title: Directed network motifs in Alzheimer's disease and mild cognitive impairment.
abstract: Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer's disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer's disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer's disease.
authors: International Computer Science Institute, Berkeley, CA, United States of America; Department of Computer Science, University of California, Berkeley, Berkeley, CA, United States of America.
authors: Eric J
authors: EJ
authors: Friedman
authors: Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States of America; VA Medical Center, San Francisco, CA, United States of America.
authors: Karl
authors: K
authors: Young
authors: Department of Computer Science, University of California, Berkeley, Berkeley, CA, United States of America.
authors: Graham
authors: G
authors: Tremper
authors: Department of Computer Science, University of California, Berkeley, Berkeley, CA, United States of America.
authors: Jason
authors: J
authors: Liang
authors: W.M. Keck Science Department, Claremont McKenna College, Pitzer College, and Scripps College, Claremont, CA, United States of America.
authors: Adam S
authors: AS
authors: Landsberg
authors: Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States of America; VA Medical Center, San Francisco, CA, United States of America.
authors: Norbert
authors: N
authors: Schuff
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000473:pathology*; D015415:Biomarkers; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D016022:Case-Control Studies; D002540:Cerebral Cortex / Q000473:pathology; D060825:Cognitive Dysfunction / Q000473:pathology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009415:Nerve Net / Q000473:pathology*; D009434:Neural Pathways
chemical_list: D015415:Biomarkers