidum, thalamus, ventricles, cortex, hippocampus, amygdala, etc.). Thus, a three-dimensional model of the rhesus monkey brain was developed, which can be used as a three-dimensional framework for mapping morphological structures as basis for a digital atlas.

Windows on the brain: the emerging role of atlases and databases in neuroscience

Brain atlases and associated databases have great potential as gateways for navigating, accessing, and visualizing a wide range of neuroscientific data. Recent progress towards realizing this potential includes the establishment of probabilistic atlases, surface-based atlases and associated databases, combined with improvements in visualization capabilities and internet access.