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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Published on: October 24, 2012
Spatiotemporal analysis of auditory "what" and "where" working memory
Claude Alain1, Kelly L McDonald, Natasa Kovacevic
1Rotman Research Institute, Baycrest Centre for Geriatric Care, Toronto, ON M6A 2E1, Canada. calain@rotman-baycrest.on.ca
Cerebral Cortex (New York, N.Y. : 1991)
|June 7, 2008
Summary
Neural segregation of sound identity ("what") and location ("where") occurs around 200 ms post-stimulus. This processing involves a widespread brain network, with working memory load effects emerging later.
Area of Science:
- Neuroscience
- Cognitive Science
- Auditory Perception
Background:
- Goal-directed attention to sound identity (what) and location (where) is linked to distinct neural activity in ventral and dorsal brain regions, respectively.
- Understanding the temporal dynamics of this neural segregation is crucial for cognitive neuroscience.
Purpose of the Study:
- To determine the precise timing of neural segregation for auditory "what" and "where" information processing.
- To investigate the neural network involved in working memory for sound identity and location.
Main Methods:
- Event-related potentials were measured during an n-back (n=1, 2) working memory task.
- Stimuli varied in semantic category and virtual location.
- Partial least squares analysis was applied to scalp and source waveform data.
Main Results:
- Domain-specific neural activity for "what" and "where" processing began around 200 ms post-stimulus onset.
- This segregation involved activity in Heschl's gyrus, medial frontal and occipital cortex, and right parietal cortex.
- Working memory load effects emerged around 400 ms post-stimulus, primarily in frontocentral regions and right temporal, frontal, and parietal cortices.
Conclusions:
- Top-down effects on processing auditory "what" and "where" information are observable approximately 200 ms after sound onset.
- This processing relies on a widely distributed neural network, with distinct temporal dynamics for information type and working memory load.
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