Pattern　classification　has　been　increasingly　used　in　functional　magnetic　resonance　imaging　(fMRI)　data　analysis.　However,　the　classification　performance　is　restricted　by　the　high　dimensional　property　and　noises　of　the　fMRI　data.　In　this　paper,　a　new　feature　selection　method　(named　as　＂NMI-F＂)　was　proposed　by　sequentially　combining　the　normalized　mutual　information　(NMI)　and　fisher　discriminant　ratio.　In　NMI-F,　the　normalized　mutual　information　was　firstly　used　to　evaluate　the　relationships　between　features,　and　fisher　discriminant　ratio　was　then　applied　to　calculate　the　importance　of　each　feature　involved.　Two　fMRI　datasets　(task-related　and　resting　state)　were　used　to　test　the　proposed　method.　It　was　found　that　classification　base　on　the　NMI-F　method　could　differentiate　the　brain　cognitive　and　disease　states　effectively,　and　the　proposed　NMI-F　method　was　prior　to　the　other　related　methods.　The　current　results　also　have　implications　to　the　future　studies.