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Functional Connectome of the Human Brain with Total Correlation
Neurons and Cognition · Submitted 6 Oct 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations.
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The abstract, sentence by sentence
Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such as correlation or mutual information. In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of total correlation and clustering results are trustable compared to ground truth values. Second, the inferred large scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies but, interestingly, can estimate additional relations beyond pair-wise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such as correlation or mutual information. | background 1.00 | 0.04 | 0.04 | asserted 0.89 |
| s1 | In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations. | contribution 1.00 | 0.94 | 0.36 | hedged possibility 0.60 |
| s2 | In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. | method 0.99 | 0.86 | 0.04 | asserted 1.00 |
| s3 | First, we prove that CorEx estimates of total correlation and clustering results are trustable compared to ground truth values. | result 0.88 | 0.92 | 0.08 | asserted 1.00 |
| s4 | Second, the inferred large scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies but, interestingly, can estimate additional relations beyond pair-wise regions. | result 0.99 | 0.93 | 0.28 | hedged possibility 0.52 |
| s5 | And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases. | result 0.75 | 0.94 | 0.41 | asserted 0.98 |
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The paper
Original abstract
Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such as correlation or mutual information. In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of total correlation and clustering results are trustable compared to ground truth values. Second, the inferred large scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies but, interestingly, can estimate additional relations beyond pair-wise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.
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What this paper does with the arXiv papers it cites
What it cites · 12 sentences without a confident label
Builds on 1 paper
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In this work we build on these empirical and theoretical results [ref] to infer a larger scale (whole brain) network based on TC for the first time.
Functional Connectivity via Total Correlation: Analytical results in Visual Areas · Introduction · 0.99
Compares with 1 paper
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Although latent factor models do not give a direct total correlation estimation as the Rotation-based Iterative Gaussianization (RBIG) [ref] and the Matrix-based Rényi's entropy [ref] did, the approach can be complementary because the construction of latent factors can help in dealing with the curse of dimensionality and for interpreting the dependencies in the data.
Information Theory Measures via Multidimensional Gaussianization · Total Correlation estimated from CorEx · 0.85
Cites as background 1 paper
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Total Correlation (TC) [ref] (also known as multi-information [ref] ) mainly describes the amount of dependence observed in the data and, by definition can be applied to multiple multivariate variables.
Information Theory Measures via Multidimensional Gaussianization · Introduction · 0.98
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