Neuroscience

how can mathematics, statistics, and machine learning help us understand how the brain works?

Background

The most interesting question in neuroscience concerns the understanding of how the brain works. A large part of the research in this area benefits from neuroimaging, which facilitates the extraction of information from neural activities.

Goals

The goal of this research program is to use mathematical and machine learning methods to uncover neural patterns common across individuals when they perform different tasks and to detect anomalies in brains of individuals with psychiatric disorders.

Broader Impacts

The methodology developed in this line of research promises to empower clinical researchers with modern quantitative tools that facilitate a data-driven diagnosis of brain diseases.

Relevant Publications

O'Reilly-Shah, V.N. Selvitella, A.M., and Schurger, A. (2026). A Caveat Regarding the Unfolding Argument: Implications of Plasticity for Computational Theories of Consciousness. Neuroscience of Consciousness, 2026 (1), niag027. https://doi.org/10.1093/nc/niag027
Seo, J., Dwivedi, R., Hoang, K.N., Selvitella, A.M., and Fujiwara, E. (2025). Attentional dynamics during emotional face processing differentiate alexithymia from mood and affective symptoms. To appear in the Journal of Personality.
Cao, B., Liu, Y.S., Selvitella, A.M., Librenza-Garcia, D., Cavalcante-Passos, I., Sawalha, J., Ballester, P. , Chen, J. , Dong, S., Wang, F., Kapczinski, F., Dursun, S., Li, X.-M., Greiner, R., and Greenshaw, A.J. (2021). Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study. Scientific Reports, 21301. https://doi.org/10.1038/s41598-021-99534-z.

Sawalha, J., Yousefnezhad, M., Selvitella, A.M., Cao, B., Greenshaw, A.J., and Greiner, R. (2021). Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces. Scientific Reports, 18, 11 (1), 16723. https://doi.org/10.1038/s41598-021-95987-4.

Sawalha, J., Cao, L., Chen, J., Selvitella, A.M., Liu, Y., Sui, J., Greiner, R., Li, X.-M., Greenshaw, A.J., Li, t., and Cao, B. (2021). Individualized identification of first-episode bipolar disorder using machine learning and cognitive tests. Journal of Affective Disorders, 282, 662-668. https://doi.org/10.1016/j.jad.2020.12.046

Yousefnezhad, M., Sawalha, J., Selvitella, A.M., and Zhang, D. (2021). Deep representational similarity learning for analyzing neural signatures in task-based fMRI datasets. Neuroinformatics, 19 (3), 417- 431.

Yousefnezhad, M., Selvitella, A.M., Han, L., and Zhang, D. (2021). Supervised hyperalignment for multi-subject fMRI data alignment. IEEE Transactions in Cognitive and Developmental Systems, 13 (3), 475- 490. https://doi.org/10.1109/TCDS.2020.2965981

Yousefnezhad, M., Selvitella, A.M., Greenshaw, A., Zhang, D., and Greiner, R. (2020). Shared space transfer learning for analyzing multi-site fMRI data. Advances in Neural Information Processing Systems, 34, 1-11. December 10th, 2020. https://proceedings.neurips.cc/paper/2020/file/b837305e43f7e535a1506fc263eee3ed-Paper.pdf

Major Events

Workshop in Mathematical and Computational Biology. 2021 and 2022. Co-organizers: K.L. Foster, Ball State University, and D. Kihara, Purdue University.

Data Science and Machine Learning Seminar Series. 2019/2020, 2020/2021, and 2021/2022.

AAAI Symposium on Survival Prediction: Algorithms, Challenges and Applications 2021. Organizers: M. van der Schaar, R. Greiner, T.A. Gerds, and N. Kumar. Thought Leader of the Discussion Group on “Counterfactual Reasoning and Causality").

Data Science Week. 2019 - ongoing. Co-organizer: K.L. Foster, Ball State University. Website.

Main Collaborators

Russell Greiner - University of Alberta Lab page
Muhammad Tony Yousefnezhad - University of Alberta Homepage
Jeffrey Sawalha - Babbly Google Scholar
Vikas O'Reilly-Shah -University of Washington Google Scholar

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