Neuroimaging Database Research Articles

Multimodal Quantitative Neuroimaging Databases and Methods: The Cuban Human Brain Mapping Project

This article reviews the contributions of the Cuban Neuroscience Center to the evolution of the statistical parametric mapping (SPM) of quantitative Multimodal Neuroimages (qMN), from its inception to more recent work. Attention is limited to methods that compare individual qMN to normative databases (n/qMN). This evolution is described in three successive stages: (a) the development of one variant of normative topographical quantitative EEG (n/qEEG-top) which carries out statistical comparison of individual EEG spectral topographies with regard to a normative database--as part of the now popular SPM of brain descriptive parameters; (b) the development of n/qEEG tomography (n/qEEG-TOM), which employs brain electrical tomography (BET) to calculate voxelwise SPM maps of source spectral features with respect to a norm; (c) the development of a more general n/qMN by substituting EEG parameters with other neuroimaging descriptive parameters to obtain SPM maps. The study also describes the creation of Cuban normative databases, starting with the Cuban EEG database obtained in the early 90s, and more recently, the Cuban Human Brain Mapping Project (CHBMP). This project has created a 240 subject database of the normal Cuban population, obtained from a population-based random sample, comprising clinical, neuropsychological, EEG, MRI and SPECT data for the same subjects. Examples of clinical studies using qMN are given and, more importantly, receiver operator characteristics (ROC) analyses of the different developments document a sustained effort to assess the clinical usefulness of the techniques.

Neuroanatomic changes and their association with cognitive decline in mild cognitive impairment: a meta-analysis

Mild cognitive impairment (MCI) is an acquired syndrome characterised by cognitive decline not affecting activities of daily living. Using a quantitative meta-analytic approach, we aimed to identify consistent neuroanatomic correlates of MCI and how they are related to cognitive dysfunction. The meta-analysis enrols 22 studies, involving 917 MCI (848 amnestic MCI) patients and 809 healthy controls. Only studies investigating local changes in grey matter and reporting whole-brain results in stereotactic coordinates were included and analysed using the activation likelihood estimation approach. Probabilistic cytoarchitectonic maps were used to compare the localization of the obtained significant effects to histological areas. A correlation between the probability of grey matter changes and cognitive performance of MCI patients was performed. In MCI patients, the meta-analysis revealed three significant clusters of convergent grey matter atrophy, which were mainly situated in the bilateral amygdala and hippocampus, extending to the left medial temporal pole and thalamus, as well as in the bilateral precuneus. A sub-analysis in only amnestic MCI revealed a similar pattern. A voxel-wise analysis revealed a correlation between grey matter reduction and cognitive decline in the right hippocampus and amygdala as well as in the left thalamus. This study provides convergent evidence of a distinct neuroanatomical pattern in MCI. The correlation analysis with cognitive-mnestic decline further highlights the impact of limbic structures and the linkage with data from a functional neuroimaging database provides additional insight into underlying functions. Although different pathologies are underlying MCI, the observed neuroanatomical pattern of structural changes may reflect the common clinical denominator of cognitive impairment.

Visual Perfusion–Diffusion Mismatch Is Equivalent to Quantitative Mismatch

The concept of stroke MRI mismatch based on qualitative evaluation of diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) has been applied in clinical practice for several years. The benefit of MRI in providing pathological evidence of ischemia before thrombolytic treatment has been demonstrated. The purpose of this study is to determine the reliability of the qualitative method and compare it with quantitative mismatch measurement in thrombolytic-treated patients. Patients (n=70) were selected from the Lesion Evolution of Stroke and Ischemic On Neuroimaging (LESION) database if they: (1) were treated with intravenous recombinant tissue plasminogen activator; (2) had a pretreatment MRI with evaluable DWI and PWI; and (3) had acute ischemic lesion volume >10 mL on DWI as determined by core imaging laboratory measurements. Quantitative mismatch was defined as a difference of >50 mL between abnormal mean transit time and DWI volumes. Sample characteristics and postdischarge modified Rankin Scale for the positive mismatch patients were compared between the subgroups identified by qualitative versus quantitative methods. Patient characteristics and thrombolytic outcomes (sex, age, National Institutes of Health Stroke Scale, mismatch volume, and modified Rankin Scale) did not differ for mismatch patients identified by qualitative versus quantitative methods. Qualitative mismatch selection among neurologists had a high sensitivity (0.82), specificity (0.80), accuracy (0.81), and positive predictive value (0.88) compared with quantitative measurements. We observed that qualitative evaluation of mismatch identified the same thrombolytic-treated patients compared with retrospective quantitative mismatch measurements.

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research.

BackgroundMassive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used.MethodWe deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas.ResultsPivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And online access to the mouse brain pivot collection can be viewed using the link http://edtech-dev.uthsc.edu/CTSI/teeDev1/unittest/PaPa/collection.html (user name: tviangte and password: demome)ConclusionsOur proposed algorithm has automated the creation of large image Pivot collections; this will enable investigators of clinical research projects to easily and quickly analyse the image collections through a perspective that is useful for making critical decisions about the image patterns discovered.

Intracranial hemorrhage in full-term newborns: a hospital-based cohort study

IntroductionIn recent years, intracranial hemorrhage (ICH) with parenchymal involvement has been diagnosed more often in full-term neonates due to improved neuroimaging techniques. The aim of this study is to describe clinical and neuroimaging data in the neonatal period and relate imaging findings to outcome in a hospital-based population admitted to a level 3 neonatal intensive care unit (NICU).MethodsFrom our neuroimaging database, we retrospectively retrieved records and images of 53 term infants (1991–2008) in whom an imaging diagnosis of ICH with parenchymal involvement was made. Clinical data, including mode of delivery, clinical manifestations, neurological symptoms, extent and site of hemorrhage, neurosurgical intervention, and neurodevelopmental outcomes, were recorded.ResultsSeventeen of the 53 term infants had infratentorial ICH, 20 had supratentorial ICH, and 16 had a combination of the two. Seizures were the most common presenting symptom (71.7%), another ten infants (18.9%) presented with apneic seizures, and five infants had no clinical signs but were admitted to our NICU because of perinatal asphyxia (n = 2), respiratory distress (n = 2), and development of posthemorrhagic ventricular dilatation (n = 1). Continuous amplitude-integrated electroencephalography recordings were performed in all infants. Clinical or subclinical seizures were seen in 48/53 (90.6%) infants; all received anti-epileptic drugs. Thirteen of all 53 (24.5%) infants died. The lowest mortality rate was seen in infants with supratentorial ICH (10%). Three infants with a midline shift required craniotomy, six infants needed a subcutaneous reservoir due to outflow obstruction, and three subsequently required a ventriculoperitoneal shunt. The group with poor outcome (death or developmental quotient (DQ) <85) had a significantly lower 5-min Apgar score (p = .006). Follow-up data were available for 37/40 survivors aged at least 15 months. Patients were assessed with the Griffiths Mental Developmental Scales, and the mean DQ of all survivors was 97 (SD = 12). Six infants (17%) had a DQ below 85 [two of them had cerebral palsy (CP)]. Three infants developed CP (8.6%); one had cerebellar ataxia, and two had hemiplegia.ConclusionICH with parenchymal involvement carries a risk of adverse neurological sequelae with a mortality of 24.5% and development of CP in 8.6%. The high mortality rate could partly be explained by associated perinatal asphyxia. Infants with supratentorial ICH had a lower, although not significant, mortality rate compared with infants with infratentorial ICH and infants with a combination of supratentorial ICH and infratentorial ICH. In spite of often large intraparenchymal lesions, 30 of the 34 survivors without CP (88.2%) had normal neurodevelopmental outcome at 15 months.

Analysis of Magnetic Resonance Imaging Data on the Study of Alzheimer’s Disease

首先综述了当前结构磁共振成像、功能磁共振成像和扩散张量磁共振成像3种技术在阿尔茨海默氏症研究中的现状;其次介绍和分析了上述3种磁共振成像数据的主要处理方法;最后介绍了基于阿尔茨海默氏症的神经影像数据库及其诊断平台的建设状况.另外,也提到了此课题在该领域的一些研究进展.;Firstly, current Alzheimer’s disease (AD) studies using structural magnetic resonance imaging (MRI), functional MRI and diffusion tensor imaging (DTI) techniques are reviewed. Then the primary processing approaches of the three sorts of MRI data are introduced and analyzed. Finally, the neuroimaging database based on AD and the construction of corresponding diagnosis platform are described. Moreover, some research advances of the research group in this field are also mentioned.

Global Exploratory Analysis of Massive Neuroimaging Collections using Microsoft Live Labs Pivot and Silverlight

Event Abstract Back to Event Global Exploratory Analysis of Massive Neuroimaging Collections using Microsoft Live Labs Pivot and Silverlight Robert W. Williams1*, Lei Yan1, Xiaodong Zhou1, Lu Lu1, Arthur Centeno1, Leonard Kuan2, Michael Hawrylycz2 and Glenn D. Rosen3 1 University of Tennessee Health Science Center, Department of Anatomy and Neurobiology, Center for Integrative and Translational Genomics, United States 2 The Allen Institute for Brain Science, United States 3 Beth Israel Deaconess Medical Center/Harvard Medical School, Department of Neurology, United States Introduction: The Mouse Brain Library (www.mbl.org) consists of high resolution images generated for 200 genetically well defined strains of mice, 2200 cases, 8800 Nissl-stained slides, and ~120,000 coronal and horizontal sections of the brain. The MBL includes representatives for most sets of recombinant inbred strains, (BXD, LXS, AXB, BXH, CXB). This collection provides a solid foundation for studies of the genetic control of brain structure, function, and behavior (Lu et al. 2001; Yang et al., 2008; Rosen et al., 2009). A key challenge is how to deliver massive image collections such as the MBL, Allen Brain Atlas projects, and BrainMaps using modern web services. In this work we tested Microsoft Live Labs Pivot (www.getpivot.com ) as an image distribution portal. Pivot provides a unique method for delivering and exploring very large collections of high-resolution images. Methods and Results: Large slides of 30-µm celloidin sections were imaged using a 20x objective (NA 0.75) and an Aperio Scanscope CS scanner at an in-plane resolution of 1-µm/pixel and at 150-µm steps along the cutting axis. Images were segmented to extract single sections. SVS format image were converted into Advanced Forensic format (AFF), JPEG2000, and Silverlight deep zoom (DZI) pyramids. A DVI is 2x larger than its JPEG parent. DZIs were assembled into a set of Pivot image collections. Metadata for sorting and display were extracted from MBL and GeneNetwork.org data tables. Metadata types include thousands of genotypes and phenotypes for all strains, as well as data for individual cases (sex, age, body weight, brain weight, litter size). Spatial coordinate tags for each section using INCF Waxholm space and BIRNLex neuroanatomical terms are being added. A Pivot server was installed on a Linux CentOS 5 8-core system (Dell R610) running Tomcat and MySQL. The system was tested on Vista and Windows 7 using a Pivot browser (www.getpivot.com) and runs well on Mac OS X 10.6 using the VMWARE Fusion 3 virtual machine. Link to http://mbl.pivotcollections.org to view the MBL. The MBL collection is large and unwieldy and our current interface (www.mbl.org) does not provide sufficient sorting flexibility and speed to effectively explore or analyze the collection. A good exploratory interface would provide both forest and tree views and a way to effectively scan the entire collection for variation in traits such as ventricular volume, callosal architecture, cortical lamination, and differences in the cytology in specific nuclei in a matter of minutes, not hours or days. Pivot is optimal for these types of rapid review and exploratory tasks. The collection can be filtered, sorted, and viewed at a wide range of magnifications—from thumbnails of whole slide to full zooms of subnuclei—almost instantly. The collection can be split, filtered, and sorted using a range of continuous and discrete metadata variables (sex, age, strain, genotype, behavior) Limitations with the current Pivot implementation can be divided into two categories: those associated with the interface itself (no nested displays, a limit of 15 displayed categories, no graphic overlay for marking or annotation), and those associated with secondary analytic and statistical functions that would typically be used to test hypotheses (no dynamic output of group statistics nor tests for differences among groups using ANOVA or t tests) Discussion: Pivot is a superb web service architecture that provides very fluid access to massive neuroimaging databases. It is extremely well suited for both the dissemination of massive 2D collections and direct exploratory analysis as part of a web service. Pivot has also been extremely helpful as part of quality control workflow and has enabled us to search for neuroanatomical differences and patterns of variation among strains of mice in ways that far surpass any other web interface.

Bridging the gap between coordinate- and keyword- based search of neuroscientific databases by UMLS-assisted semantic keyword extraction

The rapid growth of the neuroimaging literature brings the demand for integration, organization and dissemination among a highly interdisciplinary community of researchers, see e.g. (Wager, Lindquist & Kaplan 2007). Since functional localization in brain is normally represented in form of stereotaxic coordinates, it can be used directly in the process of retrieving related literature in a given functional context by the measure of coordinate distance. Current neuroimaging databases which provide coordinate-based search capabilities (Brede Database, BrainMap) contain relatively small number of publications (Szewczyk 2008), therefore an interconnection with more comprehensive bibliographical databases can extend the results pool. Recently, the BredeQuery plug-in for SPM pipeline was presented as a tool which enables coordinate-based querying of the Brede Database directly from SPM (Wilkowski, Szewczyk, Rasmussen, Hansen & Nielsen 2009). As an extension of the current plugin’s functionality, we propose methods for integration of the Brede Database with the almost complete medical publication database PubMed (http://pubmed.org).

ANDI - tools and methods for amalgamating large neuroimaging databases

ANDI - tools and methods for amalgamating large neuroimaging databases

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.

Homelessness, immigrant status, poverty and dementia: Identifying rehabilitative and care needs at the margins of society

Multivariate regression models for age estimation are a powerful tool for assessing abnormal brain morphology associated to neuropathology. Age prediction models are built on cohorts of healthy subjects and are built to reflect normal aging patterns. The application of these multivariate models to diseased subjects usually results in high prediction errors, under the hypothesis that neuropathology presents a similar degenerative pattern as that of accelerated aging. In this work, we propose an alternative to the idea that pathology follows a similar trajectory than normal aging. Instead, we propose the use of metrics which measure deviations from the mean aging trajectory. We propose to measure these deviations using two different metrics: uncertainty in a Gaussian process regression model and a newly proposed age weighted uncertainty measure. Consequently, our approach assumes that pathologic brain patterns are different to those of normal aging. We present results for subjects with autism, mild cognitive impairment and Alzheimer’s disease to highlight the versatility of the approach to different diseases and age ranges. We evaluate volume, thickness, and VBM features for quantifying brain morphology. Our evaluations are performed on a large number of images obtained from a variety of publicly available neuroimaging databases. Across all features, our uncertainty based measurements yield a better separation between diseased subjects and healthy individuals than the prediction error. Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.

1st INCF Workshop on NeuroImaging Database Integration

AbstractThe goal of this meeting was to map existing neuroimaging databases, particularly databases containing primary data, and to identify mechanisms that could facilitate integrated use of such databases, including possible fusion of databases. The report provides an overview of existing neuroimaging databases that were discussed during the workshop and examines the feasibility of database federations. The report includes several recommendations for future developments.

1st INCF Workshop on NeuroImaging Database Integration

The goal of this meeting was to map existing neuroimaging databases, particularly databases containing primary data, and to identify mechanisms that could facilitate integrated use of such databases, including possible fusion of databases. The report provides an overview of existing neuroimaging databases that were discussed during the workshop and examines the feasibility of database federations. The report includes several recommendations for future developments.

Ontology for fMRI as a Biomedical Informatics Method

Ontological engineering is one of the most challenging topics in biomedical informatics because of its key role in integrating the heterogeneous database used by biomedical information services. Ontology can translate concepts and their real-world relationships into expressions that can be processed by computer programs or web services, providing a unique taxonomic frame to describe a pathway for extracting, processing, storing, and retrieving information. In developing clinical functional neuroimaging, which requires the integration of heterogeneous information derived from multimodal measurement of the brain, these features will be indispensable. Neuroimaging ontology is remarkable in that it requires detailed description of the hypothesis, the paradigm employed, and a scheme for data generation. Neuroimaging modalities, such as functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electroencephalography (EEG), and near infrared spectroscopy (NIRS), share similar application purposes, imaging protocol, analyzing methods, and data structure; semantic gaps that remain among the modalities will be bridged as ontology develops. High-performance, global resource information database (GRID) computing and the applications organized as service-oriented computing (SOC) will support the heavy processing to integrate the heterogeneous neuroimaging system. We have been developing such a distributed intelligent neuroimaging system for real-time fMRI analysis, called BAXGRID, and a neuroimaging database. The fMRI ontology of this system will be integrated with established medical ontologies, such as the Unified Medical Language System (UMLS).

A Tale of Two Cities: A Neuroimaging Investigation of Melbourne–Sydney Rivalry Comparing Cortical Thickness in Healthy Adults

We sought to identify neurobiological correlates of Melbourne-Sydney rivalry through neuroimaging measures of a key brain region involved in cognitive and emotional regulation. Twenty subjects from each city were recruited from two large neuroimaging databases, and were scanned on a GE Signa 1.5 T magnetic resonance imaging scanner. Cortical thickness of the anterior cingulate cortex (ACC) was measured using a tessellated mesh method, after image segmentation. These measures were compared with key sporting, financial and academic variables. Residents of Melbourne had a significantly thicker ACC (p < 0.0001) than Sydney residents, and this difference remained significant when age and intracranial volume were controlled for (p = 0.001). This difference mirrored that in variables measuring wealth, sporting and academic success. The thinner ACC seen in Sydney-siders may reflect the effects of increased stress due to elevated property prices, relative lack of sporting success and other variables. An alternative explanation is that a thinner ACC is the result of increasing cortical refinement and efficiency, and a marker of a more mature city. However, if these findings are a result of latitudinal effects, this may have significant implications for residents of more northern regions of the Australian continent.

A fully automated method for quantifying and localizing white matter hyperintensities on MR images

White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found ( P < 0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies (e.g. [Taylor, W.D., MacFall, J.R., Steffens, D.C., Payne, M.E., Provenzale, J.M., Krishnan, K.R., 2003. Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539–544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.

Can cognitive processes be inferred from neuroimaging data?

There is much interest currently in using functional neuroimaging techniques to understand better the nature of cognition. One particular practice that has become common is 'reverse inference', by which the engagement of a particular cognitive process is inferred from the activation of a particular brain region. Such inferences are not deductively valid, but can still provide some information. Using a Bayesian analysis of the BrainMap neuroimaging database, I characterize the amount of additional evidence in favor of the engagement of a cognitive process that can be offered by a reverse inference. Its usefulness is particularly limited by the selectivity of activation in the region of interest. I argue that cognitive neuroscientists should be circumspect in the use of reverse inference, particularly when selectivity of the region in question cannot be established or is known to be weak.

A National Neuroimaging Database*1A call to action1

The Cardiovascular Health Study (CHS) was funded by the National Heart, Lung, and Blood Institute in 1989 to assess risk factors for myocardial infarction, stroke, and other cardiovascular diseases. Participants aged 65 years and older were recruited from random samples of Medicare eligibility lists in four communities: Sacramento County, California; Washington County, Maryland; Forsyth County, North Carolina; and Pittsburgh, Pennsylvania. MRI scans were performed at two times during the study, between 1991–1994 and between 1997–1999. The examinations included sagittal T1-weighted, axial dual echo proton density and T2-weighted scans, and axial T1-weighted scans at 5-mm intervals and with no interslice gaps. The patients who underwent a second set of scans also received a 3D axial spoiled-gradient echo scan at 1.5-mm intervals, capable of high-quality volumetry. There were 3660 participants who had an MRI during the initial evaluation period and 2317 at the second evaluation, including 2116 participants who had scans at both times. Thus the database includes a total of 5977 scans on 3861 participants. The investigators of the Cardiovascular Health Study invite neuroscience researchers to utilize the MRI images and summary data collected from the participants in the CHS study.

Mining for Associations Between Text and Brain Activation in a Functional Neuroimaging Database

We describe a method for mining a neuroimaging database for associations between text and brain locations. The objective is to discover association rules between words indicative of cognitive function as described in abstracts of neuroscience papers and sets of reported stereotactic Talairach coordinates. We invoke a simple probabilistic framework in which kernel density estimates are used to model distributions of brain activation foci conditioned on words in a given abstract. The principal associations are found in the joint probability density between words and voxels. We show that the statistically motivated associations are well aligned with general neuroscientific knowledge.

Neuroimage databases: The good, the bad and the ugly

The potential of neuroimage databases to accelerate the dissemination and use of information about brain structure and function is enormous and ever increasing. Numerous efforts are now underway to further develop the technology and sociology that are necessary to support this revolution. Each effort has its own approach and tackles some of the complex problems that are associated with creating and providing access to a database. This paper introduces many of the recent successes and future challenges that are faced by the developers and users of neuroimage databases.