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Neuroimage. 2014 Oct 1;99:14-27. doi: 10.1016/j.neuroimage.2014.05.026. Epub 2014 May 20.

Subject-specific functional parcellation via prior based eigenanatomy.

Author information

1
Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: dhillon@cis.upenn.edu.
2
Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA.
3
Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
4
Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA, USA.

Abstract

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.

KEYWORDS:

Data-driven parcellations; Default mode network; Delayed recall; MCI; PCA; ROI; fMRI

PMID:
24852460
PMCID:
PMC4382016
DOI:
10.1016/j.neuroimage.2014.05.026
[Indexed for MEDLINE]
Free PMC Article

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