Norman Scheel, Dipl.-Inf.

Photo of Norman  Scheel, Dipl.-Inf.

Raum 1.024

Institut für Neuro- und Bioinformatik
Ratzeburger Allee 160 (Geb. 64)
23562 Lübeck

+49 451 3101 5501
+49 451 3101 5504




Classification of neurodegenerative brain states based on multivariate fMRI data


In medicine, biomarkers associated with a specific disease are often used as diagnostic tools. In particular, the intrinsic functional organization of the human brain, assessed by resting-state fMRI, has gained increasing attention. Given the myriad of parameters of spatio-temporal fMRI-data, one major challenge in this research is to find those features that are most predictive for a given disease.


We use machine learning approaches on resting-state fMRI data to unravel differences in brain connectivity in different subject groups. Especiallly comparing the predictive value of fine-scale and global connectomes leads the way to standardized methods for the identification of resting-state biomarkers.




  • Scheel, N., Esswanger, A., Münte, T. F., Heldmann, M., Krämer, U. M., and Mamlouk, A. M.: Selection of Seeds for Resting-State fMRI-Based Prediction of Individual Brain Maturity: Bildverarbeitung für die Medizin, Informatik aktuell, pp. 371-376, 2015


  • Scheel, N., Chang, C., and Mamlouk, A. M.: The importance of physiological noise regression in high temporal resolution fMRI: Artificial Neural Networks - ICANN 2014, Springer, pp. 540-547, 2014