Analysis of multiple sets of data, either of the same type as in multi-subject data, or of different type as in multi-modality data, is inherent to many problems in computer science and engineering. Biomedical image analysis figures prominently among these and is particularly challenging because of the rich nature of the data made available by different imaging modalities. Data-driven methods are particularly attractive for the analysis and fusion of such data as they can achieve useful decompositions while minimizing assumptions on the model and underlying processes, and can also incorporate reliable prior information when available. One such approach recently introduced for medical image analysis and fusion is multi-dataset canonical correlation analysis (MCCA) that has proven especially useful for the analysis and fusion of rather disparate data, owing to its high flexibility and extendibility to a wide array of problem settings.
Intellectual Merit: In this proposal, the main aim is twofold. First, a number of powerful methods are developed for multi-subject (multi-set) data analysis and multi-modal data fusion based on canonical dependence analysis by significantly extending the power and flexibility of MCCA. Then, the successful application of the methods are demonstrated on a unique problem that demands these properties, namely the study of brain function and functional associations during simulated driving, a naturalistic task where data-driven methods have proven very useful. The data used in the project are complementary in nature but of very different nature: functional magnetic resonance imaging (fMRI), electroencephalography (EEG), structural MRI (sMRI), genetic array data--single nucleotide polymorphism (SNP)--and behavioral variables. The rich characteristics of the data and the problem at hand thus provide a special challenge for the methods developed and a unique testbed for the evaluation of their performance.
Broader Impacts: The broad impact of the proposed work lies in its potential to substantially impact science and information technology as well as in its educational features. Analysis of multiple datasets of the same type as well as fusion of data from different modalities/sensors is a key problem in many science and engineering disciplines. The new set of methods proposed thus form attractive solutions for many other problems beyond brain function analysis. The fully integrative nature of the proposed work is also an invaluable asset in the ongoing efforts in cross-training of students and researchers as well as increasing the participation of underrepresented groups in science and technology careers.
For further information, see the project web site at the URL: http://mlsp.umbc.edu/research_projects.html
