The Frontal and Parietal Lobes Are Activated in Transitive Inference Tasks within An Abstract Space
Poster No:
2113
Submission Type:
Abstract Submission
Authors:
Hongyimei Liu1, Taihan Chen1, Huihui Niu1, Zhongyin Liang1, Tiantian Liu1, Wenzhao Deng1, Ruiwang Huang1, Zhizhong Jiang1
Institutions:
1School of Psychology, Key Laboratory of Brain, South China Normal University, Guangzhou, Guangdong
First Author:
Hongyimei Liu
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
Co-Author(s):
Taihan Chen
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
Huihui Niu
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
Zhongyin Liang
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
Tiantian Liu
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
Wenzhao Deng
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
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
Zhizhong Jiang
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:
Transitive inference (TI) is a cognitive process that makes novel inferences based on existing experiential knowledge [1, 2]. For example, if we know A > B and B > C, we can infer that A > C, even if A and C have never been compared directly. TI has expanded from physical space to social domains and abstract domains [3, 4]. Studies suggested that medial prefrontal cortex is activated during spatial reasoning [3], and hippocampus integrates relational data in socially relevant contexts, enhancing reasoning flexibility [5, 6]. However, it is still unclear the neural mechanisms underlying hierarchical reasoning in abstract domains. The current study attempted to conduct an fMRI experiment with a TI task in a non-social spatial context for detecting the neural activity patterns associated with TI.
Methods:
Subjects
Twenty-eight healthy subjects (16F/12M, aged 18 - 25 years old) were recruited from South China Normal University (SCNU). We excluded the datasets from 13 subjects for they did not meet the threshold (head translation in any direction < 3mm, rotation < 3°) or had missing data. The study was approved by the IRB of SCNU. All subjects provided informed consent before the study and received compensation afterward.
Experimental design
Fig. 1 shows the design of behavioral training and fMRI experiment. The subjects were requested to imagine themselves as collectors, selecting objects that are either more expensive or more abstract. The study was conducted in three days, including the behavioral training and fMRI experiment. On Day 1 and Day 2, the subjects underwent behavioral training to learn relationships between pairs of objects on the dimensions of price and abstraction. On Day 3, the subjects were requested to complete a TI task and a color judgment task during fMRI scanning. The subjects completed an object placement task outside of the scanner.
Data acquisition
All the MRI data were obtained on a 3T Siemens Prisma-fit MR scanner with a 64-channel head coil. The fMRI data were collected with a single-shot simultaneous multi-slice gradient-echo EPI sequence. We also acquired high resolution brain structural images using a T1-weighted 3D MP-RAGE sequence. A field-map was obtained using double-echo gradient-echo sequence.
Data analysis
All the MRI data were preprocessed with fMRIPrep (version 23.1.4). Steps included (1) smoothing using a 5 mm full-width at half-maximum (FWHM) Gaussian kernel, and (2) high-pass filtering at 1/100 Hz to remove low-frequency confounds. We built a general linear model (GLM) by using FSL/FEAT to analyze brain activation during TI. In the calculations, we included two main regression factors (control condition F1, decision condition F2) and six head motion regression factors.
Twenty-eight healthy subjects (16F/12M, aged 18 - 25 years old) were recruited from South China Normal University (SCNU). We excluded the datasets from 13 subjects for they did not meet the threshold (head translation in any direction < 3mm, rotation < 3°) or had missing data. The study was approved by the IRB of SCNU. All subjects provided informed consent before the study and received compensation afterward.
Experimental design
Fig. 1 shows the design of behavioral training and fMRI experiment. The subjects were requested to imagine themselves as collectors, selecting objects that are either more expensive or more abstract. The study was conducted in three days, including the behavioral training and fMRI experiment. On Day 1 and Day 2, the subjects underwent behavioral training to learn relationships between pairs of objects on the dimensions of price and abstraction. On Day 3, the subjects were requested to complete a TI task and a color judgment task during fMRI scanning. The subjects completed an object placement task outside of the scanner.
Data acquisition
All the MRI data were obtained on a 3T Siemens Prisma-fit MR scanner with a 64-channel head coil. The fMRI data were collected with a single-shot simultaneous multi-slice gradient-echo EPI sequence. We also acquired high resolution brain structural images using a T1-weighted 3D MP-RAGE sequence. A field-map was obtained using double-echo gradient-echo sequence.
Data analysis
All the MRI data were preprocessed with fMRIPrep (version 23.1.4). Steps included (1) smoothing using a 5 mm full-width at half-maximum (FWHM) Gaussian kernel, and (2) high-pass filtering at 1/100 Hz to remove low-frequency confounds. We built a general linear model (GLM) by using FSL/FEAT to analyze brain activation during TI. In the calculations, we included two main regression factors (control condition F1, decision condition F2) and six head motion regression factors.
Results:
Fig. 1D shows the brain regions with significant activation associated with F1 and F2. We found significant activation in the superior frontal gyrus (SFG), superior parietal lobule (SPL), superior temporal gyrus (STG), and left insula corresponding to the TI. In addition, we also observed significant activation in the supplementary motor area (SMA). The detailed information for these clusters is listed in Table 1.
Conclusions:
We found significant brain activation in the right superior parietal, superior frontal, and left superior temporal regions, offering new insights into the neural mechanisms of TI. The activation of the SFG during the TI task is consistent with earlier studies linking this region to spatial reasoning and cognitive control processes [3, 7]. SPL activation supports the idea that TI involves spatial integration, as it plays a key role in spatial attention and information processing [8]. The STG, linked to relational processing [6], suggests that TI involves handling relational information, even in abstract spatial contexts.
Learning and Memory:
Learning and Memory Other
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Motion Correction and Preprocessing
Other Methods
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 1
Keywords:
FUNCTIONAL MRI
Other - abstract space; transitive inference
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
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Provide references using APA citation style.
[2] Schlichting, M. L., & Preston, A. R. (2015). Memory integration: neural mechanisms and implications for behavior. Current opinion in behavioral sciences, 1, 1-8.
[3] Fangmeier, T., & Knauff, M. (2009). Neural correlates of acoustic reasoning. Brain research, 1249, 181-190.
[4] Alfred, K. L., Connolly, A. C., Cetron, J. S., & Kraemer, D. J. (2020). Mental models use common neural spatial structure for spatial and abstract content. Communications biology, 3(1), 17.
[5] Park, S. A., Miller, D. S., Nili, H., Ranganath, C., & Boorman, E. D. (2020). Map making: constructing, combining, and inferring on abstract cognitive maps. Neuron, 107(6), 1226-1238.
[6] Park, S. A., Miller, D. S., & Boorman, E. D. (2021). Inferences on a multidimensional social hierarchy use a grid-like code. Nature neuroscience, 24(9), 1292-1301.
[7] Prado, J., Chadha, A., & Booth, J. R. (2011). The brain network for deductive reasoning: a quantitative meta-analysis of 28 neuroimaging studies. Journal of cognitive neuroscience, 23(11), 3483-3497.
[8] Summerfield, C., Luyckx, F., & Sheahan, H. (2020). Structure learning and the posterior parietal cortex. Progress in neurobiology, 184, 101717.