Model-based cerebrovascular markers extracted from hemodynamic data for diagnosing MCI or AD and predicting disease progression.

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"Model-based cerebrovascular markers extracted from hemodynamic data for non-invasive, portable and inexpensive diagnosis of MCI or mild AD and prediction of disease progression" PROJECT SUMMARY The goal of the proposed multi-PI project is to establish proof of concept for the utility of a new class of cerebrovascular markers that may aid in the improved diagnosis and prediction of disease progression in Mild Cognitive Impairment (MCI) and mild Alzheimer's disease (AD). The means for obtaining these markers are non-invasive, inexpensive and portable, so that they can be used for screening in a primary-care setting. The scientific rationale for this new class of cerebrovascular markers is provided by the recent promising results of our group and the mounting evidence of a strong correlation between MCI/AD and cerebrovascular dysregulation. A recently published retrospective study on a large cohort of 1,171 subjects from the ADNI database utilized multi-factorial data-driven analysis to assess the relation between MCI/AD disease progression and commonly used biomarkers (obtained from MRI/PET and plasma/CSF) and concluded that cerebrovascular dysregulation is the earliest and strongest pathologic factor associated with AD progression, corroborating the hypothesis of cerebrovascular dysregulation. Quantification of cerebrovascular dysregulation in that large-cohort study was achieved through analysis of ASL-MRI data of cerebral perfusion. We propose instead to explore a novel integrative dynamic modeling approach that analyzes the cerebral hemodynamics of persons with no cognitive impairment and MCI/AD patients with a methodology that yields input- output predictive models of the dynamic relationships between changes in beat-to-beat cerebral blood flow velocity (via Transcranial Doppler) or cerebral tissue oxygenation (via Near Infrared Spectroscopy) in response to changes in arterial blood pressure and end-tidal CO2 data. The obtained data-based models are subsequently used to compute markers of the dynamics of cerebrovascular regulation. Initial results of the advocated approach have achieved statistically significant delineation between 46 MCI patients and 20 age-matched controls on the basis of a model-based marker of dynamic vasomotor reactivity (DVR). Evaluation of the DVR marker against established MRI-based and PET-based biomarkers, as well as neuropsychological test data, from the larger cohort of the proposed project offers the promise of portable, non-invasive, inexpensive and sensitive means for detecting cerebrovascular dysregulation at the early stages of MCI or mild AD, and monitoring disease progression. Important co-variates of this study include age, gender, education, ApoE genotype, site and amyloid burden.