Update blog and links

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

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. 

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',
           },
         ],