Scale-specific analysis of fMRI data on the irregular cortical surface

Yi Chen, Radoslaw Martin Cichy, Wilhelm Stannat, John-Dylan Haynes
2018 NeuroImage  
14 To fully characterize the activity patterns on the cerebral cortex as measured with fMRI, 15 the spatial scale of the patterns must be ascertained. Here we address this problem by 16 constructing steerable bandpass filters on the discrete, irregular cortical mesh, using an 17 improved Gaussian smoothing in combination with differential operators of directional 18 derivatives. We demonstrate the utility of the algorithm in two ways. First, using 19 modelling we show that our algorithm yields
more » ... r algorithm yields superior results in numerical precision and 20 spatial uniformity of filter kernels compared to the most widely adopted approach for 21 cortical smoothing. An important interim insight hereby was that the effective scales of 22 information differ from the nominal filter sizes applied to extract them, and thus need to 23 be calculated separately to compare different algorithms on par. Second, we applied the 24 algorithm to an fMRI dataset to assess the scale and pattern form of cortical encoding of 25 information about visual objects in the ventral visual pathway. We found that filtering by 26 our method improved the detection of discriminant information about experimental 27 conditions over previous methods, that the level of categorization (subordinate versus 28 superordinate) of objects was differentially related to the spatial scale of fMRI patterns, 29 and that the spatial scale at which information was encoded increased along the ventral 30 visual pathway. In sum, our results indicate that the proposed algorithm is particularly 31 suited to assess and detect scale-specific information encoding in cortex, and promises 32 further insight into the topography of cortical encoding in the human brain. 34 peer-reviewed)
doi:10.1016/j.neuroimage.2018.07.002 pmid:30033391 fatcat:jwjw7k2fnfeironza7h3wtqppi