Current Research Focus
My current research focuses on identifying neural correlates of consciousness by examining both human and artificial intelligence systems, using ultra-high-field functional magnetic resonance imaging (fMRI) within no-report paradigms. This innovative approach aims to uncover underlying similarities in conscious processing between artificial neural networks and the human brain.
Concurrently, I am advancing several projects that push the boundaries of neuroimaging and data interpretation. These include developing sophisticated MRI motion correction algorithms, implementing super-resolution techniques in medical imaging, precise brain segmentation, and the extraction of interpretable brain biomarkers from fMRI data. Each individual projects aims to contribute to a more profound scientific understanding of the brain’s intricate workings and the potential for AI applications in neuroscience and everyday clinical practice.
Recent Publications
- Mahler, L., Wang, Q., Steiglechner, J., Birk, F., Heczko, S., Scheffler, K., & Lohmann, G. (2023, October). Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification. In International Workshop on Machine Learning in Clinical Neuroimaging (pp. 123-132). Cham: Springer Nature Switzerland.
- Mahler, L., Steiglechner, J., Wang, Q., Scheffler, K., & Lohmann, G. (2023). JudgeMI: Towards Accurate Metrics for Assessing Deep Learning Based Structural MRI Motion Correction. Poster presented at 29th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2023), Montreal, Canada.
- Wang, Q., Mahler, L., Steiglechner, J., Birk, F., Scheffler, K., & Lohmann, G. (2023). DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise Cleaning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2452-2461).
- Wang, Q., Mahler, L., Steiglechner, J., Birk, F., Scheffler, K., & Lohmann, G. (2023). A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging. arXiv preprint arXiv:2303.13900.
- Lohmann, G., Heczko, S., Mahler, L., Wang, Q., Steiglechner, J., Kumar, V. J., … & Scheffler, K. (2023). Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning. bioRxiv, 2023-11.