Neural mechanisms underlying the exploration of small city maps using magnetoencephalography

The neural mechanisms underlying spatial cognition in the context of exploring realistic city maps are unknown. We conducted a novel brain imaging study to address the question of whether and how features of special importance for map exploration are encoded in the brain to make a spatial decision....

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Published inExperimental brain research Vol. 233; no. 11; pp. 3187 - 3200
Main Authors Sakellaridi, Sofia, Christova, Peka, Christopoulos, Vassilios, Leuthold, Arthur C., Peponis, John, Georgopoulos, Apostolos P.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2015
Springer
Springer Nature B.V
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Abstract The neural mechanisms underlying spatial cognition in the context of exploring realistic city maps are unknown. We conducted a novel brain imaging study to address the question of whether and how features of special importance for map exploration are encoded in the brain to make a spatial decision. Subjects explored by eyes small city maps exemplifying five different street network types in order to locate a hypothetical City Hall, while neural activity was recorded continuously by 248 magnetoencephalography (MEG) sensors at high temporal resolution. Monitoring subjects’ eye positions, we locally characterized the maps by computing three spatial parameters of the areas that were explored. We computed the number of street intersections, the total street length, and the regularity index in the circular areas of 6 degrees of visual angle radius centered on instantaneous eye positions. We tested the hypothesis that neural activity during exploration is associated with the spatial parameters and modulated by street network type. All time series were rendered stationary and nonautocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We then assessed the associations between the prewhitened time-varying MEG time series from 248 sensors and the prewhitened spatial parameters time series, for each street network type, using multiple linear regression analyses. In accord with our hypothesis, ongoing neural activity was strongly associated with the spatial parameters through localized and distributed networks, and neural processing of these parameters depended on the type of street network. Overall, processing of the spatial parameters seems to predominantly involve right frontal and prefrontal areas, but not for all street network layouts. These results are in line with findings from a series of previous studies showing that frontal and prefrontal areas are involved in the processing of spatial information and decision making. Modulation of neural processing of the spatial parameters by street network type suggests that some street network layouts may contain other types of spatial information that subjects use to explore maps and make spatial decisions.
AbstractList The neural mechanisms underlying spatial cognition in the context of exploring realistic city maps are unknown. We conducted a novel brain imaging study to address the question of whether and how features of special importance for map exploration are encoded in the brain to make a spatial decision. Subjects explored by eyes small city maps exemplifying five different street network types in order to locate a hypothetical City Hall, while neural activity was recorded continuously by 248 magnetoencephalography (MEG) sensors at high temporal resolution. Monitoring subjects' eye positions, we locally characterized the maps by computing three spatial parameters of the areas that were explored. We computed the number of street intersections, the total street length, and the regularity index in the circular areas of 6 degrees of visual angle radius centered on instantaneous eye positions. We tested the hypothesis that neural activity during exploration is associated with the spatial parameters and modulated by street network type. All time series were rendered stationary and nonautocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We then assessed the associations between the prewhitened time-varying MEG time series from 248 sensors and the prewhitened spatial parameters time series, for each street network type, using multiple linear regression analyses. In accord with our hypothesis, ongoing neural activity was strongly associated with the spatial parameters through localized and distributed networks, and neural processing of these parameters depended on the type of street network. Overall, processing of the spatial parameters seems to predominantly involve right frontal and prefrontal areas, but not for all street network layouts. These results are in line with findings from a series of previous studies showing that frontal and prefrontal areas are involved in the processing of spatial information and decision making. Modulation of neural processing of the spatial parameters by street network type suggests that some street network layouts may contain other types of spatial information that subjects use to explore maps and make spatial decisions.
Audience Academic
Author Georgopoulos, Apostolos P.
Christopoulos, Vassilios
Peponis, John
Sakellaridi, Sofia
Leuthold, Arthur C.
Christova, Peka
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Snippet The neural mechanisms underlying spatial cognition in the context of exploring realistic city maps are unknown. We conducted a novel brain imaging study to...
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SubjectTerms Adult
Analysis
Animals
Biomedical and Life Sciences
Biomedicine
Brain - physiology
Brain Mapping
Cognition
Decision making
Female
Humans
Hypotheses
Information processing
Magnetoencephalography
Male
Middle Aged
Neurology
Neurophysiology
Neurosciences
Photic Stimulation
Reaction Time
Recognition (Psychology) - physiology
Research Article
Sensors
Spatial Navigation - physiology
Time Factors
Time series
Young Adult
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Title Neural mechanisms underlying the exploration of small city maps using magnetoencephalography
URI https://link.springer.com/article/10.1007/s00221-015-4387-5
https://www.ncbi.nlm.nih.gov/pubmed/26253309
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Volume 233
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