Epilepsy　is　a　brain　syndrome　caused　by　synchronous　abnormal　discharge　of　brain　neurons.　As　an　effective　treatment　for　epilepsy,　successful　surgical　resection　requires　accurate　localization　of　epileptic　foci　to　avoid　iatrogenic　disability.　Previous　studies　have　demonstrated　the　potential　of　restingstate　functional　magnetic　resonance　imaging　(rs-fMRI)　technique　to　localize　epileptic　foci　though　clinical　applications　of　rs-fMRI　are　still　at　an　early　stage　of　development.　fMRI　data　analysis　approaches　seek　pre-defined　regressors　modeling　contributions　to　the　voxel　time　series,　including　the　BOLD　response　following　neuronal　activation.　In　present　study,　localization　strategies　of　epileptic　foci　in　rs-fMRI　technology　were　classified　and　summarized.　To　begin　with,　data-driven　approaches　attempting　to　determine　the　intrinsic　structure　of　the　data　were　discussed　in　detail.　Then,　as　novel　fMRI　data　analysis　methods,　deconvolution　algorithms　such　as　total　activation　(TA)　and　blind　deconvolution　were　discussed,　which　were　applied　to　explore　the　underlying　activity-inducing　signal　of　the　BOLD　signal.　Lastly,　effective　connectivity　approaches　such　as　autocorrelation　function　method　and　Pearson　correlation　coefficient　have　also　been　proposed　to　identify　the　brain　regions　driving　the　generation　of　seizures　within　the　epileptic　network.　In　the　future,　fMRI　technology　can　be　used　as　a　supplement　of　intraoperative　subdural　electrode　method　or　combined　with　traditional　epileptic　focus　localization　technologies,　which　is　one　of　the　most　attractive　aspect　in　clinic.　It　may　also　play　an　important　role　in　providing　diagnostic　information　for　epilepsy　patients.