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).

Optogenetics is a set of technologies that enable optically triggered gain or loss of function in genetically specified populations of cells. Optogenetic methods have revolutionized experimental neuroscience by allowing precise excitation or inhibition of firing in specified neuronal populations embedded within complex, heterogeneous tissue. Although optogenetic tools have greatly improved our ability manipulate neural activity, they do not offer control of neural firing in the face of ongoing changes in network activity, plasticity, or sensory input. In this thesis, I develop a feedback control technology that automatically adjusts optical stimulation in real-time to precisely control network activity levels. I describe hardware and software tools, modes of optogenetic stimulation, and control algorithms required to achieve robust neural control over timescales ranging from seconds to days. I then demonstrate the scientific utility of these technologies in several experimental contexts. First, I investigate the role of connectivity in shaping the network encoding process using continuously-varying optical stimulation. I show that synaptic connectivity linearizes the neuronal response, verifying previous theoretical predictions. Next, I use long-term optogenetic feedback control to show that reductions in excitatory neurotransmission directly trigger homeostatic increases in synaptic strength. This result opposes a large body of literature on the subject and has significant implications for memory formation and maintenance. The technology presented in this thesis greatly enhances the precision with which optical stimulation can control neural activity, and allows causally related variables within neural circuits to be studied independently.