This project is to: 1. Quantify differences in axonal integrity and organization in aMS versus naPMS patients. 2. Quantify changes in axonal integrity and organization in aMS versus naPMS patients over a two-year period. 3. Validate the combination of imaging parameters that best differentiate aMS versus naPMS patients using histopathology.
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system characterized by multifocal inflammatory infiltrates, microglial activation and degradation of oligodendrocytes, myelin and axons. Clinical MS categories exhibit variable amount of central nervous system (CNS) damage and repair, depending on numerous variables including genetic, immunological, pathological and environmental factors. Therefore, understanding the interplay between axonal damage (i.e. axonal demyelination/degeneration/loss/disorganization) and (ii) axonal repair (i.e. axonal remyelination/reorganization) in living MS patients may be the key to understand disease progression, to establish accurate disease monitoring criteria and to predict disease response to future reparative therapies. New in-vivo methods are necessary to elucidate the interplay between axonal damage and repair in the brain of living patients with MS. Advanced MRI (aMRI) permits a multifaced quantification of the various components of the axons and their organization. Neurite Orientation Dispersion and Density Imaging (NODDI) and Diffusion Kurtosis (DK) are new approaches in clinical research This study is to identify in vivo the specific neuropathological pattern of axonal damage and repair exhibited by active MS (aMS) and non-active progressive MS (naPMS) patient by leveraging the information provided by model-based diffusion metrics (NODDI, DK), Magnetization Transfer Imaging (MTI), Multi-echo Susceptibility-Based imaging (SBI), Myelin Water Imaging (MWI) and quantitative T1 relaxometry (qT1). These advanced MRI contrasts provide complementary and partially redundant information about the axonal structure and its organization (i.e. density and orientation of axons and dendrites in the brain tissue, axonal integrity and myelination, presence of myelin and iron, and brain tissue architecture). Therefore, their combination may prove high sensitivity and specificity to axonal damage and repair. This project has 3 main aims: Aim 1. Quantify differences in axonal integrity and organization in aMS versus naPMS patients. Aim 2. Quantify changes in axonal integrity and organization in aMS versus naPMS patients over a two-year period. Aim 3. Validate the combination of imaging parameters that best differentiate aMS versus naPMS patients using histopathology.
Study Type
OBSERVATIONAL
Enrollment
300
Each enrolled subject will undergo a MRI at baseline and a second MRI at 2 years (+/- 3 months) follow-up.
Neurocognitive examination for healthy subjects will be performed at both baseline and follow-up
Each enrolled subject will undergo a blood sampling (10 ml) at baseline
University Hospital Basel, Department of Neurology
Basel, Switzerland
RECRUITINGMRI- change in axonal integrity and organization over 2 years in aMS, naPMS and HC, by using machine learning techniques
After magnetic resonance (MR) data preprocessing (image denoising, standardization, bias field correction) classical machine learning techniques will be used to classify a number of MRI metrics which will be averaged over a number of regions of interest (ROIs) including (i) normal-appearing white and brain matter in brain lobes and cervical spinal cord, (ii) basal ganglia, (iii) thalamus, (vi) cerebellum, MS lesions. Complex input data (voxels/patches) will be generated to learn from, then a deep learning model for supervised classification will be defined to identify the combination of aMRI parameters that characterize aMS, naPMS and HC.
Time frame: at baseline and 2 years (+/- 3 months) after baseline
Change in MUSIC Test
Neurocognitive examination. MUSIC is a rapid (about 10-12 min) multiple domain cognitive screening test reflecting the most frequently impaired cognitive domains in MS. At 20-30 points the performance is in the normal range, at 16-19 points there is at most slight cognitive dysfunction, at 11-15 points there is moderate cognitive dysfunction and at \<= 10 points there is a clear cognitive dysfunction.
Time frame: at baseline and 2 years (+/- 3 months) after baseline
Change in auditory verbal learning and memory test/ Verbaler Lern- und Merkfähigkeitstest (VLMT)
Questionnaire to investigate memory performance, learning and recall information. Five presentations of a 15-word list are given, each followed by attempted recall. This is followed by a second 15-word interference list (list B), followed by recall of list A. Delayed recall and recognition are also tested.
Time frame: at baseline and 2 years (+/- 3 months) after baseline
Change in Symbol Digital Modalities Test (SDMT)
The SDMT detects cognitive impairment by measuring the time to pair abstracts. Using a reference key, the test taker has 90 seconds to pair specific numbers with given geometric figures.
Time frame: at baseline and 2 years (+/- 3 months) after baseline
Change in Brief Visuospatial Memory Test (BVMT)
The BVMT measures visuospatial memory In three Learning Trials, the respondent views the stimulus page for 10 seconds and is asked to draw as many of the figures as possible in their correct location on a page in the response booklet. A Delayed Recall Trial is administered after a 25-minute delay. Last, a Recognition Trial, in which the respondent is asked to identify which of 12 figures were included among the original geometric figures, is administered. Slower processing speed is associated with poorer learning and memory performance.
Time frame: at baseline and 2 years (+/- 3 months) after baseline
Change in Hospital Anxiety and Depression Scale (HADS)
HADS is a fourteen-item scale with seven items each for anxiety and depression subscales. Scoring for each item ranges from zero to three. A subscale score \>8 denotes anxiety or depression.
Time frame: at baseline and 2 years (+/- 3 months) after baseline
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