While time-averaged dynamics of brain functional connectivity are known to reflect the underlying structural connections, the exact relationship between large-scale function and structure remains an unsolved issue in network neuroscience. Large-scale networks are traditionally observed by correlation of fMRI timecourses, and connectivity of source-reconstructed electrophysiological measures are less prominent. Accessing the brain by using multimodal recordings combining EEG, fMRI and diffusion MRI (dMRI) can help to refine the understanding of the spatio-temporal organization of both static and dynamic brain connectivity. In this talk I will discuss our prior findings that whole-brain connectivity derived from source-reconstructed resting-state (rs) EEG is both linked to the rs-fMRI and dMRI connectome. The EEG connectome provides complimentary information to link function to structure as compared to an fMRI-only perspective. I will present an approach extending the multimodal data integration of concurrent rs-EEG-fMRI to the temporal domain by combining dynamic functional connectivity of both modalities to better understand the neural basis of functional connectivity dynamics. The close relationship between time-varying changes in EEG and fMRI whole-brain connectivity patterns provide evidence for spontaneous reconfigurations of the brain’s functional processing architecture. Finally, I will talk about data quality of connectivity derived from concurrent EEG-fMRI recordings and how the presented multimodal framework could be applied to better understand focal epilepsy. In summary this talk will give an overview of how to integrate large-scale EEG networks with MRI-derived brain structure and function. In conclusion EEG-based connectivity measures not only are closely linked to MRI-based measures of brain structure and function over different time-scales, but also provides complimentary information on the function of underlying brain organization.

The spatial patterning of each neurodegenerative disease relates closely to a distinct structural and functional network in the human brain. This talk will mainly describe how brain network-sensitive neuroimaging methods such as resting-state fMRI and diffusion MRI can shed light on brain network dysfunctions associated with pathology and cognitive decline from preclinical to clinical dementia. I will first present our findings from two independent datasets on how amyloid and cerebrovascular pathology influence brain functional networks cross-sectionally and longitudinally in individuals with mild cognitive impairment and dementia. Evidence on longitudinal functional network organizational changes in healthy older adults and the influence of APOE genotype will be presented. In the second part, I will describe our work on how different pathology influences brain structural network and white matter microstructure. I will also touch on some new data on how brain network integrity contributes to behavior and disease progression using multivariate or machine learning approaches. These findings underscore the importance of studying selective brain network vulnerability instead of individual region and longitudinal design. Further developed with machine learning approaches, multimodal network-specific imaging signatures will help reveal disease mechanisms and facilitate early detection, prognosis and treatment search of neuropsychiatric disorders.