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Published on: August 11, 2016
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Signal Propagation in the Human Visual Pathways: An Effective Connectivity Analysis.
Vahab Youssofzadeh1, Girijesh Prasad2, Andrew J Fagan3
1Intelligent Systems Research Centre, Ulster University, Derry-Londonderry, BT48 7JL, United Kingdom, youssofzadeh-v@email.ulster.ac.uk k.wong-lin@ulster.ac.uk.
Summary
This study reveals the timing of visual signal propagation in the human brain, showing dominant forward and weaker backward connections. This research validates long-range signal timing in visual pathways.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Neuroscience
Background:
- Understanding long-range signal propagation in the human visual system is incomplete.
- Previous research has extensively investigated the visual system but lacks integrated accounts of spatiotemporal dynamics.
Purpose of the Study:
- To investigate the spatiotemporal dynamics of long-range signal propagation in human visual pathways.
- To provide insights into neural circuit dynamics using dynamic causal modeling (DCM) on concurrent EEG-fMRI data.
Main Methods:
- Utilized dynamic causal modeling (DCM) on concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data.
- Employed a recurrent forward-backward connectivity model with brain regions identified by EEG source localization and fMRI spatial priors.
- Analyzed pattern reversal visual-evoked potentials to quantify neural temporal dynamics and effective connectivity.
Main Results:
- Identified signal sources in the thalamus, primary visual cortex, and higher cortical areas along ventral and dorsal streams.
- Demonstrated signal propagation dominated by feedforward processes with weaker feedback connectivity.
- Revealed enhanced dorsal pathway connectivity and suppressed ventral pathway connectivity, with a right-hemisphere bias.
Conclusions:
- Validated, for the first time, the long-range signal propagation timing in human visual pathways.
- The findings highlight the interplay of feedforward and feedback mechanisms in visual processing.
- The computational modeling approach offers potential for understanding other cognitive processes and neurological disorders.
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