Imaging databases and neuroscience

Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas

Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.

Human neuroimaging as a "Big Data" science

The maturation of in vivo neuroimaging has led to incredible quantities of digital information about the human brain. While much is made of the data deluge in science, neuroimaging represents the leading edge of this onslaught of "big data". A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Yet few, if any, common solutions exist to support the science of neuroimaging. In this article, we discuss how modern neuroimaging research represents a multifactorial and broad ranging data challenge, involving the growing size of the data being acquired; sociological and logistical sharing issues; infrastructural challenges for multi-site, multi-datatype archiving; and the means by which to explore and mine these data. As neuroimaging advances further, e.g. aging, genetics, and age-related disease, new vision is needed to manage and process this information while marshalling of these resources into novel results. Thus, "big data" can become "big" brain science.

Emerging ethical challenges in advanced neuroimaging research: review, recommendations and research agenda

The dynamic and ever-evolving nature of neuroimaging research creates important ethical challenges. New domains of neuroscience research and improving technological capabilities in neuroimaging have expanded the scope of studies that probe the biology of the social and ethical brain, the range of eligible volunteers for research, and the extent of academic-industry relationships. Accordingly, challenges in informed consent and subject protection are surfacing. In this context, we provide an overview of the current landscape for neuroimaging and discuss specific research ethics topics arising from it. We suggest preliminary approaches to tackle current issues, and identify areas for further collaboration between neuroimagers and institutional review boards (research ethics committee).

An investigation of the structural, connectional, and functional subspecialization in the human amygdala

Although the amygdala complex is a brain area critical for human behavior, knowledge of its subspecialization is primarily derived from experiments in animals. We here employed methods for large‐scale data mining to perform a connectivity‐derived parcellation of the human amygdala based on whole‐brain coactivation patterns computed for each seed voxel. Voxels within the histologically defined human amygdala were clustered into distinct groups based on their brain‐wide coactivation maps. Using this approach, connectivity‐based parcellation divided the amygdala into three distinct clusters that are highly consistent with earlier microstructural distinctions. Meta‐analytic connectivity modelling then revealed the derived clusters' brain‐wide connectivity patterns, while meta‐data profiling allowed their functional characterization. These analyses revealed that the amygdala's laterobasal nuclei group was associated with coordinating high‐level sensory input, whereas its centromedial nuclei group was linked to mediating attentional, vegetative, and motor responses. The often‐neglected superficial nuclei group emerged as particularly sensitive to olfactory and probably social information processing. The results of this model‐free approach support the concordance of structural, connectional, and functional organization in the human amygdala and point to the importance of acknowledging the heterogeneity of this region in neuroimaging research. Hum Brain Mapp 34:3247–3266, 2013. © 2012 Wiley Periodicals, Inc.

COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets

The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies’ implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.

MBAT: a scalable informatics system for unifying digital atlasing workflows

Background

Digital atlases provide a common semantic and spatial coordinate system that can be leveraged to compare, contrast, and correlate data from disparate sources. As the quality and amount of biological data continues to advance and grow, searching, referencing, and comparing this data with a researcher's own data is essential. However, the integration process is cumbersome and time-consuming due to misaligned data, implicitly defined associations, and incompatible data sources. This work addressing these challenges by providing a unified and adaptable environment to accelerate the workflow to gather, align, and analyze the data.

Results

The MouseBIRN Atlasing Toolkit (MBAT) project was developed as a cross-platform, free open-source application that unifies and accelerates the digital atlas workflow. A tiered, plug-in architecture was designed for the neuroinformatics and genomics goals of the project to provide a modular and extensible design. MBAT provides the ability to use a single query to search and retrieve data from multiple data sources, align image data using the user's preferred registration method, composite data from multiple sources in a common space, and link relevant informatics information to the current view of the data or atlas. The workspaces leverage tool plug-ins to extend and allow future extensions of the basic workspace functionality. A wide variety of tool plug-ins were developed that integrate pre-existing as well as newly created technology into each workspace. Novel atlasing features were also developed, such as supporting multiple label sets, dynamic selection and grouping of labels, and synchronized, context-driven display of ontological data.

Conclusions

MBAT empowers researchers to discover correlations among disparate data by providing a unified environment for bringing together distributed reference resources, a user's image data, and biological atlases into the same spatial or semantic context. Through its extensible tiered plug-in architecture, MBAT allows researchers to customize all platform components to quickly achieve personalized workflows.

Multisite neuroimaging trials

PURPOSE OF REVIEW

The intent of this study is to review trends in multicenter neuroimaging trials and their value for research and implications for clinical treatment.

RECENT FINDINGS

The rise in availability of MRI for detecting disorders in the living brain has made it an attractive technology for assessing neural structure and function in a number of prominent diseases. Geographic factors underlying diseased populations coupled with complementary neuroimaging research programs have led to an increase in multicenter neuroimaging trials and consortia. Neuroimaging has become a major focus for multiinstitutional research in progressive changes in brain architecture, proxy biomarkers of treatment response, and the effects of disease on patterns of cognitive activation and connectivity. Notable consortia and research trial studies have focused on Alzheimer's disease, pediatric brain cancer, and fetal alcohol syndrome, in addition to multiinstitutional collaborative programs for mapping the normal brain. Such large-scale efforts necessitate close coordination of image data collection protocols, ontology development, computational requirements, concerted data archiving, and sharing.

SUMMARY

Multicenter neuroimaging trials, consortia, and collaboratives enable the acquisition of large-scale, purpose-driven datasets that can then be used by the broader community to model and predict clinical outcomes as well as guide clinicians in selecting treatment options for neurological disease.

Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework

Functional magnetic resonance imaging (fMRI) has led to an enormous growth in the study of cognitive neuroanatomy, and combined with advances in high-field electrophysiology (and other methods), has led to a fast-growing field of human neuroscience. Technological advances in both hardware and software will lead to an ever more promising future for fMRI. We have developed a new computational framework that facilitates fMRI experimentation and analysis, and which has led to some rethinking of the nature of experimental design and analysis. The Computational Neuroscience Applications Research Infrastructure (CNARI) incorporates novel methods for maintaining, serving, and analyzing massive amounts of fMRI data. By using CNARI, it is possible to perform naturalistic, network-based, statistically valid experiments in systems neuroscience on a very large scale, with ease of data manipulation and analysis, within reasonable computational time scales. In this article, we describe this infrastructure and then illustrate its use on a number of actual examples in both cognitive neuroscience and neurological research. We believe that these advanced computational approaches will fundamentally change the future shape of cognitive brain imaging with fMRI.

Digital atlases as a framework for data sharing

Digital brain atlases are useful as references, analytical tools, and as a data integration framework. As a result, they and their supporting tools are being recognized as potentially useful resources in the movement toward data sharing. Several projects are connecting infrastructure to these tools which facilitate sharing, managing, and retrieving data of different types, scale, and even location. With these in place, we have the ability to combine, analyze, and interpret these data in a manner not previously possible, opening the door to examine issues in new and exciting ways, and potentially leading to speedier discovery of answers as well as new questions about the brain. Here we discuss recent efforts in the use of digital mouse atlases for data sharing.

Unobtrusive integration of data management with fMRI analysis

This note describes a software utility, called X-batch which addresses two pressing issues typically faced by functional magnetic resonance imaging (fMRI) neuroimaging laboratories (1) analysis automation and (2) data management. The first issue is addressed by providing a simple batch mode processing tool for the popular SPM software package (http://www.fil.ion. ucl.ac.uk/spm/; Welcome Department of Imaging Neuroscience, London, UK). The second is addressed by transparently recording metadata describing all aspects of the batch job (e.g., subject demographics, analysis parameters, locations and names of created files, date and time of analysis, and so on). These metadata are recorded as instances of an extended version of the Protégé-based Experiment Lab Book ontology created by the Dartmouth fMRI Data Center. The resulting instantiated ontology provides a detailed record of all fMRI analyses performed, and as such can be part of larger systems for neuroimaging data management, sharing, and visualization. The X-batch system is in use in our own fMRI research, and is available for download at http://X-batch.sourceforge.net/.

Neuroethical responsibilities

Neuroscience represents a dynamic area of biomedical research where neuroethical responsibilities for researchers are emerging. This paper is the companion piece to the French-language one also published in this issue of the Canadian Journal of Neurological Sciences. It serves as a review of recent advances in neuroethics through the lens of three cases: (1) incidental finding of anomalies in neuroimaging research; (2) creation of neurotechnologies that can lead to cognitive enhancement, and (3) responsible communication of research results. We propose and discuss a multidimensional framework of neuroethical responsibilities to help tackle these issues. The framework reiterates the fundamental role of scientific integrity, puts in the foreground social responsibilities pertaining to the eventual use of neuroscience knowledge, and highlights self-reflection in research and training of researchers.

Responsabilités Neuroéthiques

Neuroscience represents a dynamic area of biomedical research where neuroethical responsibilities for researchers are emerging. This paper is the companion piece to the English-language one also published in this issue of the Canadian Journal of Neurological Sciences. It serves as a review of recent advances in neuroethics through the lens of three cases: (1) incidental finding of anomalies in neuroimaging research; (2) creation of neurotechnologies that can lead to cognitive enhancement, and (3) responsible communication of research results. We propose and discuss a multidimensional framework of neuroethical responsibilities to help tackle these issues. The framework reiterates the fundamental role of scientific integrity, puts in the foreground social responsibilities pertaining to the eventual use of neuroscience knowledge, and highlights self-reflection in research and training of researchers.

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.

LONI visualization environment

Over the past decade, the use of informatics to solve complex neuroscientific problems has increased dramatically. Many of these research endeavors involve examining large amounts of imaging, behavioral, genetic, neurobiological, and neuropsychiatric data. Superimposing, processing, visualizing, or interpreting such a complex cohort of datasets frequently becomes a challenge. We developed a new software environment that allows investigators to integrate multimodal imaging data, hierarchical brain ontology systems, on-line genetic and phylogenic databases, and 3D virtual data reconstruction models. The Laboratory of Neuro Imaging visualization environment (LONI Viz) consists of the following components: a sectional viewer for imaging data, an interactive 3D display for surface and volume rendering of imaging data, a brain ontology viewer, and an external database query system. The synchronization of all components according to stereotaxic coordinates, region name, hierarchical ontology, and genetic labels is achieved via a comprehensive BrainMapper functionality, which directly maps between position, structure name, database, and functional connectivity information. This environment is freely available, portable, and extensible, and may prove very useful for neurobiologists, neurogenetisists, brain mappers, and for other clinical, pedagogical, and research endeavors.