In the first phase of the project, we focus on developing a neural network model to predict post-operative motor status from the structural MRI. Initially we attempted to train models solely on T1-weighted with contrast agent and without any annotations, which was unsuccessful due to limited data. Subsequently we will include tumor and tract annotations in the training which should reduce the difficulty of the task. The experiments in this phase will be summarized in a publication.
In the second phase, we will create a deep feature for motor status. We will focus on three regions of the neuroimaging data: The tumor, the corticospinal tract and the part of primary motor cortex tagged with TMS positive points. This will require a preprocessing step in which we connect the TMS positive points and generate annotations to be used as part of the model input. Further, the volume of the tumor and the area of the annotation that overlaps with the tumor will be calculated and validated by medical experts. The overlapped area will measure tumor infiltration in the TMS-tagged primary motor cortex. Our hope is that this measurement could represent the motor cortex involvement. The model will be trained on annotations of the corticospinal tract, the tumor and the tagged primary motor cortex. Initially, we will train the model to reconstruct the three annotations and later we will include prediction of pre-operative motor status, tumor-tract distance, tumor infiltration and tumor size. In these experiments, we will use multi-task learning to enforce constrains on the hidden space to be used as deep feature to predict the post-operative motor status.
 T. Rosenstock, U. Grittner, G. Acker, V. Schwarzer, N. Kulchytska, P. Vajkoczy, and T. Picht. “Risk stratification in motor area–related glioma surgery based on navigated transcranial magnetic stimulation data”. In: Journal of Neurosurgery JNS 126.4 (1Apr. 2017), pages 1227 –1237. doi: 10.3171/2016.4.JNS152896.
Kirchler, M.,Khorasani, S.,Kloft, M., Lippert, C. (2019). "Two-sample Testing Using Deep Learning". In: ariXiv: 1910.06239
Konigorski, S., Khorasani S., & Lippert, C. (2018). Intergrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer’s disease. 32nd Conference on Neural Information Processing Systems (NeurIPS), arXiv:1812.00448.
Masoudi, R., Mazaheri-Asadi, L., & Khorasani, S. (2016). Partial and complete microdeletions of Y chromosome in infertile males from South of Iran. Molecular Biology Research Communications, 5(4), 247–255, PMCID: PMC5326488.