title: Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on Principal Dynamic Modes.
abstract: Sensitive and robust diagnostic biomarkers for Alzheimer's disease (AD) were sought using dynamic nonlinear models of the causal interrelationships among time-series (beat-to-beat) data of arterial blood pressure, end-tidal CO(2) and cerebral blood flow velocity collected in human subjects (4 AD patients and 4 control subjects). These models were based on Principal Dynamic Modes (PDM) and yielded a reliable biomarker for AD diagnosis in the form of the "Effective CO(2) Reactivity Index" (ECRI). The results from this initial set of subjects corroborated the efficacy of the ECRI biomarker for accurate AD diagnosis.
authors.affiliation: Department of Biomedical Engineering and the Biomedical Simulations Resource, University of Southern California, Los Angeles, CA 90089, USA. vzm@usc.edu
authors.forename: V Z
authors.initials: VZ
authors.lastname: Marmarelis
authors.forename: D C
authors.initials: DC
authors.lastname: Shin
authors.forename: R
authors.initials: R
authors.lastname: Diaz-Arrastia
authors.forename: R
authors.initials: R
authors.lastname: Zhang
mesh_terms: D000544:Alzheimer Disease / Q000097:blood* / Q000175:diagnosis*; D015415:Biomarkers / Q000097:blood*; D001794:Blood Pressure; D001921:Brain / Q000503:physiopathology; D002245:Carbon Dioxide / Q000737:chemistry; D016022:Case-Control Studies; D002560:Cerebrovascular Circulation; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010477:Perfusion; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
chemical_list: D015415:Biomarkers; D002245:Carbon Dioxide