A brief introduction to meta analysis in neuroimaging using BrainMap, Neurosynth, and NeuroVault.

1. NEUROIMAGING
META ANALYSIS 101

2. In this class you will learn
■ Theory behind doing metaanalysis in neuroimaging
■ How to use Sleuth to find papers for your metaanalysis
■ How to use GingerALE to find common patterns of
activation across studies
■ The difference between reverse and forward inference
■ How to use neurosynth.org to:
– Explore terms and topics extractedfrom text
– Decode your own unthresholded maps

3. A BIT OF THEORY

4. Metaanalysis
■ Looking for common finding across multiple studies of
the same or similar phenomenon
■ Sources of differences:
– No two studies are asking the same questions (true replications
are rare)
– Populations
– Methods (tasks, measurement apparatus, staff)

5. Metaanalysis
■ What is “a finding” in neuroimaging study?
– Brain region X is involved in cognitiveprocess Y
■ Most finding are about “where” rather than “how”
■ Location of the effect rather than it’s size

6. How do we know where the
effect is?

7. How do we know where the
effect is?

8. Four steps to success
1. Do a broad search for your topic
2. Exclude papers that fall outside of your scope
3. Extract the coordinates of reported activations for
relevant contrasts
4. Check if if the spatial overlap is statistically significant

9. Using Sleuth to look for
papers

10. Experiment?
In BrainMap Land an “experiment” is not one
experimental procedure but one contrast.

11. Using Sleuth to look for
papers

12. Using Sleuth to look for
papers

13. Using Sleuth to look for
papers
1. Define your query
2. Exclude papers
3. Exclude contrasts
4. Export

14. Task #1
1. Find a partner
2. Decide on a topic you want to preform metaanalysis on
3. Use Sleuth to find papers and export coordinates
4. Have a look at the generated .txt file

15. Results

16. Quantifying overlap

17. Quantifying overlap

18. Number of subjects

19. Task #2: Using GingerALE
■ File -> Open Foci (select the text file you have exported)
■ Preferences -> Change output directory
■ Cluster level -> 0.01
■ Threshold permutations -> 100 (for this exercise so it would
not take too long)
■ Cluster threshold FDR pID (identically distributed) - > 0.001
■ Compute!

20. Visualizing results
■ Download Mango from http://ric.uthscsa.edu/mango/
■ Download and load in Mango:
http://brainmap.org/ale/colin_tlrc_2x2x2.nii.gz
■ Add an overlay with your ALE map

21. Results!
Unthresholded ALE map Thresholded ALE map

22. REVERSE
INFERENCE

24. “In response to images of Democratic
candidates, men exhibited activity in the
medial orbital prefrontal cortex, indicating
emotional connection and positive
feelings.”
“Images of Fred Thompson led to
increased activity in the inferior frontal
cortex, a brain structure associated with
empathy.”
“Subjects who had an unfavorable view of
John Edwards responded to pictures of him
with feelings of disgust, evidenced by
increased activity in the insula, a brain area
associated with negative emotions.”

25. Reverse inference - a Bayesian view
P(process|act) =
P(process)*P(act|process)
P(act) = P(process)*P(act|process) + P(~process)*P(act|~process)
P(act) = P(processA)*P(act|processA) + P(processB)*P(act|processB) +
P(processC)*P(act|processC) + …

26. Does reverse inference work?
Insula
activitycraving
effort
pain
P(act|process)
P(process|act)
P(process|act) =
P(process)*P(act|process)

27. Insula activation is weakly selective
Some voxels active in as many of 20% of studies
Yarkoni et al., 2011

28. Using neurosynth: Terms

29. Using neurosynth: Topics

30. Using neurosynth: decoding

31. Task #3: Explore
neurosynth.org
■ Find a Term closest to the subject of your GingerALE
metaanalysis
– Look at both forward and reverse inference maps
– How many and what studies went into it?
■ Pick one Term and try to name it.
■ Use the decoder to interpret an unthresholded map if you
have one at hand

32. NeuroVault – where your
maps live

33. Papers
■ BrainMap
– Eickhoff, S. B., Laird, A. R., Grefkes,C., Wang, L. E., Zilles, K. and Fox,P. T. (2009),Coordinate-
based activation likelihood estimationmeta-analysis of neuroimaging data: A random-effects
approach based on empirical estimates of spatial uncertainty. Hum. Brain Mapp., 30:2907–
2926.
– Eickhoff, S. B., Laird, A. R., Grefkes,C., Wang, L. E., Zilles, K., & Fox,P. T. (2009).Coordinate-
based activation likelihood estimationmeta-analysis of neuroimaging data: a random-effects
approach based on empirical estimates of spatial uncertainty. Human brain mapping, 30(9),
2907–2926.Neurosynth
■ Neurosynth
– Yarkoni,T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011).Large-scale
automated synthesis of human functional neuroimaging data. Nature methods, 8(8),665–670.
■ NeuroVault
– Gorgolewski, K. J., Varoquaux,G., Rivera, G., Schwarz, Y., Ghosh, S. S., Maumet, C., Sochat, V.
V., et al. (2015).NeuroVault.org:a web-based repository for collecting and sharing
unthresholded statistical maps of the human brain. Frontiers in neuroinformatics,9. Frontiers.