The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease

Abstract

One contribution of 25 to a Theme Issue 'The virtual physiological human: integrative approaches to computational biomedicine'. Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

“…These include software applications that provide practical information for those caring for dementia patients (e.g., Dementia Support by Swedish Care International, Alzheimer’s and Other Dementias Daily Companion, MindMate) as well as tools used for mobile cognitive screening (e.g., MOBI-COG, Mobile Cognitive Screening, Dementia Screener, Sea Hero Quest, CANTAB). In addition, CDSSs, designed to aid clinical decision making by adapting computerized clinical practice guidelines to individual patient characteristics or integrating machine learning methodologies for pattern recognition, have been recently gaining more interest in expediting dementia diagnosis and disease management (Antila et al, 2013, Frame, LaMantia, Bynagari, Dexter, & Boustani, 2013, Lindgren, 2011, Lindgren, Eklund, & Eriksson, 2002). It has been shown that such systems are more sensitive in detecting an early-stage disease and more objective than diagnostic decisions made by a single practitioner (Moja et al, 2015).…”

Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

“…Several previous EEG studies of conversion from mild cognitive impairment to Alzheimer's disease have been conducted (Jelic et al, 1996, 2000; Huang et al, 2000; Stam et al, 2003; Schoonenboom et al, 2004; Rombouts et al, 2005; Babiloni et al, 2006, 2011; Kwak, 2006; Rossini et al, 2006, 2008; Lehmann et al, 2007; Moretti et al, 2007a, b, 2008, 2011; Luckhaus et al, 2008) mainly using biomarkers such as spectral measures and synchronization between brain regions. Machine-learning techniques have been used to explore differences between MCI and AD with varying success (Huang et al, 2000; Bennys et al, 2001; Prichep et al, 2006; Buscema et al, 2007; Lehmann et al, 2007; Prichep, 2007; Rossini et al, 2008), however, only few studies have tried to predict the conversion from MCI to AD (Prichep et al, 2006; Prichep, 2007; Antila et al, 2013). Many studies typically focus on a small number of biomarkers (on the order of 15 marker values), and some do not have adequate validation of their results on independent groups.…”

Study of Scenarios and Technical Requirements of a Social Assistive Robot for Alzheimer’s Disease Patients and Their Caregivers

“…According to Antila et al [1], nowadays Alzheimer's disease (AD) is the most common cause of dementia. The World Health Organization reported in 2012 that 36.5 millions of persons suffer from dementia in the world [2], that is 0.5 % of the total population (data from 2010).…”