Although Alzheimer's currently has no cure, treatments for symptoms are available. Research has come a long way since 1990s and the emerging biology-based biomarker approach will pave the way for understanding, controlling, and possibly curing this debilitative disease.
Associate Professor Juan Helen Zhou
Alzheimer's disease (AD) is the most common neurodegenerative disorder that causes cognitive impairment, disabilities, and dementia in people aged 65 years and older. AD usually begins with intermittent memory loss with atrophy-prominent damage in the different regions of the brain.
According to a 2015 study led by Singapore’s Institute of Mental Health, one in 10 people aged 60 and above in Singapore may have dementia. This translates to almost 82,000 people in 2018, and the number is expected to increase to 100,000 in the next two years.
Since the early 1990s, autopsy studies have provided evidence that AD is associated with brain neural network dysfunction. Autopsy studies, however, often provide only a poor clinical staging of cases, covering mostly end-stages of disease, which does not allow for individual follow-up.
AD is related to the accumulation of a protein called beta-amyloid (Aß) in the spaces between brain cells. The other protein implicated in Alzheimer's disease is tau, which forms tangles in the brain cells that affect cortical neuronal networks related to cognitive functions (Pievani, de Haan, Wu, Seeley & Frisoni, 2011). Soluble Aβ protein produces a toxic signalling cascade in the brain, accompanied with aggregation of tau protein and formation of neurofibrillary tangles (NFTs), which may lead to cognitive impairments and dementia (Dziewczapolski, Glogowski, Masliah & Heinemann, 2009).
Scientists are still investigating the exact roles of the Aß plaques and tau tangles in AD. Most experts believe that they play an important role in disrupting the communication between nerve cells, blocking the processes required for these cells to survive. The destruction of the nerve cells leads to the onset of AD symptoms, such as memory failure, problems performing daily activities and changes in the personality.
Multimodal neuroimaging analysis can be utilised to measure neurodegenerative effects in the brain in vivo, including molecular pathology via positron emission tomography (PET), brain atrophy via magnetic resonance imaging (MRI), altered neural function via functional MRI, and structural and functional disconnection using diffusion tensor imaging (DTI) and resting-state fMRI (rsfMRI). AD is associated with large-scale brain functional and structural network dysfunction. Researchers have demonstrated that neuroimaging approaches can map large-scale brain networks in health and detect network-level alterations in diseases.
Structural, molecular, and functional neuroimaging studies have replicated the autopsy findings and converged to a specific brain network vulnerable to AD. Firstly, patterns of brain atrophy, as revealed by MRI, most closely resembled the distribution of neurofibrillary tangles across different clinical stages of AD (Chetelat et al., 2002; Whitwell et al., 2007). Secondly, positron emission tomography (PET) of Aß and tau protein in the brain suggested differential stage-dependent amyloid and tau accumulation across AD spectrum (Thal, Attems & Ewers, 2014). Thirdly, fluorodeoxyglucose (FDG)- PET detection uncovered metabolic changes in the brain that overlaps between both the pattern of brain atrophy/tangle distribution and Aß accumulation (Choo et al., 2007).
Compared to the above-mentioned neuroimaging techniques, human brain neural networks have been more directly examined by DTI and rs-fMRI, since the turn of the century. These techniques have identified consistent structurally and functionally connected networks in the human brain (Fox et al., 2005; van den Heuvel, Mandl, Kahn & Hulshoff Pol, 2009). Any damage in these key brain networks could have an effect on human behavior and cognition (Koch et al., 2014).
The most frequently studied among these networks is the default mode network (DMN), a group of brain regions that is found deactivated during cognitive tasks requiring externally-focused attention and activated during internally-focused mental tasks, such as episodic memory retrieval, mental state attribution, and visual imagery (Buckner, Andrews-Hanna & Schacter, 2008; Mason et al., 2007; Raichle et al., 2001; Shulman et al., 1997). The main DMN nodes lie in the medial and inferior parietal, medial frontal and medial temporal lobes of the brain, which are the typical sites involved in AD-related atrophy (Buckner et al., 2005).
On this basis, an understanding of the functional and structural organisation of brain networks may further our understanding of early changes in neurodegenerative diseases. Failure of clinical trials in late-stage AD is a compelling argument to conduct studies in individuals with early disease symptoms. The field is moving from clinical diagnosis to biology- or biomarker-defined characterisation of AD, which aims to provide a better understanding of complex events that result in dementia. This will allow us to develop targeted and timely interventions of the correct disease processes along AD progression.
Imaging can provide evidence to support the hypothesis of network specificity in AD and other ageing-related disorders. For example, AD frequently co-occurs with cerebrovascular disease (CeVD) - a variety of medical conditions that affect the blood vessels of the brain, which has emerged as the leading cause of age-related cognitive impairment, especially in Asia. Evidence suggests that AD and CeVD share multiple risk factors leading to effects on cognitive decline. Neuroimaging work from our group and others have recently uncovered differential brain networks impaired in AD with and without CeVD as well as the prodromal stages (Chong et al., 2017).
Moving forward, from a clinical point of view, the development of brain network approaches may provide differential diagnosis and facilitate cognitive decline prediction at an individual level, with emerging big data and machine learning approaches (Teipel et al., 2013).
Finally, the use of brain imaging markers to assess treatment response needs to be further explored. Future work in clinical dementia research needs to close the growing gap between advances in imaging technology and a lack of progress in the development of efficient treatments.
Particularly, the novel view on mechanisms of brain resilience in healthy and pathological ageing might help to open treatment avenues beyond classical interventions. The development of multimodal individualised treatments, including medication management and stage specific cognitive training and rehabilitation would reduce the course of evident and pre-dementia stages of AD.
Advancements in neuroimaging studies focusing on brain networks serve as a significant first step towards predicting AD onset and progression. Future studies will continue to further understand the disease model and exploit translational opportunities.
Although AD currently has no cure, treatments for symptoms are available and research is ongoing to find ways to control and cure this disease. Today, there is a worldwide effort to find better ways to prevent the disease from developing, delay its onset and find more effective ways of treatment.
Associate Professor Juan Helen Zhou is an associate professor and principal investigator of the Multimodal Neuroimaging in Neuropsychiatric Disorders Laboratory, Centre for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program at Duke-NUS Medical School, Singapore. She is also a principal investigator at the Clinical Imaging Research Center, and National University of Singapore.