Dawlat El-Said

All Publications

• Brain-wide decoding of numbers and letters: Converging evidence from multivariate fMRI analysis and probabilistic meta-analysis. Cortex; a journal devoted to the study of the nervous system and behavior Liu, R., Chang, H., El-Said, D., Wassermann, D., Zhang, Y., Menon, V. 2025; 189: 256-274

  Abstract

  Previous studies exploring category-sensitive representations of numbers and letters have predominantly focused on individual brain regions. This study expands upon this research through computationally rigorous whole-brain neural decoding using Elastic Net (ND-EN), facilitating the analysis of neural patterns across the entire brain with greater precision. To establish the robustness and generalizability of our results, we also conducted innovative probabilistic meta-analyses of the extant functional neuroimaging literature. The investigation comprised both an active task, requiring participants to distinguish between numbers and letters, and a passive task where they simply viewed these symbols. ND-EN revealed that, during the active task, a distributed network-including the ventral temporal-occipital cortex, intraparietal sulcus, middle frontal gyrus, and insula-actively differentiated between numbers and letters. This distinction was not evident in the passive task, indicating that the task engagement level plays a crucial role in such neural differentiation. Further, regional neural representational similarity analyses within the ventral temporal-occipital cortex revealed similar activation patterns for numbers and letters, indicating a lack of differentiation in regions previously linked to these visual symbols. Thus, our findings indicate that category-sensitive representations of numbers and letters are not confined to isolated regions but involve a broader network of brain areas, and are modulated by task demands. Supporting these empirical findings, probabilistic meta-analyses conducted with NeuroLang and the Neurosynth database reinforced our observations. Together, the convergence of evidence from multivariate neural pattern analysis and meta-analysis advances our understanding of how numbers and letters are represented in the human brain.

  View details for DOI 10.1016/j.cortex.2025.04.017

  View details for PubMedID 40580696

• Unraveling latent cognitive, metacognitive, strategic, and affective processes underlying children's problem-solving using Bayesian cognitive modeling. bioRxiv : the preprint server for biology Mistry, P. K., Chang, H., El-Said, D., Menon, V. 2025

  Abstract

  Children exhibit remarkable variability in their mathematical problem-solving abilities, yet the cognitive, metacognitive and affective mechanisms underlying these individual differences remain poorly understood. We developed a novel Bayesian model of arithmetic problem-solving (BMAPS) to uncover the latent processes governing children's arithmetic strategy choice and efficiency. BMAPS inferred cognitive parameters related to strategy execution and metacognitive parameters related to strategy selection, revealing key mechanisms of adaptive problem solving. BMAPS parameters collectively explained individual differences in problem-solving performance, predicted longitudinal gains in arithmetic fluency and mathematical reasoning, and mediated the effects of anxiety and attitudes on performance. Clustering analyses using BMAPS parameters revealed distinct profiles of strategy use, metacognitive efficiency, and developmental change. By quantifying the fine-grained dynamics of strategy selection and execution and their relation to affective factors and academic outcomes, BMAPS provides new insights into the cognitive and metacognitive underpinnings of children's mathematical learning. This work advances powerful computational methods for uncovering latent mechanisms of complex cognition in children.

  View details for DOI 10.1101/2025.01.29.635409

  View details for PubMedID 39975184

  View details for PubMedCentralID PMC11838325

• Neuroanatomical, transcriptomic, and molecular correlates of math ability and their prognostic value for predicting learning outcomes. Science advances Liu, J., Supekar, K., El-Said, D., de Los Angeles, C., Zhang, Y., Chang, H., Menon, V. 2024; 10 (22): eadk7220

  Abstract

  Foundational mathematical abilities, acquired in early childhood, are essential for success in our technology-driven society. Yet, the neurobiological mechanisms underlying individual differences in children's mathematical abilities and learning outcomes remain largely unexplored. Leveraging one of the largest multicohort datasets from children at a pivotal stage of knowledge acquisition, we first establish a replicable mathematical ability-related imaging phenotype (MAIP). We then show that brain gene expression profiles enriched for candidate math ability-related genes, neuronal signaling, synaptic transmission, and voltage-gated potassium channel activity contributed to the MAIP. Furthermore, the similarity between MAIP gene expression signatures and brain structure, acquired before intervention, predicted learning outcomes in two independent math tutoring cohorts. These findings advance our knowledge of the interplay between neuroanatomical, transcriptomic, and molecular mechanisms underlying mathematical ability and reveal predictive biomarkers of learning. Our findings have implications for the development of personalized education and interventions.

  View details for DOI 10.1126/sciadv.adk7220

  View details for PubMedID 38820151

• Machine learning-based identification of a psychotherapy-predictive electroencephalographic signature in PTSD Nature Mental Health Zhang, Y., Naparstek, S., Gordon, J., Watts, M., Shpigel, E., El-Said, D., Badami, F. S., Eisenberg, M. L., Toll, R. T., Gage, A., Goodkind, M. S., Etkin, A., Wu, W. 2023; 1: 284-294

  View details for DOI 10.1038/s44220-023-00049-5

• Functional Connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions. Journal of neuroscience methods Rolle, C. E., Narayan, M., Wu, W., Toll, R., Johnson, N., Caudle, T., Yan, M., El-Said, D., Waats, M., Eisenberg, M., Etkin, A. 2021: 109424

  View details for DOI 10.1016/j.jneumeth.2021.109424

  View details for PubMedID 34826504

• Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nature Biomedical Engineering Zhang, Y., Wu, W., Toll, R. T., Naparstek, S., Maron-katz, A., Watts, M., Gorddodn, J., Jeeong, J., Astolfi, L., Shpigel, E., Longwell, P., Sarhadi, k., El-Said, D., Li, Y., Cooper, C., Chin-Fatt, C., Arns, M., Goodkind, M. S., Trivedi, M. H., Marmar, C. R., Etkin, A. 2020

  View details for DOI 10.1038/s41551-020-00614-8

• Development of VM-REACT: Verbal memory RecAll computerized test JOURNAL OF PSYCHIATRIC RESEARCH Naparstek, S., El-Said, D., Eisenberg, M. L., Jordan, J. T., O'Hara, R., Etkin, A. 2019; 114: 170–77

  View details for DOI 10.1016/j.jpsychires.2019.04.023

  View details for Web of Science ID 000472127300025