A method for determining a stereotactic brain target comprising at least one target point (PC'), said method being implemented at said target for a given pathology on a processor as a preparation to a neurosurgical treatment so as to improve the subsequent neurosurgical treatment prior to a neurosurgical treatment and comprising the following steps: - selecting a plurality (n) of clinical cases of patients for whom the result measured following the treatment performed for said pathology at at least one target point (PC) is greater than or equal to a threshold, postoperative imaging having been performed for each of said patients; - selecting a mathematical coordinate system, preferably an orthonormal Cartesian coordinate system; - processing said postoperative imaging so as to determine, for each selected clinical case, all or some of the (p) coordinates (x PC , y PC , z PC , v xPC , V yPC , v zPC ) of the at least one target point (PC) in the selected coordinate system; - selecting a plurality (m) of marker points (PR) of the brain; - processing the postoperative imaging so as to determine, for each selected clinical case, all or some of the (3 × m) coordinates (x PR , y PR , z PR ) of the marker points (PR) of the brain; - creating a learning database comprising the determined coordinates of the target points (PC) and the determined coordinates of the marker points (PR) of all of the (n) selected clinical cases; - determining a prediction function (F) giving the coordinates of at least one target point (PC) according to the marker points (PR) by using the learning database and a supervised statistical learning method; - processing preoperative imaging of a new patient to be treated for said pathology so as to determine all or some of the coordinates (x PR' , y PR' , z PR' ) of the marker points (PR') of said new patient; - using the prediction function (F) so as to obtain the coordinates of at least one target point (PC') for said new patient according to the coordinates of the marker points (PR') determined for said new patient. - selecting a plurality (n) of clinical cases of patients for whom the result measured following the treatment performed for said pathology at at least one target point (PC) is greater than or equal to a threshold, postoperative imaging having been performed for each of said patients; - selecting a mathematical coordinate system, preferably an orthonormal Cartesian coordinate system; - processing said postoperative imaging so as to determine, for each selected clinical case, all or some of the (p) coordinates (x PC , y PC , z PC , v xPC , V yPC , v zPC ) of the at least one target point (PC) in the selected coordinate system; - selecting a plurality (m) of marker points (PR) of the brain; - processing the postoperative imaging so as to determine, for each selected clinical case, all or some of the (3 × m) coordinates (x PR , y PR , z PR ) of the marker points (PR) of the brain; - creating a learning database comprising the determined coordinates of the target points (PC) and the determined coordinates of the marker points (PR) of all of the (n) selected clinical cases; - determining a prediction function (F) giving the coordinates of at least one target point (PC) according to the marker points (PR) by using the learning database and a supervised statistical learning method; - processing preoperative imaging of a new patient to be treated for said pathology so as to determine all or some of the coordinates (x PR' , y PR' , z PR' ) of the marker points (PR') of said new patient; - using the prediction function (F) so as to obtain the coordinates of at least one target point (PC') for said new patient according to the coordinates of the marker points (PR') determined for said new patient. <EOS>