Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Functional hippocampal redundancy as a measure of resilience to pathological aging

Aging is accompanied by declines in episodic memory and altered hippocampal function, each of which are exacerbated in response to the development of Alzheimer’s disease. Therefore, it is critical to identify factors which support resilience to such pathological aging. One proposed factor is redundancy, the existence of duplicate elements within a system that offers protection against failure. Redundancy is hypothesized to operate within the brain as a neuroprotective mechanism, though this hypothesis has not been tested in the context of neurodegenerative diseases. This dissertation presents initial evidence that hippocampal redundancy, quantified from resting-state functional brain networks, operates as a neuroprotective mechanism in aging.The role of hippocampal functional redundancy is examined in the context of clinical, cognitive, pathological, and experiential factors across three studies. The first study demonstrates that posterior hippocampal redundancy is lower in mild cognitive impairment, a precursor stage to Alzheimer’s disease, than in healthy aging, though redundancy does not differ between early and late stages of mild cognitive impairment. Further, posterior hippocampal redundancy is related to better memory performance. The second study expands upon these results, relating hippocampal redundancy to pathological markers of Alzheimer’s disease, showing that hippocampal redundancy mediates the relationship between hippocampal volume and memory performance. Additionally, the combination of low hippocampal redundancy, volume, and memory is associated with subsequent dementia conversion. The final study reveals that the positive mnemonic benefit of redundancy weakens throughout healthy older adulthood and is specific to posterior rather than anterior hippocampus. However, this study finds no evidence that redundancy is influenced by either education or physical activity, two prominent protective factors for healthy aging.Across these three studies, hippocampal redundancy, particularly in posterior regions, is shown to be associated with better clinical and cognitive outcomes. Future studies will benefit from longitudinal analysis of redundancy in relation to clinical progression and long-term measures of physical activity. Together, the results presented in this dissertation provide the initial evidence that hippocampal redundancy supports resilience to pathological aging.Doctor of Philosoph

Redundancy in functional brain connectivity from eeg recordings

The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p < 0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p < 0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5-12.5 Hz), which is known to be the most responsive channel during resting state conditions. © 2012 World Scientific Publishing Company