Functional cortex parcellation is one of the most important ways to understand the link between structure and function in the brain. Brodmann's atlas remains a fundamental pillar to understand this relationship because its areas are defined by similar cytoarchitecture and functional imaging notably had revealed that they correspond, entirely or in part, to functional areas. So, its integration to diffusion MRI (dMRI) data is pertinent, dMRI being the only non invasive and in-vivo imaging modality able to have access to a detailed geometric description of the anatomical connectivity between brain areas. This is why our method proposes to define a new connectivity profile of cortical sources based on the Brodmann's atlas. After its registration to T1 and diffusion weighted images of the same subject, we reconstructed the brain surfaces and considered the cortical sources to be the vertices of the white matter/ grey matter boundary mesh. We performed a probabilistic tractography taking each cortical sources as seeds and theBrodmann's areas astargets. Thus, we obtained the connectivity profile of a cortical source: a vectorof sizewhereis the degree of connectivity of the source to theBrodmann's area. Then, we developped a cortical parcellation method jointly analyzing the MEG forward problem and the connectivity profiles based on Brodmann's atlas of cortical sources. We computed the leadfield matrix that relates the sources to the MEG sensors. We applied a k-means algorithm to the leadfield matrix to cluster sources having a close magnetic field to the MEG sensors. Then, in each leadfield-based cluster, we clustered sources via their connectivity profile based on Brodmann's atlas. The figure presents results of this method applied on the whole brain of a subject with simulated sensors and shows suitable clusters. This automatic parcellation is an efficient preprocessing to compute a MEG inverse problem on functional data informed by its structural connectivity.