Eigenmaps Of Dynamic Functional Connectivity: Voxel-Level Dominant Patterns Through Eigenvector Centrality

Maria Giulia Preti, Dimitri Nestor Alice Van De Ville
IEEE, 2016
Article de conférence

Résumé

Dynamic functional connectivity (dFC) based on resting-state functional magnetic resonance imaging (fMRI) explores the ongoing temporal configuration of brain networks. To reduce the large dimensionality of the data, conventional dFC analysis usually foresees an atlasing step, in which the brain is parcellated into specific regions of interest, and voxels' time-courses are spatially averaged within these regions before assessing connectivity. In this study, we addressed for the first time the exploration of dFC at the voxel level; i.e., without the use of any brain parcellation prior to the connectivity analysis. We used a sliding-window approach and extracted window-specific dominant patterns. To overcome the limitations due to the huge size of voxelwise connectivity matrices, we adopted the fast eigenvector centrality method with some adaptations to make it suitable for the dFC framework. After concatenation of the dominant patterns of all subjects, principal component analysis (PCA) was used to extract the eigenmaps; i.e., the most recurring voxelwise brain patterns characterizing resting-state. The obtained eigenmaps appeared consistent with previously observed resting-state eigenconnectivities, but with the substantial advantage of characterizing brain networks at the voxel level without the need of an atlas. The effect of the connection-wise temporal demeaning, usually performed in dFC analysis to remove the influence of static connectivity, was explored and does not seem to have an influence when voxelwise brain patterns are targeted.

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https://infoscience.epfl.ch/record/224838?ln=fr

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Maria Giulia Preti, Dimitri Nestor Alice Van De Ville
IEEE, 2016
Article de conférence

Résumé

Official source

https://infoscience.epfl.ch/record/224838?ln=fr

À propos de ce résultat

Concepts associés (10)

Concepts associés

Chargement

Publications associées (9)

Publications associées

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Traces Of Human Functional Activity: Moment-To-Moment Fluctuations In Fmri Data

Luca Dodero, Djalel Eddine Meskaldji, Dimitri Nestor Alice Van De Ville

Dynamic functional connectivity (dFC) measured by functional magnetic resonance imaging (fMRI) shows evidence of large-scale networks with highly dynamic (re) configurations. We propose a novel approach to extract traces of human brain function by the construction of a trajectory in a meaningful low-dimensional space. This allows studying dFC in more detail and identify possible meaningful brain states from the moment-to-moment fluctuations of the brain signals during resting state or naturalistic conditions such as passive movie watching. Specifically, we explored dynamic organization of sub-networks derived from the time-dependent graph Laplacian in combination with Riemannian manifold distance to measure dissimilarity over time of dFC and to subsequently build the trajectory of brain activity. As a proof-of-principle, we show results for an fMRI dataset containing both rest and movie epochs in 15 healthy participants. The movie condition varied (i.e., fearful, joyful, and neutral movie excerpts) and clearly influenced the subsequent resting-state period in terms of FC brain state.

IEEE2016

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The dynamic functional connectome: State-of-the-art and perspectives

Thomas William Arthur Bolton, Maria Giulia Preti, Dimitri Nestor Alice Van De Ville

Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools.

Elsevier2017

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Human behaviour is exquisitely complex, because it is staggeringly multi-facetted and subtly varies across individuals. Characterising its yet incompletely understood underlying biological mechanisms has profound clinical implications. Here, our goal was thus to study inter-individual behavioural variability. To accomplish this feat, we monitored the dynamics of brain activity through functional magnetic resonance imaging. The acquired signals only indirectly reflect neuronal mechanisms, and can be modulated by several confounding factors. A notorious one is head motion of the scanned volunteers. As an initial step, we addressed to what extent such movement would relate to inter-individual behavioural variability. We revealed strong, yet overlooked ties between a broad range of anthropometric, cognitive, sensory and clinical measures, and spatio-temporal head motion specificities. Next, as behaviour is an intrinsically dynamic process, we scrutinised the literature to pinpoint tailored time-resolved approaches for the analysis of brain activity. In a first application, we opted to track the dynamic reconfigurations of functional interactions (or dynamic functional connectivity) between brain regions over time in the context of an audiovisual stimulation paradigm. To mitigate the deleterious influences of head motion, computations were performed across subjects (a process known as inter-subject functional correlation). We discovered that some cues from the movie would be perceived homogeneously by a healthy population, while others would not. In addition, typically developing individuals exhibited more homogeneous brain activity than subjects diagnosed with autism spectrum disorder. Furthermore, the latter differed in their individual response to the movie as a function of the extent and balance of their socio-communicative and repetitive behaviour symptoms. In a second step, we attempted to link behaviour and brain activity in the absence of an overt task. We decomposed the interactions of a dorsolateral prefrontal region of interest with the rest of the brain into a set of co-activation patterns, and observed that their expression in the resting-state could predict attentional performance. As the spatio-temporal complexity of functional brain dynamics transpired from this analysis, we then set to develop a novel sparse coupled hidden Markov model framework that enables to quantify the dynamic interplays that take place between brain networks. To try and minimise spurious effects driven by head motion, we also considered an alternative methodological avenue, called graph signal processing, which enables to inject knowledge regarding brain structure (known to constrain neuronal activity) into the analyses. By studying functional brain dynamics from this viewpoint, we revealed an extensive array of links to behaviour. Furthermore, we also extended the originally static approach to the dynamic setting, and by this mean, further broadened the set of unraveled brain/behaviour relationships. Finally, we looked back at all the methods that we applied in theoretical terms and in their ability to predict inter-individual differences in behaviour. We concluded that they render complementary aspects of functional brain dynamics, and that in future years, the neuronal significance of brain/behaviour relationships should be further strengthened by pursuing more extended multimodal analyses.

EPFL2020

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EPFL2020