Last thursday (17th January 2019), Thomas Bolton from the Medical Image Processing Lab (MIP) of Ecole Polytechnique Fédérale de Lausanne (EPFL) visited our Lab and presented his work on:
Novel ways to probe brain function: beyond stationary functional connectivity measurements
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Abstract:
Functional magnetic resonance imaging (fMRI) enables to monitor changes in brain activation over time at the whole-brain level, when subjects are either at rest, or performing a specific cognitive task.
One prominent direction towards the interpretation of fMRI data has been the assessment of functional connectivity (FC), a pair-wise measure of statistical interdependence between time courses of regional activity. Here, I will outline how this already very rich characterization can be extended to capture additional features of brain function.
I will start by moving towards a dynamic functional connectome view, where functional interplays between brain areas are successively re-estimated along sub-spans of a scanning
One prominent direction towards the interpretation of fMRI data has been the assessment of functional connectivity (FC), a pair-wise measure of statistical interdependence between time courses of regional activity. Here, I will outline how this already very rich characterization can be extended to capture additional features of brain function.
I will start by moving towards a dynamic functional connectome view, where functional interplays between brain areas are successively re-estimated along sub-spans of a scanning
session to track fluctuating brain network relationships [1]. Then, I will discuss how FC can be computed as a synchronization measures across different subjects, and by this mean, enrich our understanding of functional responses to naturalistic stimulation paradigms [2]. Third, I will highlight how structural connectivity information, reflective of physical wiring in the brain, can be used in conjunction with functional data, and enhance their interpretability by addressing the extent of overlap between structure and function [3].
Finally, I will introduce a methodological framework that enables to shift focus from FC to effective connectivity, where causal (rather than correlational) relationships between brain regions can be unraveled [4].
Finally, I will introduce a methodological framework that enables to shift focus from FC to effective connectivity, where causal (rather than correlational) relationships between brain regions can be unraveled [4].
References:
[1] Preti, Maria Giulia*, Bolton, Thomas AW*, and Dimitri Van De Ville. “The dynamic functional connectome: state-of-the-art and perspectives.” Neuroimage 160 (2017): 41-54.
[2] Bolton, Thomas AW, et al. “Interactions between large-scale functional brain networks are captured by sparse coupled HMMs.” IEEE transactions on medical imaging 37.1 (2
[1] Preti, Maria Giulia*, Bolton, Thomas AW*, and Dimitri Van De Ville. “The dynamic functional connectome: state-of-the-art and perspectives.” Neuroimage 160 (2017): 41-54.
[2] Bolton, Thomas AW, et al. “Interactions between large-scale functional brain networks are captured by sparse coupled HMMs.” IEEE transactions on medical imaging 37.1 (2
018): 230-240.
[3] Bolton, Thomas AW, et al. “Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation.” Human brain mapping 39.6 (2018): 2391-2404.
[4] Huang, Weiyu*, Bolton, Thomas AW*, et al. “A Graph Signal Processing Perspective on Functional Brain Imaging.” Proceedings of the IEEE (2018)
[3] Bolton, Thomas AW, et al. “Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation.” Human brain mapping 39.6 (2018): 2391-2404.
[4] Huang, Weiyu*, Bolton, Thomas AW*, et al. “A Graph Signal Processing Perspective on Functional Brain Imaging.” Proceedings of the IEEE (2018)
