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A time-varying source connectivity approach to reveal human somatosensory information processing
1Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China.
Neuroimage
|May 15, 2012
Summary
This study reveals how the brain processes touch information using a novel time-varying source connectivity method on electroencephalography (EEG) data. It shows sequential processing from primary somatosensory cortex (SI) to secondary (SII) and reciprocal processing involving the cingulate cortex (CC).
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Understanding neural mechanisms of sensory processing requires analyzing transient electrophysiological activities and effective connectivity.
- Current electrophysiological methods struggle to accurately estimate stimulus-activated neural sources and their millisecond-level connectivity dynamics.
Purpose of the Study:
- To develop and apply a time-varying source connectivity approach for analyzing fast-changing information flow in neural sources.
- To investigate human somatosensory information processing using high-density electroencephalography (EEG) and somatosensory evoked potentials (SEPs).
Main Methods:
- Developed a time-varying source connectivity approach using high-density EEG.
- Estimated somatosensory evoked potential (SEP) sources and activities via equivalent current dipolar source modeling.
- Inferred time-varying effective connectivity using a Kalman smoother-based method.
Main Results:
- Identified SEP generation from contralateral primary somatosensory cortex (SI), bilateral secondary somatosensory cortex (SII), and cingulate cortex (CC).
- Observed serial somatosensory information processing from SI to SII at latencies <150 ms.
- Revealed reciprocal processing between SII and CC at later latencies >200 ms.
Conclusions:
- The developed time-varying source connectivity approach effectively captures rapid neural information flow.
- Demonstrated a sequential and reciprocal processing model for human somatosensory information in the brain.
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