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Neural decoding of visual stimuli varies with fluctuations in global network efficiency
Luca Cocchi1,2, Zhengyi Yang1,3,4, Andrew Zalesky5
1Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
Human Brain Mapping
|March 26, 2017
Summary
Neural activity spontaneously shifts between high and low synchronization states. Higher global network efficiency enhances the brain's ability to represent visual stimuli like faces and motion.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Functional Neuroimaging
Background:
- Spontaneous neural activity in the brain exhibits dynamic fluctuations in global synchronization over seconds.
- These fluctuations create transient states of varying correlation across cortical areas.
- Hypothesized that these global efficiency variations influence local neuronal responses to sensory input.
Purpose of the Study:
- To investigate if spontaneous fluctuations in global network efficiency modulate stimulus-evoked neural activity patterns.
- To determine if global efficiency states affect the brain's capacity to represent distinct visual stimuli.
Main Methods:
- Employed time-resolved functional magnetic resonance imaging (fMRI) during periodic presentation of face and motion stimuli.
- Utilized a linear decoder to discriminate neural activity patterns elicited by the stimuli.
- Analyzed decoding performance in relation to states of high and low global network efficiency.
Main Results:
- Decoding accuracy for visual stimuli was significantly higher during states of high global efficiency compared to low efficiency states.
- This effect was observed across both visual and non-visual cortical regions.
- Indicates that global network efficiency variations impact stimulus-specific neural representations.
Conclusions:
- Slow fluctuations in global network efficiency are linked to variations in widespread cortical activity patterns.
- These variations influence the representation of distinct visual stimulus categories.
- Highlights the critical role of global functional connectivity dynamics in specialized, stimulus-driven neural processing.
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