Center for Medical Physics and Biomedical Engineering
siddharth.mittal@meduniwien.ac.at
Keywords
Functional Magnetic Resonance; Functional Neuroimaging; Retinotopy; Ultrahigh field MRI; Visual Cortex; Visual Perception
Research group(s)
- Functional Magnetic Resonance Imaging
Head: Christian Windischberger
Research Area: Mapping the functional organization of the human brain
Members: - MR Physics
Head: Christoph Juchem
Research Area: The Division of MR Physics is pursuing basic methodological and translational/clinical research in the areas of magnetic resonance (MR) imaging and spectroscopy.
Members:
Research interests
Siddharth’s research focuses on how the brain processes visual information by mapping the relationship between the visual field and brain activity. This process, known as visual mapping, helps us understand how different areas of the brain respond to what we see. To achieve this, he uses the population receptive field (pRF) approach, which estimates how regions in the visual cortex respond to visual stimuli based on functional MRI (fMRI) data.
His research aims to improve the accuracy and reliability of pRF mapping by refining estimation methods and addressing variability in measurements. A key challenge in this field is the lack of ground truth data for validation. To overcome this, he is developing a validation framework for systematically evaluating pRF models and visual stimulation patterns.
By combining statistical modelling, High-Performance Computing (using GPUs), and neuroimaging principles, he aims to improve visual mapping techniques and contribute to advancements in vision science and clinical research.
Techniques, methods & infrastructure
Data are collected using 3T and 7T MRI scanners, which provide high-resolution functional MRI (fMRI) signals for retinotopy experiments. To process and analyze large-scale fMRI data, he utilizes HPC systems equipped with GPU clusters. Parallelized computing with CUDA enables efficient pRF model fitting, significantly reducing computation time. He works with programming languages such as C++ and Python to develop and optimize analysis pipelines. His understanding of MR physics and fMRI helps him interpret signal properties, noise, and scanner settings. By combining advanced imaging, fast computing, and neuroimaging expertise, he aims to improve pRF mapping for both research and clinical use.
Selected publications
- Mittal, S. et al. (2025) ‘GEM-pRF: GPU-Empowered Mapping of Population Receptive Fields for Large-Scale fMRI Analysis’, Medical Image Analysis, p. 103891. Available at: https://doi.org/10.1016/j.media.2025.103891.
- Mittal, S. et al. (2025) ‘Validating run-to-run variability simulations for population receptive fields (pRF) mapping’, Journal of Vision, 25(9), p. 2365. Available at: https://doi.org/10.1167/jov.25.9.2365.
- Windischberger, C. et al. (2025) ‘Stimulus-Dependent Variability in Population Receptive Field Mapping’, Journal of Vision, 25(9), p. 2359. Available at: https://doi.org/10.1167/jov.25.9.2359.
- Mittal, S. et al. (2024) ‘A novel approach for population-receptive field mapping using high-performance computing’, Journal of Vision, 24(10), p. 536. Available at: https://doi.org/10.1167/jov.24.10.536.