TY - JOUR
T1 - Two-stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimer's disease risk
AU - Caffo, Brian S.
AU - Crainiceanu, Ciprian M.
AU - Verduzco, Guillermo
AU - Joel, Suresh
AU - Mostofsky, Stewart H.
AU - Bassett, Susan Spear
AU - Pekar, James J.
N1 - Funding Information:
This work was supported by NIH/NIA R01AG016324 (Spear Bassett, Caffo, Verduzco), NIH/NINDS grant R01 NS060910 (Crainiceanu, Caffo), NIH/NOUS R01 NS048527 , NIH/NIMH R01 MH078160 , NIH/NIMH R01 MH085328 (Mostofsky, Caffo) and NIH/NCRR grant P41-RR15241 (Pekar, Joel).
PY - 2010/7
Y1 - 2010/7
N2 - Functional connectivity is the study of correlations in measured neurophysiological signals. Altered functional connectivity has been shown to be associated with a variety of cognitive and memory impairments and dysfunction, including Alzheimer's disease. In this manuscript we use a two-stage application of the singular value decomposition to obtain data driven population-level measures of functional connectivity in functional magnetic resonance imaging (fMRI). The method is computationally simple and amenable to high dimensional fMRI data with large numbers of subjects. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and their associated loadings. We further demonstrate the utility of these decompositions in a functional logistic regression model. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found an indication of alternative connectivity in clinically asymptomatic at-risk subjects when compared to controls, which was not significant in the light of multiple comparisons adjustment. The relevant brain network loads primarily on the temporal lobe and overlaps significantly with the olfactory areas and temporal poles.
AB - Functional connectivity is the study of correlations in measured neurophysiological signals. Altered functional connectivity has been shown to be associated with a variety of cognitive and memory impairments and dysfunction, including Alzheimer's disease. In this manuscript we use a two-stage application of the singular value decomposition to obtain data driven population-level measures of functional connectivity in functional magnetic resonance imaging (fMRI). The method is computationally simple and amenable to high dimensional fMRI data with large numbers of subjects. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and their associated loadings. We further demonstrate the utility of these decompositions in a functional logistic regression model. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found an indication of alternative connectivity in clinically asymptomatic at-risk subjects when compared to controls, which was not significant in the light of multiple comparisons adjustment. The relevant brain network loads primarily on the temporal lobe and overlaps significantly with the olfactory areas and temporal poles.
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U2 - 10.1016/j.neuroimage.2010.02.081
DO - 10.1016/j.neuroimage.2010.02.081
M3 - Article
C2 - 20227508
AN - SCOPUS:77952421111
SN - 1053-8119
VL - 51
SP - 1140
EP - 1149
JO - NeuroImage
JF - NeuroImage
IS - 3
ER -