The Assessment of the Diagnosis and Prognosis of Patients With Alzheimer Disease or Parkinson Disease With Cognitive Impairment by Using Diffusion MRI
Condition: Alzheimer Disease; Parkinson Disease
MeSH terms: Parkinson Disease; Alzheimer Disease; Cognitive Dysfunction
Study type: Observational

Summary: 
      The hypothesis is that the differential extent of microstructural damages in the affected
      brain regions can be specific to the disease of interest and could reflect the clinical
      severity. Therefore, the investigator propose that whole brain parcellation of diffusion MRI
      can be used to improve diagnosis and prediction of clinical outcomes in Parkinson's Disease.

        1. A regression model between clinical severity and two year clinical outcomes and
           diffusion properties from multiple parcellated regions will be developed.

        2. Blind validation will be performed.
    

Detailed description: 
      Currently, Alzheimer's Disease (AD) and Parkinson's Disease (PD) are diagnosed mainly by
      neurologists, based on clinical symptoms. However, there are no objective criteria available
      for their diagnosis. Although magnetic resonance imaging (MRI) is often employed in
      conjunction with clinical judgement, the images are mostly used to eliminate other diseases,
      rather than to confirm the diagnosis. Other imaging methods, such as Position Emission
      Tomography or Computed Tomography, may help in the diagnosis of AD and PD, but have harmful
      effects on the human body.

      Diffusion MRI, and in particular Diffusion Tensor Imaging, are often employed in the
      evaluation of changes in connectivity in the central nervous system. As it is non-invasive
      and does not involve radiation, diffusion MRI is suitable to be used for longitudinal
      studies. It has been used for the evaluation of fiber density and cross-section in many
      diseases, including epilepsy, multiple sclerosis, and brain tumours, with good results.
      Several measurements can be obtained from diffusion MRI, including fractional anisotropy and
      mean, radial and axial diffusivity. Changes observed in diffusion MRI are related to changes
      in water content inside and outside of cells, so an increase in the diffusion coefficient
      could reflect an increase in cell membrane permeability, which may be attributed to cell
      death and rupturing. A higher diffusion coefficient may be indicative of more neuronal death.
      Therefore, using diffusion kurtosis, the investigator may be able to improve diagnoses of PD.

      From research on AD, the investigator found that the diffusion coefficient of patients with
      mild cognitive impairment and AD is significantly higher than control patients. The
      investigator will carry out analysis on 90 brain regions, including the fusiform gyrus,
      hippocampus, parahippocampus and cingulum. The listed regions have been observed to have
      differences in mean diffusivity for AD patients and those at risk for AD, as compared to
      normal controls. In previous studies, overlaps were observed between areas where the mean
      diffusivity increases and areas where brain regions shrink, but there are more regions and
      larger areas where the diffusion coefficient increases. Therefore, the mean diffusivity may
      be a more suitable clinical index than the current method of brain volume. In addition, there
      is a correlation between increased mean diffusivity and the severity of mild cognitive
      impairment or AD. Amyloid deposition is consistent with disease progression, further
      supporting that mean diffusivity can be used to reflect the progression of mild cognitive
      impairment and AD.

      The investigator plan to use Compressed Sensing to increase the speed of diffusion MRI. This
      includes image preprocessing, acquisition of Compressed Sensing observations, rebuilding the
      model, and reconstructing the algorithm. The investigator also plan to overcome the current
      limitations of region-of-interest analysis. One way of achieving this is by voxelwise
      analysis, however it has limitations caused by normalization of the image to a template
      space, and possible problems in tractography caused by rotation or distortion of the image.
      Furthermore, the use of a study specific template prevents the results from being available
      in Brodmann or Talairach coordinates. Most importantly, voxel analysis is not based on brain
      regions, so it is difficult to determine the properties of each region, and according to our
      algorithm, a large amount of voxel data would greatly reduce the resolution of the statistics
      and cause problems in statistical analysis. Therefore, the investigator have to use a common
      standard space, and develop a suitable imaging technique.

      The investigator choose to use Automatic Anatomical Labelling (AAL), as this is a commonly
      used system used in neuroscience research. The investigator also use the Montreal
      Neuroscience Institute 152 Template (MNI152) as out standard template. In the imaging and
      processing of the whole brain, the investigator use Affine Transformation, as this is
      commonly used for MRI and diffusion MRI. This includes Camino, FSL, and SPM. The investigator
      will study how the aging of a healthy brain changes the diffusion MRI and make comparisons
      between aging in males and females.

      The investigator will also use Deep Learning to increase the sensitivity and specificity and
      to improve the accuracy of classification and diagnosis, by data set sample allocation data
      preprocessing, and deep neural network design.

      Using AAL, whole brain parcellation will be performed to obtain diffusion MRI information of
      regions in the brain. Affected regions will be identified and analysed. The investigator hope
      that diffusion MRI using whole brain regions can be used for differential diagnosis and for
      identifying regions that have high correlation with clinical severity, and for accurate
      disease diagnosis and prognosis, to serve as a reference for clinicians.

      The investigator aim to use diffusion MRI to assess cognitive function and evaluate if it
      deteriorates in patients with neurodegenerative diseases. In addition, the investigator hope
      to use diffusion MRI to determine the disease severity and prognosis. Worsening of
      neurodegeneration and cognitive ability brings about increased mortality and poorer quality
      of life. The relationship between diffusion MRI results and disease severity may provide an
      objective method allowing clinicians to diagnose these diseases with greater confidence and
      earlier on in disease onset, before the worsening of symptoms.

      The study will be completed in three phases, over three years. In the first year, the
      investigator hope to establish an optimal high-quality imaging compression sensing scheme and
      image data restoration process for diffusion MRI. The investigator also hope to develop an
      optimal method for brain parcellation, and to use deep learning to improve diagnosis of
      patients with mild cognitive impairment.

      In the second year, the investigator hope to establish a process for predicting the prognosis
      of patients with typical and atypical PD, using deep learning. The investigator also hope to
      complete our evaluation of using deep learning for the diagnosis of mild cognitive
      impairment.

      In the third year, the investigator aim to complete the development of a method for
      predicting the prognosis of patients with typical and atypical PD. The investigator will also
      establish and complete our method of using deep learning to evaluate if patients with mild
      cognitive development will develop AD. Furthermore, the investigator will complete the user
      interface for image processing.
    

Observational model: Case-Control
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        For each specific disease group, the following criteria should be meet:

        Healthy volunteers: MMSE score greater than or equal to 26. Mild cognitive impairment
        patients: a) Clinical Dementia Rating score equal to 0.5, b) Be diagnosed by clinician's
        judgement of clinical information. MSA patients should fulfill the National Institute of
        Neurological Disorders and Stroke (NINDS), National Institutes of Health criteria. PSP
        patients should fulfil the NINDS-SPSP and Litvan criteria. CBD patients should fulfil the
        NINDS-SPSP criteria, including limb stiffness or hypokinesia, dystonia, myoclonus, speaking
        disorders, and cortical sensory loss. The PD patients should be besides the age of onset,
        fulfil the NINDS-SPSP criteria.
      
Keywords: magnetic resonance imaging; neurodegeneration disease; cognitive impairment; diagnosis; prognosis