An open question in neuroimaging is how to develop anatomical brain atlases for the analy- sis of functional data. Here, we present a cortical parcellation model based on macroanatomical infor- mation and test its validity on visuomotor-related cortical functional networks. The parcellation model is based on a recently developed cortical parameterization method (Auzias et al., [2013]: IEEE Trans Med Imaging 32:873–887), called HIP-HOP. This method exploits a set of primary and secondary sulci to create an orthogonal coordinate system on the cortical surface. A natural parcellation scheme arises from the axes of the HIP-HOP model running along the fundus of selected sulci. The resulting parcel- lation scheme, called MarsAtlas, complies with dorsoventral/rostrocaudal direction fields and allows inter-subject matching. To test it for functional mapping, we analyzed a MEG dataset collected from human participants performing an arbitrary visuomotor mapping task. Single-trial high-gamma activ- ity, HGA (60–120 Hz), was estimated using spectral analysis and beamforming techniques at cortical areas arising from a Talairach atlas (i.e., Brodmann areas) and MarsAtlas. Using both atlases, we con- firmed that visuomotor associations involve an increase in HGA over the sensorimotor and fronto- parietal network, in addition to medial prefrontal areas. However, MarsAtlas provided: (1) crucial func- tional information along both the dorsolateral and rostrocaudal direction; (2) an increase in statistical significance. To conclude, our results suggest that the MarsAtlas is a valid anatomical atlas for func- tional mapping, and represents a potential anatomical framework for integration of functional data arising from multiple techniques such as MEG, intracranial EEG and fMRI.