Topic 51

An automated meta-analysis of 57 studies

Top-loading terms for this topic:

rate, breathing, rates, rigid, cb, lose, respiratory, breath, rp, air, slow, acceleration, tidal, hypoxic, stereotyped, hz, stimulant, success, hold, pulse, shift, rps, deceleration, hypercapnia, rng, hypercapnic, sb, rock, challenge, holding, paper, opiate, stops, scissors, heart, stay, psu, trapping, hyperventilation, css

Forward and reverse inference meta-analysis maps

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Studies associated with Topic 51

Title Authors Journal Loading

Topic-based meta-analyses: Frequently Asked Questions

What is a "topic" in Neurosynth?

Topics reflect an effort to move beyond individual term-based analyses by modeling the covariance between different terms in article abstracts. Instead of generating separate maps for, say, "working memory" and "cognitive control", a topic modeling approach might extract a single topic that assigns a large weight to each of these closely-related terms (as well as many others). You can effectively think of a Neurosynth topic is a cluster of semantically-related words that tend to occur together in article abstracts. A meta-analysis is then performed to identify the neural correlates of each topic by searching for brain regions that are consistently more activated in articles that load highly on each topic than in articles that do not load highly on a topic. For further details, please see Poldrack et al (2012).

What do the "forward inference" and "reverse inference" maps mean?

For a detailed explanation, please see Yarkoni et al (2011). In brief, the forward inference map displays brain regions that are consistently active in studies that load highly on Topic "51". Regions with large z-scores are reported more often than one would expect them to be if activation anywhere in the brain was equally likely. Note that this is typically not so interesting, because it turns out that some brain regions are consistently reported in a lot of different kinds of studies (again, see our paper for discussion). So as a general rule of thumb, we don't recommend paying much attention to forward inference maps.

Reverse inference maps are, roughly, maps displaying brain regions that are preferentially related to a particular topic. The reverse inference map for Topic 51 displays regions that are reported more often in studies that load highly on this topic than in studies that do not load highly on the topic. Most of the time this a much more useful way of thinking about things, since reverse inference maps tell you, in some sense, which brain regions are more diagnostic of the variable in question, and not just which regions are consistently activated in studies associated with that variable.

How do you determine which studies to include in an analysis?

We use a predefined binary cut-off. For all topic-based meta-analyses, we treat all studies with a loading > 0.05 as "active" for a given topic, and all other studies as inactive. Although the choice of threshold is relatively arbitrary, in practice, varying it within a fairly broad range of values has minimal influence on the results. Adopting a continuous approach instead of dichotomizing the dataset also has a negligible effect.

Are these maps corrected for multiple comparisons?

Yes, they're corrected using a false discovery rate (FDR) approach, with an expected FDR of 0.01.

I need more details! How exactly were these maps and data generated?

If you want to know exactly how things work, we encourage you to clone the Neurosynth python tools from the github repository and work through some of the examples and code provided in the package. Everything you see on this page was generated using the default processing stream, so you should be able to easily generate the exact same images (unless the underlying database has grown or changed) for yourself.