Computational Methods for the Assessment of Brain Connectivity in Visuo-Motor Integration Processes

The identification of the networks connecting different brain areas, as well as the understanding of their role in executing complex behavioral tasks, are crucial issues in cognitive neurosciences. In this context, several time series analysis approaches are available for the investigation of brain connectivity from non-invasive electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. Among them, multivariate autoregressive (MVAR) models, studied in the frequency domain, allow quantitative assessment of connectivity separately for each specific brain rhythm. In spite of its widespread utilization and great potential, MVAR-based brain connectivity analysis is complicated by a number of theoretical and practical aspects. An important issue is that the MVAR model, commonly applied to neurophysiological time series, accounts only for lagged effects among the series, forsaking instantaneous (i.e., not lagged) effects. Despite this, instantaneous correlations among EEG/MEG signals are largely expected, mainly as a consequence of volume conduction, and the impact of their exclusion on frequency-domain connectivity measures has not been investigated yet. The aim of the present thesis was to introduce and validate a new methodological framework for the frequency-domain evaluation of brain connectivity during visuo-motor integration processes. To this end, we provided first a comprehensive description of the most common MVAR-based connectivity measures, enhancing their theoretical interpretation. Then, we introduced an extended MVAR (eMVAR) model representation explicitly accounting for instantaneous effects. Accordingly, new frequency-domain connectivity measures were defined, and procedures for improving model identification and significance assessment were given. The proposed approach was validated on theoretical illustrative examples, and then applied to EEG and MEG multichannel data recorded from subjects performing a visuo-motor task combining precise grip motor commands with sensory visual feedback. The theoretical validation showed that, in the presence of significant instantaneous correlations, the traditional MVAR formulation may yield misleading connectivity patterns, while the correct patterns can be detected from the new measures based on eMVAR model identification. The practical application showed that instantaneous correlations are non negligible in the considered neurophysiological recordings, strongly suggesting the necessity of using the proposed eMVAR model in place of the traditional one. Results showed that execution of the visuo-motor task evokes the activation of a specific network subserving sensorimotor integration, which involves occipito-parietal and precentral cortices. The new connectivity measures revealed connections which were peculiar of different brain rhythms. Specifically, in the alpha frequency band (8-13 Hz) we documented an enhanced driving role of the visual cortex on the left motor cortex, suggesting a relation between this rhythm and the lateralization of the visuo-motor task. In the beta band (13-30 Hz), task-induced connectivity changes were bilateral, suggesting an involvement of both hemispheres. In both alpha and beta bands, the new connectivity measures suggested an important role for the parietal cortex in mediating the information flow from visual to motor areas, confirming previous evidences from invasive studies based on intra-cranical recordings, TMS or PET examinations. This thesis was produced in collaboration with the Department of Physics of the University of Trento.