Proposal
Introduction:
The nervous system is divided into two parts: the central nervous system and the peripheral nervous system. The central nervous system (CNS) consists of the two most important parts of the human body, namely, the brain and the spinal cord. The brain represents the main part of the human body. It analyzes the information from sensory organs such as the eyes, ears and other senses, and sends the necessary orders to them. It also organizes the vital processes in the human body, especially the regulation of hormones. The spinal cord represents the main centre for the transfer of information to and from the brain and the reactions to different circumstances. The peripheral nervous system (PNS) transfers data, commands and information from the brain and spinal cord to all parts of the body. It also transmits information which is transmitted by the senses to the brain and spinal cord (Snell, 2010).
The brain and spinal cord contain gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). They can suffer from different types of disease that might change their structure and functions. One of these diseases is known as Multiple sclerosis (MS), which is a common disease that affects the central nervous system and affects the ability of the brain to send and receive signals (Heimer, 1983).
Multiple sclerosis is one of the most dangerous diseases worldwide. It is expected that MS will globally affect up to 2 million individuals and approximately 100,000 citizens in the UK (Constantinescu, Farooqi, O’Brien, & Gran, 2011). According to Goldenberg (2012), between 250,000 to 350,000 individuals in the United States experience of multiple sclerosis. MS lesion is an inflammation, and chronic demyelinating disease of the central nervous system of the human brainstem and spinal cord. The disease results in damage to the myelin sheath which covers and protects nerve fibres. It is the major cause of significant neurological disability through loss of communication between the human brain and other organs. There no specific cause of multiple sclerosis and it is an unpredictable and varied disease. However, there are symptoms of MS which depend upon the number of affected nerves. Some patients with chronic MS may have problems walking, vision damage, muscle weakness, failures in sensation, pain, and disorders in the function of the bladder and bowel, while others may suffer long periods of MS without any new signs (Minagar, 2014).
Multiple sclerosis has been diagnosed and detected using a variety of magnetic resonance imaging (MRI) techniques. MRI also plays a significant role in planning treatment of progressive multiple sclerosis disease (Wattjes, Steenwijk, & Stangel, 2015). Advanced MRI measures are highly recommended as professional techniques for underlying pathologies such as MS. These techniques, including magnetization transfer (MT), magnetic resonance spectroscopy (MRS), diffusion imaging, and relaxometry, are comparatively more specific and sensitive than conventional MRI, which does not provide images of damage to the human brain and spinal cord of the specificity and sensitivity required to identify the early stage of the disease (Neema, Ceccarelli, Jackson, & Bakshi, 2012).
Aim:
The main purpose of this study is to employ the Neurite Orientation Dispersion and Density imaging (NODDI) technique in (16.4T) magnetic resonance imaging (MRI) to detect ex-vivo multiple sclerosis disease changes in the lumbar spinal cord in a classical mouse model experimental autoimmune encephalomyelitis (EAE).
Justification:
According to Constantinescu et al. (2011), the model of experimental autoimmune encephalomyelitis (EAE) was developed more than 75 years ago and is still a commonly used model to this day. Also, it is the most popular experimental technique used for multiple sclerosis and inflammatory disease of the CNS in both human beings and animals. However, EAE has limitations when applied to individual diseases. In addition, EAE is a complex procedure because it involves the interaction of an assortment of mechanisms of immunological and neurological diseases. Furthermore, EAE plays an important role in estimating the main pathological features of multiple sclerosis in the CNS, such as inflammatory demyelinating disease, axonal loss, and gliosis. Finally, EAE has been proposed as a method to explore, test, develop the concepts of the immune system as well as to develop standard therapeutic plans for multiple sclerosis.
Diffusion weighted imaging (DWI) has become the gold standard technique in MRI because it provides specific microstructure information from white matter and is a non-invasive method for detecting the impairments of water molecules in brain tissue (Panagiotaki et al., 2012).
The Diffusion tensor (DT) technique, which is related to the DWI technique, can be used as a simple and common model. This technique is useful to illustrate the microstructural signals of tissue using fractional anisotropy mapping (FA) and mean diffusivity (MD). Nonetheless, these indices have limited specificity, because they can be affected by the features of the microstructure (Panagiotaki et al., 2012). The Diffusion tensor imaging (DTI) technique involves the movement of spins in 3D with a b value of zero (Gaussian distribution). It is achieved by fitting the apparent diffusion tensor to 6 or more orthogonally encoded directions (DWIs). However, the DT model does not account for restricted diffusion because the b factor in the diffusion weighting becomes large (Panagiotaki et al., 2012).
Neurite orientation dispersion and density imaging (NODDI) is interfused as a practical diffusion MRI procedure for assessing microstructural difficulties in neurites (the dendrites and axons) in MRI systems. NODDI provides a model of tissue that differentiates three types of microstructural maps: intra-cellular tissue volume fraction (, which measures restricted diffusion in neurites; extra-cellular, which is the orientation dispersion index (ODI); and CSF, which is the isotopic volume fraction ( (Zhang, Schneider, Wheeler-Kingshott, & Alexander, 2012). The NODDI protocol consists of two HARDI shells which measure the white matter, gray matter, and CSF more accurately than the DTI technique, which uses only one shell.
Hypothesis:
The hypothesis of this project is that NODDI parameters can detect the microstructural changes in the EAE mouse model. The expectation is that the experimental results of the group of mice affected by MS disease would be different in the DTI and NODDI indices. The data acquisition of classical EAE-mice will be compared with control, sham-mice and mild EAE mice from a previously acquired dataset. It is expected that the diffusion parameters in the classical EAE mice would indicate stronger neuronal damage than the other groups. For example, there would be estimated changes of the TDI maps, such as decreasing the fractional anisotropy map (FA), MD, and RD/AD maps. Also, I expect that the NODDI maps, ODI and will decrease in the GM and WM due to demyelination and axonal loss, while the map might increase as a result of increasing extracellular space.
Methods:
The classical EAE mice will be prepared by our collaborator’s (Prof Maree Smith) group (School of Pharmacy). EAE mice will be injected using synthetic myelin-derived protein (MOG35-55) mixed with Quil A adjuvant and pertussis toxin (PT) (Khan, Gordon, Woodruff, & Smith, 2015). The animals will be euthanized after 35 days and the lumbar spinal cord will be extracted for ex-vivo imaging. They will be scanned at 16.4T magnetic strength using the NODDI protocol. The imaging results from the mouse model with classical EAE will be compared with control, sham and mild EAE mice from a previously acquired dataset.
Objective:
The objectives of this project will be as follows:
- Detecting the progressing EAE disease by measuring the changes in the spinal cord of the (ex-vivo) mouse model by using NODDI and DTI procedures with a high magnetic field of 16.4T.
- The data acquisition will require 2 HARDI shells for NODDI that will consist of 30 directions at , and , and will be used for DTI.
- The NODDI results will be compared with DTI technique findings.
- The data obtained from the classical EAE disease that is acquired in this project will be compared with a previous study that has been done in control, sham and mild EAE mouse model.
Equipment:
- An ultra-high field scanner (16.4T) that has a micro 2.5 gradient (1.5T/m), a coil with 20 mm volume.
- Parameters of the NODDI and DTI: TR/TE= 24/300 ms, FOV=25.6 X 10 X 10 mm, Resolution = 0.156X 0.156 X 0.156, number of slices = 3D, bandwidth = 50 kHz and δ/Δ= 2.5/10 ms.
- NODDI Matlab toolbox, MRtrix3 (for DTI processing), ITksnap (ROI segmentation) and FSL programs in CAI workstations to analyze and assess EAE data.
- Searches of the UQ library, Google Scholar, Pub Med, Medscape, and web of Science will also be conducted.
Bibliography
Constantinescu, C. S., Farooqi, N., O’Brien, K., & Gran, B. (2011). Experimental autoimmune encephalomyelitis (EAE) as a model for multiple sclerosis (MS). British Journal of Pharmacology, 164(4), 1079-1106. doi:10.1111/j.1476-5381.2011.01302.x
Goldenberg, M. M. (2012). Multiple Sclerosis Review. Pharmacy and Therapeutics, 37(3), 175-184.
Heimer, L. (1983). The Human Brain and Spinal Cord Functional Neuroanatomy and Dissection Guide. New York, NY: New York, NY : Springer US.
Khan, N., Gordon, R., Woodruff, T. M., & Smith, M. T. (2015). Antiallodynic effects of alpha lipoic acid in an optimized RR-EAE mouse model of MS-neuropathic pain are accompanied by attenuation of upregulated BDNF-TrkB-ERK signaling in the dorsal horn of the spinal cord. Pharmacology Research & Perspectives, 3(3), e00137. doi:10.1002/prp2.137
Minagar, A. (2014). Multiple sclerosis: an overview of clinical features, pathophysiology, neuroimaging, and treatment options. Paper presented at the Colloquium Series on Integrated Systems Physiology: From Molecule to Function to Disease.
Neema, M., Ceccarelli, A., Jackson, J. S., & Bakshi, R. (2012). Magnetic Resonance Imaging in Multiple Sclerosis Multiple Sclerosis (pp. 136-162): Wiley-Blackwell.
Panagiotaki, E., Schneider, T., Siow, B., Hall, M. G., Lythgoe, M. F., & Alexander, D. C. (2012). Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison. NeuroImage, 59(3), 2241-2254. doi:http://dx.doi.org/10.1016/j.neuroimage.2011.09.081
Snell, R. S. (2010). Clinical neuroanatomy: Lippincott Williams & Wilkins.
Wattjes, M. P., Steenwijk, M. D., & Stangel, M. (2015). MRI in the Diagnosis and Monitoring of Multiple Sclerosis: An Update. Clinical Neuroradiology, 25(2), 157-165. doi:10.1007/s00062-015-0430-y
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000-1016. doi:http://dx.doi.org/10.1016/j.neuroimage.2012.03.072
