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“Transforming” the neuroscience of language: Estimating pattern-to-pattern transformations of brain activity
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Poster A115 in Poster Session A, Tuesday, October 24, 10:15 am - 12:00 pm CEST, Espace Vieux-Port
Olaf Hauk1, Rebecca L Jackson1,2, Setareh Rahimi1; 1University of Cambridge, 2University of York
The cognitive neuroscience of language aims at revealing how linguistic information is represented and manipulated in the brain to enable communication and meaningful behaviour. An important aspect of the underlying brain processes is the integration and transformation of information across multiple brain systems. In order to understand these processes, a detailed characterisation of brain connectivity is key. In order to characterize brain connectivity most accurately, connectivity methods should make use of the full multivariate and multidimensional information available from neuroimaging data. This should include a characterization of transformations between patterns of activation across brain regions, and their dependence on stimulus features, task and context. Methods for this type of analysis in event-related experimental designs have only recently begun to emerge (Anzellotti & Coutanche, 2018; Basti, Nili, Hauk, Marzetti, & Henson, 2020). Here, we describe novel methods developments to estimate the multidimensional relationships between patterns of brain activity from different brain regions. In particular, we will highlight their potential to estimate the voxel-to-voxel transformations between these patterns. This opens up opportunities to characterise these transformation with metrics such as sparsity, divergence, convergence, etc. We will specifically focus on methods that are suitable for event-related experimental designs. A few recent studies employed ridge regression to estimate linear transformation matrices. In fMRI data from an object recognition experiment this revealed that transformations between early visual cortex and inferior temporal areas are relatively sparse (Basti et al., 2019). In dynamic EEG/MEG data, this approach supported a central role for bilateral ATLs with a wider semantic brain network (Rahimi, Jackson, Farahibozorg, & Hauk, 2022). The latter results have been confirmed using a nonlinear extension of this method, indicating that linear methods provide an efficient approximation of multidimensional brain connectivity (Rahimi, Jackson, & Hauk, 2023). A multivariate as well as multidimensional extension of this method has also recently been proposed. We propose methods for analysing pattern transformations in language research in more detail. We illustrate this on simplified examples from the neuroscience of word recognition. References: Anzellotti, S., & Coutanche, M. N. (2018). Beyond Functional Connectivity: Investigating Networks of Multivariate Representations. Trends in Cognitive Sciences, 22(3), 258-269. doi:10.1016/j.tics.2017.12.002 Basti, A., Mur, M., Kriegeskorte, N., Pizzella, V., Marzetti, L., & Hauk, O. (2019). Analysing linear multivariate pattern transformations in neuroimaging data. PLoS One, 14(10), e0223660. doi:10.1371/journal.pone.0223660 Basti, A., Nili, H., Hauk, O., Marzetti, L., & Henson, R. N. (2020). Multi-dimensional connectivity: a conceptual and mathematical review. Neuroimage, 221, 117179. doi:10.1016/j.neuroimage.2020.117179 Rahimi, S., Jackson, R., Farahibozorg, S., & Hauk, O. (2022). Time Lagged Multidimensional Pattern Connectivity (TL MDPC): An EEG/MEG Pattern Transformation Based Functional Connectivity Metric. Neuroimage. doi:https://doi.org/10.1016/j.neuroimage.2023.119958 Rahimi, S., Jackson, R., & Hauk, O. (2023). Identifying nonlinear Functional Connectivity with EEG/MEG using Nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC). bioRxiv. doi:https://doi.org/10.1101/2023.01.19.524690
Topic Areas: Methods, Meaning: Lexical Semantics