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Uncovering Dynamic Semantic Networks in the Brain Using Novel Approaches for EEG/MEG Connectome Reconstruction Uncovering Dynamic Semantic Networks in the Brain Using Novel Approaches for EEG/MEG Connectome Reconstruction SEYEDEHREZVAN FARAHIBOZORG SELWYN COLLEGE MRC COGNITION AND BRAIN SCIENCES UNIT SCHOOL OF CLINICAL MEDICINE UNIVERSITY OF CAMBRIDGE This dissertation is submitted for the degree of Doctor of Philosophy February 2018 Uncovering Dynamic Semantic Networks in the Brain Using Novel Approaches for EEG/MEG Connectome Reconstruction Seyedehrezvan Farahibozorg The current thesis addresses some of the unresolved predictions of recent models of the semantic brain system, such as the hub-and-spokes model. In particular, we tackle different aspects of the hypothesis that a widespread network of interacting heteromodal (hub(s)) and unimodal (spokes) cortices underlie semantic cognition. For this purpose, we use connectivity analyses, measures of graph theory and permutation-based statistics with source reconstructed Electro- /MagnetoEncephaloGraphy (EEG/MEG) data in order to track dynamic modulations of activity and connectivity within the semantic networks while a concept unfolds in the brain. Moreover, in order to obtain more accurate connectivity estimates of the semantic networks, we propose novel methods for some of the challenges associated with EEG/MEG connectivity analysis in source space. We utilised data-driven analyses of EEG/MEG recordings of visual word recognition paradigms and found that: 1) Bilateral Anterior Temporal Lobes (ATLs) acted as potential processor hubs for higher- level abstract representation of concepts. This was reflected in modulations of activity by multiple contrasts of semantic variables; 2) ATL and Angular Gyrus (AG) acted as potential integrator hubs for integration of information produced in distributed semantic areas. This was observed using Dynamic Causal Modelling of connectivity among the main left-hemispheric candidate hubs and modulations of functional connectivity of ATL and AG to semantic spokes by word concreteness. Furthermore, examining whole-brain connectomes using measures of graph theory revealed modules in the right ATL and parietal cortex as global hubs; 3) Brain oscillations associated with perception and action in low-level cortices, in particular Alpha and Gamma rhythms, were modulated in response to words with those sensory-motor attributes in the corresponding spokes, shedding light on the mechanism of semantic representations in spokes; 4) Three types of hub-hub, hub-spoke and spoke-spoke connectivity were found to underlie dynamic semantic graphs. Importantly, these results were obtained using novel approaches proposed to address two challenges associated with EEG/MEG connectivity. Firstly, in order to find the most suitable of several connectivity metrics, we utilised principal component analysis (PCA) to find commonalities and differences of those methods when applied to a dataset and identified the most suitable metric based on the maximum explained variance. Secondly, reconstruction of EEG/MEG connectomes using anatomical or fMRI-based parcellations can be significantly contaminated by spurious leakage- induced connections in source space. We, therefore, utilised cross-talk functions in order to optimise the number, size and locations of cortical parcels, obtaining EEG/MEG-adaptive parcellations. In summary, this thesis proposes approaches for optimising EEG/MEG connectivity analyses and applies them to provide the first empirical evidence regarding some of the core predictions of the hub- and-spokes model. The key findings support the general framework of the hub(s)-and-spokes, but also suggest modifications to the model, particularly regarding the definition of semantic hub(s). To mum and dad… v vi PREFACE This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. The work was carried out between October 2014 and February 2018 at the Medical Research Council Cognition and Brain Sciences Unit (MRC CBU), University of Cambridge, Cambridge, UK and under supervision of Dr. Olaf Hauk and advice of Professor Richard Henson. No part of this thesis is substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution. Collaborations: In Chapters 3 and 4, I have utilised a pre-existing dataset to address new questions. The experiment was designed and collected by Olaf Hauk, Anna Woollams, Karalyn Patterson, Elisa Cooper, Gemma Evans and Yuanyuan Chen at the MRC CBU. In Chapter 3, Dynamic Causal Modelling was conducted mostly under supervision of Richard Henson and some of the utilised code was kindly provided by him. Chapters 3 and 4 have been carried out in collaboration with Karalyn Patterson and Anna Woollams. Journal publications: Chapter 6 and relevant subsections of Chapters 2 and 8 have been published with no modification in: Farahibozorg, S.-R., Henson, R. N., & Hauk, O. (2018). Adaptive cortical parcellations for source reconstructed EEG/MEG connectomes. NeuroImage, volume 169. doi:10.1016/j.neuroimage.2017.09.009. Chapters 3, 4, 5 and 7 are also in preparation and are expected to be submitted for publications without substantial modifications to the content. Conferences presentations: Chapters 3 and 4 have been presented as posters at: 22nd and 23rd Annual Meeting of the Organization for the Human Brain Mapping, (OHBM) Geneva, Switzerland, 2016 and Vancouver, Canada, 2017, 8th Annual Meeting of the Society for Neurobiology of Language (SNL), London, UK, 2016 as well as local meetings in Cambridge. These chapters will be vii presented as a talk (merit abstract award, honourable mention) at the 10th annual meeting of SNL, Quebec City, Canada. Chapter 6 has been presented at: 22nd (poster) and 23rd (poster and oral presentation, winner of a merit abstract award) Annual Meeting of the OHBM (Geneva, Switzerland, 2016 and Vancouver, Canada, 2017) as well as MEG UK Meeting (oral presentation), Oxford, UK, 2017. Some parts of this chapter together with some parts of chapter 7 will be presented as a symposium talk at the 21st international conference on Biomagnetism, Philadelphia, USA. Chapters 5 and 7 were accepted for poster presentations at the 24th Annual Meeting of the OHBM (Singapore, 2018). Chapter 7 will be presented as a poster at the 10th annual meeting of SNL, Quebec City, Canada. In all the aforementioned collaborations, journal publication and conference presentations, I have been the first author, preparing the manuscripts, abstracts, posters and talks and revising them in collaboration with the co-authors. Additionally, all the data analysis codes, unless stated otherwise, have been written by myself. This thesis does not exceed 60,000 words (excluding figure, tables, appendices and bibliography), and contains less than 150 figures as prescribed by the Degree Committee at the School of Clinical Medicine. viii SUMMARY The current thesis addresses some of the unresolved predictions of recent models of the semantic brain system, such as the hub-and-spokes model. In particular, we tackle different aspects of the hypothesis that a widespread network of interacting heteromodal (hub(s)) and unimodal (spokes) cortices underlie semantic cognition. For this purpose, we use connectivity analyses, measures of graph theory and permutation-based statistics with source reconstructed Electro- /MagnetoEncephaloGraphy (EEG/MEG) data in order to track dynamic modulations of activity and connectivity within the semantic networks while a concept unfolds in the brain. Moreover, in order to obtain more accurate connectivity estimates of the semantic networks, we propose novel methods for some of the challenges associated with EEG/MEG connectivity analysis in source space. We utilised data-driven analyses of EEG/MEG recordings of visual word recognition paradigms and found that: 1) Bilateral Anterior Temporal Lobes (ATLs) acted as potential processor hubs for higher- level abstract representation of concepts. This was reflected in modulations of activity by multiple contrasts of semantic variables; 2) ATL and Angular Gyrus (AG) acted as potential integrator hubs for integration of information produced in distributed semantic areas. This was observed using Dynamic Causal Modelling of connectivity among the main left-hemispheric candidate hubs and modulations of functional connectivity of ATL and AG to semantic spokes by word concreteness. Furthermore, examining whole-brain connectomes using measures of graph theory revealed modules in the right ATL and parietal cortex as global hubs; 3) Brain oscillations associated with perception and action in low-level cortices, in particular Alpha and Gamma rhythms, were modulated in response to words with those sensory-motor attributes in the corresponding spokes, shedding light on the mechanism of semantic representations in spokes; 4) Three types of hub-hub, hub-spoke and spoke-spoke connectivity were found to underlie dynamic semantic graphs. Importantly, these results were obtained using novel approaches proposed to address two challenges associated with EEG/MEG connectivity. Firstly, in order to find the most
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