Subject-specific functional parcellation via Prior Based Eigenanatomy

• Published in: NeuroImage (Orlando, Fla.) Vol. 99; pp. 14 - 27
• Main Authors: Dhillon, Paramveer S., Wolk, David A., Das, Sandhitsu R., Ungar, Lyle H., Gee, James C., Avants, Brian B.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.10.2014; Elsevier; Elsevier Limited
• Subjects: Aged; Algorithms; Alzheimer Disease - pathology; Alzheimer Disease - psychology; Biological and medical sciences; Classification; Cohort Studies; Data-driven parcellations; Default mode network; Delayed recall; Female; fMRI; Fundamental and applied biological sciences. Psychology; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; MCI; Mental Recall - physiology; Methods; Nerve Net - pathology; Neuroimaging - methods; PCA; Principal Component Analysis; Principal components analysis; ROI; Studies; Time series; Vertebrates: nervous system and sense organs; fMRI; Delayed recall; Default mode network; MCI; ROI; Data-driven parcellations; PCA; Human; IMRI
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.05.026

We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist. We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained ℓ1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling of the inter-subject variability in the functional parcel boundaries and allows us to construct subject-specific networks with reduced sensitivity to ROI placement. We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity. •A novel method for prior constrained decomposition of high-dimensional matrices•A novel algorithm for its mathematical optimization•Publicly available implementation of our approach in C++•Application of our approach to improve network extraction from BOLD data•Evaluation on mild cognitive impairment and delayed recall prediction tasks