TY - JOUR
T1 - Signal Propagation in the Human Visual Pathways: An Effective Connectivity Analysis
AU - Youssofzadeh, Vahab
AU - Prasad, Girijesh
AU - Fagan, Andrew J.
AU - Reilly, Richard B.
AU - Martens, Sven
AU - Meaney, James F.
AU - Wong-Lin, KongFatt
PY - 2015/9/30
Y1 - 2015/9/30
N2 - Although the visual system has been extensively investigated, an integrated account of the spatiotemporal dynamics of long-range signal propagation along the human visual pathways is not completely known or validated. In this work, we used dynamic causal modeling approach to provide insights into the underlying neural circuit dynamics of pattern reversal visual-evoked potentials extracted from concurrent EEG-fMRI data. A recurrent forward– backward connectivity model, consisting of multiple interacting brain regions identified by EEG source localization aided by fMRI spatial priors, best accounted for the data dynamics. Sources were first identified in the thalamic area, primary visual cortex, as well as higher cortical areas along the ventral and dorsal visual processing streams. Consistent with hierarchical early visual processing, the model disclosed and quantified the neural temporal dynamics across the identified activity sources. This signal propagation is dominated by a feedforward process, but we also found weaker effective feedback connectivity. Using effective connectivity analysis, the optimal dynamic causal modeling revealed enhanced connectivity along the dorsal pathway but slightly suppressed connectivity along the ventral pathway. A bias was also found in favor of the right hemisphere consistent with functional attentional asymmetry. This study validates, for the first time, the long-range signal propagation timing in the human visual pathways. A similar modeling approach can potentially be used to understand other cognitive processes and dysfunctions in signal propagation in neurological and neuropsychiatric disorders.
AB - Although the visual system has been extensively investigated, an integrated account of the spatiotemporal dynamics of long-range signal propagation along the human visual pathways is not completely known or validated. In this work, we used dynamic causal modeling approach to provide insights into the underlying neural circuit dynamics of pattern reversal visual-evoked potentials extracted from concurrent EEG-fMRI data. A recurrent forward– backward connectivity model, consisting of multiple interacting brain regions identified by EEG source localization aided by fMRI spatial priors, best accounted for the data dynamics. Sources were first identified in the thalamic area, primary visual cortex, as well as higher cortical areas along the ventral and dorsal visual processing streams. Consistent with hierarchical early visual processing, the model disclosed and quantified the neural temporal dynamics across the identified activity sources. This signal propagation is dominated by a feedforward process, but we also found weaker effective feedback connectivity. Using effective connectivity analysis, the optimal dynamic causal modeling revealed enhanced connectivity along the dorsal pathway but slightly suppressed connectivity along the ventral pathway. A bias was also found in favor of the right hemisphere consistent with functional attentional asymmetry. This study validates, for the first time, the long-range signal propagation timing in the human visual pathways. A similar modeling approach can potentially be used to understand other cognitive processes and dysfunctions in signal propagation in neurological and neuropsychiatric disorders.
KW - Concurrent EEG-fMRI
KW - dorsal pathway
KW - dynamic causal modeling
KW - ventral pathway
KW - visual-evoked potentials
UR - https://pure.ulster.ac.uk/en/publications/signal-propagation-in-the-human-visual-pathways-an-effective-conn-3
UR - http://www.jneurosci.org/content/35/39/13501.short
U2 - 10.1523/JNEUROSCI.2269-15.2015
DO - 10.1523/JNEUROSCI.2269-15.2015
M3 - Article
SN - 1529-2401
VL - 35
SP - 13501
EP - 13510
JO - The Journal of Neuroscience
JF - The Journal of Neuroscience
IS - 39
ER -