1. A method of identifying regions within the brain, the method comprising the steps of: a. providing a collection of N-dimensional data points, each data point being representative of a location in the brain and containing N>1 different types of data therefrom; b. sampling N different types of data from a location in the brain, thereby defining a sampled N-dimensional data point; c. automatically defining within a processor M discrete data point clusters (M>1) from the collected data points and from the sampled data point, wherein: i. each data point cluster contains data points which are proximate in N-dimensional space, and ii. the M data point clusters correspond to M discrete regions within the grey matter of the brain, and further wherein the M discrete data point clusters are defined by the following steps: (1) within a data set defined by the collected data points and the sampled data point, identifying the data point which has the greatest proportion of closely proximate data points in N-space, thereby identifying an index data point; (2) defining a data cluster nucleus which contains the index data point and closely proximate data points; (3) defining in N-space the centroid of the data points of the data cluster nucleus, thereby defining a nucleus centroid; and (4) expanding the data cluster nucleus to include data points which are outside of, but closely proximate in N-space to, the data cluster nucleus, wherein the expanded data cluster nucleus defines one of the M data point clusters; d. removing from the data set the data points corresponding to the defined data point cluster; e. defining a subsequent data point cluster by use of the foregoing step c.; and f. indicating whether the sampled data point is within a particular data point cluster, and therefore within a particular one of the M regions of the brain.