A disease state fingerprint for evaluation of Alzheimer's disease

Jussi Mattila (Corresponding Author), Juha Koikkalainen, Arho Virkki, Anja Simonsen, Mark van Gils, Gunhild Waldemar, Hilkka Soininen, Jyrki Lötjönen, The Alzheimer’s Disease Neuroimaging Initiative

    Research output: Contribution to journalArticleScientificpeer-review

    69 Citations (Scopus)

    Abstract

    Diagnostic processes of Alzheimer's disease (AD) are evolving. Knowledge about disease-specific biomarkers is constantly increasing and larger volumes of data are being measured from patients. To gain additional benefits from the collected data, a novel statistical modeling and data visualization system is proposed for supporting clinical diagnosis of AD. The proposed system computes an evidence-based estimate of a patient's AD state by comparing his or her heterogeneous neuropsychological, clinical, and biomarker data to previously diagnosed cases. The AD state in this context denotes a patient's degree of similarity to previously diagnosed disease population. A summary of patient data and results of the computation are displayed in a succinct Disease State Fingerprint (DSF) visualization. The visualization clearly discloses how patient data contributes to the AD state, facilitating rapid interpretation of the information. To model the AD state from complex and heterogeneous patient data, a statistical Disease State Index (DSI) method underlying the DSF has been developed. Using baseline data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the ability of the DSI to model disease progression from elderly healthy controls to AD and its ability to predict conversion from mild cognitive impairment (MCI) to AD were assessed. It was found that the DSI provides well-behaving AD state estimates, corresponding well with the actual diagnoses. For predicting conversion from MCI to AD, the DSI attains performance similar to state-of-the-art reference classifiers. The results suggest that the DSF establishes an effective decision support and data visualization framework for improving AD diagnostics, allowing clinicians to rapidly analyze large quantities of diverse patient data.
    Original languageEnglish
    Pages (from-to)163-176
    Number of pages14
    JournalJournal of Alzheimer's Disease
    Volume27
    Issue number1
    DOIs
    Publication statusPublished - 2011
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Alzheimer's disease
    • automatic
    • biomarkers
    • computer-assisted
    • decision making
    • information processing
    • projections and predictions

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