From Brain Patterns to Academic Success: Unveiling Multimodal Signatures in Reading and Mathematics

1029 

Abstract Submission 

Ping Long¹, Rui Chen¹, Dongmei Zhi¹, Vince Calhoun², Sha Tao¹, Jing Sui¹

¹State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, ²Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, Georgia, United States

Ping Long  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Rui Chen  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Dongmei Zhi  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Vince Calhoun  
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, Georgia, United States

Sha Tao  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Jing Sui  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Children's reading and mathematics are two essential abilities that are critical to academic achievements and future career development. Existing neuroimaging research often concentrated on a single ability (reading or mathematical processing) [1][3], or compared them using one specific MRI modality[2][4][5]. However, the multimodal neuroimaging signatures significantly associated with reading and mathematics remained unexplored. Here, via a supervised three-way MRI fusion (fALFF, FA, and GMV), we identified the MRI signatures for comprehensive reading and mathematical processing for 562 children to reveal the common and unique underlying neurobiological basis. Moreover, the longitudinal predictability of the identified baseline MRI signatures for estimating 5 types of cognitive scores one year later (including attention, memory, reasoning, visual perception, and cognitive composite) was examined and validated.

This study recruited 562 typically developing children aged 9-11 years from two independent cohorts (N = 441 and N = 121) with MRI scans, academic attainment scores, and cognitive scores. Firstly, academic-guided fusion was performed in children from the PKU dataset. Specifically, reading and mathematics scores were used respectively as a reference to guide a three-way MRI feature fusion,i.e., fALFF, FA, and GMV, by multimodal canonical correlation analysis with reference plus joint independent component analysis (MCCAR + jICA)[6]. Then, structural similarity index measure (SSIM) was used to measure the degree of similarity for joint components between reading, math and cognitive domains for each modality. Next, the multimodal brain patterns obtained from PKU were used to predict five cognitive scores one year later with linear support vector regression (SVR) and validated in another cohort.

Results highlighted the prefrontal regions and posterior default mode network as the most commonly prominent brain networks for academic achievements. The Broca's area and posterior cingulate cortex in fractional amplitude of low-frequency fluctuation, and anterior cingulate cortex in grey matter volume were more extensively involved in reading[1][2], while the middle temporal gyrus and angular gyrus in grey matter volume was specifically engaging in mathematics[3][4][5]. Moreover, academic achievements were both highly associated with reasoning, whereas mathematics was more extensively involved in visual perception than reading. Most importantly, the identified multimodal signatures can successfully predict children's current academic achievements and distinct cognitive domains at one year later, especially the highest predictive power of cognitive composite validated by another independent cohort (r > 0.35).

In this study, we searched for the common and unique multimodal signatures between reading and mathematics, as well as their relationships with distinct cognitive domains by a supervised multimodal fusion. To the best of our knowledge, this is the first attempt to utilize reading and mathematics as references to guide the three-way multimodal MRI fusion, where the multimodal signatures can significantly predict one-year-later cognitive abilities, especially the cognitive composite, which were also validated in another independent cohort. Our findings contribute to a better understanding of the brain relationships between academic achievement and cognitive abilities, which would have important implications for the early detection of learning difficulties and the development of targeted interventions to support children's academic growth.

Education, History and Social Aspects of Brain Imaging

Reading and Writing ¹

Learning and Memory Other ²

Multivariate Approaches

Multi-Modal Imaging

Learning

MRI

Other - Reading; mathematics; multimodal signatures