title: Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods.
abstract: Graph theory is increasingly being used to study brain connectivity across the spectrum of Alzheimer's disease (AD), but prior findings have been inconsistent, likely reflecting methodological differences. We systematically investigated how methods of graph creation (i.e., type of correlation matrix and edge weighting) affect structural network properties and group differences. We estimated the structural connectivity of brain networks based on correlation maps of cortical thickness obtained from MRI. Four groups were compared: 126 cognitively normal older adults, 103 individuals with Mild Cognitive Impairment (MCI) who retained MCI status for at least 3 years (stable MCI), 108 individuals with MCI who progressed to AD-dementia within 3 years (progressive MCI), and 105 individuals with AD-dementia. Small-world measures of connectivity (characteristic path length and clustering coefficient) differed across groups, consistent with prior studies. Groups were best discriminated by the Randić index, which measures the degree to which highly connected nodes connect to other highly connected nodes. The Randić index differentiated the stable and progressive MCI groups, suggesting that it might be useful for tracking and predicting the progression of AD. Notably, however, the magnitude and direction of group differences in all three measures were dependent on the method of graph creation, indicating that it is crucial to take into account how graphs are constructed when interpreting differences across diagnostic groups and studies. The algebraic connectivity measures showed few group differences, independent of the method of graph construction, suggesting that global connectivity as it relates to node degree is not altered in early AD.
authors.affiliation: Department of Mathematics, United States Naval Academy, Annapolis, MD 21401, USA.
authors.forename: David J
authors.initials: DJ
authors.lastname: Phillips
authors.affiliation: Department of Mathematics, United States Naval Academy, Annapolis, MD 21401, USA.
authors.forename: Alec
authors.initials: A
authors.lastname: McGlaughlin
authors.affiliation: Department of Mathematics, United States Naval Academy, Annapolis, MD 21401, USA.
authors.forename: David
authors.initials: D
authors.lastname: Ruth
authors.affiliation: Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
authors.forename: Leah R
authors.initials: LR
authors.lastname: Jager
authors.affiliation: Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
authors.forename: Anja
authors.initials: A
authors.lastname: Soldan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000473:pathology*; D016022:Case-Control Studies; D002540:Cerebral Cortex / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000473:pathology*; D063132:Connectome; D018450:Disease Progression; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009434:Neural Pathways / Q000473:pathology; D009929:Organ Size
keywords: Alzheimer's disease; Connectomics; Cortical thickness networks; Graph theory; Mild cognitive impairment; Structural MRI