Neurosynth: Frequently Asked Questions

• Activation coordinates are extracted from published neuroimaging articles using an automated parser.
• The full text of all articles is parsed, and each article is 'tagged' with a set of terms that occur at a high frequency in that article.
• A list of several thousand terms that occur at high frequency in 20 or more studies is generated.
• For each term of interest (e.g., 'emotion', 'language', etc.), the entire database of coordinates is divided into two sets: those that occur in articles containing the term, and those that don't.
• A giant meta-analysis is performed comparing the coordinates reported for studies with and without the term of interest. In addition to producing statistical inference maps (i.e., z and p value maps), we also compute posterior probability maps, which display the likelihood of a given term being used in a study if activation is observed at a particular voxel.

We're not very principled about this. Every time the database expands, we generate a list of all possible terms that meet some minimum cut-off for number of studies. Then, we manually go through the list of several thousand terms and keep only the ones that seem to us to carry psychological or anatomical content (e.g., 'emotion' and 'language' would make the cut; 'activation' and 'necessary' wouldn't. It's entirely possible (likely, in fact) that we miss some terms that other people would include, or include some terms that don't have any interesting content.

There are three reasons why images may take a while to display in the interactive viewer when you first load a page. First, the image files themselves are fairly large (typically around 1 MB each), and most pages on Neurosynth display multiple images simultaneously. This means that your browser is usually receiving 1 - 3 MB of data on each request. Needless to say, the images can't be rendered until they've been received, so users on slower connections may have to wait a bit.

Second, there's some overhead incurred by reading the binary Nifti image into memory in JavaScript; on most browsers, this adds another second or two of delay.

Lastly, some images are only generated on demand--for example, if you're the first user to vist a particular brain location, you'll enjoy the privilege of waiting for the coactivation image for that voxel to be generated anew. This will typically only take a second or two, but can take as long as a minute in rare cases (specifically, when too many users are accessing Neurosynth and we need to spin up another background worker to handle the load).

Custom online analyses are no longer supported on neurosynth.org. This functionality was experimental, and we've decided to take it off-line for now. The precipitating reasons for this were that (a) the interface was buggy, (b) the results were insensible much (most?) of the time, and (c) what was intended to be purely an exploratory, experimental tool was being used to generate questionable results that users were in our view reading far too much into (and in some cases, reporting in publications).

We're continuing to develop a more comprehensive and robust suite of tools for online meta-analysis, but for the time being, if you want to conduct custom analyses using Neurosynth data, you'll have to use the Python core tools (which contain all of the same functionality and more, but require some basic Python proficiency to use).

The images you see are thresholded to correct for multiple comparisons. We use a false discovery rate (FDR) criterion of .01, meaning that, in theory, and on average, you can expect about 1% of the voxels you see activated in any given map to be false positives (though the actual proportion will vary and is impossible to determine). That said, we haven't done any extensive investigation to determine how well these thresholds are calibrated with gold-standard approaches based on permutation, so it's possible the thresholds might be somewhat more liberal or conservative than the nominal value suggests.

When you click the download link below the map you're currently viewing, you'll start downloading a 3D image corresponding to the map you currently have loaded. The downloaded images are in NIFTI format, and the files are gzipped to conserve space. All major neuroimaging software packages should be able to read these images in without any problems. The images are all nominally in MNI152 2mm space (the default space in SPM and FSL), though there's a bit more to it than that, because technically we don't account very well for stereotactic differences between studies in the underlying database (we convert Talairach to MNI, but it's imperfect, and we don't account for more subtle differences between, e.g., FSL and SPM templates). For a more detailed explanation, see the paper.

Note that the downloaded images are not dynamically adjusted to reflect the viewing options you currently have set in your browser. For instance, if you've adjusted the settings to only display negative activations at a threshold of z = -7 or lower, clicking the download link won't give you an image with only extremely strong negative activations--it'll give you the original (FDR-corrected) image. Of course, you can easily recreate what you're seeing in your browser by adjusting the thresholds correspondingly in your off-line viewer.

Short answer:

• uniformity test map: z-scores from a one-way ANOVA testing whether the proportion of studies that report activation at a given voxel differs from the rate that would be expected if activations were uniformly distributed throughout gray matter.
• association test map: z-scores from a two-way ANOVA testing for the presence of a non-zero association between term use and voxel activation.

Long answer: The uniformity test and association test maps are statistical inference maps; they display z-scores for two different kinds of analyses. The uniformity test map can be interpreted in roughly the same way as most standard whole-brain fMRI analysis: it displays the degree to which each voxel is consistently activated in studies that use a given term. For instance, the fact that the uniformity test map for the term 'emotion' displays high z-scores in the amygdala implies that studies that use the word emotion a lot tend to consistently report activation in the amygdala--at least, more consistently than one would expect if activation were uniformly distributed throughout gray matter. Note that, unlike most meta-analysis packages (e.g., ALE or MKDA), z-scores aren't generated through permutation, but using a chi-square test. (We use the chi-square test solely for pragmatic reasons: we generate thousands of maps at a time, so it's not computationally feasible to run thousands of permutations for each one.)

The association test maps provides somewhat different (and, in our view, typically more useful) information. Whereas the uniformity test maps tell you about the consistency of activation for a given term, the association test maps tell you whether activation in a region occurs more consistently for studies that mention the current term than for studies that don't. So for instance, the fact that the amygdala shows a large positive z-score in the association test map for emotion implies that studies whose abstracts include the word 'emotion' are more likely to report amygdala activation than studies whose abstracts don't include the word 'emotion'. That's important, because it controls for base rate differences between regions. Meaning, some regions (e.g., dorsal medial frontal cortex and lateral PFC) play a very broad role in cognition, and hence tend to be consistently activated for many different terms, despite lacking selectivity. The association test maps let you make slightly more confident claims that a given region is involved in a particular process, and isn't involved in just about every task.