Clustering　methods　are　commonly　used　for　fMRI　(functional　Magnetic　Resonance　Imaging)　data　analysis.　Based　on　an　effective　clustering　algorithm　called　Affinity　Propagation　(AP)　and　a　new　defined　similarity　measure,　we　present　a　method　for　detecting　activated　brain　regions.　In　the　proposed　method,　autocovariance　function　values　and　the　Euclidean　distance　metric　of　time　series　are　firstly　calculated　and　combined　into　a　new　similarity　measure,　then　the　AP　algorithm　with　the　measure　is　carried　out　on　all　time　series　of　data,　and　at　last　regions　with　which　their　cross-correlation　coefficients　are　greater　than　a　threshold　are　taken　as　activations.　Without　setting　the　number　of　clusters　in　advance,　our　method　is　especially　appropriate　for　the　analysis　of　fMRI　data　collected　with　a　periodic　experimental　paradigm.　The　validity　of　the　proposed　method　is　illustrated　by　experiments　on　a　simulated　dataset　and　a　benchmark　dataset.　It　can　detect　all　activated　regions　in　the　simulated　dataset　accurately,　and　its　error　rate　is　smaller　than　that　of　K-means.　On　the　benchmark　dataset,　the　result　is　very　similar　to　SPM.　©　Springer-Verlag　Berlin　Heidelberg　2010.