Synopsis
In this education workshop, I will motivate the use of resting-state
fMRI (rs-fMRI) and functional connectivity to study the human brain. I will also
present example studies that use rs-fMRI as a tool to investigate brain
organization, disorder and behavior. I will conclude with some existing challenges
about rs-fMRI.Highlights
1. We can understand the brain in terms of specialization and integration
2. Functional integration can be studied with resting-state fMRI
(rs-fMRI), which allows us to answer questions about brain organization,
disorder and behavior
3. While there are unresolved caveats, rs-fMRI promises to be important
for data-driven discovery science
Target Audience
Faculty and trainees who are
interested in using resting-state fMRI for their studies.
Objectives
To understand how resting-state fMRI can be utilized to study healthy
brain organization, diseases and behavior notwithstanding certain caveats that
have arisen recently.
Synopsis
We can try to understand the brain in terms of functional specialization
and integration [1]. While task-based fMRI allows us to study functional specialization,
connectivity allows us to study integration [1]. The “resting” brain
dynamically fluctuates during rest. Bharat Biswal was the first to demonstrate
that resting-state fMRI (rs-fMRI) fluctuations within the motor system are
correlated [2]. The correlations between fMRI time courses are called
functional connectivity and are thought to reflect multisynaptic connectivity
[3, 4]. Intriguing, there are strong correspondences between networks recruited
during tasks and networks of regions functionally connected during rest [5, 6,
7]. The correspondences suggest that we can use rs-fMRI to extract functional
brain networks [5, 8, 9] instead of having to run multiple task-based fMRI
sessions with different tasks. These networks can be used to study healthy
brain organization, diseases and behavior [10, 11, 12]. However, it is worth
noting that rs-fMRI is not the panacea that people originally thought. Even
though rs-fMRI is “task-free”, it is affected by motion [14], fatigue [15] and
internal thought processes during rest [16]. This can introduce biases for
example when studying aging because older people move more, are more easily
fatigued and have different kinds of thoughts when compared with young people. Nevertheless,
the enthusiasm over rs-fMRI has resulted in large-scale rs-fMRI datasets that
are collected in roughly the same way (e.g., [16, 17, 18]), providing great opportunity
for data-driven discovery science [19, 20].
Acknowledgements
No acknowledgement found.References
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17. http://neuroinformatics.harvard.edu/gsp/
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