Comparing Matrix Decomposition Methods for Meta-Analysis and Reconstruction of Cognitive Neuroscience Results

The results of 2,256 neuroimaging experiments were an- alyzed using singular value decomposition (SVD) and non-negative matrix factorization (NMF) to extract pat- terns in the data. To evaluate the techniques’ efficacy at capturing regularities in the data, one positive and one negative result from each of 100 random experi- ments were treated as missing, and the values were it- eratively reconstructed using each technique for dimen- sionality reduction. Under the best conditions, preci- sion and recall of roughly 78% was achieved for each method. Weighting the domain matrix and area matrix to have equal first eigenvalues before combining them, a technique known as blending, significantly improved re- sults for both methods. While using unnormalized data appeared to produce a peak in results for 10-15 dimen- sions, normalizing to take into account variation in the popularity of experiment types removed the effect. The basis vectors produced by each method do not support the idea that current cognitive ontologies map well to individual brain areas.