The data challenge

Integrating several disciplines and dealing with different information and data represent a big challenge in terms of neuroscience data management. In fact, data may range from simple textual descriptions to time series (Electroencefalography (EEG)), anatomical data (Computer Tomography (CT), Magnetic Resonance Imaging (MRI)) and functional data (Functional MRI (fMRI), Positron Emission Tomography (PET)/ Single Photon Emission Computer Tomography (SPECT)). Data size and type can vary from few Kilobytes of a textual annotation on a patient, or a psychological questionnaire, to the hundreds of Megabytes of modern MRI/CT study or all day long multichannel EEG recordings[2].

Molecular genetics and the investigation of the whole genome by means of techniques such as the whole genome sequencing, whole exome sequencing or genome wide association studies may generate an overwhelming amount of digitized data[3] that need to be managed through ever developing bioinformatics tools, at the price of more and more data, metadata, and storage space to handle. As a response to these challenges, software environments and tools must be extensible in data modalities as well as scalable in storage and computational resources.

The information challenge

Data management in neuroinformatics needs to provide not only adequate infrastructures to store, search and retrieve large data sets, but also tools and standards for defining their structure, exploring their content and understanding their meaning. In other words, scientists need to focus on aspects such as accessibility, integration and annotation of data. Providing extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments is even more difficult. In these cases, software environments should be flexible enough to allow the dynamical definition of new types of data and metadata, to support different associations between data and metadata, to dinamically modify data models and structures, to redefine the search paths and to add distributed sources of data.

State of the art

Several data models have been defined to describe structured data for storage in data management systems for biomedical applications. The main aim of such data models has been to provide the definition and format of data for supporting the development of suitable information systems.

XCEDE (XML-based Clinical and Experimental Data Exchange)[4] is a widespread data model providing an extensive metadata hierarchy for describing and documenting research and clinical studies. The subdivision of experimental data at various granularity levels is granted by a taxonomy of hierarchical data types. At each hierarchical level, elements contain level-specific information, whose schema may be used to store metadata specific for the experiment or related to the data modality. The linking rules between levels are flexible enough to allow the omission of other levels if the user finds them unnecessary. Due to its powerful general schema and to the inherent flexibility of its approach, the XCEDE model, describing a complete metadata hierarchy for clinical experiments, has been taken as a starting point for building the structure of our data model. Some elements at the base of the XCEDE model (projects, subjects, visits, studies, episodes and acquisitions) have been extracted and further developed in our work together with some data and metadata hierarchies used by the BIRN (Biomedical Informatics Research Network) initiative[5, 6].

Also the Functional Genomics Experiment (FuGE) object model is aimed at providing a framework for the development of standards in life science. As such, FuGE provides a solid foundation for other technology-specific, life-science standards and data formats and is currently being used to develop formats for microarrays, proteomics, metabolomics and various other technologies[7]. As we had to manage also data in the field of genomics, references to FuGE have been considered during our work. From another point of view, ontologies try to define, through a common vocabulary, a structured representation of knowledge that can be used by either humans or automated software agents on a particular domain[8]. Within this work, ontologies are used to support users in defining the names of attributes of data types. This is very helpful to build data types with standard annotations. Two ontologies belonging to the Open Biomedical Ontologies (OBO)[9, 10], providing a list of other useful ontologies, have been taken into account. They are:

Several software platforms have been proposed in the last years to manage multimodality neuroscience and neuroimaging data, but just a few of them give the possibility to costumize data models and structures. Most of them are highly devoted to a specified field, thus resulting poor in flexibility. Actually, a number of these platforms have been designed to deal either with complex neuroimaging data schemes or with distributed resources on data and computational sides, but usually they do not take into account the possibility of considering extensible data models to add new types of data as well.

SenseLab[15] is a project of Yale University for flexibly storing data and metadata to be displayed within a Web-based user interface. The approach to address these issues is named EAV/CR (Entity-Attribute-Value with Classes and Relationships) and combines object-oriented concepts with the basic EAV model. Specifically, an object in the database must belong to a particular class, its attributes are constrained based on its class, and relationships are set between data objects. An EAV/CR database is based on a relational database using mostly an EAV-based storage mechanism. Flexibility is taken into account just as regards tables to be created and/or classes to be added to tables.

The GridPACS system, developed at Ohio State University similarly provides a distributed data archive and utilizes Grid technology for implementing processing pipelines[16]. Nevertheless, it doesn’t address issues about extensibility in data models and is strictly devoted to managing medical images.

Few other data management systems are mostly aimed at allowing XML-based descriptions of relational databases. Among them, we may cite Silkroute[17] and DataServer[18]. But none of them, actually, neither consider complex data schemes for neuroscience applicatons nor address the issue related to the use of distributed data and computational resources.

However, starting from these previous experiences, a reference platform for managing multimodality neuroscience data has emerged in the last years. Having extensibility as its reason for being, the eXtensible Neuroimaging Archive Toolkit (XNAT)[19] is a powerful software platform designed to facilitate the management and maintenance of neuroimaging data and related information developed by the Neuroinformatics Research Group in collaboration with the National Institute of Health (NIH). XNAT enables users to access the archive through a secure web application which provides a number of quality control and productivity features, supports a wide variety of methods to upload data, allows users to inspect, validate, and process the data and extend the default data schema. Moreover, backup processes can be scheduled and security protocols can be enforced, user interactions with the data are logged and modifications to data can be recorded and validated. Also, common processing routines can be automated and the resulting measures can be captured back into the archive. Moreover, the XNAT framework relies heavily on XML and XML Schema for its data representation, security system, and generation of user interface content. For all the features mentioned above and because of its use of XML for building extensible data models, XNAT has been considered as a reference platform for our work. However, after a comprehensive evaluation, some constraints in the overall approach of XNAT have been proven to be impractical in our application. The major obstacles were related to the manipulation and extension of the data model, the low flexibility in data management and the difficulty to access distributed computing resources and storage facilities. In particular, as regards extensibility, XNAT still needs computer science skills to manually write XML schemes to add a new data type as well as the rebuilding of the database and the re-deployment of the webapp. Moreover, a Grid based deployment of applications is not considered within such a platform. Further details on the comparison between our and XNAT’s approaches will be given in the methods section. As a further improvement, the Human Imaging Database (HID), developed by the Biomedical Informatics Research Network (BIRN), provides an extensible data management system for clinical neuroimaging, accessible through a web-based user interface[20], operating in a distributed environment. In respect to XNAT, HID allows the federation of different databases and relies on a schema, based on the XCEDE schema described above, with abstract data types, thus providing more efficient expansions without modifying the schema itself. HID can also mantain a virtual directory system of local and remote files managed through the Storage Resource Broker.

The Grid approach

Actually, to address issues about distributed resources regarding both storage and computation, the Grid approach has been a successful way to manage some specific challenges about complex experiments in the biomedical field[21]. These challenges include the management of storage resources using different access protocols, the authentication and authorization across systems using different identity management systems, uniform management policies across institutions having differing access requirements and the need of a wide-area-network access.

iRODS, the Integrated Rule-Oriented Data System, is a data Grid middleware system developed by the Data Intensive Cyber Environments research group. The iRODS system is a generic middleware that can be configured to implement any desired data management application based on a set of major components including a data Grid Architecture based on a client/server model controlling interactions with distributed storage and computational resources, a Metadata Catalog maintained in a database system for managing the attributes of data and state information generated by remote operations and a Rule Engine enforcing and executing adaptive Rules. iRods is distributed with an open source BSD license and has been chosen as the reference Grid middleware for our software environment. More reasons about the choice of iRODS as Grid middleware are explained in the Methods section.

The process-event structure

An event is defined as either any “atomic” operation that can be performed on patients, or any processing of data, or any other action related to the administration and management of the repository. If needed, it can contain correlations between data, metadata and, in addition, algorithms. Each event is associated with a process. The process is defined as a group of sequential events and/or sub-processes related to an activity, allowing the creation of a sort of hierarchical structure. Custom process-event types and their relationships can be defined, thus describing the taxonomy better fitting the needs of the application. An example of the process-events structure concerning a possible clinical scenario is shown in Figure1. It is worth noting the existing relationships between processes (blue boxes) and events (yellow boxes) and the association of data and metadata to each specific event. A pre-surgical analysis sequence is considered. This is divided into different phases consisting, each, of different steps, ranging from the acquisition of data to their analysis. According to the described structure, a Pre-surgical Process (P) can be considered as a top level process composed by different sequential subprocesses: (SP1) Data Acquisition, (SP2) Image Post-Processing, (SP3) Trajectories study and (SP4) Surgery Area Estimation. Each of these sub-processes represents a specific part of the main process and is composed by a number of events, each of them connected to the related data and metadata. It is also worth noting that the whole process can be easily modified to fit either changes in the analysis sequence or different requirements for another case study, just by changing or adding new events, creating new processes composed of different events or combining existing processes and events. The defined process-event taxonomy can be used to store the information about each step in the process and the related data and metadata, thus allowing the definition of a detailed time line of performed operations. This can also improve the repeatability of experiments by providing both a detailed recording of the analysis process and a complete description of relationships between data and actions.

The XCEDE model has been tailored to fit this process/events model in a very simple and efficient way. As shown in Figure1, the visit and study elements have been associated to process entities with a father-son relationship while episodes and acquisitions have been collapsed into events.

The data model

The analysis of the state of the art pointed out that just a few data models/repositories have the ability to add new data types. However, in fact, in most cases, as their databases are based on the already defined data, they require the rebuilding of the database each time a new type of data is added.

For example, the procedure to add a new data type in XNAT requires the intervention of a specialist in order to:

The proposed approach solves these issues providing a new methodology for the data and related metadata management to make the repository easier to use, more dynamic and therefore improving the overall extensibility and customization. This is based on the definition of a “meta” data model enabling the user to build his own data types independently from the application context. With this approach, in order to add a new data type, a user (even a clinical one) has just to:

A “data type” is identified as a minimum set of information describing a data instance (i.e., the set of records associated to a clinical study, the parameters for a particular biomedical image) that may or may not be associated to physical files. As an example, clinical data can be defined as datatype but are not associated to a file, unlike MRI data.

Each data type is described by an XML metadata schema associated to XSD and XSL files file to define, respectively, its structure and display. The XSD and the XSL adopted for transformations are the same for all XML files. From XML files, DHTML web forms are built, using XSL transformations. The XML can be stored in MySQL or in other SQL databases. In this case, an XML file URL can result not in a physical file but in a query to the DB (this is transparent to the component requiring the XML). This can be considered as a caching mechanism.

The XML representation of a data type metadata is divided into two main sections: a header, containing general information about the schema, and the metadata description, representing the detailed description of the information. The metadata description is composed by one or more groups of information each composed by attributes, loops and their combinations. An attribute defines a single metadata and is composed by different parameters and subelements. The formers describe the information related to the typology of the attribute (type, whether it is required, etc.). Subelements, instead, describe what the attribute represents: they include name, value or possible values and references to existing ontology definitions. Actually, through the interface used to build data types, it is possible to choose an ontology among those defined within the platform. Once an ontology has been chosen, it is temporarily loaded within the system and its terms are made available to support users in the attributes’ name definition. This is done thanks to a “suggest” mechanism. In fact, whenever a user digits the name field, an ontology based suggestion will appear according to the written text. This approach is very important in order to build data types using standard annotations and to make them being easily integrated with external data sources.

The loop is probably the most relevant improvement with respect to existing data models. Usually, when modelling clinical information, the information model (in terms of number of attributes) is clear but the amount of information (the number of occurences) that will characterize the specific data to be recorded is not. This is the case, for example, of reagents in microarray studies: ”reagent” is the information to be recorded, but for different microarray studies, different types of reagents can be used. Therefore, the information to be recorded under the “reagent” metadata is in fact characterized by description, concentration and amount for every used reagent. A similar issue in clinical studies arises describing risk factors: the presence of a risk factor is the information, and it can be modeled with details on every sub-factor presence and relevance (smoke, alchool, sedentary job).

Overall system architecture

The main components of the Repository are (Figure2):

Two important aspects are related to authentication and software as a service issues. As regards authentication, users and system administrators authenticate to the system using an existing LDAP or a database account available on the server infrastructure. The access is via web browser without any client installation and in a secure way through the HTTPS (secure HTTP) protocol. When a user needs to access the repository resources he has to authenticate himself through the web portal interface using his username and password. Each user is associated to Access Control Lists in order to guarantee security and auditing. System administrators are able to define different groups of users associated with different access permission to different pages and functions of the repository. This way, users can see only a subset of pages and perform a limited number of different actions (depending on their role in the project). The Web based SaaS (Software as a Service) approach has been preferred because the backend hardware can be scaled up enough to satisfy the user needs without requiring users to implement their own infrastructure. Moreover, this allows, at any moment, to migrate from a standard hardware infrastructure to a service based one, e.g. using the Grid or other kind of on demand IT services.

The repository portal

The repository portal is designed to make the storage and the navigation of data and information easy, through a simple and transparent web interface. It is a Java 2 Enterprise Edition (J2EE) web application based on several existing open source tools for the development of web applications. The basis of the portal consists in a framework that relies on an Apache Tomcat web application container[22]. It incorporates a database interface layer built through iBATIS, a persistence framework which automates the mapping between SQL databases and objects in Java[23]. To provide users with highly interactive interfaces, some components are designed using the AJAX (Asynchronous Javascript and XML) programming technique. Messages are exchanged in XML or JSON (JavaScript Object Notation)[24] format wherever possible. Also, wherever possible, XSL transformations (to transform XML data into human readable HTML pages) are performed[25].

This component represents the main access point to all the functionalities available through the overall integration platform, and exposes both user and administrator interfaces. Administrators are able to control users’ access by creating groups and their association with pages and functions, define processes (visits and studies), events and all their relationships, define new data types and related metadata, associate them with the related events and manage available ontologies. Normal users, according to their assigned permissions, are able to insert new data, retrieve patients’ information and view all the related data, download stored data, explore visits, studies and their interconnection and all the related events, data and metadata to have a global picture.

As an additional feature, in order to make the insertion of metadata easier, an automated approach is available for some predefined datatypes like MRI, fMRI, PET and SPECT. Using libraries like Java dcm4che[26], the portal can automatically extract metadata contained within uploaded data files and incorporate them correctly in the database, creating associated events depending on image modality. Such as automatic procedure permits to avoid human errors and provides a further file type checking before the uploading. If needed, a visualization tool can be made available within the portal interface in order to allow users to interact with neuroimages through a remote visualization service. This is made possible by using a client-side application that uses the VNC protocol to connect to a sharable work session that is running server-side, with a significant speed up of diagnostic processes.

The database

The Repository is based on a MySQL database. The database design has been a crucial part of the repository development. In fact this component is fundamental in order to make the repository highly flexible and easily extensible. The core of the database is formed by the two previously described entities: processes and events and their relationship to data and metadata. The information inside the data table represents the data inserted in the repository. These data can be associated with one o more files, thus keeping the association with one or more file entities accordingly to their datatype. The File table contains the URIs of all the stored files. The repository can be configured to store the metadata totally or partially within the database. In this latter case, the metadata are stored as XML descriptions inside the data table, to display the data in a rapid and dynamic way using XSLT Transformations and as records of specific metadata tables, to perform complex queries in an easier way.

The Grid middleware

A crucial aspect of the the repository design is related to the choice of the Grid middleware used to build the underlying infrastructure. The storage subsystem has been built around the iRods tool, the successor of SRB (Storage Resource Broker, by the San Diego Supercomputing Center)[27]. iRods has been chosen, among others (e.g. gLlite Storage Element subcomponent) because it allows to build a federated and distributed data storage system without the need of central components. In fact, the gLite middleware provides a storage component with many features (command line and Java APIs, integration with x.509 certificates, integration with key-stores for crypted data storage) and, in a separate component, a metadata catalog (AMGA). However, it requires almost dedicated servers, with a specific Linux flavour on top and a bit of infrastructure (resource brokers) to deal with, thus requiring (almost partially) specific technical staff. Therefore, as our platform is mostly aimed at small laboratories mostly focused on biomedical skills and with a low level hardware infrastructure, the choice of iRODS as Grid storage middleware has been preferred despite the advanced features of gLite. Moreover, in the planned experiment, the requested amount of storage was really basic and the storage infrastructure had not to be shared on a public Grid virtual organization, thus suggesting a simpler solution. Finally, we also decided to use iRODS as the basis of the storage architecture of the repository because it permits the use of heterogeneous storage resources and allows the creation of microservices and rules to easily perform operations on the stored data and metadata. Anyway, it is worth mentioning that iRODS can be interoperable with the gLite Storage Resource Manager (SRM)[28] interface thanks to the work carried out at the Academia Sinica Grid Computing group, Taiwan[29].

The samples manager

The system described in this paper has been realized with the main goal of providing a tool for everyday activity of data collection and search, with a really pragmatic and practice oriented perspective. One of the peculiarity of our multi-centre study is that blood samples taken in Genoa, Italy have to be sent to the Health Science Center, Lubbock, Texas, for the genetic analysis to be carried out. Samples have to be stored in freezers, as different analyses are carried out over time. To streamline the process of storing and retrieving samples, an ad-hoc Web application has been developed, fully integrated in the platform. The application allows biologists to register the actual location of the sample in the freezer benches (-80C and -45C), also allowing technicians to configure graphically the available racks and slots in their freezers. When they put a blood sample (or a processed DNA/RNA sample) in the freezer, they can input the coordinates of the sample, from the freezer identification number down to the x-y coordinate in the sample box, simply by clicking on the application interface. They can also save the courier mailing number and a reference to the patient. This way, a sample can be retrieved at any time and is unequivocally associated with other patient data.

The scenario

Our platform is being tested within a research project currently carried out through an international collaborative effort between the Laboratory of Molecular Neurogenetics at the Texas Tech University Health Sciences Center (TTUHSC) and two Departments at the University of Genoa, Italy: the Department of Neuroscience, Ophthalmology and Genetics (DINOG) - Section of Clinical Neurophysiology and the Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS).

The aim of the study is to develop criteria for the early diagnosis of Alzheimer’s disease (AD) and algorithms to predict the progression of the disease, by combining neuropsychological tests, imaging methods, genetic tests and biomarker screening. The results of the implementation of the described platform within such a scenario are presented below just as an assessment of the feasibility of our approach in case of small multi-modality and multi-level experiments. Even though the architecture has been explicitely thought for facing extensibility issues, not only in terms of data model but also in terms of larger datasets and more distributed hardware infrastructure, the scalability of our approach has not been tested yet, due to the limited dimension of the experiment. However, a first set of 20 patients with their clinical data and brain scans, as well as results from the related genetic screening, have been inserted, so far, through our platform. We plan to insert about 50 patients a year due to the strict policies adopted for patient eligibility.

The steps identified within the collaboration in order to achieve the project goals are as follows:

After neurophysiologists identified subjects eligible for the study, they have inserted the related personal data into the system together with clinical data, neuropsychological tests and neuro imaging data. Neuroimaging data are associated to files, while clinical data and neuropsychological tests are simple data without any file association. Data have been fully anonymized (and also defaced in case of morphological images) and cannot be linked in any way to patient’s names. The link between data in the repository and patients identities is done by using unique and anonymous identifiers managed only by clinicians. This solution was one of the project requirements and satisfies the guidelines authors have been asked to follow about data privacy.

Then, neurologists have provided the blood samples drawn from the subjects. In order to make samples management easy, a specific interface has been created to add samples, associate them with the corresponding patients and store the related information. Finally, these samples have been collected, frozen and sent, using couriers, to the Texas Tech group to generate genetic data.

On their turn, at first, geneticists have configured the genetic screening setup by inserting information about the genes to be screened, their exons and their relevant known variants. This configuration has been performed through the integration with online resources such as NCBI (National Center for Biotechnology Information)[30] and Molgen (Alzheimer Disease and Frontotemporal Dementia Mutation Database)[31] in order to retrieve and/or compare information. Users can directly view/access both the NCBI and the Molgen sites, the first pointing to the gene page and the second pointing to the gene variants section, in order to retrieve and store the correct and complete information within the repository. This is a very powerful tool to integrate the information coming from public databases into the repository.

A second step has been related to the insertion of new data obtained by genetic screening on the patients samples. This has been carried out through a devoted section in the user interface.

Finally, geneticists have completed the samples management process by inserting information about the delivery and storage of samples. In regard to this last aspect, the platform includes an application described in methods section, able to describe the real configuration of the storage fridges in each laboratory, thus allowing users to put and move samples in such structures and to track them over time. By means of such an integration, authorized users are able to manage samples and their storage information through a unique application.

In general, the access to patient information can be restricted because of privacy policies and only authorized users can insert, view or modify data. Users can view more or less information according to their permissions: for example, geneticists users are not allowed to see patients personal data but only their clinical data. This is another feature able to match important requirements for these kinds of experiments usally based on strict privacy policies.

As regards data mining, patient-related variables like diagnosis, sex and birth date can be combined with data information in order to compose specific queries. In fact, it is possible to add many different conditions in a dynamic way. As such, by choosing any of the defined data types, the corresponding “field” parameter is automatically filled with its specific defined attributes. Once a field has been selected, it is sufficient to insert the desired “value” parameter to complete the condition. This method makes it possible to query patients having data with one or more specific metadata attributes for any arbitrary combination of their values. It is therefore easy to imagine how to build complex queries choosing and combining patient related values (diagnosis and sex) with complex conditions on specific data and metadata. For example a query might be built to retrieve all male Amnestic MCI patients that are smokers, having variants for MAPT gene and one or more MRI data. After the query has been performed and patients have been retrieved, users can have a quick overview of all their data using an interface divided into three main sections. The first one is the same used to build the query as described above and can be used to refine the query. The second section contains the list of retrieved patients. For each selected patient in the list a third section shows a synthetic view of the related data. Each field can be further examined in details.

From the architectural point of view, a Data Grid paradigm allows keeping a unified control on the access and storage of data in resources geografically distributed among different centers. This can be done through secure protocols, also hiding the real infrastructure from users. In details, a main iRODS server has been installed within the DIBRIS infrastructure and further iRODS storage resources are located at Texas Tech University (Figure3).

Testing the platform

Both clinicians and genetists were then asked to test the functionality of our platform in inserting and retrieving data and metadata. Four different scenarios were defined for testing the usability and the effectiveness of the Xtens environment. The first scenario involved the insertion by clinicians of the clinical history, results of general medical and neurological examinations, neuropsychological test scores and neuroimaging data including structural MRI and FDG-PET for a 74-year old woman with just mild impairment in memory test, but not demented. In the second scenario, the group of geneticists had to access, through an action toolbar, a page within the repository that is specifically set to support and facilitate the record of genetic data. Candidate genes can be selected from a drop-down menu and known single nucleotide polymorphisms can be entered manually or downloaded from the most relevant field specific websites (NCBI and Molgen) for each selected gene. Consequently, the geneticists are enabled to match their original findings with the ones from Ncbi and Molgen and to catalogue them as known or novel variants or mutations. As for the third scenario, the system was asked to list all subjects scoring less than 6 on the delayed recall measure of the Rey Auditory Learning Verbal Test, the standard test in use to detect short-term memory impairment. These subjects are more likely to be affected by a neurodegenerative disease than subjects with deficit in other cognitive domains. The results were displayed in a system window listing the identity number of those subjects whose information could be easily accessed by simply clicking on the identity number itself. The latter scenario was about the definition of a new data type concerning a generic serum biomarker in order to test the user friendliness of extending the data model. Users can either modify already defined processes, by selecting them from a list, or create new ones in an easy way, by means of the interface shown in Figure4. Once a data type was built, related XML file and database structures are automatically created and stored as described before, thus completely hiding the underlying structures and layers from users. After the data scheme was defined, users started filling in their data into the repository. However, the schema can still be modified and extended after the insertion of data without any need of rebuilding the database.

From such usability validation some qualitative results may be evidenced.

A more quantitative evaluation, together with a scalability report, is planned to be presented in a further paper when the deployment of the repository will be completed.