Dynamic visual cortical connectivity analysis based on functional magnetic resonance imaging

Autor: Jiajun Yang, Shi Yuhu, Zeng Weiming, Weifang Nie, Le Zhao
Rok vydání: 2020
Předmět:
Computer science
Neuroimaging
050105 experimental psychology
lcsh:RC321-571
03 medical and health sciences
Behavioral Neuroscience
symbols.namesake
0302 clinical medicine
Consistency (statistics)
Sliding window protocol
Neural Pathways
dynamic effective connectivity
medicine
Humans
0501 psychology and cognitive sciences
visual cortex
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Original Research
Dynamic functional connectivity
Brain Mapping
medicine.diagnostic_test
business.industry
fMRI
05 social sciences
Perspective (graphical)
Brain
Pattern recognition
Human brain
Magnetic Resonance Imaging
Pearson product-moment correlation coefficient
Visual cortex
medicine.anatomical_structure
Granger causality
symbols
dynamic functional connectivity
Artificial intelligence
Functional magnetic resonance imaging
business
030217 neurology & neurosurgery
Zdroj: Brain and Behavior, Vol 10, Iss 7, Pp n/a-n/a (2020)
Brain and Behavior
ISSN: 2162-3279
Popis: Background Studies of brain functional connectivity (FC) and effective connectivity (EC) using the functional magnetic resonance imaging (fMRI) have advanced our understanding of functional organization on visual cortex of human brain. The current studies mainly focus on static or dynamic connectivity, while the relationships between them have not been well characterized especially for static EC (sEC) and dynamic EC (dEC), as well as the consistency characteristics of changing trend of dFCs and dECs, which is of great importance to reveal the neural information processing mechanism in visual cortex region. Method In this study, we explore these relationships among several subareas of human visual cortex (V1–V5) by calculating the connection intensity and information flow among them over time by sliding window method, which are defined by Pearson correlation coefficient and Granger causality analysis, respectively, in each window. Results The results demonstrate that there are extensive connections existing in human visual network, which are time‐varying both in resting and task‐related states. sFC intensity is negatively correlated with the variance of dFC, while sEC intensity is positively correlated with the variance of dEC. Furthermore, we also find that dFC within visual cortex at rest shows more consistency, while dEC shows less compared with task state in changing trend. Conclusion Therefore, this study provides novel findings about dynamics of connectivity in human visual cortex from the perspective of functional and effective connectivity.
We explore relationships among several subareas of human visual cortex (V1–V5) by calculating the connection intensity and information flow among them over time by sliding window method, which are defined by the Pearson correlation coefficient and Granger causality analysis, respectively, in each window. The study provides novel findings about dynamics of connectivity in human visual cortex from the perspective of functional and effective connectivity.
Databáze: OpenAIRE
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