Rapid advances over the past several decades in neuroimaging and cyberinfrastructure technologies have brought explosive growth in the web-based warehousing, availability, and accessibility of imaging data on a broad array of neurodegenerative and neuropsychiatric disorders and conditions.[1-4] This growth has been driven largely by the demand for multi-scale data in the investigation of fundamental disease processes; the need for interdisciplinary cooperation to integrate, query, and interpret the data; and the movement of science in general toward freely available and openly accessible information. In response to this substantial need for capacity to store and exchange data online in meaningful ways in support of data analysis, hypothesis testing, and future reanalysis or even repurposing, the electronic collection, organization, annotation, storage, and distribution of clinical, genetic, and imaging data are by now essential activities in the contemporary biomedical and translational discovery process. The result has been the prolific development and emergence of complex computational infrastructures that serve as repositories of databases and provide critical functionalities such as sophisticated image analysis algorithm pipelines and powerful three-dimensional visualization and statistical tools.[5-9] The statistical and operational advantages of collaborative, distributed team science in the form of multi-site consortia continue to push this approach in a diverse range of population-based investigations.
New era of collaborative, interdisciplinary team science
The ongoing convergence and integration of neuroscientific infrastructures worldwide is heading ultimately to the creation of a global virtual imaging laboratory. Through ordinary web browsers, large-scale image data sets and related clinical data and biospecimens, algorithm pipelines, computational resources, and visualization and statistical toolkits are easily accessible to users regardless of their physical location or disciplinary orientation. The promise of this investigatory environment-without-walls, and its incipient marshalling of scientific talent and facilitation of collaboration across multiple disciplines, is accelerating various translational initiatives with high societal impact, such as early or pre-symptomatic diagnosis and prevention of Alzheimer’s disease.
Neuroimaging is now a major focus for multi-institutional research on progressive changes in brain architecture, biomarkers of treatment response, and the differential effects of disease on patterns of cognitive activation and connectivity. Prominent research consortia and multi-site clinical trials have focused on Alzheimer’s disease, pediatric brain cancer, and fetal alcohol syndrome, in addition to multi-institutional collaborative programs for mapping the normal brain.[11, 12] Current leading-edge mapping consortia are focusing on the human brain as a complex network of connectivity and aim for a comprehensive structural description of the brain’s network architecture. This collaborative effort, the human connectome (http://www.humanconnectomeproject.org/), is exploring and generating new insights on the organization of the brain’s structural connections and their role in shaping functional dynamics and brain plasticity. Such large-scale efforts necessitate close coordination of image data collection protocols, ontology development, computational requirements and sharing.
Key areas of e-science impact in neuroscience and neurology
Multi-site neuroimaging studies are dramatically accelerating the pace and volume of discoveries regarding major brain disease and the contrasts between normal and abnormal brain structure and function. The large-scale, purpose-driven data sets generated by these consortia can then be used by the broader community to model and predict clinical outcomes as well as guide clinicians in selecting treatment options for various neurological diseases. Multisite trials are an important element in the study of a disease or the process of evaluating an intervention. Linking together multiple sites facilitates the recruitment of large samples that yield high statistical power for both main analyses as well as secondary analyses of subgroups. Generalizability of results to the level of the population also is maximized. Because data come from multiple sites, investigators can explore how a treatment’s effects vary across geographically diverse sites and how such variation relates to site characteristics, and to cultural and socioeconomic characteristics of the patients who participated in the studies. Such information can directly inform clinical decision-making at the level of the patient and guide the selection of treatment options. These research efforts are imperative for guiding treatment recommendations for neurological disorders domestically and internationally as well as at the level of the individual patient. Multicenter collaborations strengthen understanding of brain diseases that affect all walks of life, all ages, and all cultures, thus enabling accelerated translation of neuroimaging trial outcomes directly into clinical applications.
Large archives of neuroimaging data are also creating innumerable opportunities for re-analysis and mining that can lead to new findings of use in basic research or in the characterization of clinical syndromes. Access to databases of neuroanatomical morphology has led to the development of content-driven approaches for exploration of brains that are anatomically similar, revealing patterns embedded within entire (sub)sets of neuroimaging data.
Provenance, or the description of the history of a set of data, has grown more important with the proliferation of research consortia-related efforts in neuroimaging.[5, 15] Knowledge about the origin and history of an image is crucial for establishing data and results quality; detailed information about how it was processed, including the specific software routines and operating systems that were used, is necessary for proper interpretation, high-fidelity replication, and re-use and re-purposing of the data. New mechanisms have emerged for describing provenance in a simple and easy-to-use environment, alleviating the burden of documentation from the user while still providing a rich description of an image’s source history. This combination of ease of use and highly descriptive metadata is greatly facilitating the collection of provenance and subsequent sharing of large data sets.
Multimodal classification of images has advanced the utility of atlases of neuropathology through standardized 3D coordinate systems that integrate data across patients, techniques, and acquisitions.[11, 16, 17] Atlases with a well-defined coordinate space, together with algorithms to align data with them, have enabled the pooling of brain mapping data from multiple subjects and sources, including large patient populations, and facilitated reconstruction of the trajectories of neurodegenerative diseases like Alzheimer’s as they progress in the living brain.[18-22] Automated algorithms can then utilize atlas descriptions of anatomical variance to guide image segmentation, tissue classification, functional analysis, and pathology detection.[7, 23, 24] Statistical representations of anatomy resulting from the application of atlasing strategies to specific subgroups of diseased individuals have revealed charactersitics of structural brain differences in a number of diseases, including Alzheimer’s disease, HIV/AIDS, unipolar depression, Tourette syndrome, and autism.
Atlas-based descriptions of variance offer statistics on degenerative rates and can elucidate clinically relevant features at the systems level. Atlases have identified differences in atrophic patterns between Alzheimer’s disease and Lewy body dementia, and differences between atrophy rates across clinically defined subtypes of psychosis. Atlases have also revealed the association between genes and brain structure. Based on well-characterized patient groups, population-based atlases contain composite maps and visualizations of structural variability, asymmetry, and group-specific differences. Pathological change can be tracked over time, and generic features resolved, enabling these atlases to offer biomarkers for a variety of pathological conditions as well as morphometric measures for genetic studies or drug trials.
Brain atlases can now accommodate observations from multiple modalities and from populations of subjects collected at different laboratories around the world. These probabilistic systems show promise for identifying patterns of structural, functional, and molecular variation in large imaging databases, for pathology detection in individuals and groups, and for determining the effects of age, gender, handedness, and other demographic or genetic factors on brain structures in space and time. Integrating these observations to enable statistical comparison has already provided a deeper understanding of the relationship between brain structure and function.
This chapter considers and assesses the clinical implications of enabling large numbers of scientists to work in tandem with the same large data sets in the context of one such effort, the Alzheimer’s Diseases Neuroimaging Initiative (ADNI). Two facets in particular of this project exemplify the clinical value of large-scale neuroimaging databases in research and in patient care: (1) disease diagnosis and progression tracking, including the diagnostic value of image databases in demarcating abnormal and normal ranges of biospecimens; and (2) role of neuroimages in statistical powering, subject stratification, and incisive endpoints and outcomes of clinical trials.