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

58 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

Fingerprint

Dermatoglyphics
Alzheimer Disease
Aptitude
Biomarkers
Information Systems
Neuroimaging
Disease Progression

Keywords

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

Cite this

Mattila, J., Koikkalainen, J., Virkki, A., Simonsen, A., van Gils, M., Waldemar, G., ... The Alzheimer’s Disease Neuroimaging Initiative (2011). A disease state fingerprint for evaluation of Alzheimer's disease. Journal of Alzheimer's Disease, 27(1), 163-176. https://doi.org/10.3233/JAD-2011-110365
Mattila, Jussi ; Koikkalainen, Juha ; Virkki, Arho ; Simonsen, Anja ; van Gils, Mark ; Waldemar, Gunhild ; Soininen, Hilkka ; Lötjönen, Jyrki ; The Alzheimer’s Disease Neuroimaging Initiative. / A disease state fingerprint for evaluation of Alzheimer's disease. In: Journal of Alzheimer's Disease. 2011 ; Vol. 27, No. 1. pp. 163-176.
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Mattila, J, Koikkalainen, J, Virkki, A, Simonsen, A, van Gils, M, Waldemar, G, Soininen, H, Lötjönen, J & The Alzheimer’s Disease Neuroimaging Initiative 2011, 'A disease state fingerprint for evaluation of Alzheimer's disease', Journal of Alzheimer's Disease, vol. 27, no. 1, pp. 163-176. https://doi.org/10.3233/JAD-2011-110365

A disease state fingerprint for evaluation of Alzheimer's disease. / Mattila, Jussi (Corresponding Author); Koikkalainen, Juha; Virkki, Arho; Simonsen, Anja; van Gils, Mark; Waldemar, Gunhild; Soininen, Hilkka; Lötjönen, Jyrki; The Alzheimer’s Disease Neuroimaging Initiative.

In: Journal of Alzheimer's Disease, Vol. 27, No. 1, 2011, p. 163-176.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Mattila, Jussi

AU - Koikkalainen, Juha

AU - Virkki, Arho

AU - Simonsen, Anja

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AU - Waldemar, Gunhild

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AU - Lötjönen, Jyrki

AU - The Alzheimer’s Disease Neuroimaging Initiative

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AB - 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.

KW - Alzheimer's disease

KW - automatic

KW - biomarkers

KW - computer-assisted

KW - decision making

KW - information processing

KW - projections and predictions

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