Objective:　Identifying　mild　cognitive　impairment　(MCI)　subjects　who　will　progress　to　Alzheimer＇s　disease　(AD)　is　not　only　crucial　in　clinical　practice,　but　also　has　a　significant　potential　to　enrich　clinical　trials.　The　purpose　of　this　study　is　to　develop　an　effective　biomarker　for　an　accurate　prediction　of　MCI-to-AD　conversion　from　magnetic　resonance　images.　Methods:　We　propose　a　novel　grading　biomarker　for　the　prediction　of　MCI-to-AD　conversion.　First,　we　comprehensively　study　the　effects　of　several　important　factors　on　the　performance　in　the　prediction　task　including　registration　accuracy,　age　correction,　feature　selection,　and　the　selection　of　training　data.　Based　on　the　studies　of　these　factors,　a　grading　biomarker　is　then　calculated　for　each　MCI　subject　using　sparse　representation　techniques.　Finally,　the　grading　biomarker　is　combined　with　age　and　cognitive　measures　to　provide　amore　accurate　prediction　of　MCI-to-AD　conversion.　Results:　Using　the　Alzheimer＇s　Disease　Neuroimaging　Initiative　(ADNI)　dataset,　the　proposed　global　grading　biomarker　achieved　an　area　under　the　receiver　operating　characteristic　curve　(AUC)　in　the　range　of　79-81%　for　the　prediction　of　MCI-to-AD　conversion　within　three　years　in　tenfold　cross　validations.　The　classification　AUC　further　increases　to　84-92%　when　age　and　cognitive　measures　are　combined　with　the　proposed　grading　biomarker.　Conclusion:　The　obtained　accuracy　of　the　proposed　biomarker　benefits　from　the　contributions　of　different　factors:　a　tradeoff　registration　level　to　align　images　to　the　template　space,　the　removal　of　the　normal　aging　effect,　selection　of　discriminative　voxels,　the　calculation　of　the　grading　biomarker　using　AD　and　normal　control　groups,　and　the　integration　of　sparse　representation　technique　and　the　combination　of　cognitive　measures.　Signifi-cance:　The　evaluation　on　the　ADNI　dataset　shows　the　efficacy　of　the　proposed　biomarker　and　demonstrates　a　significant　contribution　in　accurate　prediction　of　MCI-to-AD　conversion.