Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.

This blog reports latest progresses in
(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home
(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures
(3) My Works

Saturday, March 17, 2012

The future of FMRI connectivity

There is a nice review paper on FMRI connectivity in Neuroimage:

Stephen M. Smith, The future of FMRI connectivity, NeuroImage, accepted by 2012.

Clearly, it attracts the interests of many researchers in this field, since it has been one of the most downloaded papers in the journal (but now it is just an accepted paper)!

Here is the abstract:

“FMRI connectivity” encompasses many areas of research, including resting-state networks, biophysical modelling of task-FMRI data and bottom-up simulation of multiple individual neurons interacting with each other. In this brief paper I discuss several outstanding areas that I believe will see exciting developments in the next few years, in particular concentrating on how I think the currently separate approaches will increasingly need to take advantage of each others' respective complementarities.

And the outline of the contents:
Introduction - brief review of concepts
      Spatial patterns of connectivity
      Connectivity modelling from multiple subjects
Model complexity
      Bottom-up modelling
      Graph theory
      FMRI network modelling methods
      Patterns of conditional independence; observational vs. interventional studies
      Dynamic biological Bayesian models
Nonlinearities and temporal nonstationarities
Other issues… and conclusions

Although this is a brief review paper, the author has tried to cover many important aspects of fMRI connectivity. But I think there are two aspects may need to put more words. One is the sparsity based models in the section of  model complexity. The second is how to verify the fidelity of an estimated connectivity network. Hope I can see the two issues especially the second one are discussed in details in future's review papers.

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