Prefrontal and Parietal Cortices Engagement in Cognitive Maps for Social Navigation
Poster No:
794
Submission Type:
Abstract Submission
Authors:
Taihan Chen1, Xinrui Li2, Xia Liu1, Runchen Gan3, Yidan Qiu2, Ruiwang Huang4
Institutions:
1South China Normal University, Guangzhou, China, 2South China Normal University, Guangzhou, Guangdong, 3South China Normal Unversity, Guangzhou, Guangdong, 4School of Psychology, Key Laboratory of Brain, South China Normal University, Guangzhou, Guangdong
First Author:
Co-Author(s):
Ruiwang Huang
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong
Introduction:
A cognitive map is an internal representation of the relationships between entities to support flexible prediction and decision [1]. Previous studies found that the hippocampus, entorhinal cortex, prefrontal cortex are key regions in cognitive mapping. The hippocampus and entorhinal cortex encode spatial information, and the prefrontal cortex drives the hippocampus to update cognitive maps for efficient navigation [2, 3]. These maps are not limited to spatial navigation but extend to abstract social space, facilitating the understanding of social relationships and aiding in making informed social interaction decisions [4, 5, 6]. However, it remains unclear how cognitive maps are transformed across the hippocampal-prefrontal circuits to represent the abstract information, and previous studies also found that brain regions such as the inferior parietal cortex and posterior cingulate cortex function in social navigation [5, 6, 7, 8]. Thus, we re-analyzed the task-fMRI data [9, 10], which were obtained from subjects performing a naturalistic, role-playing task, which was adopted by following Tavares et al. [6], to study the neural mechanisms underlying the representation of cognitive maps in social space.
Methods:
Participants. We recruited 40 adult healthy participants (17M/23F, age = 20.4 ± 1.5 years) from the campus of South China Normal University (SCNU). The fMRI datasets from 2 subjects were excluded for excessive head movement. The study was approved by the IRB of SCNU.
Data acquisition and preprocessing. The MRI data was acquired on a 3T Siemens Trio MRI scanner equipped with a 32-channel phased-array head coil. Both the anatomical and functional MRI data were preprocessed with fMRIPrep (Ver 23.1.4). The obtained images were first smoothed with a 5-mm FWHM Gaussian kernel, and then were filtered with a high-pass filtering (with a cutoff period of 100s) to eliminate the low-frequency artifacts.
Experimental design and procedures. Figs. 1a and 1b show that the participants interacted with five main characters by choosing one of two given options. Each participant went through 3 trials (narrative condition, optional condition, and baseline) in each of the social interaction blocks during the scanning. We recorded the trajectory of main characters and calculated the vector length (v), vector angle (θ), vector length variation (∆v), and vector angular variation (∆θ) according to the equations in Fig. 1b.
Whole-brain GLM analysis. We performed two univariate GLM analyses for the fMRI data by using FEAT/FSL. Both GLM analyses include three main regressors (narrative condition, optional condition, and baseline) and six head movement regressors. We set ∆v as another regressor in GLM1 and set ∆θ in GLM2 to estimate the corresponding brain activation, respectively.
Data acquisition and preprocessing. The MRI data was acquired on a 3T Siemens Trio MRI scanner equipped with a 32-channel phased-array head coil. Both the anatomical and functional MRI data were preprocessed with fMRIPrep (Ver 23.1.4). The obtained images were first smoothed with a 5-mm FWHM Gaussian kernel, and then were filtered with a high-pass filtering (with a cutoff period of 100s) to eliminate the low-frequency artifacts.
Experimental design and procedures. Figs. 1a and 1b show that the participants interacted with five main characters by choosing one of two given options. Each participant went through 3 trials (narrative condition, optional condition, and baseline) in each of the social interaction blocks during the scanning. We recorded the trajectory of main characters and calculated the vector length (v), vector angle (θ), vector length variation (∆v), and vector angular variation (∆θ) according to the equations in Fig. 1b.
Whole-brain GLM analysis. We performed two univariate GLM analyses for the fMRI data by using FEAT/FSL. Both GLM analyses include three main regressors (narrative condition, optional condition, and baseline) and six head movement regressors. We set ∆v as another regressor in GLM1 and set ∆θ in GLM2 to estimate the corresponding brain activation, respectively.
Results:
Fig. 1c shows the ∆v related significant activation in the right medial prefrontal cortex (mPFC), right orbitofrontal cortex (OFC) and bilateral inferior parietal cortex (IPC). Fig. 1d shows the ∆θ related significant activation in the right cingulate cortex, right OFC, and right IPC. The detail information of these clusters is listed in Table 1.
Conclusions:
We found that the PFC and IPC were involved in encoding the variation of vector parameters. These results indicated that these two regions may be correlated to the process of encoding the variations in social relationship, and implied that hippocampal-prefrontal circuits may encode social information into cognitive maps. Further studies are required to confirm and expand upon our initial observations.
Emotion, Motivation and Social Neuroscience:
Social Cognition 1
Social Neuroscience Other
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Keywords:
FUNCTIONAL MRI
Other - Cognitive Maps; Social Navigation
1|2Indicates the priority used for review
Provide references using author date format
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