Intersubject variability in functional neuroanatomy of silent verb generation: assessment by a new activation detection algorithm based on amplitude and size information.

We present an experimental evaluation of a new algorithm for the detection of activated areas in brain functional maps. The new algorithm, named HMSD, is based on a hierarchical multiscale description of the difference image in terms of connected objects. Size and magnitude of each object are simultaneously tested with respect to a bidimensional frequency distribution derived using Monte-Carlo simulations under the null hypothesis. In the present work. HMSD was applied to the analysis of a silent verb generation PET activation protocol conducted in six right-handed subjects. Applied to single-subject data. HMSD reveals activation located in the left inferior frontal gyrus in three subjects (two in the pars opercularis, one in the pars triangularis), and in the pars opercularis of the right inferior frontal gyrus in one case, the latter being combined to a crossed cerebellar activation. Overall, single-case results were consistent with the analyses of stereotactically averaged data. Despite a 2D implentation. HMSD detection performances of averaged data were better than that obtained with the 2D version of statistical parametric mapping (SPM) and comparable to that of the 3D version of SPM.