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We propose an approach to analyzing functional neuroimages in which (1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show...
We propose an approach to analyzing functional neuroimages in which: (1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data; and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). In an on-off design we model the spatial activation pattern as a sum of an unknown...
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