1Case Study¶

NeuroxLink is a mini python package to parse mdast, facilitating cross-paper import of article content from MyST servers. It also introduces some Plotly functionality to work with data from interactive figures!

Importing the papers¶

🔗 importing 10.55458/neurolibre.alberta1 from 🌎 https://cdn.neurolibre.org/content/alberta1/paper.json
The Role of Hippocampal Volume, Brain Density, and Network Efficiency in Alien Memory Function: ALien Brain ExtRacTion Analytics (ALBERTA) Consortium
-------------------------------------

🔗 importing 10.55458/neurolibre.alberta2 from 🌎 https://cdn.neurolibre.org/content/alberta2/paper.json
Size Matters, but So Does Connectivity: The Amygdala's Role in Emotional Intelligence: ALien Brain ExtRacTion Analytics (ALBERTA) Consortium
-------------------------------------

🔗 importing 10.55458/neurolibre.alberta3 from 🌎 https://cdn.neurolibre.org/content/alberta3/paper.json
More Than Just Words: Temporal Cortex Volume Correlates with Language Ability: ALien Brain ExtRacTion Analytics (ALBERTA) Consortium
-------------------------------------
🔗 importing 10.55458/neurolibre.alberta4 from 🌎 https://cdn.neurolibre.org/content/alberta4/paper.json
Navigating the Void: How Parietal Cortex Volume Predicts Spatial Orientation in Zero Gravity: ALien Brain ExtRacTion Analytics (ALBERTA) Consortium
-------------------------------------

🔗 importing 10.55458/neurolibre.alberta5 from 🌎 https://cdn.neurolibre.org/content/alberta5/paper.json
Attention is all you need, and a chunky prefrontal cortex: ALien Brain ExtRacTion Analytics (ALBERTA) Consortium
-------------------------------------

Peek into the paper (but programmatically)¶

paper = nlx.papers[0]
print("\n------------> AUTHORS AND AFFILIATIONS")
print(paper.get_authors(), "\n------------> DEPENDENCIES")
print(paper.get_dependencies(), "\n------------> HEADINGS")
print(paper.headings)

------------> AUTHORS AND AFFILIATIONS
[{'nameParsed': {'literal': 'Alien Brain Consortium', 'given': 'Alien Brain', 'family': 'Consortium'}, 'name': 'Alien Brain Consortium', 'affiliations': ['Where aliens live'], 'id': 'contributors-myst-generated-uid-0'}, {'nameParsed': {'literal': 'Robo Neuro', 'given': 'Robo', 'family': 'Neuro'}, 'name': 'Robo Neuro', 'affiliations': ['Where robots live'], 'id': 'contributors-myst-generated-uid-1'}] 
------------> DEPENDENCIES
[{'url': '/alberta1/analysis', 'label': 'fig0cell', 'kind': 'Notebook', 'slug': 'analysis', 'location': '/content/analysis.ipynb'}] 
------------> HEADINGS
[{'depth': 1, 'text': 'Introduction', 'identifier': 'introduction'}, {'depth': 1, 'text': 'Methods', 'identifier': 'methods'}, {'depth': 1, 'text': 'Results', 'identifier': 'results'}, {'depth': 2, 'text': 'Alienarity Index', 'identifier': 'alienarity-index'}, {'depth': 2, 'text': 'Hippocampal Volume', 'identifier': 'hippocampal-volume'}, {'depth': 2, 'text': 'Brain Density', 'identifier': 'brain-density'}, {'depth': 2, 'text': 'Network Efficiency', 'identifier': 'network-efficiency'}, {'depth': 1, 'text': 'Conclusion', 'identifier': 'conclusion'}]

From your paper into my paper - The MyST way¶

The MyST ecosystem allows embedding outputs cross-reference style, i.e., you can render someone else’s output in your article (display-only) and reference it in your text. Here’s a list of example cross-references to the scoping review MRI article published on NeuroLibre:

• Fig. 1
• Fig. 2
• Fig. 3
• Fig. 7

From your paper into my paper - The NeuroxLink way¶

paper.inspect_plotly_figures()

These are the plotly figures I found:
-------------------------------------
- html-link [fig0] enumerated as (Figure 1)

- html-link [fig1] enumerated as (Figure 2)

- html-link [fig2] enumerated as (Figure 3)

- html-link [fig3] enumerated as (Figure 4)

fig = paper.create_plotly_object_from('fig0')
fig.show()

Interesting... I am not sure what Notebook-code is telling me. What about the AI of the second article? Let’s find out...

nlx.papers[doi2].create_plotly_object_from('fig0').show()

I wonder if there is something extraterrestrial going on in Notebook-code here, each AI has 6 points, but what’s the message they are trying to give???

Let’s take a look at the third one...

👽 Okay, this is getting weird and I cannot picture what’s going on here.

Time to put these together and see if AI has any insights hidden for us. Are not those aliens weird!

🥁🥁🥁 Time to reveal the aliens’ AI message!¶

2Now we are talking! Or, is it papers talking to each other?¶

#TalkAboutInsight

Let’s start our meta-analysis by importing 15 figures from 5 ALBERTA studies into this article!

Correlation matrix¶

Remember that 5 studies are seeking to understand the relationship between different brain measurements across different brain regions associated with different cognitive functions. However, despite the simplistic nature of the overall methodology, nearly each article represented the bivariate relationship between two variables using a different method!

If this was a traditional paper, we would not be able to run any meta-analysis on these results, since we cannot combine the data across these studies with the reported methods.

God news is, this is not a traditional paper! We are in a realm where papers can talk to each other, and we can leverage this power to run a meta-analysis on these results!

If you are not convinced that the alines are sooo linear in the brain, check out this PCA plot!¶

and this looks almost exactly like the correlation between the score and volume:

Take home message: Hang out with the aliens with bigger heads!¶

If you were wondering whether the type of correlation method used in each study affects the results of the meta-analysis, here’s your meta-meta-analysis:

2.1Enhanced Collaboration and Integration¶

Traditionally, scientific papers have existed in silos, each presenting its findings in isolation. However, the integration of interactive figures and data across multiple papers, as enabled by NeuroLibre, allows for a seamless flow of information. This interconnectedness fosters enhanced collaboration among researchers, enabling them to build upon each other’s work more effectively. By remixing data from interactive figures, researchers can combine insights from various studies, leading to more comprehensive and robust conclusions.

2.2Accelerated Discovery and Innovation¶

The ability for papers to communicate and share data accelerates the pace of discovery. Researchers no longer need to manually extract and reanalyze data from individual studies. Instead, tools like the myst content server and mdast facilitate the automatic import and parsing of data, streamlining the research process. This efficiency not only saves time but also reduces the likelihood of errors, allowing researchers to focus on innovative analysis and interpretation.

2.3Democratization of Data¶

Interconnected papers democratize access to data, making it easier for researchers from diverse backgrounds and institutions to contribute to and benefit from collective knowledge. This inclusivity is crucial for addressing complex, multidisciplinary challenges that require diverse perspectives and expertise. By enabling data remixing and integration, platforms like NeuroLibre ensure that valuable insights are not confined to a single study but are accessible to the broader scientific community.

2.4Comprehensive Meta-Analyses¶

Meta-analyses, such as the one demonstrated in this notebook, are significantly enhanced by the ability to integrate data from multiple sources. The NeuroLibre platform’s capability to remix data from interactive figures across different papers allows for a more thorough and nuanced analysis. This comprehensive approach leads to more accurate and generalizable findings, ultimately advancing the field and informing future research directions.

2.5Conclusion¶

The concept of “papers talking to each other” is not just a futuristic vision but a present reality enabled by cutting-edge tools and platforms. By facilitating the integration and remixing of data from multiple studies, NeuroLibre, the myst content server, and mdast are paving the way for next-generation scientific literature. This interconnected approach enhances collaboration, accelerates discovery, democratizes data access, and enables comprehensive meta-analyses, ultimately driving the advancement of knowledge and innovation in the scientific community.

2.6Introduction and Setup¶

The notebook begins with an introduction to the ALBERTA studies and the NeuroxLink package, which facilitates cross-paper import of article content and interactive figures.

• Package Installation and Initialization

NeuroxLink is installed and initialized to import and parse data from the specified articles.

• Data Import and Inspection

The notebook imports data from five ALBERTA articles and inspects the available Plotly figures within these articles.

• Data Extraction and Visualization

  • Interactive figures are fetched and rendered using Plotly.
  • Data from these figures is extracted and stored in a structured format.

• Meta-Analysis

  A meta-analysis is performed by combining data from multiple figures across the five articles. The combined data is visualized using various Plotly visualizations, including scatter plots and scatter matrix plots.

• Principal Component Analysis (PCA)

PCA is conducted on the combined dataset to reduce dimensionality and visualize the principal components.

• Correlation Analysis

Correlation matrices (Pearson, Kendall, Spearman) are computed and visualized using heatmaps to understand the relationships between different variables.

• Insights and Conclusions

The notebook concludes with insights derived from the visualizations and analyses, highlighting the variability and relationships between different brain measurements across the studies.

Overall, this notebook showcases the integration of multiple research articles, extraction of interactive data, and comprehensive analysis to derive meaningful insights from the combined dataset.