Evidence map›Paper›PMID 37350917›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023

Mining Correlation between Fluid Intelligence and Whole-brain Large Scale Structural Connectivity.

Sumita Garai, Frederick Xu, Duy Anh Duong-Tran, Yize Zhao, Li Shen

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Gene-Modulated Network Diffusion for Improved Modeling of Amyloid-bioRxiv : the preprint server for biology · 2026
    Article
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  5. Characterizing Amyloid Pathogenic Spread in Alzheimer's Disease Through A Network Diffusion Model.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025
    Article
  6. Article
  7. Causality-based Subject and Task Fingerprints using fMRI Time-series Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2024
    Article
  8. Caudal and Thalamic Segregation in White Matter Brain Network Communities in Alzheimer's Disease Population.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024
    Article
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  12. PRESERVING HUMAN LARGE-SCALE BRAIN CONNECTIVITY FINGERPRINT IDENTIFIABILITY WITH RANDOM PROJECTIONS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sumita GaraiUniversity of Pennsylvania, Philadelphia, PA, USA.
Frederick XuUniversity of Pennsylvania, Philadelphia, PA, USA.
Duy Anh Duong-TranUniversity of Pennsylvania, Philadelphia, PA, USA.
Yize ZhaoYale University, New Haven, CT, USA.
Li ShenUniversity of Pennsylvania, Philadelphia, PA, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

Exploring the neural basis of intelligence and the corresponding associations with brain network has been an active area of research in network neuroscience. Up to now, the majority of explorations mining human intelligence in brain connectomics leverages whole-brain functional connectivity patterns. In this study, structural connectivity patterns are instead used to explore relationships between brain connectivity and different behavioral/cognitive measures such as fluid intelligence. Specifically, we conduct a study using the 397 unrelated subjects from Human Connectome Project (Young Adults) dataset to estimate individual level structural connectivity matrices. We show that topological features, as quantified by our proposed measurements: Average Persistence (AP) and Persistent Entropy (PE), has statistically significant associations with different behavioral/cognitive measures. We also perform a parallel study using traditional graph-theoretical measures, provided by Brain Connectivity Toolbox, as benchmarks for our study. Our findings indicate that individual's structural connectivity indeed offers reliable predictive power of different behavioral/cognitive measures, including but not limited to fluid intelligence. Our results suggest that structural connectomes provide complementary insights (compared to using functional connectomes) in predicting human intelligence and warrants future studies on human intelligence and/or other behavioral/cognitive measures involving multi-modal approach.

Identifiers

PMID37350917
PMCPMC10283120

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.