Maryann Martone
Keynote lecture
Will talk about: The possibility and probability of a global neuroscience information framework
Maryann Martone received her B.A. from Wellesley College in biological psychology and Ancient Greek, and her Ph.D. in neuroscience in 1990 from the University of California, San Diego. Her thesis work on the neurochemical organization of the mammalian neostriatum was performed in the laboratory of Dr. Philip Groves. After receiving her degree, she joined the National Center for Microscopy and Imaging Research (NCMIR), then newly founded at the University of California, San Diego by Dr. Mark Ellisman. NCMIR is an NIH-established research resource dedicated to the advancement of 3D multiscale imaging technologies for unraveling the molecular and structural complexity of the nervous system. She is currently the Co-Director of NCMIR and a Professor-in-Residence in the Department of Neuroscience at UCSD.
Although she continues to investigate the structure of the nervous system using light and electron microscopy, for the past several years, she has been involved in the creation neuroinformatics resources for neuroscience. Dr. Martone is the principal investigator of the Neuroinformatics Framework project, a national pilot project awarded by the NIH to establish a uniform resource description framework for neuroscience. She is head of the Cell Centered Database project, an on-line database for electron tomography and correlated light and electron microscopic data. Her recent work has focused on building ontologies for neuroscience to facilitate data exchange and integration, and integrating such ontologies into image analysis and data mining tools. She chairs the Neuroinformatics Committee for the Society for Neurosciences and is the US scientific representative to the International Neuroinformatics Coordinating Facility (INCF). Within the INCF, she chairs the Program on Ontologies for Neural Structures (PONS).
Understanding the brain strains the limits of current human ingenuity. Perhaps more than any other organ system, the problem of understanding the brain is fundamentally multiscale, with relevant data derived from spatial and temporal scales spanning many orders of magnitude. Because of the complexity and breadth of these networks, unraveling functional circuits underlying complex behaviors or pinpointing the locus of disease processes, even where the genetic defect is known, has confounded scientists, who by the limitations of experimental methods glimpse only a pinhole view of a vast interconnected landscape.
Neuroscientists rely heavily on technological advances to expand our capacity to deal with this enormous complexity. Certainly, neuroscience has been the direct beneficiary of recent revolutions in molecular biology, imaging technology and computational technology. These convergent revolutions are producing views of the brain of increasing size, breadth and detail, as we acquire data spanning multiple scales across increasing expanses of brain tissue. With the concomitant increase in computing power, the increased data generation is leading to production of ever more realistic computational models, allowing scientists to probe the consequences of the structural and biochemical complexity in ways not amenable to direct experimentation.
The potential power of these integrated approaches is exemplified in large-scale projects such as the Blue Brain, the Allen Brain and Genes to Cognition. These projects realize huge monetary and manpower investments into the generation of large amounts of data. Because data within these projects are mainly acquired within a single framework, they are able to build powerful informatics infrastructure to serve and analyze these data. Mining these richly integrated data sets is starting to yield new insights into how the brain is organized.
The vast majority of neuroscience, however, is still conducted by individual researchers, who contribute their data and unique insights through less well structured venues such as the literature and websites or the creation of smaller custom databases. Although the amount of data is increasing daily, neuroscience as a whole, with its exceptionally large scope and diverse research community, lacks a coherent community framework for bringing these data together. Because such a framework has not been readily available, each source tends to use its own terminology and is structured, reasonably so, around its own particular data needs. Such customization is a significant barrier to data integration, because it requires considerable human effort to access each resource, understand the context and content of the data, and determine the conditions under which it can be compared to other similar results. The effect of this customization is that much neuroscience data is opaque to modern computational and bioinformatics tools that can operate across vast amounts of data, but require information to be parsed by a machine in order to be accessed and utilized.
Why is the data integration problem so difficult in neuroscience? Neuroscience, unlike molecular biology, does not have a single data type like a gene sequence that is easily stored or exchanged. Nor, like the geosciences, does it have a single well characterized spatial framework in which to place data. Without these types of “hooks”, it is difficult to create common tools like Google Earth that “mash up” data coming from diverse sources. Thus, building a successful framework for neuroscience requires a multipronged approach to accommodate the diversity of data and the multiple temporal and spatial scales over which they are acquired. Essentially, a framework should be able to specify for each piece of data what, when and where and provide the means for tying them together; that is 1) a coherent semantic framework encapsulating the concepts that neuroscientists use to communicate about the content of their data, the experimental conditions under which they were acquired and the conceptual and temporal relationships among them; 2) a spatial framework for tying data to its general location in the nervous system; 3) a community infrastructure software base where researchers can share tools and data easily. Building these types of frameworks is hard, but through significant national and international investments in infrastructure over the past decade, the base elements of such a framework are beginning to emerge. However, the promise of these frameworks will only be realized if researchers begin to utilize them to make data and tools more discoverable and interoperable. In this presentation, I will discuss the current landscape of neuroscience resources and our experiences in establishing standards for global neuroscience information exchange through projects like the Neuroscience Information Framework, the Whole Brain Catalog and the INCF Programs on atlasing and semantic interoperability.
