title: Thickness network features for prognostic applications in dementia.
abstract: Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease but not its interregional covariation of thickness. We present novel features based on the inter-regional covariation of cortical thickness. Initially, the cortical labels of each subject are partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between 2 nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, a thickness network is computed using nodal degree, betweenness, and clustering coefficient measures. Fusing them with multiple kernel learning, it is observed that thickness network features discriminate mild cognitive impairment (MCI) converters from controls (CN) with an area under curve (AUC) of 0.83, 74% sensitivity and 76% specificity on a large subset obtained from the Alzheimer's Disease Neuroimaging Initiative data set. A comparison of predictive utility in Alzheimer's disease and/or CN classification (AUC of 0.92, 80% sensitivity [SENS] and 90% specificity [SPEC]), in discriminating CN from MCI (converters and nonconverters combined; AUC of 0.75, SENS and SPEC of 64% and 73%, respectively) and in discriminating between MCI nonconverters and MCI converters (AUC of 0.68, SENS and SPEC of 65% and 64%) is also presented. ThickNet features as defined here are novel, can be derived from a single magnetic resonance imaging scan, and demonstrate the potential for the computer-aided prognostic applications.
authors.affiliation: Department of Engineering Science, School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.
authors.forename: Pradeep Reddy
authors.initials: PR
authors.lastname: Raamana
authors.affiliation: Department of Radiology, Center for Imaging of Neurodegenerative Diseases, University of California, San Francisco, CA, USA.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
authors.affiliation: Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Engineering Science, School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada. Electronic address: mfbeg@sfu.ca.
authors.forename: Mirza Faisal
authors.initials: MF
authors.lastname: Beg
mesh_terms: D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology*; D015415:Biomarkers; D002540:Cerebral Cortex / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000473:pathology; D003704:Dementia / Q000175:diagnosis* / Q000473:pathology*; D003937:Diagnosis, Differential; D042241:Early Diagnosis; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D011379:Prognosis; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers
keywords: Alzheimer; Cortical thickness; Early detection; Fusion; Mild cognitive impairment; Multiple kernel learning; Network properties