Structural and functional brain plasticity for statistical learning
University of Cambridge
Doctor of Philosophy (PhD)
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Karlaftis, V. M. (2018). Structural and functional brain plasticity for statistical learning (Doctoral thesis). https://doi.org/10.17863/CAM.26159
Extracting structure from initially incomprehensible streams of events is fundamental to a range of human abilities: from navigating in a new environment to learning a language. These skills rely on our ability to extract spatial and temporal regularities, often with minimal explicit feedback, that is known as statistical learning. Despite the importance of statistical learning for making perceptual decisions, we know surprisingly little about the brain circuits and how they change when learning temporal regularities. In my thesis, I combine behavioural measurements, Diffusion Tensor Imaging (DTI) and resting-state fMRI (rs-fMRI) to investigate the structural and functional circuits that are involved in statistical learning of temporal structures. In particular, I compare structural connectivity as measured by DTI and functional connectivity as measured by rs-fMRI before vs. after training to investigate learning-dependent changes in human brain pathways. Further, I combine the two imaging modalities using graph theory and regression analyses to identify key predictors of individual learning performance. Using a prediction task in the context of sequence learning without explicit feedback, I demonstrate that individuals adapt to the environment’s statistics as they change over time from simple repetition to probabilistic combinations. Importantly, I show that learning of temporal structures relates to decision strategy that varies among individuals between two prototypical distributions: matching the exact sequence statistics or selecting the most probable outcome in a given context (i.e. maximising). Further, combining DTI and rs-fMRI, I show that learning-dependent plasticity in dissociable cortico-striatal circuits relates to decision strategy. In particular, matching relates to connectivity between visual cortex, hippocampus and caudate, while maximisation relates to connectivity between frontal and motor cortices and striatum. These findings have potential translational applications, as alternate brain routes may be re-trained to support learning ability when specific pathways (e.g. memory-related circuits) are compromised by age or disease.
statistical learning, brain plasticity, cortico-striatal circuits, Diffusion Tensor Imaging, resting-state fMRI, graph theory, decision strategy, matching, maximization, PLS regression, tractography, ICA
This work was supported by grants to Professor Zoe Kourtzi from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), the Leverhulme Trust (RF-2011-378), the Alan Turing Institute (TU/B/000095), the Wellcome Trust (205067/Z/16/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2011-290011, and to Dr Andrew E Welchman from the Wellcome Trust (095183/Z/10/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2012-316746.
This record's DOI: https://doi.org/10.17863/CAM.26159
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