Abstract
Recently a new technique called multiband imaging was introduced, it allows extremely low repetition times for functional magnetic resonance imaging (fMRI). As these ultra fast imaging scans can increase the Nyquist rate by an order of magnitude, there are many new effects, that have to be accounted for. As more frequencies can now be sampled directly, we want to analyze especially those that are due to physiological noise, such as cardiac and respiratory signals. Here, we adapted RETROICOR [4] to handle multiband fMRI data. We show the importance of physiological noise regression for standard temporal resolution fMRI and compare it to the high temporal resolution case. Our results show that especially for multiband fMRI scans, it is of the utmost importance to apply physiological noise regression, as residuals of these noises are clearly detectable in non noise independent components if no prior physiological noise has been applied.
| Original language | English |
|---|---|
| Title of host publication | Artificial Neural Networks and Machine Learning - ICANN 2014 |
| Editors | Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P. Villa |
| Number of pages | 8 |
| Volume | 8681 |
| Publisher | Springer Verlag |
| Publication date | 01.01.2014 |
| Edition | 1 |
| Pages | 829-836 |
| ISBN (Print) | 978-3-319-11178-0 |
| ISBN (Electronic) | 978-3-319-11179-7 |
| DOIs | |
| Publication status | Published - 01.01.2014 |
| Event | 24th International Conference on Artificial Neural Networks - Depat. of Informatics, Knowledge Technology, University of hamburg, Hamburg, Germany Duration: 15.09.2014 → 19.09.2014 http://icann2014.org |
