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Journal Article

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization


Steele,  Christopher
Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montréal, QC, Canada;
Department of Psychology, Concordia University, Montréal, QC, Canada;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Patel, R., Steele, C., Chen, A. G. X., Patel, S., Devenyi, G. A., Germann, J., et al. (2019). Investigating microstructural variation in the human hippocampus using non-negative matrix factorization. NeuroImage, 116348. doi:10.1016/j.neuroimage.2019.116348.

Cite as: http://hdl.handle.net/21.11116/0000-0005-848E-6
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses.