Meta-Analytic Connectivity Modeling of Mathematical Cognition Networks in Relation to Working Memory
Poster No:
793
Submission Type:
Late-Breaking Abstract Submission
Authors:
Xavier Lim1, SH Annabel Chen1,2,3, Chiao-Yi Wu4
Institutions:
1Psychology, School of Social Sciences, Nanyang Technological University, Singapore, Singapore, 2Centre for Research and Development in Learning, Nanyang Technological University, Singapore, Singapore, 3Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, 4National Institute of Education, Nanyang Technological University, Singapore, Singapore
First Author:
Xavier Lim
Psychology, School of Social Sciences, Nanyang Technological University
Singapore, Singapore
Psychology, School of Social Sciences, Nanyang Technological University
Singapore, Singapore
Co-Author(s):
SH Annabel Chen, PhD
Psychology, School of Social Sciences, Nanyang Technological University|Centre for Research and Development in Learning, Nanyang Technological University|Lee Kong Chian School of Medicine, Nanyang Technological University
Singapore, Singapore|Singapore, Singapore|Singapore, Singapore
Psychology, School of Social Sciences, Nanyang Technological University|Centre for Research and Development in Learning, Nanyang Technological University|Lee Kong Chian School of Medicine, Nanyang Technological University
Singapore, Singapore|Singapore, Singapore|Singapore, Singapore
Introduction:
Mathematical cognition subserves many life skills, ranging from arithmetic to problem-solving. Past studies have theorized a link between working memory and mathematical processing, where the latter taps into information storage and manipulation over time (Raghubar et al., 2010). This link is further supported by behavioral studies observing individuals with math difficulties performing worse than typically-developing controls on working memory tasks, with these difficulties ameliorated after working memory training (Layes et al., 2018). Here, we meta-analytically examined these propositions by investigating the neural networks associated with mathematical processing and working memory. We hypothesized brain activation and functional connectivity within the fronto-parietal regions (e.g., inferior parietal lobule, middle frontal gyrus) to emerge as the common neural network for both skills.
Methods:
Using the PRISMA framework (Page et al., 2021), we retrieved fMRI/PET studies on mathematical processing and working memory separately from the PubMed, PsycINFO, Scopus, and Web of Sciences databases. Studies were included if they reported whole-brain task-based contrasts relevant to math/working memory in MNI/Talairach coordinate space and involved healthy participants aged 5-35. 141 studies on math (n=3511, Foci=3004, Experiments=165) and 141 studies on working memory (n=5117, Foci=2914, Experiments=160) were included in the analyses. Using Activation Likelihood Estimation (ALE; Eickhoff et al., 2009), we identified the common brain regions for math and working memory via conjunction analysis. Next, we used Meta-Analytic Connectivity Modelling (MACM; Kotkowski et al., 2018) to identify the functional connectivity profiles in the common brain regions. Functional decoding was carried out to explore the behavioral profiles associated with these brain regions.
Results:
Nine regions emerged common to math and working memory: left superior parietal lobule (SPL), right inferior parietal lobule (IPL), left precentral gyrus (PCG), left medial frontal gyrus (MeFG), right middle frontal gyrus (MFG), bilateral insulae, and bilateral subgyral of the frontal lobe. The regions unique to math was the bilateral precuneus, and unique to working memory were the right MFG, right cerebellum, and right superior frontal gyrus. MACM shows significant co-activation patterns within the fronto-parietal network and salience network for the regions common to both skills: specifically, we observed significant positive bidirectional connections between the left SPL, bilateral insulae, and left MeFG (all pCorrected < .00069). Functional decoding suggests that the left SPL was significantly associated with attention, reasoning, language, working memory, vision, and semantic language. The other regions were generally linked to higher-order cognition (reasoning, attention) and executive functions (cognitive control, working memory).
Conclusions:
Our study affirmed the brain regions and functional coupling within the fronto-parietal network in contributing to both math and working memory (Ren & Libertus, 2023). The fronto-parietal network, given its relevance to cognitive control mandated by many cognitive abilities, is thought to be flexibly connected to other brain networks depending on task demands (Zanto & Gazzaley, 2013). Given that both math and working memory rely on higher-order cognitive control (i.e., sustained attention and task switching), the fronto-parietal network likely plays an influential role (Asplund et al., 2010). The bilateral insulae also emerged as common regions as both tasks mandate attentional control which is subserved by the salience network (Menon & Uddin, 2010). Altogether, our study holds implications for educational practices, such as advocacy for working memory-based training programs, or brain-evidenced interventions targeting the fronto-parietal network, to improve mathematical disabilities.
Higher Cognitive Functions:
Space, Time and Number Coding 1
Learning and Memory:
Working Memory 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI
PET
Keywords:
Cognition
FUNCTIONAL MRI
Memory
Meta- Analysis
Other - Functional Connectivity, Math
1|2Indicates the priority used for review
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Provide references using APA citation style.
Eickhoff, S. B. (2009). Coordinate‐based activation likelihood estimation meta‐analysis of neuroimaging data: A random‐effects approach based on empirical estimates of spatial uncertainty. Human Brain Mapping, 30(9), 2907–2926. https://doi.org/10.1002/hbm.20718
Kotkowski, E. (2018). The hippocampal network model: A transdiagnostic metaconnectomic approach. NeuroImage: Clinical, 18, 115–129. https://doi.org/10.1016/j.nicl.2018.01.002
Layes, S. (2018). Effectiveness of working memory training among children with dyscalculia: Evidence for transfer effects on mathematical achievement—a pilot study. Cognitive Processing, 19(3), 375–385. https://doi.org/10.1007/s10339-017-0853-2
Menon, V. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214(5–6), 655–667. https://doi.org/10.1007/s00429-010-0262-0
Page, M. J. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Raghubar, K. P. (2010). Working memory and mathematics: A review of developmental, individual difference, and cognitive approaches. Learning and Individual Differences, 20(2), 110–122. https://doi.org/10.1016/j.lindif.2009.10.005
Ren, X. (2023). Identifying the Neural Bases of Math Competence Based on Structural and Functional Properties of the Human Brain. Journal of Cognitive Neuroscience, 35(8), 1212–1228. https://doi.org/10.1162/jocn_a_02008
Zanto, T. P. (2013). Fronto-parietal network: Flexible hub of cognitive control. Trends in Cognitive Sciences, 17(12), 602–603. https://doi.org/10.1016/j.tics.2013.10.001