Novel Methods for the Visualization and Analysis of Functional Maps in Cortex

Flat Maps 27 Figure 12 shows some of the programs that are used to finally create a flat data set: The low- resolution anatomical inplane images are aligned with the high-resolution anatomical 3D vA- natomy data set. mrLoadRet uses the alignment matrix that was created by mrAlign during this procedure to transform the functional data from the inplane coordinates into the high- resolution coordinates by interpolation. This works because there is an intrinsic alignment be- tween the anatomical inplanes and the functional data; the physical location of inplanes is the same as the location of the functional slices. The gray matter within the vAnatomy is classified and can be unfolded to yield a flat data set. The same transformation that is used for the gray matter flattening can be used for the functional data. Functional activity is restricted to the gray matter, i.e., only functional data that lies within the gray matter is kept. Therefore, mrLoadRet can transform the functional data, which is now in the high- resolution vAnatomy coordinate space (reference coordinate system), to flat coordinates by using 3D to flat coordinate mapping from the flattening procedure (mrFlatMesh). The flat- tened functional data can then be overlaid onto the flattened cortical surface. The functional data is saved into a file called corAnal.mat (coherence analysis). This file includes different MATLAB matrices. mrFindBorders uses the phase matrices and the coherence matrices. These are explained below in the section that deals with functional flat maps.