Raphaël Pierre Liégeois

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Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signature ...

Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping phy ...

Component analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain n ...

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the r ...

Nodal properties are of particular importance when investigating patterns in brain networks. Nodal information is usually studied by comparing a few nodal measurements (up to three), resulting in analyses in three-dimensional spaces, at maximum. We offer a ...

Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fM ...

Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from correlations between functional time series, and more recently from partial correlations o ...

Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions-for example, based on wavel ...

Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. ...

Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts ...

Graphs are extensively used to represent networked data. In many applications, especially when considering large datasets, it is a desirable feature to focus the analysis onto specific subgraphs of interest. Slepian theory and its extension to graphs allow ...

The extraction of "fingerprints" from human brain connectivity data has become a new frontier in neuroscience. However, the time scales of human brain identifiability are still largely unexplored. We here investigate the dynamics of brain fingerprints alon ...

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying ...

In the absence of external stimuli, neural activity continuously evolves from one configuration to another. Whether these transitions or explorations follow some underlying arrangement or lack a predictable ordered plan remains to be determined. Here, usin ...

Raphaël Pierre Liégeois

Chat with Graph Search

Brain structure-function coupling provides signatures for task decoding and individual fingerprinting

The arrow-of-time in neuroimaging time series identifies causal triggers of brain function

Dynamic mode decomposition of resting-state and task fMRI

Questions and controversies in the study of time-varying functional connectivity in resting fMRI

Connectome embedding in multidimensional graph spaces

Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis

Revisiting correlation-based functional connectivity and its relationship with structural connectivity

Guided graph spectral embedding: Application to the C. elegans connectome

On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI

Resting brain dynamics at different timescales capture distinct aspects of human behavior

Time-resolved analysis of dynamic graphs: an extended Slepian design

When makes you unique: Temporality of the human brain fingerprint

Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest