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Resting-state fMRI: Beautiful Noise?

Micah Allen
Micah Allen

際際滷s from my recent in-house talk reviewing and defending the use of rest-state techniques in functional MRI.

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RESTING STATE NETWORKS Beautiful Noise?
Overview of Talk Conclusion Methodological Recipe and Caveats Background Future directions
Resting-state fMRI: Beautiful Noise?
Resting-state fMRI: Beautiful Noise?
Conclusion Resting state networks are: Highly robust across cultures, laboratories, methods Localized to gray matter and predicted by tractography Found in fMRI, EEG, MEG, and intracranial recordings Implicated in information processing, multi- tasking, memory, learning, development and emotion Easy to obtain, easy to analyze, and damn interesting Human connectomics - dont get left behind!
A Methodological Disclaimer WARNING: shameless analysis of low-frequency fluctuations Source: Smith chapter in Functional MRI: An Introduction to Methods
Resting-state fMRI: Beautiful Noise?
simple recipe for rsfMRI 5-7 minutes of rest Fixation, or no-fixation. Place at beginning of scan Duplicate for maximum effect Extra ROI timeseries/foci ICA vs a priori seed-r Regression with whole brain and/or task
Life at < .1 HZ
Acquisition
99 problems but acquisition aint one
Pre-processing Band-pass filter: remove constant offsets and linear trends, retain <0.08 Hz Regression of nuisance variables; motion, global signal, average lateral ventricle, deep cerebral white matter.
just breath
Analysis: ICA vs seed-based Data-driven vs a priori
Other Analysis Task-induced de-activation Rest-Stimulus interaction Post-scan rest Correlation of resting connectivity with cognition, personality, or psychiatric inventories.
Task induced De-activation
Rest-Stimulus Interaction
Post-Rest
Rest-Inventory Correlation
Why Rest? Theory and Background
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A little history The default mode of brain function originally proposed by Raichle and Fox (2001) Attracted initial controversy; why we should resist the baseline Has since exploded, evolved into the functional and human connectome projects. Confusion springs from a failure to distinguish between psychological, physiological and anatomical accounts. The Rt. Hon. Lord Brain (Brain 1969) From a brief history of the DMN (Raichle, 2007)
Human Connectome?
Towards a Discovery Science Of Human Brain Function N = 1,093 24 Centers
Resting States are Predicted by Structure Predicting human resting-state functional connectivity from structural connectivity 1. C. J. Honeya, O. Spornsa,1, L. Cammounb, X. Gigandetb, J. P. Margulies et al. (2009) Thiranb, R. Meulicand P. Hagmannb,c
What is Anti-Correlation?
Problems with Anti-r
Resting-state fMRI: Beautiful Noise?
Resting-state fMRI: Beautiful Noise?
Resting-state fMRI: Beautiful Noise?
To be safe If you want to look at anti-r networks: Record respiration Avoid global signal regression Include a task likely to engage CEN and SAL networks.
A Default Mode? Marianas Trench Argument: Dont conflate structure with content! Rumination, introspection, social cognition Free-energy Consciousness?
Picking apart DMN Although exact function remains unclear, DMN connectivity at rest and during task-processing now implicated in: Schizophrenia OCD Autism ADHD Levels of consciousness Task-irrelevant thoughts
Measuring the Default The Resting State Questionnaire (RSQ) Experience Sampling (Schooler et al) Differential engagement by task Correlation with individual differences
Resting-state fMRI: Beautiful Noise?
Future Directions
Resting-state fMRI: Beautiful Noise?
Real Conclusion There is simply too much multi-modal, multi-cultural evidence to dismiss slow-wave processing as a feature of mammalian brains Simple stories; DMN as consciousness, pure anti-correlation, 1:1 mapping between structure and function = FAIL If you are spending the time and energy doing fMRI, you might as well spend 5-7 minutes at rest.
THANK YOU $ +

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Resting-state fMRI: Beautiful Noise?

  • 1. RESTING STATE NETWORKS Beautiful Noise?
  • 2. Overview of Talk Conclusion Methodological Recipe and Caveats Background Future directions
  • 5. Conclusion Resting state networks are: Highly robust across cultures, laboratories, methods Localized to gray matter and predicted by tractography Found in fMRI, EEG, MEG, and intracranial recordings Implicated in information processing, multi- tasking, memory, learning, development and emotion Easy to obtain, easy to analyze, and damn interesting Human connectomics - dont get left behind!
  • 6. A Methodological Disclaimer WARNING: shameless analysis of low-frequency fluctuations Source: Smith chapter in Functional MRI: An Introduction to Methods
  • 8. simple recipe for rsfMRI 5-7 minutes of rest Fixation, or no-fixation. Place at beginning of scan Duplicate for maximum effect Extra ROI timeseries/foci ICA vs a priori seed-r Regression with whole brain and/or task
  • 9. Life at < .1 HZ
  • 11. 99 problems but acquisition aint one
  • 12. Pre-processing Band-pass filter: remove constant offsets and linear trends, retain <0.08 Hz Regression of nuisance variables; motion, global signal, average lateral ventricle, deep cerebral white matter.
  • 14. Analysis: ICA vs seed-based Data-driven vs a priori
  • 15. Other Analysis Task-induced de-activation Rest-Stimulus interaction Post-scan rest Correlation of resting connectivity with cognition, personality, or psychiatric inventories.
  • 20. Why Rest? Theory and Background
  • 21. +
  • 22. A little history The default mode of brain function originally proposed by Raichle and Fox (2001) Attracted initial controversy; why we should resist the baseline Has since exploded, evolved into the functional and human connectome projects. Confusion springs from a failure to distinguish between psychological, physiological and anatomical accounts. The Rt. Hon. Lord Brain (Brain 1969) From a brief history of the DMN (Raichle, 2007)
  • 24. Towards a Discovery Science Of Human Brain Function N = 1,093 24 Centers
  • 25. Resting States are Predicted by Structure Predicting human resting-state functional connectivity from structural connectivity 1. C. J. Honeya, O. Spornsa,1, L. Cammounb, X. Gigandetb, J. P. Margulies et al. (2009) Thiranb, R. Meulicand P. Hagmannb,c
  • 31. To be safe If you want to look at anti-r networks: Record respiration Avoid global signal regression Include a task likely to engage CEN and SAL networks.
  • 32. A Default Mode? Marianas Trench Argument: Dont conflate structure with content! Rumination, introspection, social cognition Free-energy Consciousness?
  • 33. Picking apart DMN Although exact function remains unclear, DMN connectivity at rest and during task-processing now implicated in: Schizophrenia OCD Autism ADHD Levels of consciousness Task-irrelevant thoughts
  • 34. Measuring the Default The Resting State Questionnaire (RSQ) Experience Sampling (Schooler et al) Differential engagement by task Correlation with individual differences
  • 38. Real Conclusion There is simply too much multi-modal, multi-cultural evidence to dismiss slow-wave processing as a feature of mammalian brains Simple stories; DMN as consciousness, pure anti-correlation, 1:1 mapping between structure and function = FAIL If you are spending the time and energy doing fMRI, you might as well spend 5-7 minutes at rest.