|
|
|
|
LEADER |
03603nam a2200745Ia 4500 |
001 |
10.1002-hbm.25269 |
008 |
220427s2021 CNT 000 0 und d |
020 |
|
|
|a 10659471 (ISSN)
|
245 |
1 |
0 |
|a Perceptual learning of tone patterns changes the effective connectivity between Heschl's gyrus and planum temporale
|
260 |
|
0 |
|b John Wiley and Sons Inc
|c 2021
|
856 |
|
|
|z View Fulltext in Publisher
|u https://doi.org/10.1002/hbm.25269
|
520 |
3 |
|
|a Learning of complex auditory sequences such as music can be thought of as optimizing an internal model of regularities through unpredicted events (or “prediction errors”). We used dynamic causal modeling (DCM) and parametric empirical Bayes on functional magnetic resonance imaging (fMRI) data to identify modulation of effective brain connectivity that takes place during perceptual learning of complex tone patterns. Our approach differs from previous studies in two aspects. First, we used a complex oddball paradigm based on tone patterns as opposed to simple deviant tones. Second, the use of fMRI allowed us to identify cortical regions with high spatial accuracy. These regions served as empirical regions-of-interest for the analysis of effective connectivity. Deviant patterns induced an increased blood oxygenation level-dependent response, compared to standards, in early auditory (Heschl's gyrus [HG]) and association auditory areas (planum temporale [PT]) bilaterally. Within this network, we found a left-lateralized increase in feedforward connectivity from HG to PT during deviant responses and an increase in excitation within left HG. In contrast to previous findings, we did not find frontal activity, nor did we find modulations of backward connections in response to oddball sounds. Our results suggest that complex auditory prediction errors are encoded by changes in feedforward and intrinsic connections, confined to superior temporal gyrus. © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
|
650 |
0 |
4 |
|a adult
|
650 |
0 |
4 |
|a Adult
|
650 |
0 |
4 |
|a article
|
650 |
0 |
4 |
|a auditory cortex
|
650 |
0 |
4 |
|a auditory cortex
|
650 |
0 |
4 |
|a auditory cortex
|
650 |
0 |
4 |
|a Auditory Cortex
|
650 |
0 |
4 |
|a Auditory Perception
|
650 |
0 |
4 |
|a blood oxygenation
|
650 |
0 |
4 |
|a causal modeling
|
650 |
0 |
4 |
|a connectome
|
650 |
0 |
4 |
|a Connectome
|
650 |
0 |
4 |
|a controlled study
|
650 |
0 |
4 |
|a DCM
|
650 |
0 |
4 |
|a diagnostic imaging
|
650 |
0 |
4 |
|a effective connectivity
|
650 |
0 |
4 |
|a excitation
|
650 |
0 |
4 |
|a female
|
650 |
0 |
4 |
|a Female
|
650 |
0 |
4 |
|a fMRI
|
650 |
0 |
4 |
|a functional magnetic resonance imaging
|
650 |
0 |
4 |
|a hearing
|
650 |
0 |
4 |
|a human
|
650 |
0 |
4 |
|a Humans
|
650 |
0 |
4 |
|a learning
|
650 |
0 |
4 |
|a learning
|
650 |
0 |
4 |
|a Learning
|
650 |
0 |
4 |
|a Magnetic Resonance Imaging
|
650 |
0 |
4 |
|a Models, Theoretical
|
650 |
0 |
4 |
|a music
|
650 |
0 |
4 |
|a Music
|
650 |
0 |
4 |
|a nerve cell network
|
650 |
0 |
4 |
|a Nerve Net
|
650 |
0 |
4 |
|a nuclear magnetic resonance imaging
|
650 |
0 |
4 |
|a perceptual learning
|
650 |
0 |
4 |
|a physiology
|
650 |
0 |
4 |
|a precision-weighting
|
650 |
0 |
4 |
|a prediction
|
650 |
0 |
4 |
|a predictive coding
|
650 |
0 |
4 |
|a sound
|
650 |
0 |
4 |
|a superior temporal gyrus
|
650 |
0 |
4 |
|a superior temporal gyrus
|
650 |
0 |
4 |
|a temporal lobe
|
650 |
0 |
4 |
|a Temporal Lobe
|
650 |
0 |
4 |
|a theoretical model
|
650 |
0 |
4 |
|a young adult
|
650 |
0 |
4 |
|a Young Adult
|
700 |
1 |
|
|a Dietz, M.J.
|e author
|
700 |
1 |
|
|a Hansen, N.C.
|e author
|
700 |
1 |
|
|a Lumaca, M.
|e author
|
700 |
1 |
|
|a Quiroga-Martinez, D.R.
|e author
|
700 |
1 |
|
|a Vuust, P.
|e author
|
773 |
|
|
|t Human Brain Mapping
|