Publications of G Lohmann

Mahler, L.; Steiglechner, J.; Bender, B.; Lindig, T.; Ramadan, D.; Bause, J.; Birk, F.; Heule, R.; Charyasz, E.; Erb, M. et al.; Kumar, V.; Hagberg, G.; Martin, P.; Lohmann, G.; Scheffler, K.: UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations. Nature 12 (1), 635 (2025)

Doehler, J.; Northall, A.; Liu, P.; Fracasso, A.; Chrysidou, A.; Speck, O.; Lohmann, G.; Wolbers, T.; Kuehn, E.: The 3D Structural Architecture of the Human Hand Area is Non-Topographic. The Journal of Neuroscience 43 (19), pp. 3456 - 3476 (2023)

Blum, C.; Baur, D.; Achauer, L.-C.; Berens, P.; Biergans, S.; Erb, M.; Hömberg, V.; Huang, Z.; Kohlbacher, O.; Liepert, J. et al.; Lindig, T.; Lohmann, G.; Macke, J.; Römhild, J.; Rösinger-Hein, C.; Zrenner, B.; Ziemann, U.: Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha) - a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke. BMC Neurology 22 (1), 238 (2022)

Lacosse, E.; Scheffler, K.; Lohmann, G.; Martius, G.: Jumping over baselines with new methods to predict activation maps from resting-state fMRI. Scientific Reports 11 (1), 3480, pp. 1 - 15 (2021)

Stelzer, J.; Lacosse, E.; Bause, J.; Scheffler, K.; Lohmann, G.: Brainglance: visualizing group level MRI data at one glance. Frontiers in Neuroscience 13, 972, pp. 1 - 13 (2019)

Scheffler, K.; Heule, R.; Baez-Yanez, M.; Kardatzki, B.; Lohmann, G.: The BOLD sensitivity of rapid steady-state sequences. Magnetic Resonance in Medicine 81 (4), pp. 2526 - 2535 (2019)

Lohmann, G.; Stelzer, J.; Lacosse, E.; Kumar, V.; Mueller, K.; Kuehn, E.; Grodd, W.; Scheffler, K.: LISA improves statistical analysis for fMRI. Nature Communications 9, 4014, pp. 1 - 9 (2018)

Koelsch, S.; Skouras, S.; Lohmann, G.: The auditory cortex hosts network nodes influential for emotion processing: An fMRI study on music-evoked fear and joy. PLoS One 13 (1), pp. 1 - 22 (2018)

Mueller, K.; Lepsien, J.; Möller, H.; Lohmann, G.: Commentary: Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Frontiers in Human Neuroscience 11, 345, pp. 1 - 3 (2017)

Fomina, T.; Lohmann, G.; Erb, M.; Ethofer, T.; Schölkopf, B.; Grosse-Wentrup, M.: Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS. Journal of Neural Engineering 13 (6), 066021 (2016)

Lohmann, G.; Stelzer, J.; Zuber, V.; Buschmann, T.; Margulies, D.; Bartels, A.; Scheffler, K.: Task-Related Edge Density (TED): A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain. PLoS ONE 11 (6), pp. 1 - 22 (2016)

Kuehn, E.; Mueller, K.; Lohmann, G.; Schuetz-Bosbach, S.: Interoceptive awareness changes the posterior insula functional connectivity profile. Brain Structure and Function 221 (3), pp. 1555 - 1571 (2016)

Stelzer, J.; Lohmann, G.; Mueller, K.; Buschmann, T.; Turner, R.: Deficient approaches to human neuroimaging. Frontiers in Human Neuroscience 8, 462, pp. 1 - 16 (2014)

Schaefer, A.; Margulies, D.; Lohmann, G.; Gorgolewski, K.; Smallwood, J.; Kiebel, S.; Villringer, A.: Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI. Frontiers in Human Neuroscience 8, 195, pp. 1 - 13 (2014)

Stelzer, J.; Buschmann, T.; Lohmann, G.; Margulies, D.; Trampel, R.; Turner, R.: Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping. Frontiers in Neuroscience 8, 66, pp. 1 - 8 (2014)

Wang, Q.; Mahler, L.; Steiglechner, J.; Birk, F.; Scheffler, K.; Lohmann, G.: DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise Cleaning. In: IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 2452 - 2461. 2nd Workshop on Computer Vision for Automated Medical Diagnosis: International Conference on Computer Vision (ICCV/CVF 2023) , Paris, France, October 02, 2023 - October 06, 2023. (2023)

Mahler, L.; Wang, Q.; Steiglechner, J.; Birk, F.; Heczko, S.; Scheffler, K.; Lohmann, G.: Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification. In: Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, pp. 123 - 132 (Eds. Abdulkadir, A.; Bathula, D.; Dvornek, N.; Govindarajan, S.; Habes, M. et al.). 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023), Vancouver, BC, Canada, October 08, 2023. Springer, Cham, Switzerland (2023)

Wang, Q.; Mahler, L.; Steiglechner, J.; Birk, F.; Scheffler, K.; Lohmann, G.: A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging. In: Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, pp. 23 - 33 (Eds. Abdulkadir, A.; Bathula, D.; Dvornek, N.; Govindarajan, S.; Habes, M. et al.). 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023), Vancouver, BC, Canada, October 08, 2023. Springer, Cham, Switzerland (2023)

Steiglechner, J.; Wang, Q.; Ramadan, D.; Mahler, L.; Scheffler, K.; Bender, B.; Lindig, T.; Lohmann, G.: FLEXseg: Next Generation Brain MRI Segmentation at 9.4 T. In: Medical Imaging with Deep Learning (MIDL 2022). Medical Imaging with Deep Learning (MIDL 2022), Zürich, Swtzerland, July 06, 2022 - July 08, 2022. (2022)

Wang, Q.; Steiglechner, J.; Lindig, T.; Bender, B.; Scheffler, K.; Lohmann, G.: Super-Resolution for Ultra High-Field MR Images. In: Medical Imaging with Deep Learning (MIDL 2022). Medical Imaging with Deep Learning (MIDL 2022) , Zürich, Swtzerland , July 06, 2022 - July 08, 2022. (2022)