**Abstract** : OBJECTIVE: Statistical analysis of patients previously operated on may improve our methods of performing subsequent surgical procedures. In this article, we introduce a method for studying the functional properties of cerebral structures from electrophysiological and neuroimaging data by using the probabilistic functional atlas (PFA). The PFA provides a spatial distribution of the clinically most effective contacts normalized to a common space. This distribution is converted into a probability function for a given point in space to be inside an effective contact. The PFA was used to analyze spatial properties of the functional subthalamic nucleus (STN), defined as the spatial volume corresponding to the distribution of effective contacts. These results may potentially be useful in planning subthalamic implantation of electrodes. METHODS: In all, 168 bilateral subthalamic stimulations were examined. An algorithm was developed for converting these data into the PFA. The PFA for the STN (here called "atlas") was calculated with 0.25-mm3 resolution, and several features characterizing the left and right STN were studied. The analysis was performed with and without lateral compensation against the width of the third ventricle. The key feature introduced here used for analysis of the functional STN is a (probabilistic) functional volume of structure (defined for a given probability as the volume of a region whose every point has a probability equal to or greater than this given probability). RESULTS: The analysis has been performed for two situations: with and without lateral compensation against the width of the third ventricle. Without lateral compensation, the differences between the mean values and standard deviations of their barycenter coordinates for the left and right functional STNs are 0.31 and 0.18 mm, respectively. The left STN and right STN exhibit differences in functional volume size and probability distribution. The entire functional volume is 240 mm3 for the left and 229 mm3 for the right STN. A more prominent difference exists in the region of high probabilities (0.7 or higher), called "hot STN." The volume of the left hot STN is 5.52 mm3, whereas that of the right is 3.92 mm3. The left hot STN is 1.41 times bigger and 20% more dense than the right hot STN. For a given probability, the corresponding functional volume for the left hot STN is up to 43 times larger than that for the right STN. Practically speaking, lateral compensation does not change these results qualitatively. Quantitatively, differentiation between the left and right STNs is lower. For instance, for a given probability, the corresponding functional volume for the left hot STN is only up to 11 times larger than that for the right one. In either situation (i.e., with and without lateral compensation), the size of the hot STN in relation to the whole STN remains very small (1-2%). In addition, statistical analysis shows that in either situation, the means of the left and right functional STNs are significantly different. CONCLUSION: PFA-based planning may be superior to the current practice of using anatomic atlases that provide delineation of the target structure only, because it is more precise and provides a unique target point in the stereotactic space. This best stereotactic target is the point in the individualized atlas with the highest probability, meaning the highest probability of having the best target on the basis of the patients previously operated on. This best target is located in the hot STN, the size of which determines the precision of targeting. Because the size of the hot STN in comparison to the whole STN remains very small (1-2%) independent of whether or not lateral compensation is applied, target planning and execution have to be performed with high precision. The methodology presented, based on the PFA and on the functional volume, is general and can be applied to other structures and data sets. As numerous centers keep gathering large amounts of electrophysiological human and animal data, this work may facilitate opening new avenues in exploiting these data.