̽»¨¾«Ñ¡

Career

• since 2023 Assistant Professor, ̽»¨¾«Ñ¡, Division of Physiotherapy, Franzén group & Affiliated to Theme Women’s Health and Allied Health Professionals, Medical unit Occupational Therapy and Physiotherapy, Karolinska University Hospital
• 2019-2023 Postdoc, ̽»¨¾«Ñ¡, Division of Physiotherapy, Franzén group &Affiliated to Theme Women’s Health and Allied Health Professionals, Medical unit Occupational Therapy and Physiotherapy, Karolinska University Hospital
• 2016-2019 PhD Student, Max-Planck-Institute for Human Cognitive and Brain Sciences,   &International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity (IMPRS NeuroCom)
• 2013-2016 Research Assistant, Max-Planck-Institute for Human Cognitive and Brain Sciences,  , CRC 1052 Obesity Mechanisms

Academic Education

• 2013-2015  Master of Science, Neurobiology and Behavior, University of Leipzig
• 2010-2013  Bachelor of Science, Biology, Martin-Luther University of Halle-Wittenberg

Merits

• Part of the
• at ̽»¨¾«Ñ¡, 2024

For my research, I have applied data-driven methods ranging from meta-analyses, voxel-based morphometry, and machine learning to graph theory to investigate disease-specific neural correlates of neurodegenerative diseases as well as predict such syndromes individually.

During my , I conducted quantitative and systematic meta-analyses on neuroimaging data to identify disease-specific patterns that can disentangle clinically hard distinguishable Parkinsonian syndromes. Further, I worked on multi-centric neuroimaging data from the . I used supervised machine learning to discriminate clinical syndrome and unique symptoms in single patients.

As a postdoc, I worked on neuroimaging and behavioral analyses of a large randomized controlled trial that compared a highly challenging balance and gait training with an intense speech and voice training in people with Parkinson’s disease. I applied multi-level analyses, voxel-based morphometry, effective functional connectivity analyses, and structural covariance networks to investigate behavioral and brain changes. Taking a step towards personalized medicine, I used the aforementioned multi-modal dataset of neuroimaging, clinical, motor, and cognitive variables to identify Parkinson’s disease subtypes using unsupervised machine learning.

Besides my research work, I am passionate about and active in education at ̽»¨¾«Ñ¡. I was awarded the ’’ at ̽»¨¾«Ñ¡ grant for 2024. Further, I have experience giving lectures, IT hands-on practices, seminars, and exam grading in several doctoral and master courses. Since 2021, with a colleague, I have been organizing a monthly journal club for the Ph.D. students at the Division of Physiotherapy. Since 2021, I have been part of the "Methods for systematic review- from idea to project plan" course where I have held lectures, done seminar supervision, and proofread/ graded assignments. I am further part of the bi-annual doctoral course "" where I facilitate lectures and IT hands-on practices/ software demonstration using sMRI analysis software. Additionally, I have held a lecture within the doctoral education module ’Neurodegenerative Disorders I - From Molecule to Treatment’, yearly since 2021.