Large prospective studies of aphasia recovery that incorporate anatomical, physiological, and behavioral data are virtually non-existent. This has a significant impact on virtually all research into the diagnosis, prognosis, and treatment of aphasia, since we do not know the natural course of the disease, and thus cannot adequately inform patients and families or assess the effects of therapeutic interventions. We believe that the complexities of data management, particularly regarding anatomical and physiological data, represent a major stumbling block to the design and execution of such studies. With such diverse sources of information as demographic and medical data, cognitive and linguistic test results, electrophysiological recordings, and many types of brain images, it is hard enough to perform single case studies that attempt to relate these data to each other, let alone studies that include statistically meaningful numbers of participants. Even when the problem is restricted to a single data type, such as functional MRI data, we do not have the ability to scale up the methods used in individual subjects to larger groups. Both the large volume of data and the complexity of data processing cause difficulties. We thus propose to build computational infrastructure (R21 phase) to facilitate the prospective investigation of aphasia recovery (R33 phase). The infrastructure is based on the use of (a) database technology to represent diverse data types within a single representational framework; and (b) """"""""grid"""""""" computing to distribute data and data processing over many storage devices and computers, using software developed in federally (NSF) funded basic computational research that allows investigators to express complex data processing algorithms in a convenient manner. The longitudinal aphasia study will use structural and functional MRI and diffusion tensor imaging, along with language and cognitive measures, to characterize the natural course of physiological and behavioral recovery from aphasia. The physiology of recovery will be quantified in neural network models of individual patient imaging data and their mathematical """"""""fit"""""""" to normative templates derived from imaging data on healthy age-matched adults. The changes in these models over time will be related to the behavioral changes to construct a theory of recovery. The computational infrastructure will provide the means to encode the diverse types of data needed for aphasia recovery research in such a way that complex queries involving multiple data types (e.g., brain activation and language performance) can be retrieved easily, and that queries requiring significant computer processing (e.g., peak detection in imaging time series) can be answered quickly due to grid computing. Finally, this infrastructure and data will be shared, and a user of the system from virtually anywhere could pose such questions using the relational database query interface. ? ? ?
| Andric, Michael; Small, Steven L (2010) Functional imaging of putative human mirror neuron systems in neurological disease. Exp Neurol 221:5-9 |
| Small, Steven L; Wilde, Michael; Kenny, Sarah et al. (2009) Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework. Int J Psychophysiol 73:62-72 |
| Small, Steven L; Llano, Daniel A (2009) Biological approaches to aphasia treatment. Curr Neurol Neurosci Rep 9:443-50 |
| Hasson, Uri; Nusbaum, Howard C; Small, Steven L (2009) Task-dependent organization of brain regions active during rest. Proc Natl Acad Sci U S A 106:10841-6 |
| Hasson, Uri; Skipper, Jeremy I; Wilde, Michael J et al. (2008) Improving the analysis, storage and sharing of neuroimaging data using relational databases and distributed computing. Neuroimage 39:693-706 |
| Stef-Praun, Tiberiu; Clifford, Benjamin; Foster, Ian et al. (2007) Accelerating medical research using the swift workflow system. Stud Health Technol Inform 126:207-16 |
