Exploring new methods for statistical inference in fMRI data analysis: The spatial specificity paradox

Brain activity in fMRI studies is represented by voxels; units of graphic information defining a small location in the brain. In a typical case, the brain is visualized using somewhere around 200.000 voxels. To measure activity every location or voxel is tested individually, with every voxel using a separate hypothesis test; this leads to a massive multiple testing problem. One way this problem is solved is by Bonferroni-like corrections on single voxels, however Bonferroni is notorious for it’s conservativeness (Samuel-Cahn, 1996). Instead of correcting for every test at the voxel level, one can also test groups (called clusters) of voxels. Hypothesis-testing on clusters reduces the multiple testing problem by accept- or rejecting entire clusters, but leads to a new problem known as the ‘spatial specificity paradox’: inference on the voxel level accurately locates activation at the cost of having low power for each test, whereas inference on the cluster level has more power but...Show moreBrain activity in fMRI studies is represented by voxels; units of graphic information defining a small location in the brain. In a typical case, the brain is visualized using somewhere around 200.000 voxels. To measure activity every location or voxel is tested individually, with every voxel using a separate hypothesis test; this leads to a massive multiple testing problem. One way this problem is solved is by Bonferroni-like corrections on single voxels, however Bonferroni is notorious for it’s conservativeness (Samuel-Cahn, 1996). Instead of correcting for every test at the voxel level, one can also test groups (called clusters) of voxels. Hypothesis-testing on clusters reduces the multiple testing problem by accept- or rejecting entire clusters, but leads to a new problem known as the ‘spatial specificity paradox’: inference on the voxel level accurately locates activation at the cost of having low power for each test, whereas inference on the cluster level has more power but cannot localize activation any more accurate than ”there is at least one voxel active in this cluster”. Recently a solution called All-Resolutions Inference (ARI) was developed based on closed-testing to tackle this problem (Rosenblatt, Finos, Weeda, Solari, & Goeman, 2018). This method offers one way to quantify activation within clusters, without losing too much power. This project aims to assess and compare the quality of these new methods using simulation studies and real data applications.Show less

Leiden University Student Repository

Documents

In Collections

Exploring new methods for statistical inference in fMRI data analysis: The spatial specificity paradox