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. 2012 Nov 15;63(3):1478-86.
doi: 10.1016/j.neuroimage.2012.07.059. Epub 2012 Aug 3.

A computational neurodegenerative disease progression score: method and results with the Alzheimer's disease Neuroimaging Initiative cohort

Bruno M Jedynak  1 , Andrew Lang, Bo Liu, Elyse Katz, Yanwei Zhang, Bradley T Wyman, David Raunig, C Pierre Jedynak, Brian Caffo, Jerry L Prince; Alzheimer's Disease Neuroimaging Initiative
Affiliations

A computational neurodegenerative disease progression score: method and results with the Alzheimer's disease Neuroimaging Initiative cohort

Bruno M Jedynak et al. Neuroimage. .

Abstract

While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer's disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer's DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.

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Figures

Figure 1
Figure 1
This graph represents a conceptualization of the timing of key biomarkers transitions from “Normal” to “Abnormal” as subjects go through the three stages of Alheimer’s disease: “Cognitively Normal”, “MCI”, and “Dementia.” This plot is reproduced from “Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade,” Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ., Lancet Neurol. 2010 Jan;9(1):119-28.
Figure 2
Figure 2
The values of seven biomarkers, measured at all visits of all ADNI subjects, are plotted on the normalized ADPS. Each connected polyline represents the consecutive visits of a single subject, and each line segment is colored according to the subject’s clinical diagnoses between visits (see legend). The gray curves are the sigmoid functions representing the fitted behavior of each biomarker in the normalized space.
Figure 3
Figure 3
Bootstrapping yields different biomarker sigmoids with each random substitution. These plots give all the computed sigmoids over the entire bootstrapping exercise. Tight agreement overall is observed.
Figure 4
Figure 4
Rate of the ADPS as function of the ADPS for baseline visits. Black: Normal subjects. Red: MCI subjects Green: AD subjects.
Figure 5
Figure 5
(a) Estimated biomarker dynamics as a function of the normalized ADPS. Estimation of the normalized ADPS for all ADNI subjects was carried out, and common biomarker dynamics represented by sigmoidal functions were simultaneously fitted as part of the ADPS normalization algorithm. Each sigmoidal function was scaled and flipped in order to fit on a scale going from -1 representing “Normal” to 1 representing “Abnormal”. The positions of vertical lines representing progression from Normal to MCI and MCI to AD were fitted as optimal separating thresholds between the clinical diagnoses provided in the ADNI database. (b) 90% confidence intervals for the inflection point of each biomarker
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