From 88f535fd052a3cb9144735ea14bf2d9d46b006f1 Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Tue, 19 Dec 2023 18:19:56 -0600 Subject: [PATCH 01/16] Add blog post for December --- blog/2023-12-21-december.md | 46 +++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 blog/2023-12-21-december.md diff --git a/blog/2023-12-21-december.md b/blog/2023-12-21-december.md new file mode 100644 index 0000000..ac26211 --- /dev/null +++ b/blog/2023-12-21-december.md @@ -0,0 +1,46 @@ +--- +slug: december-2023-updates +title: December 2023 Updates +authors: alejandro +tags: [neurosynth] +--- +Hello Neurosynth Users, + +We're excited to share our December updates with you! It’s been a great month, having announced the release of Neurosynth Compose on social media. In the last month we’ve seen **over 1,200 new user visits**, with **200 users signing up for an account**! 🚀 + +Help us keep this growth going by [sharing our announcement](http://localhost:3000/compose-docs/blog/announcing-ns-compose) with your colleagues. 🧑‍🔬 + +# 🌟 What’s New 🌟 + +We’ve also continued to introduce new features and improve the user experience. Here’s some highlights: + +**Large-scale association tests** + +A key feature that set aside Neurosynth were large-scale “association tests” (previously referred to as “reverse inference” test) + +Whereas a typical meta-analysis tells you about the consistency of activation in a set of studies, an association test tells you whether activation in a region occurs *more consistently for studies in your set of studies versus a large reference sample of studies*. + +Neurosynth leverages large-scale neuroimaging datasets as a reference to which you can compare the specific studies in given meta-analysis. + +That's important, because this effectively allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence tend to be consistently activated for many different terms. Thus, if you see insula activity in your meta-analysis, you can erroneously conclude that the insula is involved in the process of your study. A large-scale association test lets you determine if the activity you observe in a region occurs to a greater extent in your meta-analysis than in other studies, making it possible to make more confident claims that a given region is involved in a particular process, and isn't involved in just about every task. + +Previously these association tests were available for the automatically generated maps on neurosynth.org. **Now you can perform large-scale association tests for your custom meta-analyses in Neurosynth Compose.** In addition, we also support other types of “pairwise” algorithms, such as ALE Subtraction, and you are free to choose from a variety of reference datasets, including the Neurosynth corpus. + +**User Experience Enhancements** + +Based on your valuable feedback, we've made numerous bug fixes and improvements, notably: + +* **Simplified Curation**: The review import page has been removed, and summary information is now added directly to the tag step. + +* **Searching UI**: We've replaced the dropdown with a selection gallery, making it easier to choose your preferred search method, and we now pre-populate search names for Neurostore. + +* **Streamlined Duplicate Resolution**: The 'resolve duplicates' function will only appear if duplicates are present. + +* **Improved Editing Workflow**: The process of editing studies has been made more straightforward, allowing you to start editing with ease. + +We hope you enjoy these changes, and be sure to leave us feedback or ask questions if you have any problems or suggestions. + + +Cheers, + +The Neurosynth Team 🧠 From ea832805dffba5565f98526e879d882a86983ff8 Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Tue, 19 Dec 2023 18:20:12 -0600 Subject: [PATCH 02/16] Add draft of MKDA Chi Squared tutorial --- docs/tutorial/automated.md | 1 + docs/tutorial/index.mdx | 16 +++- docs/tutorial/manual.md | 1 + docs/tutorial/mkda_association.md | 77 ++++++++++++++++++ static/img/compose_ecosystem_paths.png | Bin 0 -> 54178 bytes static/tutorial/mkda_chi_squared_algo.png | Bin 0 -> 36300 bytes .../tutorial/mkda_chi_squared_reference.png | Bin 0 -> 38453 bytes 7 files changed, 92 insertions(+), 3 deletions(-) create mode 100644 docs/tutorial/mkda_association.md create mode 100644 static/img/compose_ecosystem_paths.png create mode 100644 static/tutorial/mkda_chi_squared_algo.png create mode 100644 static/tutorial/mkda_chi_squared_reference.png diff --git a/docs/tutorial/automated.md b/docs/tutorial/automated.md index 86fbfd5..5e15ffc 100644 --- a/docs/tutorial/automated.md +++ b/docs/tutorial/automated.md @@ -4,6 +4,7 @@ sidebar_position: 3 --- # Automated Meta-Analysis +*How to create a fully automated meta-analysis* :::caution This tutorial, and the functionality for automated meta-analysis, is under construction. diff --git a/docs/tutorial/index.mdx b/docs/tutorial/index.mdx index 74bedb1..898d986 100644 --- a/docs/tutorial/index.mdx +++ b/docs/tutorial/index.mdx @@ -5,11 +5,15 @@ import { Grid, Card, CardContent, Typography, Button } from '@mui/material'; import { Link } from 'react-router-dom'; +import DocCardList from '@theme/DocCardList'; -Neurosynth Compose supports a range of different workflows, from exploratory large-scale automated analyses to highly curated and rigorous manual analyses. +### Overview -The choice of workflow depends on the research question and the available resources. Note that you can choose to blend approaches from these two -ends of the spectrum, depending on your needs. Learn more about the different workflows by following the tutorials below. +Neurosynth Compose supports a range of different workflows, +from exploratory large-scale automated analyses to highly curated and rigorous manual analyses. + +The choice of workflow depends on your research question and resources available for study curation. +We reccomend starting with the **manual meta-analysis** tutorial if you are new.
@@ -49,3 +53,9 @@ ends of the spectrum, depending on your needs. Learn more about the different wo
+ + +### All tutorials +Select a tutorial to get started. + + diff --git a/docs/tutorial/manual.md b/docs/tutorial/manual.md index 2b22877..c3852a0 100644 --- a/docs/tutorial/manual.md +++ b/docs/tutorial/manual.md @@ -4,6 +4,7 @@ sidebar_position: 2 --- # Manual Meta-Analysis +*How to create a custom, manual meta-analysis.* import { Card, CardContent, Typography, Button } from '@mui/material'; import { FaDownload } from 'react-icons/fa' diff --git a/docs/tutorial/mkda_association.md b/docs/tutorial/mkda_association.md new file mode 100644 index 0000000..a70d3a5 --- /dev/null +++ b/docs/tutorial/mkda_association.md @@ -0,0 +1,77 @@ +--- +sidebar_label: 'MKDA Chi-Squared Association' +sidebar_position: 3 +--- + +# MKDA Chi-Squared and large-scale association tests +*How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis* + +import { Card, CardContent, Typography, Button } from '@mui/material'; +import { FaDownload } from 'react-icons/fa' + +## The Problem + +A key feature that set aside the original Neurosynth platform was large-scale “association" maps (which we previously called "reverse inference" maps). + +In a neuroimaging meta-analysis, researchers pool a set of studies that invoke a common psychological construct or task in order to determine if and where brain activity is consistently activated. For example, in our manual meta-analysis tutorial, we selected 13 studies where subject underwent brain imaging during nicotine administration. By combining the results of individual studies with a meta-analysis (e.g. with the ALE or MKDA Density algorithms), we can see which brain regions that consistently activate for this task. + +Although this is a useful approach, there is a significant inferential challenge-- namely, determining how specific the relationship between activity in a given region and the cognitive state invoked by the target task. This is difficult, in part because brain regions vary widely in how specifically they activate for different tasks. Some brain regions, such as the insula or lateral prefrontal cortex, play a very broad role in cognition, and hence consistently activate for many different tasks and cognitive constructs. + +Thus, perhaps a more useful question is not only how consistently a given set of studies of a given task elicit brain activity, but if this brain activity occurs *more consistently* for these studies compared to a large, diverse reference dataset of studies. + +Back to our example, we want to know if nicotine administration shows *more consistent activity* than all other studies that do not perform nicotine administration, and if so where? + +## MKDA Chi-Squared association test + +One way to answer this question is by comparing activity in a given set of studies to a large reference dataset. The Neurosynth dataset is a natural candidate, as this dataset encompasses a sample of tens of thousands of studies automatically retrieved from the neuroimaging literature. This should provide a reasonable (although imperfect) baseline to control for base rate differences across the brain. + +We can perform this test using the `Multilevel kernel density (MKDA) analysis- Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, can then test if studies in our meta-analysis consistent activate greater than what we would expect given the large-scale Neurosynth dataset. + +:::info +**What happened to the "forward inference" and "reverse inference" analysis** + +On Neurosynth.org, we renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps, performed by the MKDA Chi-Squared algorithm. + +Although the method we used hasn't change,d the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for probabilistic maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from frequentist inferential tests. These maps are more difficult to interpret and use correctly, which is why we don't currently support this approach. +::: + +## Specifying MKDA Chi-Squared Meta-Analysis + +Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of studies as you would for any other meta-analysis, using either automated or manual selection methods. + +In Step 3 ("Create Meta-Analysis Specification") of your Project, select *MKDAChi2* as the *algorithm*. + + +![MKDA Chi Squared](/tutorial/mkda_chi_squared_algo.png). + +Next, select the annotation inclusion column you want to use, as before (by default, the "included" column will be used). + +Now, select a reference dataset from the dropdown list below. The Neurosynth dataset represents the latest release of the legacy *Neurosynth* dataset (version 7), released July, 2018. The *Neurostore* dataset represents the latest update of our continuously updating "live" dataset, spanning over 20,000 neuroimaging studies. + +![MKDA Chi Squared Reference](/tutorial/mkda_chi_squared_reference.png). + +Now simply complete the rest of the meta-analysis specification wizard to finish. + +## Executing your analysis + +The *MKDA Chi-Squared* algorithm is more computational expensive than a traditional *MKDA Density* analysis. As a result, it's unlikely to complete with the freely available resources available on Google Colab. + +You can run this workflow locally in one line using docker by copying the command your screen. See the *execution* documentation page for more information. + +:::tip +We recommend at least 32GB of RAM to perform a MKDA Chi-Squared analysis +::: + +## Intepreting results + +- Consistency Maps +- Association Maps +- Not getting carried away with results + +## Further Reading +Pair-wise tests, ALE SUbstraction.... + + + + + diff --git a/static/img/compose_ecosystem_paths.png b/static/img/compose_ecosystem_paths.png new file mode 100644 index 0000000000000000000000000000000000000000..86d29220a2caf3bda1d3a6003f07959294330e71 GIT binary patch literal 54178 zcmX_n1yCGK7ws(WEWzD^1&82nA0c>fClK7-B}kAUA-EIVHMo25;O_43_9lP5_iAgl zYI+u0%P7QpTUua)p`l{w)Jb zRDe}yXJ&74;P;wxjPd}AJJ^kbjodvdnw3Nx?H>1TYVzG>Cy!G>QFGNKQx|z4#Wr>S zi}>)4dhcJxt}A#7BT>@?ti)U#@593k{-P5j?*zZq%;|z1W&}9N4L;K(?sJ6m;d~qW z6}qU(pQUdyv7=L+MK`3&8OhomSels5lCd~rGw7nA-+M9C`IF-1BHdGXi!J#_@Te9{m28&ikpm+<+=kyqtr8 z4^Jr=Vg5R>z4O$%tZOQlhanAUG9cMC#z8uU8EM+|CKph+ghLXB)$m+2umNf^b7vQ| 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HcmV?d00001 From d3ad2cb6ace69cf6847664d41423fdd9742e5d4a Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Wed, 20 Dec 2023 15:24:20 -0600 Subject: [PATCH 03/16] Update MKDA tutorial --- docs/tutorial/advanced/index.mdx | 12 +++ docs/tutorial/advanced/mkda_association.md | 92 +++++++++++++++++++++ docs/tutorial/automated.md | 2 +- docs/tutorial/index.mdx | 13 ++- docs/tutorial/manual.md | 2 +- docs/tutorial/mkda_association.md | 77 ----------------- static/tutorial/neurosynth_paper_fig2.jpg | Bin 0 -> 89141 bytes 7 files changed, 112 insertions(+), 86 deletions(-) create mode 100644 docs/tutorial/advanced/index.mdx create mode 100644 docs/tutorial/advanced/mkda_association.md delete mode 100644 docs/tutorial/mkda_association.md create mode 100644 static/tutorial/neurosynth_paper_fig2.jpg diff --git a/docs/tutorial/advanced/index.mdx b/docs/tutorial/advanced/index.mdx new file mode 100644 index 0000000..7e15190 --- /dev/null +++ b/docs/tutorial/advanced/index.mdx @@ -0,0 +1,12 @@ +--- +title: Advanced tutorials +sidebar_position: 3 +--- + +import DocCardList from '@theme/DocCardList'; + +# Advanced tutorials + +After you've completed the core Manual and Advanced tutorials, you can continue your learning journey with these advanced tutorials. + + diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md new file mode 100644 index 0000000..48397ed --- /dev/null +++ b/docs/tutorial/advanced/mkda_association.md @@ -0,0 +1,92 @@ +--- +sidebar_label: 'MKDA Chi-Squared Association' +sidebar_position: 3 +--- + +# MKDA Chi-Squared and large-scale association tests +*How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis* + +import { Card, CardContent, Typography, Button } from '@mui/material'; +import { FaDownload } from 'react-icons/fa' + +## The Problem + +A key feature that sets aside the Neurosynth platform is large-scale “association" maps (which we previously called "reverse inference" maps). + +In a neuroimaging meta-analysis, researchers pool a set of studies that invoke a common psychological construct or task to determine where brain activity is consistently activated. For example, in our [manual meta-analysis](../manual) tutorial, we identified 13 studies where subjects underwent nicotine administration. By combining the results of individual studies, we can see which brain regions that consistently activate for this task. + +Although this is a useful approach, there is a significant inferential challenge-- namely, determining how *specific* the relationship between activity in a given region and the cognitive state invoked by the target task. This is difficult, in part because brain regions vary widely in how specifically they activate for different tasks. Some brain regions, such as the insula or lateral prefrontal cortex, play a very broad role in cognition, and hence consistently activate for many different tasks and cognitive constructs. + +Thus, perhaps a more useful question is if and where brain activity occurs *more consistently* for studies investigating a task or construct (in our case, nictoine administration) than studies that *do not* elicit that task or construct. The Neurosynth dataset (or any other large-scale neuroimaging datasets) is a useful reference, as it consists of tens of thousands of diverse neuroimaging studies automatically sample from the literature. + +## MKDA Chi-Squared + +In our example we want to know if and where studies of nicotine administration show more consistent brain activiation, than *all other studies* in the Neurosynth database (15,000+ studies). + +We can perform this test using the `Multilevel kernel density (MKDA) analysis- Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, can then test if a greater proportion of studies in our meta-analysis consistently activate a given voxel greater than what we observe in a large set of studies that we did not select for our meta-analysis. + +Conceptually, this tests if there's evidence of a *population level* association between the task or psychological construct in our meta-analysis and brain activation (for every voxel). + +:::info +**What happened to the "forward inference" and "reverse inference" analysis?** + +On Neurosynth.org, we renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps, performed by the MKDA Chi-Squared algorithm. + +Although the method we used hasn't change,d the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for probabilistic maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from frequentist inferential tests. These maps are more difficult to interpret and use correctly, which is why we don't currently support this approach. + +See "Further Reading" below to read more about why this change was made +::: + +## Specifying MKDA Chi-Squared Meta-Analysis + +Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of studies as you would for any other meta-analysis, using either automated or manual selection methods. + +In Step 3 ("Create Meta-Analysis Specification") of your Project, select *MKDAChi2* as the *algorithm*. + + +![MKDA Chi Squared](/tutorial/mkda_chi_squared_algo.png). + +Next, select the annotation inclusion column you want to use, as before (by default, the "included" column will be used). + +Now, select a reference dataset from the dropdown list below. The Neurosynth dataset represents the latest release of the legacy *Neurosynth* dataset (version 7), released July, 2018. The *Neurostore* dataset represents the latest update of our continuously updating "live" dataset, spanning over 20,000 neuroimaging studies. + +![MKDA Chi Squared Reference](/tutorial/mkda_chi_squared_reference.png). + +Now simply complete the rest of the meta-analysis specification wizard to finish. + +## Executing your analysis + +The *MKDA Chi-Squared* algorithm is more computational expensive than a traditional *MKDA Density* analysis. As a result, it's unlikely to complete with the freely available resources available on Google Colab. + +You can run this workflow locally in one line using docker by copying the command your screen. See the *execution* documentation page for more information. + +:::tip +We recommend at least 32GB of RAM to perform a MKDA Chi-Squared analysis +::: + +## Interpreting results + +![Neurosynth Maps](/tutorial/neurosynth_paper_fig2.jpg). + +**Figure 1**. Comparison of Uniformity and Association maps from the original Neurosynth article, on three automatically generated meta-analyses. + +The *MKDA Chi-Squared* Workflow outputs two types of maps: **uniformity** and **association** test maps. + +- **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. + +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, for a meta-analysis of "emotion" 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 other coordinate based meta-analysis algorithms (e.g., ALE or MKDA), z-scores aren't generated through permutation, but using a chi-square test + +- **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. + +The association test maps tell you whether activation in a region occurs more consistently for studies in your meta-analysis m than for other studies in the reference dataset. In other words, a large positive z-score implies that studies in a meta-analysis are more likely to report XXX activation than studies whose abstracts don't include the word 'emotion'. + +Note that association maps *do not* tell you what the probability of a given psychological concept or task is. High Z-scores do not imply that a certain region or voxel is *selective* for a given concept or task. Instead, it just means there is evidence that there is at least a non-zero difference between reference studies, and studies in the meta-analysis. + +## Further Reading + +If you want to understand the nuances of what inferences you can and cannot make using these maps, we recommend reading Tal Yarkoni's blog posts on how these maps do not provide evidence that the dACC is select for pain: [Post 1](https://www.talyarkoni.org/blog/2015/12/05/no-the-dorsal-anterior-cingulate-is-not-selective-for-pain-comment-on-lieberman-and-eisenberger-2015/), [Post 2](https://www.talyarkoni.org/blog/2015/12/14/still-not-selective-comment-on-comment-on-comment-on-lieberman-eisenberger-2015/), as well as a commentary by [Tor Wager et al., 2016](https://www.pnas.org/doi/10.1073/pnas.1600282113) + + + + + diff --git a/docs/tutorial/automated.md b/docs/tutorial/automated.md index 5e15ffc..0520b77 100644 --- a/docs/tutorial/automated.md +++ b/docs/tutorial/automated.md @@ -1,6 +1,6 @@ --- sidebar_label: 'Automated Meta-Analysis' -sidebar_position: 3 +sidebar_position: 2 --- # Automated Meta-Analysis diff --git a/docs/tutorial/index.mdx b/docs/tutorial/index.mdx index 898d986..2331d06 100644 --- a/docs/tutorial/index.mdx +++ b/docs/tutorial/index.mdx @@ -7,12 +7,12 @@ import { Grid, Card, CardContent, Typography, Button } from '@mui/material'; import { Link } from 'react-router-dom'; import DocCardList from '@theme/DocCardList'; -### Overview +### Quickstart -Neurosynth Compose supports a range of different workflows, -from exploratory large-scale automated analyses to highly curated and rigorous manual analyses. +Neurosynth Compose supports a range of workflows, +from exploratory large-scale automated analyses to highly rigorous manual analyses. -The choice of workflow depends on your research question and resources available for study curation. +The choice of workflow depends on your research question and resources available for manual curation. We reccomend starting with the **manual meta-analysis** tutorial if you are new.
@@ -55,7 +55,6 @@ We reccomend starting with the **manual meta-analysis** tutorial if you are new.
-### All tutorials -Select a tutorial to get started. +### Advanced tutorials +After you've completed the core tutorials above, you can continue your learning journey with [advanced tutorials](./advanced). - diff --git a/docs/tutorial/manual.md b/docs/tutorial/manual.md index c3852a0..d593e0d 100644 --- a/docs/tutorial/manual.md +++ b/docs/tutorial/manual.md @@ -1,6 +1,6 @@ --- sidebar_label: 'Manual Meta-Analysis' -sidebar_position: 2 +sidebar_position: 1 --- # Manual Meta-Analysis diff --git a/docs/tutorial/mkda_association.md b/docs/tutorial/mkda_association.md deleted file mode 100644 index a70d3a5..0000000 --- a/docs/tutorial/mkda_association.md +++ /dev/null @@ -1,77 +0,0 @@ ---- -sidebar_label: 'MKDA Chi-Squared Association' -sidebar_position: 3 ---- - -# MKDA Chi-Squared and large-scale association tests -*How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis* - -import { Card, CardContent, Typography, Button } from '@mui/material'; -import { FaDownload } from 'react-icons/fa' - -## The Problem - -A key feature that set aside the original Neurosynth platform was large-scale “association" maps (which we previously called "reverse inference" maps). - -In a neuroimaging meta-analysis, researchers pool a set of studies that invoke a common psychological construct or task in order to determine if and where brain activity is consistently activated. For example, in our manual meta-analysis tutorial, we selected 13 studies where subject underwent brain imaging during nicotine administration. By combining the results of individual studies with a meta-analysis (e.g. with the ALE or MKDA Density algorithms), we can see which brain regions that consistently activate for this task. - -Although this is a useful approach, there is a significant inferential challenge-- namely, determining how specific the relationship between activity in a given region and the cognitive state invoked by the target task. This is difficult, in part because brain regions vary widely in how specifically they activate for different tasks. Some brain regions, such as the insula or lateral prefrontal cortex, play a very broad role in cognition, and hence consistently activate for many different tasks and cognitive constructs. - -Thus, perhaps a more useful question is not only how consistently a given set of studies of a given task elicit brain activity, but if this brain activity occurs *more consistently* for these studies compared to a large, diverse reference dataset of studies. - -Back to our example, we want to know if nicotine administration shows *more consistent activity* than all other studies that do not perform nicotine administration, and if so where? - -## MKDA Chi-Squared association test - -One way to answer this question is by comparing activity in a given set of studies to a large reference dataset. The Neurosynth dataset is a natural candidate, as this dataset encompasses a sample of tens of thousands of studies automatically retrieved from the neuroimaging literature. This should provide a reasonable (although imperfect) baseline to control for base rate differences across the brain. - -We can perform this test using the `Multilevel kernel density (MKDA) analysis- Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, can then test if studies in our meta-analysis consistent activate greater than what we would expect given the large-scale Neurosynth dataset. - -:::info -**What happened to the "forward inference" and "reverse inference" analysis** - -On Neurosynth.org, we renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps, performed by the MKDA Chi-Squared algorithm. - -Although the method we used hasn't change,d the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for probabilistic maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from frequentist inferential tests. These maps are more difficult to interpret and use correctly, which is why we don't currently support this approach. -::: - -## Specifying MKDA Chi-Squared Meta-Analysis - -Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of studies as you would for any other meta-analysis, using either automated or manual selection methods. - -In Step 3 ("Create Meta-Analysis Specification") of your Project, select *MKDAChi2* as the *algorithm*. - - -![MKDA Chi Squared](/tutorial/mkda_chi_squared_algo.png). - -Next, select the annotation inclusion column you want to use, as before (by default, the "included" column will be used). - -Now, select a reference dataset from the dropdown list below. The Neurosynth dataset represents the latest release of the legacy *Neurosynth* dataset (version 7), released July, 2018. The *Neurostore* dataset represents the latest update of our continuously updating "live" dataset, spanning over 20,000 neuroimaging studies. - -![MKDA Chi Squared Reference](/tutorial/mkda_chi_squared_reference.png). - -Now simply complete the rest of the meta-analysis specification wizard to finish. - -## Executing your analysis - -The *MKDA Chi-Squared* algorithm is more computational expensive than a traditional *MKDA Density* analysis. As a result, it's unlikely to complete with the freely available resources available on Google Colab. - -You can run this workflow locally in one line using docker by copying the command your screen. See the *execution* documentation page for more information. - -:::tip -We recommend at least 32GB of RAM to perform a MKDA Chi-Squared analysis -::: - -## Intepreting results - -- Consistency Maps -- Association Maps -- Not getting carried away with results - -## Further Reading -Pair-wise tests, ALE SUbstraction.... - - - - - diff --git a/static/tutorial/neurosynth_paper_fig2.jpg b/static/tutorial/neurosynth_paper_fig2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..44fe571b1c3a27dc24d54c47144d09f177e2af70 GIT binary patch literal 89141 zcmbTdWmp``7A`!v28ZB7fP?_SA;AX;G7uQtU4jhmu7ThhAh^2)8C-*VaCdiiyX+(T zob&zq?yY{-Q>(hGYie~>SFd^3ThBAkD*zlRaj-Z54h{|w|8fAHS0&zyxtM(g0Ki}d z04e|gKmp*vApqcBw7-ND4(T7c!HZ7zFWvk_fB&yK@Gm+W96aFFOK$ff>@PaOOYZX` zX5SJ2aqo{8J?}+ce(oRTf9Dcl896F;W;Rx44%U}DS=l*w+1Pp6S*h5#c-h%`IoV!n z!)5%ZFE6(P&;U<>f42Hp?*MTB)c4g8% z|EdQE|Ic5Z3JMw;Dmo^? zD@+UwOcFeNYyt{WASDGUIXM+AGaVK62O4tn_n+uLu(EM-asugi1i0DxnK?Mw|7rvd 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deletions(-) create mode 100644 static/tutorial/lobos_pinto_figa.png diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md index 649ca1d..5c0aef5 100644 --- a/docs/tutorial/advanced/mkda_association.md +++ b/docs/tutorial/advanced/mkda_association.md @@ -4,7 +4,7 @@ sidebar_position: 3 --- # MKDA Chi-Squared and large-scale association tests -*How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis* +*How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis, with a Social Processing example* import { Card, CardContent, Typography, Button } from '@mui/material'; import { FaDownload } from 'react-icons/fa' @@ -13,16 +13,18 @@ import { FaDownload } from 'react-icons/fa' A key feature that sets aside the Neurosynth platform is large-scale “association" maps (which we previously called "reverse inference" maps). -In a neuroimaging meta-analysis, researchers pool a set of studies that invoke a common psychological construct or task to determine where brain activity is consistently activated. For example, in our [manual meta-analysis](../manual) tutorial, we identified 13 studies where subjects underwent nicotine administration. By combining the results of individual studies, we can see which brain regions consistently activate for this task. +In a neuroimaging meta-analysis, researchers pool a set of studies that invoke a common psychological construct or task to determine where brain activity is consistently activated. Although this is a useful approach, there is a significant inferential challenge-- namely, determining how *specific* the relationship between activity in a given region and the cognitive state invoked by the target task. This is difficult, in part because brain regions vary widely in how specifically they activate for different tasks. Some brain regions, such as the insula or lateral prefrontal cortex, play a very broad role in cognition, and hence consistently activate for many different tasks and cognitive constructs. -Thus, perhaps a more useful question is if and where brain activity occurs *more consistently* for studies investigating a task or construct (in our case, nicotine administration) than studies that *do not* elicit that task or construct. The Neurosynth dataset (or any other large-scale neuroimaging datasets) is a useful reference, as it consists of tens of thousands of diverse neuroimaging studies automatically sample from the literature. +For example, in a recent high-quality meta-analysis of RDoC social constructs across 864 fMRI contrasts, [Pintos Lobo et al., (2022)](https://pubmed.ncbi.nlm.nih.gov/36436737/) found converging activation across a variety of regions for "All Social Processing Tasks", including mPFC, anterior cingulate cortex (ACC), PCC, TPJ, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. However, some of these regions are known to be involved general task maintenance and other domain-general processes, making it difficult to know how specific the relationship is between activity in these regions and social processing. + +![Lobos Pinto](/tutorial/lobos_pinto_figa.png) + +Thus, perhaps a more useful question is if brain activity occurs *more consistently* for studies investigating a task or construct (in this case, social processing) than studies that *do not* elicit that task or construct. The Neurosynth dataset (or any other large-scale neuroimaging datasets) is a useful reference, as it consists of tens of thousands of diverse neuroimaging studies automatically sample from the literature. ## MKDA Chi-Squared -In our example we want to know if and where studies of nicotine administration show more consistent brain activation, than *all other studies* in the Neurosynth database (15,000+ studies). - We can perform this test using the `Multilevel kernel density (MKDA) analysis - Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that *we did not select* for our inclusion criteria. Conceptually, this tests if there's evidence of a *population level* association between the task or psychological construct in our meta-analysis and brain activation (for every voxel). It is equivalent to conducting a chi-squared test of independence for a 2-by-2 table of counts for each voxel, where the binary variables are foci occurrence in the meta-analysis of interest and foci occurrence in the reference set of unselected studies. @@ -39,38 +41,32 @@ See "Further Reading" below for more information on this problem. ## Specifying MKDA Chi-Squared Meta-Analysis -:::tip -For this example, we'll use the default parameters for the `MKDAChi2` algorithm. -For in depth documentation on the specific parameters of each algorithm, refer to the NiMARE documentation: [`MKDAChi2`](https://nimare.readthedocs.io/en/stable/generated/nimare.meta.cbma.mkda.MKDAChi2.html) - Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of Analyses to include from your StudySet as you would for any other meta-analysis. In Step 3 ("Create Meta-Analysis Specification") of your Project, select *MKDAChi2* as the *algorithm*. +![MKDA Chi Squared](/tutorial/mkda_chi_squared_algo.png) -![MKDA Chi Squared](/tutorial/mkda_chi_squared_algo.png). +:::note +By default, the `FDRCorrector` is selected, which will perform cluster correction using False Detection Rate with an *alpha* of 0.05. +This is a fast algorithm, however, it is recommended to use `FWECorrector` (family-wise-error) with the `montecarlo` method for more accurate, publication-quality results. +::: Next, select the annotation inclusion column you want to use, as before (by default, the "included" column will be used). Now, select a reference dataset from the dropdown list below. The Neurosynth dataset represents the latest release of the legacy *Neurosynth* dataset (version 7), released July, 2018. The *Neurostore* dataset represents the latest update of our continuously updating "live" dataset, spanning over 20,000 neuroimaging studies. -![MKDA Chi Squared Reference](/tutorial/mkda_chi_squared_reference.png). +![MKDA Chi Squared Reference](/tutorial/mkda_chi_squared_reference.png) Now simply complete the rest of the meta-analysis specification wizard to finish. -:::tip -When comparing a manually-curated meta-analysis to a reference dataset there are several potential issues to be aware of. First, the reference dataset is composed of automatically extracted coordinates, while a manual curated dataset has corrected coordinate data. Second, unless you include all relevant studies in your target StudySet, some studies relevant to your question of interest may remain in the reference dataset, modestly biasing results -::: - - ## Executing your analysis -The *MKDA Chi-Squared* algorithm is more computational expensive than a traditional *MKDA Density* analysis. As a result, it's unlikely to complete with the freely available resources available on Google Colab. - -You can run this workflow locally in one line using docker by copying the command your screen. See the *execution* documentation page for more information. +As usual, you can execute your meta-analysis using Google Colab or on a local computational resource using Docker. -:::tip -We recommend at least 32GB of RAM to perform a MKDA Chi-Squared analysis +:::tip +The `MKDAChi2` algorithm should take between ~30s-2minutes to execute. However, that the `FWECorrector` with 5,000+ montecarlo iterations can take several hours to complete. +We recommend executing this on a workstation or high performance computer with multiple parallel cores ::: ## Interpreting results @@ -87,8 +83,21 @@ The association test maps tell you whether activation in a region **XXX** occurs Note that association maps *do not* tell you what the probability of a given psychological concept or task is. High Z-scores do not imply that a certain region or voxel is *selective* for a given concept or task. Instead, it just means there is evidence that there is at least a non-zero difference between reference studies, and studies in the meta-analysis. +## Example: Pintos Lobo (2022) - All Social Processing Tasks + +To demonstrate, we used Neurosynth-Compose to replicate the `Pintos Lobo et al., (2022)` meta-analysis we referenced earlier. For this example, we have already created a `Project` and `StudySet` with the coordinates used in this meta-analysis. + +We then specified a `MKDAChi2` Meta-Analysis with `FWECorrector` with the `montecarlo` method with 5,000 iterations. + +### Add Neurovault Links +### Add Links to Output +### Explain results + + ## Further Reading +#### Add Link to James's Notebook + If you want to understand the nuances of what inferences you can and cannot make using these maps, we recommend reading Tal Yarkoni's blog posts on how these maps do not provide evidence that the dACC is select for pain: [Post 1](https://www.talyarkoni.org/blog/2015/12/05/no-the-dorsal-anterior-cingulate-is-not-selective-for-pain-comment-on-lieberman-and-eisenberger-2015/), [Post 2](https://www.talyarkoni.org/blog/2015/12/14/still-not-selective-comment-on-comment-on-comment-on-lieberman-eisenberger-2015/), as well as a commentary by [Tor Wager et al., 2016](https://www.pnas.org/doi/10.1073/pnas.1600282113) diff --git 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-> 0 bytes static/tutorial/pinto_lobos_figa.png | Bin 0 -> 84166 bytes ...ster_corr-FWE_method-montecarlo.nii.gz.png | Bin 0 -> 32805 bytes ...ster_corr-FWE_method-montecarlo.nii.gz.png | Bin 0 -> 33084 bytes static/tutorial/prob-A_neurosynth.png | Bin 0 -> 30608 bytes 6 files changed, 61 insertions(+), 29 deletions(-) delete mode 100644 static/tutorial/lobos_pinto_figa.png create mode 100644 static/tutorial/pinto_lobos_figa.png create mode 100644 static/tutorial/pinto_lobos_z_desc-associationMass_level-cluster_corr-FWE_method-montecarlo.nii.gz.png create mode 100644 static/tutorial/pinto_lobos_z_desc-uniformityMass_level-cluster_corr-FWE_method-montecarlo.nii.gz.png create mode 100644 static/tutorial/prob-A_neurosynth.png diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md index 5c0aef5..fa5f9c6 100644 --- a/docs/tutorial/advanced/mkda_association.md +++ b/docs/tutorial/advanced/mkda_association.md @@ -9,37 +9,33 @@ sidebar_position: 3 import { Card, CardContent, Typography, Button } from '@mui/material'; import { FaDownload } from 'react-icons/fa' -## The Problem +## The Reverse Inference Problem -A key feature that sets aside the Neurosynth platform is large-scale “association" maps (which we previously called "reverse inference" maps). +A common goal of neuroimaging meta-analysis, is to pool a set of studies that invoke common psychological constructs to identify where brain activity is consistently activated. -In a neuroimaging meta-analysis, researchers pool a set of studies that invoke a common psychological construct or task to determine where brain activity is consistently activated. +Although this is a useful approach, there is a significant inferential challenge-- namely, determining how *specific* the relationship between activity in a given region and the cognitive state invoked by the target task. Ideally, we would like to infer the probability of a mental state given activity in a given region. However, this is exceedingly difficult due to the well-established problem of *reverse inference* (Poldrack, 2011). -Although this is a useful approach, there is a significant inferential challenge-- namely, determining how *specific* the relationship between activity in a given region and the cognitive state invoked by the target task. This is difficult, in part because brain regions vary widely in how specifically they activate for different tasks. Some brain regions, such as the insula or lateral prefrontal cortex, play a very broad role in cognition, and hence consistently activate for many different tasks and cognitive constructs. +Even if we establish that a given task (e.g. working memory) activates a region 100% of the time (e.g. lateral prefrontal cortex), this observation only establishes that working memory engagement is a sufficient condition for LPFC activity, but *not* that LPFC activity indicates working memory engagement (Poldrack & Yarkoni, 2015). In practice, we know that brain regions are activated by a variety of cognitive processes, and that certain regions of the brain- such as the insula, lateral PFC and medial frontal cingulate cotex (MFCC)- have a high base rate of activation, making it difficult to establish specificity. Using the Neurosynth database (15,000+ studies), we can map the probability of activation of all voxels. Across this large and diverse dataset, certain voxels in MFCC and insula are activate in as many as 20% of studies. -For example, in a recent high-quality meta-analysis of RDoC social constructs across 864 fMRI contrasts, [Pintos Lobo et al., (2022)](https://pubmed.ncbi.nlm.nih.gov/36436737/) found converging activation across a variety of regions for "All Social Processing Tasks", including mPFC, anterior cingulate cortex (ACC), PCC, TPJ, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. However, some of these regions are known to be involved general task maintenance and other domain-general processes, making it difficult to know how specific the relationship is between activity in these regions and social processing. +![Prob-A](/tutorial/prob-A_neurosynth.png) +*Probability of Activity for all Voxels across the Neurosynth Dataset* -![Lobos Pinto](/tutorial/lobos_pinto_figa.png) +The reverse inference problem is a challenge even for rigorous, high-quality meta-analyses. For example, a recent meta-analysis of RDoC social constructs across 864 fMRI contrasts, [Pintos Lobo et al., (2022)](https://pubmed.ncbi.nlm.nih.gov/36436737/) found converging activation across a variety of regions for "All Social Processing Tasks", including mPFC, ACC, PCC, TPJ, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. However, some of the regions have a high base rate of activation, making it difficult to know how strongly associated their activity is with social processing. -Thus, perhaps a more useful question is if brain activity occurs *more consistently* for studies investigating a task or construct (in this case, social processing) than studies that *do not* elicit that task or construct. The Neurosynth dataset (or any other large-scale neuroimaging datasets) is a useful reference, as it consists of tens of thousands of diverse neuroimaging studies automatically sample from the literature. +![Lobos Pinto](/tutorial/pinto_lobos_figa.png) + +*Fig 5a (condensed) from Pintos Lobo et al., (2022). Convergent Activation Patterns Across all social processing tasks (864 contrasts, 1,109 total annotations). * + +Although reverse inference poses a serious challenge, there are certain questions we can ask using large-scale meta-analytic databases that can help. Specifically: **does activity occur *more consistently* for studies that elicit by the mental construct of interest (in this case, social processing) than studies that *do not* elicit that construct** Large-scale meta-analytic datasets can serve as a useful reference, as they consists of tens of thousands of diverse neuroimaging studies automatically sampled from the literature. ## MKDA Chi-Squared -We can perform this test using the `Multilevel kernel density (MKDA) analysis - Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that *we did not select* for our inclusion criteria. +We can answer this question using a `Multilevel kernel density (MKDA) analysis - Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that *we did not select* for our inclusion criteria. Conceptually, this tests if there's evidence of a *population level* association between the task or psychological construct in our meta-analysis and brain activation (for every voxel). It is equivalent to conducting a chi-squared test of independence for a 2-by-2 table of counts for each voxel, where the binary variables are foci occurrence in the meta-analysis of interest and foci occurrence in the reference set of unselected studies. -:::info -**What happened to the "forward inference" and "reverse inference" analysis?** - -On Neurosynth.org, we renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps that we discuss here. - -Although the method we used hasn't changed (MKDA Chi-Squared), the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for posterior probability maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. Probability maps are more difficult to interpret and use correctly, as they depend on the *prior* assumed by the researcher. Since setting an appropriate prior is highly non-trivial, these maps are disabled by default. - -See "Further Reading" below for more information on this problem. -::: - -## Specifying MKDA Chi-Squared Meta-Analysis +## How to run MKDA Chi-Squared on Neurosynth Compose +### Specification Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of Analyses to include from your StudySet as you would for any other meta-analysis. @@ -60,18 +56,18 @@ Now, select a reference dataset from the dropdown list below. The Neurosynth dat Now simply complete the rest of the meta-analysis specification wizard to finish. -## Executing your analysis +### Executing your analysis As usual, you can execute your meta-analysis using Google Colab or on a local computational resource using Docker. :::tip -The `MKDAChi2` algorithm should take between ~30s-2minutes to execute. However, that the `FWECorrector` with 5,000+ montecarlo iterations can take several hours to complete. -We recommend executing this on a workstation or high performance computer with multiple parallel cores +The `MKDAChi2` algorithm takes between ~30s-2minutes to run. However, the `FWECorrector` with 5,000+ montecarlo iterations can take several hours to complete. +We recommend using a workstation or HPC and specifying `--n-cores` at run-time. ::: ## Interpreting results -The *MKDA Chi-Squared* Workflow outputs two types of maps: **uniformity** and **association** test maps. +The *MKDA Chi-Squared* Workflow outputs two key maps: **uniformity** and **association** test maps. - **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. @@ -83,24 +79,60 @@ The association test maps tell you whether activation in a region **XXX** occurs Note that association maps *do not* tell you what the probability of a given psychological concept or task is. High Z-scores do not imply that a certain region or voxel is *selective* for a given concept or task. Instead, it just means there is evidence that there is at least a non-zero difference between reference studies, and studies in the meta-analysis. +:::note +*NiMARE* outputs a variety of maps, including cluster-corrected and uncorrected versions of all maps. + +See the documentation sections on [Outputs of NIMARE](https://nimare.readthedocs.io/en/stable/outputs.html) and [Monte Carlo multiple comparisons](https://nimare.readthedocs.io/en/stable/cbma.html#the-monte-carlo-multiple-comparisons-correction-method) for more details. +::: + ## Example: Pintos Lobo (2022) - All Social Processing Tasks -To demonstrate, we used Neurosynth-Compose to replicate the `Pintos Lobo et al., (2022)` meta-analysis we referenced earlier. For this example, we have already created a `Project` and `StudySet` with the coordinates used in this meta-analysis. +To demonstrate, we used Neurosynth-Compose to replicate the [Pintos Lobo et al., (2022)]([text](https://pubmed.ncbi.nlm.nih.gov/36436737/)) meta-analysis for All Social Processing Tasks. For this example, we have already created a `Project` and `StudySet` with the coordinates used in this meta-analysis. We then specified a `MKDAChi2` Meta-Analysis with `FWECorrector` with the `montecarlo` method with 5,000 iterations. -### Add Neurovault Links -### Add Links to Output -### Explain results + + +### Results + +First, let's look at the FWE cluster corrected **uniformity test** map. + +`z_desc-uniformityMass_level-cluster_corr-FWE_method-montecarlo` +![Uniformity](/tutorial/pinto_lobos_z_desc-uniformityMass_level-cluster_corr-FWE_method-montecarlo.nii.gz.png) + +In this analysis, we replicate the findings of Pinto Lobos (2022), showing consistent activation for social processing across a variety of regions. + +Next, let's look at the FWE cluster corrected **association map**: + +`z_desc-associationMass_level-cluster_corr-FWE_method-montecarlo` +![Association](/tutorial/pinto_lobos_z_desc-associationMass_level-cluster_corr-FWE_method-montecarlo.nii.gz.png) + +As before, regions which have been previously implicated with social processing, such as the tempo-parietal junction (TPJ), and dorso-medial and ventro-medial PFC are present, meaning that activity in these social processing studies report activity in these regions with greater frequency than other studies in the Neurosynth database. + +However, certain regions which we know to have low specificity, such as the insula, medial frontal cingulate cortex (MFCC) and parts of dorso-lateral PFC, are absent, meaning that there is *no evidence* that social processing tasks report activity in these regions *more frequently* than other studies in the database. + +This example demonstrates how `MKDA Chi-Squared` association analysis can help determine the specificity activity and tasks in a meta-analysis, even for high-quality manual meta-analyses. + + +## Footnotes & Caveats + +**What happened to the "forward inference" and "reverse inference" maps?** + +We renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps that we discuss here. + +Although the method we used hasn't changed (`MKDA Chi-Squared`), the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for posterior probability maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. Probability maps are more difficult to interpret and use correctly, as they depend on the *prior* assumed by the researcher. Since setting an appropriate prior is highly non-trivial, these maps are disabled by default. -## Further Reading +## References & Further Reading -#### Add Link to James's Notebook If you want to understand the nuances of what inferences you can and cannot make using these maps, we recommend reading Tal Yarkoni's blog posts on how these maps do not provide evidence that the dACC is select for pain: [Post 1](https://www.talyarkoni.org/blog/2015/12/05/no-the-dorsal-anterior-cingulate-is-not-selective-for-pain-comment-on-lieberman-and-eisenberger-2015/), [Post 2](https://www.talyarkoni.org/blog/2015/12/14/still-not-selective-comment-on-comment-on-comment-on-lieberman-eisenberger-2015/), as well as a commentary by [Tor Wager et al., 2016](https://www.pnas.org/doi/10.1073/pnas.1600282113) +Poldrack RA. Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron. 2011 Dec 8;72(5):692-7. doi: 10.1016/j.neuron.2011.11.001. PMID: 22153367; PMCID: PMC3240863. +Poldrack RA, Yarkoni T. From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. Annu Rev Psychol. 2016;67:587-612. doi: 10.1146/annurev-psych-122414-033729. Epub 2015 Sep 21. 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zO7-g7$soL-48Xx`wiWVMh~m;>2_sdt-xnO>S|{3q;CLl9MRgGfgL{r-FWS)y_Yx*+ zyd?@Iz#(<&b_we$WU1G@R;QpX;$q*_ao)xH{s!RUrh|i|B^^priD@!4 zvdw7TYuaLG#3=g@OHzwtNr;d|D9#OyhS>PJ4QRd(c0T3w! z>6CQl>$681tM7-~ac8?34cQmlCWc$U3wfiAibl5aFjTuM9yTU(0-ilvnWF6x2}uh{ov znjlhErhc*;3m@$O3aA0c;?}L!B3Y*fS4N`f<#FwkRsBS4=~6arXGrMH%GsDfU>Qc8s%pZ7`u2RcL`s z63P}UaHT!SqC>**0}NcOjXwCy3hZLQMNR||>aJx?7fVY8zkm~2k5EDaucwb< zZ!4erpF)@-005TWMK;wfgZhTystIMKjO_qtJwD}1V-z!wk!8W;!0SUc4H*K6MGF(H zS>Rzs%xVA%hgyOph=>v`8$n@BZeweT#8Gv?p_r^nW`VQ|)HMjGMj(UJS~VO+BV}*@ z06kCmuk`@X&i?@No2uJZz)5aU&6T(?9=Mcy6o@Q^ z+z>f@*vtT+YES?r6F7Xj^N?klj$RedpXfYev@e;2nk`#*4` z;;hjcpAF0{0oB6Imi`TgTvN4R&%cP+Uno6E5{kHQg0eyD@GT7Q2HCizhW7&6lsg>M zYHW^@RvE^ z*Rk9^qqCM4)PB$Ph{r3R{K}Hkhx$svYOM;#D8Os-NjM!EIFxjM3#r09{y1T&$@p~* zO&^1QfzR|C{5za|KjL%zeze|f%@c3K{u#tI_xN=U&J$_+AB`cb(m&eHe$Vgt|Jj~g BS04ZX literal 0 HcmV?d00001 From 179a9fd76144c30ed22bb83c20481dfcac67c0ef Mon Sep 17 00:00:00 2001 From: nicholst Date: Thu, 21 Dec 2023 08:45:48 +0000 Subject: [PATCH 04/16] Trivial and content edits to MKDA association --- docs/tutorial/advanced/mkda_association.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md index 48397ed..ca844e8 100644 --- a/docs/tutorial/advanced/mkda_association.md +++ b/docs/tutorial/advanced/mkda_association.md @@ -23,16 +23,16 @@ Thus, perhaps a more useful question is if and where brain activity occurs *more In our example we want to know if and where studies of nicotine administration show more consistent brain activiation, than *all other studies* in the Neurosynth database (15,000+ studies). -We can perform this test using the `Multilevel kernel density (MKDA) analysis- Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, can then test if a greater proportion of studies in our meta-analysis consistently activate a given voxel greater than what we observe in a large set of studies that we did not select for our meta-analysis. +We can perform this test using the `Multilevel kernel density (MKDA) analysis - Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that we did not select for our meta-analysis. -Conceptually, this tests if there's evidence of a *population level* association between the task or psychological construct in our meta-analysis and brain activation (for every voxel). +Conceptually, this tests if there's evidence of a *population level* association between the task or psychological construct in our meta-analysis and brain activation (for every voxel). It is equivalent to conducting a chi-squared test of independence for a 2-by-2 table of counts for each voxel, where the binary variables are foci occurrence in the meta-analysis of interest and foci occurrence in the reference set of unselected studies. :::info **What happened to the "forward inference" and "reverse inference" analysis?** On Neurosynth.org, we renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps, performed by the MKDA Chi-Squared algorithm. -Although the method we used hasn't change,d the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for probabilistic maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from frequentist inferential tests. These maps are more difficult to interpret and use correctly, which is why we don't currently support this approach. +Although the method we used hasn't changed, the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for probabilistic maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. These maps are more difficult to interpret and use correctly, which is why we don't currently support this approach. See "Further Reading" below to read more about why this change was made ::: From 412caff437b591f8e682ca9bf4d27d842f16646b Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Thu, 21 Dec 2023 12:24:10 -0600 Subject: [PATCH 05/16] Fix reverse inferense / prior discussion --- docs/tutorial/advanced/mkda_association.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md index ca844e8..bc1e8aa 100644 --- a/docs/tutorial/advanced/mkda_association.md +++ b/docs/tutorial/advanced/mkda_association.md @@ -13,28 +13,28 @@ import { FaDownload } from 'react-icons/fa' A key feature that sets aside the Neurosynth platform is large-scale “association" maps (which we previously called "reverse inference" maps). -In a neuroimaging meta-analysis, researchers pool a set of studies that invoke a common psychological construct or task to determine where brain activity is consistently activated. For example, in our [manual meta-analysis](../manual) tutorial, we identified 13 studies where subjects underwent nicotine administration. By combining the results of individual studies, we can see which brain regions that consistently activate for this task. +In a neuroimaging meta-analysis, researchers pool a set of studies that invoke a common psychological construct or task to determine where brain activity is consistently activated. For example, in our [manual meta-analysis](../manual) tutorial, we identified 13 studies where subjects underwent nicotine administration. By combining the results of individual studies, we can see which brain regions consistently activate for this task. Although this is a useful approach, there is a significant inferential challenge-- namely, determining how *specific* the relationship between activity in a given region and the cognitive state invoked by the target task. This is difficult, in part because brain regions vary widely in how specifically they activate for different tasks. Some brain regions, such as the insula or lateral prefrontal cortex, play a very broad role in cognition, and hence consistently activate for many different tasks and cognitive constructs. -Thus, perhaps a more useful question is if and where brain activity occurs *more consistently* for studies investigating a task or construct (in our case, nictoine administration) than studies that *do not* elicit that task or construct. The Neurosynth dataset (or any other large-scale neuroimaging datasets) is a useful reference, as it consists of tens of thousands of diverse neuroimaging studies automatically sample from the literature. +Thus, perhaps a more useful question is if and where brain activity occurs *more consistently* for studies investigating a task or construct (in our case, nicotine administration) than studies that *do not* elicit that task or construct. The Neurosynth dataset (or any other large-scale neuroimaging datasets) is a useful reference, as it consists of tens of thousands of diverse neuroimaging studies automatically sample from the literature. ## MKDA Chi-Squared -In our example we want to know if and where studies of nicotine administration show more consistent brain activiation, than *all other studies* in the Neurosynth database (15,000+ studies). +In our example we want to know if and where studies of nicotine administration show more consistent brain activation, than *all other studies* in the Neurosynth database (15,000+ studies). -We can perform this test using the `Multilevel kernel density (MKDA) analysis - Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that we did not select for our meta-analysis. +We can perform this test using the `Multilevel kernel density (MKDA) analysis - Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that *we did not select* for our inclusion criteria. Conceptually, this tests if there's evidence of a *population level* association between the task or psychological construct in our meta-analysis and brain activation (for every voxel). It is equivalent to conducting a chi-squared test of independence for a 2-by-2 table of counts for each voxel, where the binary variables are foci occurrence in the meta-analysis of interest and foci occurrence in the reference set of unselected studies. :::info **What happened to the "forward inference" and "reverse inference" analysis?** -On Neurosynth.org, we renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps, performed by the MKDA Chi-Squared algorithm. +On Neurosynth.org, we renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps that we discuss here. -Although the method we used hasn't changed, the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for probabilistic maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. These maps are more difficult to interpret and use correctly, which is why we don't currently support this approach. +Although the method we used hasn't changed (MKDA Chi-Squared), the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for posterior probability maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. Probability maps are more difficult to interpret and use correctly, as they depend on the *prior* assumed by the researcher. Since setting an appropriate prior is highly non-trivial, these maps are disabled by default. -See "Further Reading" below to read more about why this change was made +See "Further Reading" below for more information on this problem. ::: ## Specifying MKDA Chi-Squared Meta-Analysis From a1518e535e0240e7bdd868b7af58e4300e2e3198 Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Fri, 22 Dec 2023 16:28:10 -0600 Subject: [PATCH 06/16] Update docs/tutorial/advanced/mkda_association.md Co-authored-by: Taylor Salo --- docs/tutorial/advanced/mkda_association.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md index bc1e8aa..c313658 100644 --- a/docs/tutorial/advanced/mkda_association.md +++ b/docs/tutorial/advanced/mkda_association.md @@ -78,7 +78,7 @@ The uniformity test map can be interpreted in roughly the same way as most stand - **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. -The association test maps tell you whether activation in a region occurs more consistently for studies in your meta-analysis m than for other studies in the reference dataset. In other words, a large positive z-score implies that studies in a meta-analysis are more likely to report XXX activation than studies whose abstracts don't include the word 'emotion'. +The association test maps tell you whether activation in a region **XXX** occurs more consistently for studies in your meta-analytic sample **m** than for other studies in the reference dataset. In other words, a large positive z-score implies that studies in a meta-analysis are more likely to report **XXX** activation than studies whose abstracts don't include the word 'emotion'. Note that association maps *do not* tell you what the probability of a given psychological concept or task is. High Z-scores do not imply that a certain region or voxel is *selective* for a given concept or task. Instead, it just means there is evidence that there is at least a non-zero difference between reference studies, and studies in the meta-analysis. From cceb3f3f515a930e442ec937b26b4805170efc76 Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Fri, 22 Dec 2023 16:28:19 -0600 Subject: [PATCH 07/16] Update blog/2023-12-21-december.md Co-authored-by: Taylor Salo --- blog/2023-12-21-december.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/2023-12-21-december.md b/blog/2023-12-21-december.md index ac26211..81004d4 100644 --- a/blog/2023-12-21-december.md +++ b/blog/2023-12-21-december.md @@ -22,7 +22,7 @@ Whereas a typical meta-analysis tells you about the consistency of activation in Neurosynth leverages large-scale neuroimaging datasets as a reference to which you can compare the specific studies in given meta-analysis. -That's important, because this effectively allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence tend to be consistently activated for many different terms. Thus, if you see insula activity in your meta-analysis, you can erroneously conclude that the insula is involved in the process of your study. A large-scale association test lets you determine if the activity you observe in a region occurs to a greater extent in your meta-analysis than in other studies, making it possible to make more confident claims that a given region is involved in a particular process, and isn't involved in just about every task. +That's important, because this effectively allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence tend to be consistently activated for many different terms. Thus, if you see insula activity in your meta-analysis, you might erroneously conclude that the insula is involved in the process of your study. A large-scale association test lets you determine if the activity you observe in a region occurs to a greater extent in your meta-analysis than in other studies, making it possible to make more confident claims that a given region is involved in a particular process, and isn't involved in just about every task. Previously these association tests were available for the automatically generated maps on neurosynth.org. **Now you can perform large-scale association tests for your custom meta-analyses in Neurosynth Compose.** In addition, we also support other types of “pairwise” algorithms, such as ALE Subtraction, and you are free to choose from a variety of reference datasets, including the Neurosynth corpus. From 03a8964cbb3dea73767e80a33a7f78e06f56a4e4 Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Fri, 22 Dec 2023 16:28:54 -0600 Subject: [PATCH 08/16] Update blog/2023-12-21-december.md Co-authored-by: Taylor Salo --- blog/2023-12-21-december.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/2023-12-21-december.md b/blog/2023-12-21-december.md index 81004d4..972982f 100644 --- a/blog/2023-12-21-december.md +++ b/blog/2023-12-21-december.md @@ -16,7 +16,7 @@ We’ve also continued to introduce new features and improve the user experience **Large-scale association tests** -A key feature that set aside Neurosynth were large-scale “association tests” (previously referred to as “reverse inference” test) +A key feature that set aside Neurosynth were large-scale “association tests” (previously referred to as “reverse inference” tests). Whereas a typical meta-analysis tells you about the consistency of activation in a set of studies, an association test tells you whether activation in a region occurs *more consistently for studies in your set of studies versus a large reference sample of studies*. From b47a7fa4c32240c2d69fdb76106e63b7ea73e3c5 Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Tue, 2 Jan 2024 17:21:04 -0600 Subject: [PATCH 09/16] Update and rename --- ...3-12-21-december.md => 2024-1-2-new-year.md} | 15 +++++++++++---- docs/tutorial/advanced/mkda_association.md | 17 +++++++++++------ 2 files changed, 22 insertions(+), 10 deletions(-) rename blog/{2023-12-21-december.md => 2024-1-2-new-year.md} (83%) diff --git a/blog/2023-12-21-december.md b/blog/2024-1-2-new-year.md similarity index 83% rename from blog/2023-12-21-december.md rename to blog/2024-1-2-new-year.md index ac26211..26ea889 100644 --- a/blog/2023-12-21-december.md +++ b/blog/2024-1-2-new-year.md @@ -1,12 +1,14 @@ --- -slug: december-2023-updates -title: December 2023 Updates +slug: january-2024-updates +title: New Year Updates authors: alejandro tags: [neurosynth] --- Hello Neurosynth Users, -We're excited to share our December updates with you! It’s been a great month, having announced the release of Neurosynth Compose on social media. In the last month we’ve seen **over 1,200 new user visits**, with **200 users signing up for an account**! 🚀 +Happy New Year! + +2023 was a very exciting year for Neurosynth, having launched our Compose platform to the public and announced it on social media. In the December we’ve saw **over 1,200 new user visits**, with **200 users signing up for an account**! 🚀 Help us keep this growth going by [sharing our announcement](http://localhost:3000/compose-docs/blog/announcing-ns-compose) with your colleagues. 🧑‍🔬 @@ -24,7 +26,12 @@ Neurosynth leverages large-scale neuroimaging datasets as a reference to which y That's important, because this effectively allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence tend to be consistently activated for many different terms. Thus, if you see insula activity in your meta-analysis, you can erroneously conclude that the insula is involved in the process of your study. A large-scale association test lets you determine if the activity you observe in a region occurs to a greater extent in your meta-analysis than in other studies, making it possible to make more confident claims that a given region is involved in a particular process, and isn't involved in just about every task. -Previously these association tests were available for the automatically generated maps on neurosynth.org. **Now you can perform large-scale association tests for your custom meta-analyses in Neurosynth Compose.** In addition, we also support other types of “pairwise” algorithms, such as ALE Subtraction, and you are free to choose from a variety of reference datasets, including the Neurosynth corpus. +Previously these association tests were available for the automatically generated maps on neurosynth.org. **Now you can perform large-scale association tests for your custom meta-analyses in Neurosynth Compose.** + +import Button from '@mui/material/Button'; + **User Experience Enhancements** diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md index bc1e8aa..54b928f 100644 --- a/docs/tutorial/advanced/mkda_association.md +++ b/docs/tutorial/advanced/mkda_association.md @@ -39,7 +39,11 @@ See "Further Reading" below for more information on this problem. ## Specifying MKDA Chi-Squared Meta-Analysis -Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of studies as you would for any other meta-analysis, using either automated or manual selection methods. +:::tip +For this example, we'll use the default parameters for the `MKDAChi2` algorithm. +For in depth documentation on the specific parameters of each algorithm, refer to the NiMARE documentation: [`MKDAChi2`](https://nimare.readthedocs.io/en/stable/generated/nimare.meta.cbma.mkda.MKDAChi2.html) + +Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of Analyses to include from your StudySet as you would for any other meta-analysis. In Step 3 ("Create Meta-Analysis Specification") of your Project, select *MKDAChi2* as the *algorithm*. @@ -54,6 +58,11 @@ Now, select a reference dataset from the dropdown list below. The Neurosynth dat Now simply complete the rest of the meta-analysis specification wizard to finish. +:::tip +When comparing a manually-curated meta-analysis to a reference dataset there are several potential issues to be aware of. First, the reference dataset is composed of automatically extracted coordinates, while a manual curated dataset has corrected coordinate data. Second, unless you include all relevant studies in your target StudySet, some studies relevant to your question of interest may remain in the reference dataset, modestly biasing results +::: + + ## Executing your analysis The *MKDA Chi-Squared* algorithm is more computational expensive than a traditional *MKDA Density* analysis. As a result, it's unlikely to complete with the freely available resources available on Google Colab. @@ -66,15 +75,11 @@ We recommend at least 32GB of RAM to perform a MKDA Chi-Squared analysis ## Interpreting results -![Neurosynth Maps](/tutorial/neurosynth_paper_fig2.jpg). - -**Figure 1**. Comparison of Uniformity and Association maps from the original Neurosynth article, on three automatically generated meta-analyses. - The *MKDA Chi-Squared* Workflow outputs two types of maps: **uniformity** and **association** test maps. - **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. -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, for a meta-analysis of "emotion" 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 other coordinate based meta-analysis algorithms (e.g., ALE or MKDA), z-scores aren't generated through permutation, but using a chi-square test +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, for a meta-analysis of "emotion" 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. - **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. From d74cade082e639717626add05318a728d970dfad Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Thu, 4 Jan 2024 13:26:16 -0600 Subject: [PATCH 10/16] Update blog and links --- blog/2024-1-2-new-year.md | 31 ++++++++++------------ docs/tutorial/advanced/mkda_association.md | 2 +- docusaurus.config.js | 4 +-- 3 files changed, 17 insertions(+), 20 deletions(-) diff --git a/blog/2024-1-2-new-year.md b/blog/2024-1-2-new-year.md index 130ece9..cc76ade 100644 --- a/blog/2024-1-2-new-year.md +++ b/blog/2024-1-2-new-year.md @@ -1,5 +1,4 @@ --- -slug: january-2024-updates title: New Year Updates authors: alejandro tags: [neurosynth] @@ -8,44 +7,42 @@ Hello Neurosynth Users, Happy New Year! -2023 was a very exciting year for Neurosynth, having launched our Compose platform to the public and announced it on social media. In the December we’ve saw **over 1,200 new user visits**, with **200 users signing up for an account**! 🚀 +2023 was a very exciting year for Neurosynth, having launched our Compose platform to the public and announced it on social media. In the December we’ve saw **over 500 new user visits**, with **200 users signing up for an account**! 🚀 -Help us keep this growth going by [sharing our announcement](http://localhost:3000/compose-docs/blog/announcing-ns-compose) with your colleagues. 🧑‍🔬 +Help us keep this growth going by [sharing our announcement](./announcing-ns-compose) with your colleagues. 🧑‍🔬 # 🌟 What’s New 🌟 We’ve also continued to introduce new features and improve the user experience. Here’s some highlights: -**Large-scale association tests** +### Large-scale association tests -A key feature that set aside Neurosynth were large-scale “association tests” (previously referred to as “reverse inference” tests). +A key feature that set Neurosynth aside were large-scale association maps (previously known as “reverse inference”). -Whereas a typical meta-analysis tells you about the consistency of activation in a set of studies, an association test tells you whether activation in a region occurs *more consistently for studies in your set of studies versus a large reference sample of studies*. +Whereas a typical meta-analysis tells you about if activity is consistently reported in a target set of studies, an association test tells you if **activation occurs more consistently in this set of studies versus a large and diverse reference sample**. -Neurosynth leverages large-scale neuroimaging datasets as a reference to which you can compare the specific studies in given meta-analysis. +That's important, because this allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence are consistently activated for many different tasks and cognitive states. Thus, if you see insula activity in your meta-analysis, you might erroneously conclude that the insula is involved in the cognitive state you're studying. A large-scale association test lets you determine if the activity you observe in a region occurs *more consistently* in your meta-analysis than in other studies, making it possible to make more confident claims that a given region is involved in a particular process, and isn't involved in just about every task. -That's important, because this effectively allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence tend to be consistently activated for many different terms. Thus, if you see insula activity in your meta-analysis, you might erroneously conclude that the insula is involved in the process of your study. A large-scale association test lets you determine if the activity you observe in a region occurs to a greater extent in your meta-analysis than in other studies, making it possible to make more confident claims that a given region is involved in a particular process, and isn't involved in just about every task. - -Previously these association tests were available for the automatically generated maps on neurosynth.org. **Now you can perform large-scale association tests for your custom meta-analyses in Neurosynth Compose.** +Previously association tests were available for the automatically generated maps on neurosynth.org. **Now you can perform large-scale association tests for your custom meta-analyses in Neurosynth Compose.** import Button from '@mui/material/Button'; - -**User Experience Enhancements** +### UX Enhancements ✨ -Based on your valuable feedback, we've made numerous bug fixes and improvements, notably: +Based on your valuable feedback, we've made numerous bug fixes and improvements: * **Simplified Curation**: The review import page has been removed, and summary information is now added directly to the tag step. -* **Searching UI**: We've replaced the dropdown with a selection gallery, making it easier to choose your preferred search method, and we now pre-populate search names for Neurostore. +* **Searching UI**: We've replaced the dropdown with a selection gallery, making it easier to choose your preferred search method, and we now auto-generate search import names. In addition, resolving duplicates is skipped if none are present. -* **Streamlined Duplicate Resolution**: The 'resolve duplicates' function will only appear if duplicates are present. +* **Improved Editing Workflow**: The editing interface has been improved, streamlining the extraction process. -* **Improved Editing Workflow**: The process of editing studies has been made more straightforward, allowing you to start editing with ease. -We hope you enjoy these changes, and be sure to leave us feedback or ask questions if you have any problems or suggestions. +We hope you enjoy these changes. Be sure to leave us feedback or ask questions if you have any problems or suggestions. Cheers, diff --git a/docs/tutorial/advanced/mkda_association.md b/docs/tutorial/advanced/mkda_association.md index d3165a0..649ca1d 100644 --- a/docs/tutorial/advanced/mkda_association.md +++ b/docs/tutorial/advanced/mkda_association.md @@ -20,7 +20,7 @@ Although this is a useful approach, there is a significant inferential challenge Thus, perhaps a more useful question is if and where brain activity occurs *more consistently* for studies investigating a task or construct (in our case, nicotine administration) than studies that *do not* elicit that task or construct. The Neurosynth dataset (or any other large-scale neuroimaging datasets) is a useful reference, as it consists of tens of thousands of diverse neuroimaging studies automatically sample from the literature. ## MKDA Chi-Squared - + In our example we want to know if and where studies of nicotine administration show more consistent brain activation, than *all other studies* in the Neurosynth database (15,000+ studies). We can perform this test using the `Multilevel kernel density (MKDA) analysis - Chi-square` analysis, originally introduced in [Wager et al.,](https://doi.org/10.1093/scan/nsm015). For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that *we did not select* for our inclusion criteria. diff --git a/docusaurus.config.js b/docusaurus.config.js index 10989e3..0b8b63d 100644 --- a/docusaurus.config.js +++ b/docusaurus.config.js @@ -77,8 +77,8 @@ const config = { }, {to: '/blog', label: 'Blog', position: 'left'}, { - href: 'https://github.com/neurostuff/compose-docs', - label: 'GitHub', + href: 'https://compose.neurosynth.org', + label: 'Compose Home', position: 'right', }, ], From 8b6bed186af5205a9e70d32d4a9827d819f0a4bf Mon Sep 17 00:00:00 2001 From: Alejandro de la Vega Date: Wed, 10 Jan 2024 16:48:58 -0600 Subject: [PATCH 11/16] Reformat and add run instructions --- .../Studies => Explore}/Searching.md | 0 docs/guide/Explore/index.mdx | 26 ++++++ .../{walkthrough => }/Project/Curation.md | 0 .../{walkthrough => }/Project/Extraction.md | 0 .../Project/Specification.md | 0 .../guide/{walkthrough => }/Project/index.mdx | 3 +- docs/guide/Running/index.mdx | 85 ++++++++++++++++++ docs/guide/glossary.md | 2 +- docs/guide/walkthrough/Studies/index.mdx | 6 -- docs/guide/walkthrough/_category_.json | 4 - docs/guide/walkthrough/index.mdx | 14 --- docs/guide/walkthrough/meta-analyses.md | 18 ---- docs/guide/walkthrough/search.md | 14 --- docs/guide/walkthrough/studysets.md | 4 - static/guide/nsc_colab_notebook.png | Bin 0 -> 133433 bytes 15 files changed, 113 insertions(+), 63 deletions(-) rename docs/guide/{walkthrough/Studies => Explore}/Searching.md (100%) create mode 100644 docs/guide/Explore/index.mdx rename docs/guide/{walkthrough => }/Project/Curation.md (100%) rename docs/guide/{walkthrough => }/Project/Extraction.md (100%) rename docs/guide/{walkthrough => }/Project/Specification.md (100%) rename docs/guide/{walkthrough => }/Project/index.mdx (82%) create mode 100644 docs/guide/Running/index.mdx delete mode 100644 docs/guide/walkthrough/Studies/index.mdx delete mode 100644 docs/guide/walkthrough/_category_.json delete mode 100644 docs/guide/walkthrough/index.mdx delete mode 100644 docs/guide/walkthrough/meta-analyses.md delete mode 100644 docs/guide/walkthrough/search.md delete mode 100644 docs/guide/walkthrough/studysets.md create mode 100644 static/guide/nsc_colab_notebook.png diff --git a/docs/guide/walkthrough/Studies/Searching.md b/docs/guide/Explore/Searching.md similarity index 100% rename from docs/guide/walkthrough/Studies/Searching.md rename to docs/guide/Explore/Searching.md diff --git a/docs/guide/Explore/index.mdx b/docs/guide/Explore/index.mdx new file mode 100644 index 0000000..09a3fdd --- /dev/null +++ b/docs/guide/Explore/index.mdx @@ -0,0 +1,26 @@ +--- +title: Explore +sidebar_position: 1 +--- + +# Explore + +Here, you can browse and search existing public `Studies`, `StudySets` and `Meta-Analyses` created on the platform. + +## Studies + +The `Studies` page lets you browse and search all studies that exist on the NeuroStore server. This interface is similar to what you'll see when importing studies into your `Project`. However, here it's simply provided for your browsing pleasure. + +For more information on how advanced search functionally, see [Searching Studies](./Explore/Searching) + +## StudySets and Meta-Analyses + +For `StudySets` and `Meta-Analyses`, you can browse and search any user-contributed items, including those from other users. + +Note that although you see all publically available items, you cannot edit somebody else's content. + +:::note +We are currently working on a way to allow users to fork other users' `StudySets` and `Meta-Analyses` to create their own versions. +Stay tuned! +:::: + diff --git a/docs/guide/walkthrough/Project/Curation.md b/docs/guide/Project/Curation.md similarity index 100% rename from docs/guide/walkthrough/Project/Curation.md rename to docs/guide/Project/Curation.md diff --git a/docs/guide/walkthrough/Project/Extraction.md b/docs/guide/Project/Extraction.md similarity index 100% rename from docs/guide/walkthrough/Project/Extraction.md rename to docs/guide/Project/Extraction.md diff --git a/docs/guide/walkthrough/Project/Specification.md b/docs/guide/Project/Specification.md similarity index 100% rename from docs/guide/walkthrough/Project/Specification.md rename to docs/guide/Project/Specification.md diff --git a/docs/guide/walkthrough/Project/index.mdx b/docs/guide/Project/index.mdx similarity index 82% rename from docs/guide/walkthrough/Project/index.mdx rename to docs/guide/Project/index.mdx index 2fe8bae..5fed978 100644 --- a/docs/guide/walkthrough/Project/index.mdx +++ b/docs/guide/Project/index.mdx @@ -11,9 +11,8 @@ Within a project you will be able to: 1. **[Curate](./Project/Curation)** studies of interest and select the ones to be included in the meta-analysis 2. **[Extract](./Project/Extraction)** the relevant data such as activation coordinates and other meta-data 3. **[Specify](./Project/Specification)** the algorithm and corrector you would like to use - 4. **Run** the meta-analysis and **View** the results -In each project, you can define a define a single StudySe (i.e. a collection of related studies), and one or more MetaAnalysis specifications. +In each project, you can define a define a single StudySet (i.e. a collection of related studies), and one or more MetaAnalysis specifications. You can open a specific project by logging in, navigating to the diff --git a/docs/guide/Running/index.mdx b/docs/guide/Running/index.mdx new file mode 100644 index 0000000..890524f --- /dev/null +++ b/docs/guide/Running/index.mdx @@ -0,0 +1,85 @@ +--- +title: Running Analyses +sidebar_position: 2 +--- + +# Running Analyses + +You have a several options for running the analysis. In all cases, you will need your unique ``, which you can access for each Meta-Analysis within your Project. + +![Meta-analysis run](/tutorial/ma_run.png) + +Under the hood, analyses are managed by the [nsc-runner](https://github.com/neurostuff/nsc-runner) Python package, and executed by the [NiMARE](https://nimare.readthedocs.io/en/stable/) (Neuroimaging Meta-Analysis Research Environment) Python package. + +## Google Colab + +[![text](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neurostuff/neurosynth-compose-notebook/blob/main/run_and_explore.ipynb) + +The easiest way to run an analysis is to use the [Google Colab](https://colab.research.google.com/) notebook linked above. + +The provided notebook runs entirely in the cloud, and does not require any local installation of software. +To use simply paste your analysis ID into the first cell (`META_ID`), and using the Toolbar selet (Runtime -> Run All) +or the keyboard shortcut (Ctrl or ⌘ + F9) to run the notebook. + +![Colab notebook](/guide/nsc_colab_notebook.png) + +The notebook will install all required software, run the analysis, and upload the results to Neurosynth Compose. +Once the analysis is complete, you can use the notebook to explore the results using the interative report, download an archive +of the results, or browse the results in the Neurosynth Compose web interface, in the Meta-Analysis section of your Project. + +:::tip +The Colab notebook has limited and varying freely available resources, and may not be able to run large analyses. +If your analysis fails, try running it again, or using one of the other methods below. +::: + +## Docker + +The easiest way to run analyses locally is to use the `nsc-runner` [Docker](https://www.docker.com/) image provided by Neurosynth Compose. + +Docker is a containerization technology that allows you to run software in a consistent environment, regardless of the underlying operating system. + +To run the Docker image, you will need to install Docker on your local machine. +Instructions for installing Docker can be found [here](https://docs.docker.com/get-docker/). + +Once Docker is installed, you can run your analysis using the using the following command: + +``` +docker run -it -v -v /local/dir:/results ghcr.io/neurostuff/nsc-runner:latest +``` + +where `/local/dir` is the path to a local directory where you would like to save the results of your analysis, and `` is the ID of the meta-analysis you would like to run. + +The Docker image will download all required software, run the analysis, and upload the results to Neurovault & Neurosynth Compose. +An HTML report will be saved in the results directory, and the results will be available in the Meta-Analysis section of your Project on Neurosynth Compose. + +### Updating the Docker image + +For every release of `nsc-runner`, we publish a corresponding Docker image. + +You can manually download a specific neuroscout-cli release as follows: + +``` +docker pull ghcr.io/neurostuff/nsc-runner: +``` + +where `` is the version of `nsc-runner` that you want to download. If you omit version, the latest stable version will be downloaded. + +You can see the tags available for download on [GitHub](https://github.com/neurostuff/compose-runner/pkgs/container/nsc-runner) + +## Manually prepared environment using pip + +:::warning +Manually installing `nsc-runner` may be difficult due to complex dependencies in the SciPy stack, or fMRI-specific tooling. Proceed only if you know what you’re doing. +::: + +Use pip to install `nsc-runner` from PyPI: + +``` +pip install nsc-runner +``` + +and then run the analysis using the following command: + +``` +nsc-runner +``` diff --git a/docs/guide/glossary.md b/docs/guide/glossary.md index ae0810a..bc475a1 100644 --- a/docs/guide/glossary.md +++ b/docs/guide/glossary.md @@ -1,6 +1,6 @@ --- title: Glossary -sidebar_position: 1 +sidebar_position: 3 --- # Glossary diff --git a/docs/guide/walkthrough/Studies/index.mdx b/docs/guide/walkthrough/Studies/index.mdx deleted file mode 100644 index c4cc0e4..0000000 --- a/docs/guide/walkthrough/Studies/index.mdx +++ /dev/null @@ -1,6 +0,0 @@ ---- -title: Studies Page -sidebar_position: 0 ---- - -# Studies Page \ No newline at end of file diff --git a/docs/guide/walkthrough/_category_.json b/docs/guide/walkthrough/_category_.json deleted file mode 100644 index eacaa9c..0000000 --- a/docs/guide/walkthrough/_category_.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "label": "Walkthrough", - "position": 0 -} diff --git a/docs/guide/walkthrough/index.mdx b/docs/guide/walkthrough/index.mdx deleted file mode 100644 index 8d4ac5d..0000000 --- a/docs/guide/walkthrough/index.mdx +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Walkthrough -sidebar_position: 0 ---- - -import DocCardList from '@theme/DocCardList'; - -# Walkthrough - -This is a comprehensive walkthrough and reference for each page of Neurosynth Compose. - -If you're looking for a quickstart, check out our [Tutorials](/tutorial). - - \ No newline at end of file diff --git a/docs/guide/walkthrough/meta-analyses.md b/docs/guide/walkthrough/meta-analyses.md deleted file mode 100644 index ab3cc24..0000000 --- a/docs/guide/walkthrough/meta-analyses.md +++ /dev/null @@ -1,18 +0,0 @@ ---- -title: Meta-Analyses Page -sidebar_position: 2 ---- - -# a - -## a.a - -## a.b - -## a.b.a - -# b - -## b.b - -# c \ No newline at end of file diff --git a/docs/guide/walkthrough/search.md b/docs/guide/walkthrough/search.md deleted file mode 100644 index 913bba1..0000000 --- a/docs/guide/walkthrough/search.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Searching -sidebar_position: 4 ---- - -# Intro To Searching - -### Searching Studies - -Neurosynth-Compose supports multiple ways to search for studies. - -### Searching Studysets - -### Searching Meta-Analyses \ No newline at end of file diff --git a/docs/guide/walkthrough/studysets.md b/docs/guide/walkthrough/studysets.md deleted file mode 100644 index 83e7701..0000000 --- a/docs/guide/walkthrough/studysets.md +++ /dev/null @@ -1,4 +0,0 @@ ---- -title: Studysets Page -sidebar_position: 1 ---- \ No newline at end of file diff --git a/static/guide/nsc_colab_notebook.png b/static/guide/nsc_colab_notebook.png new file mode 100644 index 0000000000000000000000000000000000000000..fadd1d81946ea839126c31a58aacabcf3ff3f18b GIT binary patch literal 133433 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