This work involved the design of a multi-view manifold learning algorithm that capitalizes on various types of structure in high-dimensional time-series data to model dynamic signals in low dimensions. The resulting embeddings of human functional brain imaging data unveil trajectories through brain states that predict cognitive processing during diverse experimental tasks.
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References
Turk-Browne, N. B. Functional interactions as big data in the human brain. Science 342, 580–584 (2013). A review article that presents the properties and complexity of functional neuroimaging data.
Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019). This paper presents the PHATE algorithm, which our study extends.
Chen, J. et al. Shared memories reveal shared structure in neural activity across individuals. Nat. Neurosci. 20, 115–125 (2017). This paper describes one of the data sets used in our study.
Hanke, M. et al. A studyforrest extension, simultaneous fMRI and eye gaze recording during prolonged natural stimulation. Sci. Data 3, 160092 (2016). This paper describes one of the data sets used in our study.
Baldassano, C. et al. Discovering event structure in continuous narrative perception and memory. Neuron 95, 709–721 (2017). This paper presents the event segmentation framework for modeling neural dynamics.
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This is a summary of: Busch, E. L. et al. Multi-view manifold learning of human brain-state trajectories. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00419-0 (2023).
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Revealing trajectories of the mind via non-linear manifolds of brain activity. Nat Comput Sci 3, 192–193 (2023). https://doi.org/10.1038/s43588-023-00423-4
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DOI: https://doi.org/10.1038/s43588-023-00423-4
