The Impact of Task Difficulty on Multiplication: An fMRI Study in Adults
Poster No:
1891
Submission Type:
Abstract Submission
Authors:
Asya Istomina1, Andrei Faber1, Andrei Manzhurtsev2, Maxim Ublinskiy2, Marie Arsalidou3
Institutions:
1Higher School of Economics, Moscow, Russian Federation, 2Clinical and Research Institute of Emergency Pediatric Surgery and Trauma, Moscow, Russian Federation, 3York University, Toronto, Ontario
First Author:
Co-Author(s):
Andrei Manzhurtsev
Clinical and Research Institute of Emergency Pediatric Surgery and Trauma
Moscow, Russian Federation
Clinical and Research Institute of Emergency Pediatric Surgery and Trauma
Moscow, Russian Federation
Maxim Ublinskiy
Clinical and Research Institute of Emergency Pediatric Surgery and Trauma
Moscow, Russian Federation
Clinical and Research Institute of Emergency Pediatric Surgery and Trauma
Moscow, Russian Federation
Introduction:
Mathematics provide the quantitative foundation for navigating today's data-driven world. However, mathematical processes are widely studied using neuroimaging techniques, however, few studies investigated neural correlates of multiplication problems of increasing difficulty (Jost et al., 2009; De Visscher et al., 2015; Heidekum et al., 2021). We examine for the first time, using functional magnetic resonance imaging (fMRI), brain activity associated with three levels of difficulty during multiplication in young adults. Changing the difficulty level in math over three levels is a powerful paradigm to study brain networks involved in math cognition.
Methods:
Structural and functional MRI data were collected from the group of 20 adults (12 females; 21–29 years using a Philips Achieva dStream 3.0T MRI scanner. Participants completed1-digit, 2-digit and 3-digit dmultiplication on tasks organized in a block design, where each block lasted 32 seconds. They were instructed to produce as many correct answers as possible. All procedures and materials were approved by the local ethics committee. Data preprocessing and analysis were performed using AFNI software (version 23.2.04 for Mac OS; Cox, 2009). A high-resolution T1-weighted anatomical scan underwent nonlinear warp estimation using the AFNI function 3dQwarp (Cox & Glen, 2013). Functional data were preprocessed to correct for slice-time differences, head motion, linear drifts, and low-frequency noise. The functional images were aligned to the participant's T1-weighted anatomical scan, normalized to the Montreal Neurological Institute (MNI) space, and spatially smoothed with an 8-mm Full Width at Half Maximum (FWHM) Gaussian smoothing kernel. Whole-brain responses for each participant were modeled using a general linear model (GLM), with regressors corresponding to each experimental condition. Individual parametric maps were combined into a mixed-effects group GLM analysis using the 3dMEMA function in AFNI (Chen et al., 2012). Multiple comparisons were controlled using a false discovery rate (FDR) threshold with a q-value of 0.05.
Results:
Behavioral results indicate that adults solved easy 1-digit multiplication problems significantly faster and with higher accuracy compared to difficult 2-digit and 3-digit tasks. Single-digit multiplication activated the left angular gyrus consistent with a memory retrieval strategy (Sokolowski et al., 2022). Two-digit multiplication is expressed by increased activity in the inferior parietal lobe and the left inferior frontal gyrus, areas related to procedural problem solving (Istomina & Arsalidou, 2024). Three-digit multiplication tasks reliably activated extensive fronto-parietal regions and the middle temporal gyrus (Liu et al., 2019), as well as bilateral cerebellum. The cerebellum, though not commonly emphasized in mathematical cognition research, is increasingly recognized for its involvement in solving complex multiplication tasks and other arithmetic operations. Extending beyond its traditional sensorimotor role, its engagement highlights a broader spectrum of cognitive functions, previously underexplored due to historical neuroimaging limitations (Anteraper et al., 2023).
Conclusions:
Multiplication problem difficulty is expressed by increased activity specifically in the various brain regions as a function of difficulty. This highlights the different systems involved in solving multiplication problems of different levels of complexity. This work contributed to the development of a mapping for mathematical processes in adults.
Limitations
Motion is the most prevalent artifact, which was controlled during preprocessing. Although we recruited participants without specific mathematical background knowledge in mathematics could be a limitation.
Limitations
Motion is the most prevalent artifact, which was controlled during preprocessing. Although we recruited participants without specific mathematical background knowledge in mathematics could be a limitation.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Reasoning and Problem Solving 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Motion Correction and Preprocessing
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
ADULTS
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
MRI
Open-Source Software
1|2Indicates the priority used for review
·Figure 1. Examples of clusters specific to the contrasts: two-digit multiplication > control task and three-digit multiplication > control task
Abstract Information
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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.
De Visscher, A., Berens, S. C., Keidel, J. L., Noël, M. P., & Bird, C. M. (2015). The interference effect in arithmetic fact solving: An fMRI study. NeuroImage, 116, 92-101.
Heidekum, A. E., De Visscher, A., Vogel, S. E., De Smedt, B., & Grabner, R. H. (2021). Can the interference effect in multiplication fact retrieval be modulated by an arithmetic training? An fMRI study. Neuropsychologia, 157, 107849.
Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical research, 29(3), 162-173.
Cox, R. W., & Glen, D. R. (2013). Nonlinear warping in AFNI. In Poster presented at the 19th Annual Meeting of the Organization for Human Brain Mapping.
Chen, G., Saad, Z. S., Nath, A. R., Beauchamp, M. S., & Cox, R. W. (2012). FMRI group analysis combining effect estimates and their variances. Neuroimage, 60(1), 747-765.
Sokolowski, H. M., Hawes, Z., & Ansari, D. (2022). The neural correlates of retrieval and procedural strategies in mental arithmetic: A functional neuroimaging meta‐analysis. Human Brain Mapping.
Istomina, A., & Arsalidou, M. (2024). Add, subtract and multiply: Meta-analyses of brain correlates of arithmetic operations in children and adults. Developmental Cognitive Neuroscience, 101419.
Liu, J., Yuan, L., Chen, C., Cui, J., Zhang, H., & Zhou, X. (2019). The semantic system supports the processing of mathematical principles. Neuroscience, 404, 102-118.
Anteraper, S., Guell, X., & Whitfield-Gabrieli, S. (2022). Big contributions of the little brain for precision psychiatry. Frontiers in psychiatry, 13, 1021873.