Neurodegenerative diseases such as Alzheimer’s Disease (AD), Parkinson disease (PD), Huntington Disease (HD) and Amyotrophic Lateral Sclerosis (ALS) involve the loss of structure or function of neurons, including neuronal death (see Martin (2002)
; Shaw (2005)
). During the earliest stages of these diseases, the progression is slow, on the time scale of years, (see Sperling et al. (2011)
for the case of AD). It is widely believed that these early stages are the most promising for therapeutic intervention, before irremediable neuronal loss occurs. Developing a therapeutic remedy requires a precise measure of disease progression, i.e., a quantity which would be specific to a particular disease and sensitive to subtle changes. However, obtaining accurate measures of disease progression during the earliest phases of the disease is difficult. Indeed, these phases are essentially non-symptomatic and the clinical tests which characterize the acute phase of the disease are not sensitive enough to qualify as a measure of disease progression. In response, the medical research community has contributed to developing and validating biomarkers. Biomarkers for neurodegenerative diseases include protein counts (in the cerebrospinal fluid), blood analysis, brain imaging, including molecular and MR, genetic analysis and neuropsychological tests. Structural imaging biomarkers are unique in that they allow one to characterize the size, shape, and health of various brain substructures at the organ level while being noninvasive (see e.g. Qiu et al. (2008)
for AD, Rizk-Jackson et al. (2011)
for HD). Functional imaging provides a spatially localized image of the physiological processes occurring in the brain. See Brooks and Pavese (2011)
for a review of imaging biomarkers in PD and Turner et al. (2011)
for ALS. Due to the complexity of the neurodegenerative diseases and variabilities within the human population, research efforts have been pooled in order to create datasets with a large number of subjects, time-points and biomarkers. The Alzheimer’s Disease Neuroimaging Initiative (ADNI), see http://adni.loni.ucla.edu/
, was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public/private partnership. A related effort is taking place for PD. The Parkinson Progression Marker Initiative (PPMI), see http://www.ppmi-info.org/
, is a comprehensive observational, international, multicenter study designed to identify PD progression biomarkers both to improve understanding of disease etiology and course and to provide crucial tools to enhance the likelihood of success of PD modifying therapeutic trials. Huntington disease is caused by a mutation in a single gene, HTT, with full penetrance, making it feasible to identify presymptomatic individuals who will develop the disease but do not yet show yet any clinical symptoms, see Hayden (1981)
. At least two large studies (Predict-HD, see https://www.predict-hd.net/
and TrackOn-HD, see http://hdresearch.ucl.ac.uk/current-studies/trackon-hd/
) are underway to identify sensitive biomarkers for HD. Similar efforts are recently taking place for ALS, see Turner et al. (2009)
; Labbe (2012)
. The availability of large datasets for neurodegenerative diseases opens new opportunities for computational methods which could have a strong impact in the study, the development of therapeutics and the follow-up of patients with neurodegenerative diseases.
We present in this article a generic computational method for computing a disease progression score (DPS) by combining biomarkers. ADNI is, as of today, the largest publicly available longitudinal dataset of biomarkers related to a neurodegenerative disease. It is therefore the dataset which we have chosen to evaluate our method. Since we will work with the ADNI dataset, we recall some preliminary information on AD as well as the validated biomarkers for AD in section 2. The method for computing a DPS, which is the main contribution of this paper, is presented in section 3. Results with the ADNI dataset appear in section 4 and finally in section 5, we discuss the results in the context of ADNI, and their consequence in the study of AD and of neurodegenerative diseases.