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MKDA Chi Squared tutorial, December blog post, and How to run instructions #10

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56 changes: 56 additions & 0 deletions blog/2024-1-2-new-year.md
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---
title: New Year Updates
authors: alejandro
tags: [neurosynth]
---
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 500 new user visits**, with **200 users signing up for an account**! 🚀

Help us keep this growth going by [sharing our announcement](./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 Neurosynth aside were large-scale association maps (previously known as “reverse inference”).

Whereas a typical meta-analysis tells you 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**.

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.

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.**

We have created a full primer and tutorial on MKDA Chi-Squared, including an example from a recent meta-analysis on social processing. Check it out!

import Button from '@mui/material/Button';

<Button variant="contained" color="primary" href='tutorial/advanced/mkda_association'>
MKDA Chi-Squared Tutorial 🧑‍🎓
</Button>

### UX Enhancements ✨

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 auto-generate search import names. In addition, resolving duplicates is skipped if none are present.

* **Improved Editing Workflow**: The editing interface has been improved, streamlining the extraction process.

* **Various UX Improvements and Fixes**: We fixed many papercuts, especially in the *Extraction* phase.


We hope you enjoy these changes.

Email us any [feedback](mailto:[email protected]), or ask a question on [NeuroStars](https://neurostars.org/tag/neurosynth-compose) if you have issues.


Cheers,

The Neurosynth Team 🧠
26 changes: 26 additions & 0 deletions docs/guide/Explore/index.mdx
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---
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!
::::

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Expand Up @@ -15,7 +15,7 @@ After the curation phase is complete, the user is redirected to the extraction p
Here, the extraction phase starts when
a wizard that pops up and guides the user through the process of initializing the extraction phase. On top of creating the
initial [**annotation columns**](./Extraction#annotations), this wizard also guides the user through the
process of [**ingestion** ](./Extraction#ingestion) of the curated studies to create a new [**studyset**](../../glossary#studyset).
process of [**ingestion** ](./Extraction#ingestion) of the curated studies to create a new [**studyset**](../glossary#studyset).

## Ingestion

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Expand Up @@ -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
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85 changes: 85 additions & 0 deletions docs/guide/Running/index.mdx
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---
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 `<meta-analysis-id>`, 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 <meta-analysis-id>
```

where `/local/dir` is the path to a local directory where you would like to save the results of your analysis, and `<meta-analysis-id>` 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:<version>
```

where `<version>` 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 <meta-analysis-id>
```
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---
title: Glossary
sidebar_position: 1
sidebar_position: 3
---

# Glossary
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12 changes: 12 additions & 0 deletions docs/tutorial/advanced/index.mdx
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---
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.

<DocCardList/>
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