A Novel Multimodality MRI Biomarker of Asymptomatic Alzheimer's Disease
Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia. There is a pressing need to find biomarkers that can accurately predict future disease progression and evaluate the effects of disease-modifying agents in asymptomatic patients. Here we propose to develop and validate multimodality magnetic resonance imaging (MRI) techniques that can help to detect and quantify asymptomatic AD progression. The techniques proposed could lead to the development of novel and highly reliable biomarkers of AD to guide future prevention, diagnoses and therapies, especially in asymptomatic patients when behavioral testing cannot be used for diagnosis and to verify therapeutic effects.
Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia. With no known cures or disease-modifying therapies, there is a pressing need to ﬁnd biomarkers that can accurately assess and predict disease progression in asymptomatic patients. However, despite recent efforts and significant progress in research, finding such a biomarker remains a major challenge. Given that AD is hypothesized to lead to changes in neuronal function long before detectable behavioral impairments and/or brain structure changes, functional magnetic resonance imaging (fMRI), with its ability to image brain function, has the potential to be a critical tool in the early detection of AD and a non-invasive technique that can be used in the evaluation of treatments. Functional MRI is a functional brain imaging technique that uses blood oxygen level dependent (BOLD) signal as an indirect measure of neural activity, and has been widely used to relate cognitive performance to brain activations.
However, traditional fMRI studies of AD, focusing on amplitude, have produced some conflicting results: while some have observed decreased hippocampal activity in individuals with AD or at risk, others have observed comparable or even enhanced neural activity in hippocampus or other brain regions. These apparent contradictions might reflect the technical limitations of conventional fMRI techniques. Furthermore, most fMRI techniques focus on detecting changes within regions of gray matter, which mainly consist of neuronal cell bodies and branching dendrites. Thus some aspects of early AD pathological changes might be difficult to assess with these fMRI techniques, including disrupted functional connectivity between brain regions and impaired integrity within white matter, which mainly relate to nerve fibers. Therefore, integrating this fMRI technique with other MRI modalities – especially resting state fMRI connectivity and diffusion tensor imaging (DTI) – is expected to lead to an increase in sensitivity and accuracy in assessing AD progression.
To test this hypothesis, we propose to conduct a cross-sectional study using a combination of advanced fMRI techniques with other MRI techniques, with an ultimate goal of developing and validating a multimodal MRI biomarker of asymptomatic AD that is highly sensitive to early AD pathological changes and can accurately detect and assess AD progression. First the relationship between AD progression and MRI data for each individual MRI modality will be examined separately, then we will develop and evaluate multimodal MRI biomarkers of AD using powerful machine learning algorithms on multimodal MRI data to assess current disease status.
About the Researcher
Xiong Jiang, PhD, received a bachelor’s degree in computer science, a master’s degree in biophysics, a PhD degree in experimental psychology (with a minor in cognitive neuroscience), and postdoctoral training in computational neuroscience and neuroimaging. Currently he is an assistant professor of neuroscience at Georgetown University Medical Center. His research interests are to use neuroimaging techniques like magnetic resonance imaging (MRI) to study brain function and dysfunction. During recent years, his research has been directed towards developing MRI techniques that can detect and assess neuropathological changes at early stages of brain disease, such as Alzheimer’s disease.
I have a multidisciplinary training background, with a deep interest in finding out how the brain functions and dysfunctions. As a researcher rooted in basic science, I am deeply worried about the potential burden and cost associated with varying brain diseases, including Alzheimer's disease (AD). With a rapidly aging population, AD is going to affect many, many families, including our own family. My wife's grandpa was diagnosed with possible AD-related dementia at the age of 93.
I strongly believe it is an obligation for the science community to tackle this problem, since all our research work are funded by the society (via government, philanthropy, foundations, and other mechanisms). My passion for the next 5-10 years is to develop MRI-based biomarkers for varying neurodegenerative diseases, which might have the potential to assist in clinical diagnosis, as well as to guide drug development and targeted interventional therapies.
Working with scientists from different fields, I am confident that we will find a solution to prevent and/or slow down the progression of AD and other neurodegenerative diseases. That will help to improve the life quality for people who might be at risk and their family members, and to less the burden on society and the caregivers (who most times are family members).
First published on: July 20, 2016
Last modified on: April 29, 2020