Non-negative partial least squares for meta-analytic parcellation: A functional atlas for the human brain |
| | Abstract | Algorithms can automatically analyze human brain mapping studies represented in a neuroinformatics database. We describe one such meta-analytic method that relies on a combination of text mining, functional volumes modeling, partial least squares and non-negative matrix factorization (NMF): NMF decomposes the product between a bag-of-words matrix, constructed from abstract words, and a voxelization matrix constructed by kernel density modeling of the stereotaxic coordinates in the scientific papers contained in a database. The components found allows us to construct a functional atlas where voxels and words get loaded on components interpretable as brain functions. When applied on the Brede Database with 186 papers we find components such as memory, emotion, pain and audition. Furthermore, we present a cluster validation procedure based on permutation and cluster matching that quantifies the variability of the functional atlas. | | Keywords | Neuroinformatics, text mining, functional volumes modeling, non-negative matrix factorization, partial least squares, human brain, positron emission tomography, functional magnetic resonance imaging (fMRI), meta-analysis | | Type | Journal paper [Submitted] | | Year | 2006 Month September | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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