1 code implementation • 12 Dec 2023 • Lukas Fisch, Michael O. Heming, Andreas Schulte-Mecklenbeck, Catharina C. Gross, Stefan Zumdick, Carlotta Barkhau, Daniel Emden, Jan Ernsting, Ramona Leenings, Kelvin Sarink, Nils R. Winter, Udo Dannlowski, Heinz Wiendl, Gerd Meyer zu Hörste, Tim Hahn
While ubiquitous in research and clinical practice, flow cytometry requires gating, i. e. cell type identification which requires labor-intensive and error-prone manual adjustments.
no code implementations • 14 Aug 2023 • Lukas Fisch, Stefan Zumdick, Carlotta Barkhau, Daniel Emden, Jan Ernsting, Ramona Leenings, Kelvin Sarink, Nils R. Winter, Benjamin Risse, Udo Dannlowski, Tim Hahn
Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines.
no code implementations • 10 Feb 2023 • Jan Ernsting, Nils R. Winter, Ramona Leenings, Kelvin Sarink, Carlotta B. C. Barkhau, Lukas Fisch, Daniel Emden, Vincent Holstein, Jonathan Repple, Dominik Grotegerd, Susanne Meinert, NAKO Investigators, Klaus Berger, Benjamin Risse, Udo Dannlowski, Tim Hahn
The brain-age gap is one of the most investigated risk markers for brain changes across disorders.
no code implementations • 20 Dec 2021 • Nils R. Winter, Ramona Leenings, Jan Ernsting, Kelvin Sarink, Lukas Fisch, Daniel Emden, Julian Blanke, Janik Goltermann, Nils Opel, Carlotta Barkhau, Susanne Meinert, Katharina Dohm, Jonathan Repple, Marco Mauritz, Marius Gruber, Elisabeth J. Leehr, Dominik Grotegerd, Ronny Redlich, Andreas Jansen, Igor Nenadic, Markus Nöthen, Andreas Forstner, Marcella Rietschel, Joachim Groß, Jochen Bauer, Walter Heindel, Till Andlauer, Simon Eickhoff, Tilo Kircher, Udo Dannlowski, Tim Hahn
Discussion: We provide a large-scale, multimodal analysis of univariate biological differences between MDD patients and controls and show that even under near-ideal conditions and for maximum biological differences, deviations are extremely small and similarity dominates.
no code implementations • 14 Dec 2021 • Tim Hahn, Hamidreza Jamalabadi, Erfan Nozari, Nils R. Winter, Jan Ernsting, Marius Gruber, Marco J. Mauritz, Pascal Grumbach, Lukas Fisch, Ramona Leenings, Kelvin Sarink, Julian Blanke, Leon Kleine Vennekate, Daniel Emden, Nils Opel, Dominik Grotegerd, Verena Enneking, Susanne Meinert, Tiana Borgers, Melissa Klug, Elisabeth J. Leehr, Katharina Dohm, Walter Heindel, Joachim Gross, Udo Dannlowski, Ronny Redlich, Jonathan Repple
We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses.
no code implementations • 21 Jul 2021 • Tim Hahn, Hamidreza Jamalabadi, Daniel Emden, Janik Goltermann, Jan Ernsting, Nils R. Winter, Lukas Fisch, Ramona Leenings, Kelvin Sarink, Vincent Holstein, Marius Gruber, Dominik Grotegerd, Susanne Meinert, Katharina Dohm, Elisabeth J. Leehr, Maike Richter, Lisa Sindermann, Verena Enneking, Hannah Lemke, Stephanie Witt, Marcella Rietschel, Katharina Brosch, Julia-Katharina Pfarr, Tina Meller, Kai Gustav Ringwald, Simon Schmitt, Frederike Stein, Igor Nenadic, Tilo Kircher, Bertram Müller-Myhsok, Till F. M. Andlauer, Jonathan Repple, Udo Dannlowski, Nils Opel
We quantified the theoretical energy required for each patient and time-point to reach a symptom-free state given individual symptom-network topology (E 0 ) and 1) tested if E 0 predicts future symptom improvement and 2) whether this relationship is moderated by Polygenic Risk Scores (PRS) of mental disorders, childhood maltreatment experience, and self-reported resilience.
no code implementations • 21 Jul 2021 • Tim Hahn, Nils R. Winter, Jan Ernsting, Marius Gruber, Marco J. Mauritz, Lukas Fisch, Ramona Leenings, Kelvin Sarink, Julian Blanke, Vincent Holstein, Daniel Emden, Marie Beisemann, Nils Opel, Dominik Grotegerd, Susanne Meinert, Walter Heindel, Stephanie Witt, Marcella Rietschel, Markus M. Nöthen, Andreas J. Forstner, Tilo Kircher, Igor Nenadic, Andreas Jansen, Bertram Müller-Myhsok, Till F. M. Andlauer, Martin Walter, Martijn P. van den Heuvel, Hamidreza Jamalabadi, Udo Dannlowski, Jonathan Repple
Conclusions: We show that network controllability is related to genetic, individual, and familial risk in MDD patients.
no code implementations • 16 Jul 2021 • Tim Hahn, Jan Ernsting, Nils R. Winter, Vincent Holstein, Ramona Leenings, Marie Beisemann, Lukas Fisch, Kelvin Sarink, Daniel Emden, Nils Opel, Ronny Redlich, Jonathan Repple, Dominik Grotegerd, Susanne Meinert, Jochen G. Hirsch, Thoralf Niendorf, Beate Endemann, Fabian Bamberg, Thomas Kröncke, Robin Bülow, Henry Völzke, Oyunbileg von Stackelberg, Ramona Felizitas Sowade, Lale Umutlu, Börge Schmidt, Svenja Caspers, German National Cohort Study Center Consortium, Harald Kugel, Tilo Kircher, Benjamin Risse, Christian Gaser, James H. Cole, Udo Dannlowski, Klaus Berger
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research.
1 code implementation • 22 Mar 2021 • Lukas Fisch, Jan Ernsting, Nils R. Winter, Vincent Holstein, Ramona Leenings, Marie Beisemann, Kelvin Sarink, Daniel Emden, Nils Opel, Ronny Redlich, Jonathan Repple, Dominik Grotegerd, Susanne Meinert, Niklas Wulms, Heike Minnerup, Jochen G. Hirsch, Thoralf Niendorf, Beate Endemann, Fabian Bamberg, Thomas Kröncke, Annette Peters, Robin Bülow, Henry Völzke, Oyunbileg von Stackelberg, Ramona Felizitas Sowade, Lale Umutlu, Börge Schmidt, Svenja Caspers, German National Cohort Study Center Consortium, Harald Kugel, Bernhard T. Baune, Tilo Kircher, Benjamin Risse, Udo Dannlowski, Klaus Berger, Tim Hahn
For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples.
no code implementations • 13 Feb 2020 • Ramona Leenings, Nils Ralf Winter, Lucas Plagwitz, Vincent Holstein, Jan Ernsting, Jakob Steenweg, Julian Gebker, Kelvin Sarink, Daniel Emden, Dominik Grotegerd, Nils Opel, Benjamin Risse, Xiaoyi Jiang, Udo Dannlowski, Tim Hahn
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development.
no code implementations • 13 Feb 2020 • Ramona Leenings, Nils Ralf Winter, Kelvin Sarink, Jan Ernsting, Xiaoyi Jiang, Udo Dannlowski, Tim Hahn
Despite the tremendous efforts to democratize machine learning, especially in applied-science, the application is still often hampered by the lack of coding skills.