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Diagnostic research holds the key to defeat dementia

Alzheimer’s disease (AD) is the most common form of dementia. It is a neurodegenerative disease that occurs, in most cases, at older age (i.e. people over 65 years of age). The disease has a slow start, initially affecting the ability to remember recent events (i.e. loss of newly formed episodic memories). As the disease progresses memory functions worsen, other cognitive functions begin to decline (e.g., language, planning, orientation) and behavioural changes become noticeable (e.g. mood, self-care, inappropriate behaviour).

Despite numerous attempts, no treatments that stop or reverse the disease progression are currently available. Recently, Alzheimer’s Research UK announced that the phase 3 trials of the drug Solanezumab (an anti-amyloid antibody) failed to slow cognitive decline in people with mild Alzheimer’s disease [1]. Yet another setback in our commitment to defeat dementia. Previous trails had been promising, with the drug being most beneficial for patients with Mild Alzheimer’s disease. This indicates the importance to treat the disease early on in the disease progress. Reliable diagnostic tools for the detection of early AD are therefore necessary. They will play a vital role to increase our chances to bring the disease to a halt before severe symptoms occur.

Unfortunately, distinguishing early symptoms of AD from cognitive aging or disorders with similar cognitive symptoms as AD (e.g., depression or other types of dementia) is not an easy task at present. Currently, initial diagnosis for AD is usually based on performance on a battery of cognitive tests assessing memory, language, reasoning abilities etc. These tests might be sensitive for AD, though not at the earliest stages in the disease process. Moreover, such symptoms are also related to cognitive aging, depression and other forms of dementia, making it difficult to determine whether the tested individual actually suffers from AD. Additional investigation can be done in specialized centres using structural or functional imaging techniques (CT, MRI, fMRI, PET scans) or by examining body fluids (i.e. cerebrospinal fluid). These techniques are invasive, expensive and not widely available. Moreover, diagnosis based on such biomarkers is still uncommon as they remain unreliable for diagnostic purposes (i.e. not specific to AD). For these reasons, we argue that it is time for an alternative approach to diagnose signs of Early AD: one that is affordable and theory driven (for review see Hoefeijzers, Calia & Parra, 2016).

Respect to this approach, our research team aims to identify if tests assessing the ability to form and memorise item features (e.g. the object’s shape and colour) can be used to (a) distinguish people with Mild Cognitive Impairments (MCI) from people who age normally and (b) whether such tests are able to predict which Mild Cognitive Impairment (MCI) patients will convert to AD.
Our findings are promising and show that early on in the development of AD, before other signs of AD-related symptoms are observed (i.e. during the preclinical phase of AD), the ability to bind item features in visual short-term memory becomes impaired. Importantly, visual short-term memory binding is not affected by healthy aging or impaired in disorders with similar symptoms as AD. Furthermore, test performance of MCI patients shows to be a reliable indicator for conversion of MCI to AD. Altogether, the short-term memory binding task developed in our lab holds promising marker properties for early AD that could fulfil the need to treat as early as possible affected patients.

Can we further strengthen the diagnostic value of our task and its ability to monitor the effectiveness of certain treatment strategies (i.e. drug / memory training)? We think so. We are currently investigating the cognitive underpinnings of visual short-term memory binding (VSTMB). More specifically, we are incorporating the use of Electro-Encephalogram (EEG) and functional Magnetic Resonance Imaging (fMRI) techniques to investigate the neuronal networks active in patients with early symptoms of AD while performing our VSTMB task. We will compare their network activity with the neuronal patterns found in people aging normally (i.e., healthy elderly). A clear understanding of how such brain networks become impaired and are affected throughout the disease process (i.e. when converting from MCI to AD) will (a) further improve the diagnosis of early AD and (b) inform about the effectiveness of certain treatment strategies on both behavioural and brain network level.

Combining cognitive research with EEG/fMRI is the most popular technique to study the living human brain nowadays [2], especially in the western world (i.e. the USA and West of Europe) and Asia (i.e. China and Japan) [3]. Though how these findings, mainly obtained by studying the human brain of western Caucasians and Asians, translate to the rest of the world remains to be determined.
This is an important issue regarding our quest to develop an affordable and culturally unbiased marker for the detection of early AD. Indeed, to develop reliable AD screening tools that retain diagnostic power in low and middle income countries (e.g., Africa, South America etc.), where to this day substantial number of suffers remain unidentified, we need to test the sensitivity of our VSTMB test across populations with different cultural backgrounds. We investigated the test sensitivity of our VSTMB test to detect early AD across populations in South America and Western Europe. Despite huge differences in education and age between groups across countries, performance of patients and controls across countries did not differ significantly (Hoefeijzers et al., 2016, under review; Parra et al., 2011) showing that feature binding in VSTMB is insensitive to the educational level of the assessed individual. This implies that our VSTMB test can be used for the assessment of elderly in countries with low socio-cultural backgrounds.

Early diagnosis of AD is the first, yet crucial, step to stop or reverse the disease progression. While pharmaceutical treatments have consistently failed, rehabilitation techniques relying on computerised technologies such as virtual reality (VR) have started to show promising outcomes. VR is applied as a cognitive training tool in AD patients to improve their ability to navigate and orientate themselves in novel and familiar environments, to improve face recognition, and to maintain activities of daily living such as cooking, driving and shopping. The benefit of VR is that the to-be-performed activities, and the environment in which these activities are performed, are adaptable to the needs of each individual patient. Moreover, training takes place in a safe virtual environment [4]. By performing certain tasks, games or everyday activities in virtual environments, the patients’ cognitive skills required to complete the tasks such as attention, executive functions, memory and orientation are trained (for overview on Dementia-related VR research see García-Betances et al., 2015). Virtual training programs have shown to be promising rehabilitation tools to retain everyday-life functions and quality of life in patients with early AD.

Our lab is currently developing a customised Virtual Reality program for the assessment and rehabilitation of everyday-tasks in people with early signs of Dementia 5. Our method should be able to (a) determine, on an individual basis, the cognitive problems (e.g. attention, memory, planning) that limit early AD patients to perform tasks of everyday life and (b) overcome limitations of current VR training by providing a flexible and individual-centred platform for cognitive training. More specifically, we will recreate, in virtual environments, sections of the participants’ house wherein they perform their daily living tasks (i.e., everyday tasks they struggle with). Participant will take part in a 6-month memory training program where they will train these daily everyday life tasks (and as result train the cognitive skills required to complete such tasks) in their own customised “familiar” VR environment (e.g. kitchen, living room) [5].

Our research framework:
DAEM: Detecting Alzheimer’s & Enhancing Memory.

As Alzheimer’s Research UK states: Research holds the power to defeat dementia. We cannot agree more. In this aspect we believe that diagnostic research holds the key to success.

Dr Serge Hoefeijzers & Dr Mario Parra

21 January 2017



García-Betances, R. I., Arredondo Waldmeyer, M. T., Fico, G., & Cabrera-Umpiérrez, M. F. (2015). A succinct overview of virtual reality technology use in Alzheimer’s disease. Frontiers in aging neuroscience, 7, 80.

Hoefeijzers S, Calia C, Parra MA (2016) Is it Time to Change the Way we Detect Alzheimer’s Disease and Monitor its Progression? Towards Affordable and Theory-Driven Approaches from Cognitive Neurosciences. JSM Alzheimer’s Dis Related Dementia 3(2): 1028.

Hoefeijzers, S., Hernandez, A. G., Rios, A. M., and Parra M. A. (2016). Feature binding of common everyday items is not affected by age and education: A culturally unbiased marker of cognitive aging trajectories. Frontiers in Aging Neuroscinece (under review).

Parra, Mario A, Sergio Della Sala, Sharon Abrahams, Robert H Logie, Luis Guillermo Méndez, and Francisco Lopera. 2011. “Specific Deficit of Colour–colour Short-Term Memory Binding in Sporadic and Familial Alzheimer’s Disease.” Neuropsychologia 49 (7): 1943–52. doi:http://dx.doi.org/10.1016/j.neuropsychologia.2011.03.022.

[1] http://us7.campaign-archive2.com/?u=c46bc216d82187266285591a0&id=a6cf5637af&e=873c6f3225

[2] http://sapienlabs.co/500000-human-neuroscience-papers/

[3] http://sapienlabs.co/eeg-and-fmri-research-numbers-by-country/

[4] http://www.neurophysiologylab.hw.ac.uk/vrais-kitchen.html

[5] https://www.alzheimers.org.uk/site/scripts/documents_info.php?documentID=3465



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      Interesting Links

   Alzheimer's virtual reality app simulates dementia - Link

   EU membership and dementia research- Link

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