title: Graph analysis of structural brain networks in Alzheimer's disease: beyond small world properties.
abstract: Changes in brain connectivity in patients with early Alzheimer's disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations. In this study, we constructed structural brain networks out of 87 regions using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm. At the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed that small worldness diminished with AD only in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. The second sub-network, which did not present significant AD-induced modifications of 'classical' small world parameters, nonetheless showed a trend towards an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Beyond small world properties, complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks. These results show that neurodegenerative processes impact volumetric networks in a non-global fashion. Our findings provide new quantitative insights into topological principles of structural brain networks and their modifications during early stages of Alzheimer's disease.
authors.affiliation: The Feinstein Institute for Medical Research, North Shore-LIJ Health System, Manhasset, NY, USA.|The Zucker Hillside Hospital, Psychiatry Research, North Shore-LIJ Health System, Glen Oaks, NY, USA.|Department of Mathematics, Hofstra University, Hempstead, NY, USA.
authors.forename: Majnu
authors.initials: M
authors.lastname: John
authors.affiliation: Department of Communication Sciences and Disorders, University of Mississippi, Oxford, USA.
authors.forename: Toshikazu
authors.initials: T
authors.lastname: Ikuta
authors.affiliation: Department of Physiology and Pharmacology, SUNY Health Science Center at Brooklyn, Brooklyn, NY, 11203, USA. janina.ferbinteanu@downstate.edu.|Department of Neurology, SUNY Health Science Center at Brooklyn, Brooklyn, NY, USA. janina.ferbinteanu@downstate.edu.
authors.forename: Janina
authors.identifier: http://orcid.org/0000-0002-0453-518X
authors.initials: J
authors.lastname: Ferbinteanu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000473:pathology*; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000473:pathology
keywords: Alzheimer's disease; Clustering algorithm; Complex networks; Complexity; Degenerative processes; Entropy; Small world properties