Publications (3)8.31 Total impact
- SourceAvailable from: Birgit Gaschler-Markefski[Show abstract] [Hide abstract]
ABSTRACT: Working memory (WM) tasks involve several interrelated processes during which past information must be transiently maintained, recalled, and compared with test items according to previously instructed rules. It is not clear whether the rule-specific comparisons of perceptual with memorized items are only performed in previously identified frontal and parietal WM areas or whether these areas orchestrate such comparisons by feedback to sensory cortex. We tested the latter hypothesis by focusing on auditory cortex (AC) areas with low-noise functional magnetic resonance imaging in a 2-back WM task involving frequency-modulated (FM) tones. The control condition was a 0-back task on the same stimuli. Analysis of the group data identified an area on right planum temporale equally activated by both tasks and an area on the left planum temporale specifically involved in the 2-back task. A region of interest analysis in each individual revealed that activation on the left planum temporale in the 2-back task positively correlated with the task performance of the subjects. This strongly suggests a prominent role of the AC in 2-back WM tasks. In conjunction with previous findings on FM processing, the left lateralized effect presumably reflects the complex sequential processing demand of the 2-back matching to sample task.Cerebral Cortex 12/2007; 17(11):2544-52. DOI:10.1093/cercor/bhl160 · 8.31 Impact Factor
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ABSTRACT: Functional magnetic resonance imaging (fMRI) gained a lot of interest in medical and human research in the last years. FMRI is a noninvasive method used to study human brain functions by localizing activated brain areas. There are a lot of interesting questions in neurobiology, one of these is the processing of learning-related processes in the human brain and how these processes can be described and analyzed. To analyze fMRI data different hypothesis-based and data-based methods can be used. The Independent Component Analysis (ICA) is an information-theoretic statistical and computational technique used to identify hidden factors of observed multivariate data. The mathematical background of ICA is investigated in this thesis and relations to other methods like Principal Component Analysis (PCA) are elaborated. The advantages of ICA in comparison to classical methods for analyzing fMRI data under the aspect of learning-related processes are investigated in real fMRI data as well as in simulations studies. Thereby dynamic changes in the fMRI time series are systematically analyzed and described.