University of Heidelberg
Medical Faculty Mannheim
University Hospital Mannheim
CKM receives DFG funding for Project "Prädiktion von Therapieansprechen und Outcome beim lokal fortgeschrittenen Rektum-Karzinom mittels Radiomics und Deep Learning: eine beispielhafte Anwendung für eine allgemein verwendbare, Deep Learning basierte Prozessierungs-Pipeline für die Bild-Klassifikation" read more.
Alena-Kathrin Schnurr presented on our research on AI in medical imaging @ Research Plus forum
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Dr. Mathias Davids wins the I. I. Rabi Award of the International Society for Magnetic Resonance in Medicine (ISMRM),
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Collaborate Research Projects

Resting-state functional magnetic resonance imaging


Resting-state functional magnetic resonance imaging (rs-fMRI) measures spontaneous, low frequency fluctuations in the BOLD signal in the absence of any sensory or cognitive stimulus. This was first presented by Biswal et al. [1] who introduced this method for investigation of the functional architecture of the brain.
In analysing functional brain connectivity during wakeful rest, different resting state networks arose depicting specific functions and varied spatial topology (for an overview see [2]).
And even by applying different mathematical methods to analyse rs-fMRI connectivity, the results are more or less consistent [3]. Therefore, it might be important looking more closely at rs-fMRI connectivity because of its complementary nature compared to task-based fMRI.

Clinical Applications

Research using rs-fMRI is applied in clinical contexts while assessing many different diseases as for example

  • epilepsy
  • pain disorder
An detailed overview is given by [4] and [5].


[1] Biswal et al., Magn Reson Med, 1995, 34, pp. 537 - 541.
[2] Lee et al., Am J Neuroradiol, 2013, 34, pp. 1866 - 1872.
[3] Damoiseaux et al., P Natl Acad Sci USA, 2006, 103, pp. 13848 - 13853.
[4] Fox and Greicius, Front Sys Neurosci, 2010, 4, Article 19.
[5] Lee et al., Top Magn Reson Imag, 2016, 25, pp. 11 - 18.

Further reading

  1. F. Zidda, M. Griebe, A. Ebert, M. Ruttorf, C. Roßmanith, A. Gass, J. Andoh, F. Nees and K. Szabo. Resting-state connectivity alterations during transient global amnesia. NeuroImage Clin, 2019, 23, 101869.
  2. F. Zidda, J. Andoh, S. Pohlack, T. Winkelmann, R. Dinu-Biringer, J. Cavalli, M. Ruttorf, F. Nees and H. Flor. Default mode network connectivity of fear- and anxiety-related cue and context conditioning. NeuroImage, 2018, 165, 190 - 199.
Contact: Dr. Frank Zöllner last modified: 09.12.2019
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