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Diagnosis of neurologic and neuropsychiatric disorders typically involves considerable assessment including clinical observation, neuroimaging, and biological and neuropsychological measurements. While it is reasonable to expect that the integration of neuroimaging data and complementary non-imaging measures is likely to improve early diagnosis on individual basis, due to technical challenges associated with the task of combining different data types, medical image pattern recognition analysis has been largely focusing solely on neuroimaging evaluations. In this paper, we explore the potential of integrating neuroimaging and clinical information within a pattern classification framework, and propose that the multi-kernel learning (MKL) paradigm may be suitable for building a multimodal classifier of a disorder, as well as for automatic identification of the relevance of each information type. We apply our approach to the problem of detecting cognitive decline in healthy older adults from single-visit evaluations, and show that the performance of a classifier can be improved when nouroimaging and clinical evaluations are used simultaneously within a MKL-based classification framework.

Multi-Kernel Classification for Integration of Clinical and Imaging Data: Application to Prediction of Cognitive Decline in Older Adults