Visual Systems for Interactive Exploration and Mining of Large-Scale Neuroimaging Data Archives

While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the populating of large-scale neuroimaging databases. As they do and these archives grow in size, a particular challenge lies in examining and interacting with the information that these resources contain through the development of compelling, user-driven approaches for data exploration and mining. In this article, we introduce the informatics visualization for neuroimaging (INVIZIAN) framework for the graphical rendering of, and dynamic interaction with the contents of large-scale neuroimaging data sets. We describe the rationale behind INVIZIAN, detail its development, and demonstrate its usage in examining a collection of over 900 T1-anatomical magnetic resonance imaging (MRI) image volumes from across a diverse set of clinical neuroimaging studies drawn from a leading neuroimaging database. Using a collection of cortical surface metrics and means for examining brain similarity, INVIZIAN graphically displays brain surfaces as points in a coordinate space and enables classification of clusters of neuroanatomically similar MRI images and data mining. As an initial step toward addressing the need for such user-friendly tools, INVIZIAN provides a highly unique means to interact with large quantities of electronic brain imaging archives in ways suitable for hypothesis generation and data mining

Interactive Exploration of Neuroanatomical Meta-Spaces

Large-archives of neuroimaging data present many opportunities for re-analysis and mining that can lead to new findings of use in basic research or in the characterization of clinical syndromes. However, interaction with such archives tends to be driven textually, based on subject or image volume meta-data, not the actual neuroanatomical morphology itself, for which the imaging was performed to measure. What is needed is a content-driven approach for examining not only the image content itself but to explore brains that are anatomically similar, and identifying patterns embedded within entire sets of neuroimaging data. With the aim of visual navigation of large- scale neurodatabases, we introduce the concept of brain meta-spaces. The meta-space encodes pair-wise dissimilarities between all individuals in a population and shows the relationships between brains as a navigable framework for exploration. We employ multidimensional scaling (MDS) to implement meta-space processing for a new coordinate system that distributes all data points (brain surfaces) in a common frame-of-reference, with anatomically similar brain data located near each other. To navigate within this derived meta-space, we have developed a fully interactive 3D visualization environment that allows users to examine hundreds of brains simultaneously, visualize clusters of brains with similar characteristics, zoom in on particular instances, and examine the surface topology of an individual brain's surface in detail. The visualization environment not only displays the dissimilarities between brains, but also renders complete surface representations of individual brain structures, allowing an instant 3D view of the anatomies, as well as their differences. The data processing is implemented in a grid-based setting using the LONI Pipeline workflow environment. Additionally users can specify a range of baseline brain atlas spaces as the underlying scale for comparative analyses. The novelty in our approach lies in the user ability to simultaneously view and interact with many brains at once but doing so in a vast meta-space that encodes (dis) similarity in morphometry. We believe that the concept of brain meta-spaces has important implications for the future of how users interact with large-scale archives of primary neuroimaging data

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu

A Multi-facetted Visual Analytics Tool for Exploratory Analysis of Human Brain and Function Datasets

Brain research typically requires large amounts of data from different sources, and often of different nature. The use of different software tools adapted to the nature of each data source can make research work cumbersome and time consuming. It follows that data is not often used to its fullest potential thus limiting exploratory analysis. This paper presents an ancillary software tool called BRAVIZ that integrates interactive visualization with real-time statistical analyses, facilitating access to multi-facetted neuroscience data and automating many cumbersome and error-prone tasks required to explore such data. Rather than relying on abstract numerical indicators, BRAVIZ emphasizes brain images as the main object of the analysis process of individuals or groups. BRAVIZ facilitates exploration of trends or relationships to gain an integrated view of the phenomena studied, thus motivating discovery of new hypotheses. A case study is presented that incorporates brain structure and function outcomes together with different types of clinical data

An Investigation towards Challenges in medical image processing

Imaging is important in today's healthcare since it is used at every stage of the clinical process, from diagnosis and treatment planning to surgery and follow-up investigations. Large data volumes provide issues for medical image processing because most imaging modalities have gone completely digital with ever- increasing resolution. This work, address difficulties in the range of Kilo- to Terabytes related to bioimaging, virtual reality in medical visualisations, bioimage management, and neuroimaging. Algorithms for image processing and visualisation must be modified due to the growing volume of data. With the aid of graphical processing units, scalable algorithms and sophisticated parallelization strategies have been created. This publication provides a summary of them. Although these methods are managing the difficulty from Kilo to Terabyte, the Petabyte level is quickly approaching. Medical image processing is still an important area of study because of this

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline

The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols (Rex et al., 2003). It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides three layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available online (http://Pipeline.loni.ucla.edu)