Cornelia Laule

Associate Professor

Research Interests

Auto-Immune Diseases
Central Nervous System Inflammatory Diseases
Cerebral Atrophy
image analysis
magnetic resonance imaging
Magnetic resonance spectroscopy
multiple sclerosis
Nervous System Development
Neurodegenerative diseases
Neurological diseases
Neurological diseases
Neuronal Systems
Spinal Cord Diseases
spinal cord
Spinal cord injury

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.

Research Methodology

Magnetic Resonance Imaging
magnetic resonance spectroscopy
image analysis
human post mortem brain and spinal cord tissue


Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round
I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).
I am interested in hiring Co-op students for research placements.

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Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Exploring myelin water imaging: from application to atlases to algorithms (2021)

No abstract available.

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Correlations of serum neurofilament light chain and quantitative magnetic resonance imaging metrics in multiple sclerosis (2021)

No abstract available.

Concurrent transcranial magnetic stimulation and magnetic resonance spectroscopy in phantoms and humans (2019)

No abstract available.

In vivo measurement of absolute metabolite concentrations with quantitative magnetic resonance imaging and spectroscopy (2019)

Magnetic resonance spectroscopy (MRS) measures relative signals arising from spins on different metabolites, e.g. N-Acetyl aspartate (NAA). To improve the interpretability of changes caused by disease, it is optimal to convert these relative signals to absolute concentrations e.g. by referencing it to the MR signal of water. Segmentation of high-resolution qualitative magnetic resonance images (MRI) is an accessible and easy-to-use method to estimate the properties of tissue water in the spectroscopic volume of interest (VOI), including water content, [H₂O], and relaxation properties (T₁, T₂) with pre-determined literature values. However, these tissue properties can change in disease and with age. Therefore, we proposed the use of a quantitative MRI approach to reference metabolite concentrations by measuring subject-specific T₁ and T₂ relaxation as well as water content maps. The approach was first validated by measuring a range of biologically relevant water contents and metabolite concentrations in vitro. [H₂O] was overestimated by 4.8% on average, while NAA concentrations were underestimated by 9.9%. In a study of ten healthy controls comparing the traditional segmentation quantification with the novel quantitative MRI method, we observed larger variabilities for subject-specific water properties, which did not propagate to the variability of the absolute metabolite concentrations of the neurochemicals (p > 0.37). Metabolite concentrations were lower with the quantitative MRI approach by -5.4% (p=0.002) in a white matter volume of interest (VOI) and -2.4% (p=0.002) in a grey matter VOI compared to the segmentation-based quantification.The quantitative MRI method for calculating absolute metabolite concentrations in MRS showed promising results, offering a potential alternative for the currently widely used segmentation approach.

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Current Students & Alumni

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