Meta analysis in neuroimaging 101

NEUROIMAGING META ANALYSIS 101

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 extracted from text– Decode your own unthresholded maps

A BIT OF THEORY

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)

Metaanalysis

■ What is “a finding” in neuroimaging study?– Brain region X is involved in cognitive process Y

■ Most finding are about “where” rather than “how”

■ Location of the effect rather than it’s size

How do we know where the effect is?

How do we know where the effect is?

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

Using Sleuth to look for papers

Experiment?

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

Using Sleuth to look for papers

Using Sleuth to look for papers

Using Sleuth to look for papers1. Define your query

2. Exclude papers

3. Exclude contrasts

4. Export

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

Results

Quantifying overlap

Quantifying overlap

Number of subjects

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!

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

Results!

Unthresholded ALE map Thresholded ALE map

REVERSE INFERENCE

“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.”

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) + …

Does reverse inference work?

Insulaactivitycraving

effort

pain

P(act|process)

P(process|act)

P(process|act) =P(process)*P(act|process)

Insula activation is weakly selective

Some voxels active in as many of 20% of studiesYarkoni et al., 2011

Using neurosynth: Terms

Using neurosynth: Topics

Using neurosynth: decoding

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

NeuroVault – where your maps live

Papers

■ BrainMap– Eickhoff, S. B., Laird, A. R., Grefkes, C., Wang, L. E., Zilles, K. and Fox, P. T. (2009), Coordinate-

based activation likelihood estimation meta-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 estimation meta-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.