Evidence map›Paper›PMID 41427113›Full record

ArticleFrontiers in radiology2025

H-VIP: quantifying regional topological contributions of the brain network to cognition.

Sumita Garai, Sandra Vo, Lucy Blank, Frederick Xu, Jiong Chen, Duy Duong-Tran, Yize Zhao, Brielin C Brown, Li Shen

Abstract read
In one paragraph

Article in Frontiers in radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Sumita GaraiDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Sandra VoDepartment of Biology, Regis University, Denver, CO, United States.
Lucy BlankDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Frederick XuDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Jiong ChenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Duy Duong-TranDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Yize ZhaoDepartment of Biostatistics, Yale University, New Haven, CT, United States.
Brielin C BrownDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.
Li ShenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States.

Funding

An integrative Bayesian approach for linking brain to behavioral phenotypeR01EB034720 · NIBIB · YALE UNIVERSITY · PI R Todd Constable, Yize Zhao · 2023 to 2026
$2.4M
Integrative analysis for patient-centered outcomes and time-to-event data in Alzheimer's diseaseRF1AG081413 · NIA · YALE UNIVERSITY · PI SUN, YIFEI, ZHAO, YIZE · 2023 to 2023
$2.3M
Novel integrative imaging genetics analysis for Alzheimer's disease riskand progressionR01AG068191 · NIA · YALE UNIVERSITY · PI ZHAO, YIZE · 2024 to 2025
$1.2M
NIA NIH HHS R01 AG068191NIA NIH HHS RF1 AG081413NIBIB NIH HHS R01 EB034720
6 · The paper itself

Abstract

Introduction: Understanding the role of various brain regions of interest (ROIs) in various cognitive functions or tasks, across healthy or neurodegenerative conditions and multiple degrees of separation, remains a key challenge in neuroscience. Conventional network measures can only capture localized or quasi-localized features of brain ROIs. Topological data analysis (TDA), particularly persistent homology, provides a threshold-free, mathematically rigorous framework for identifying topologically salient features in complex networks. In this paper, we introduce a new metric, the Homological Vertex Importance Profile (H-VIP), designed to assess the relevance of vertices that participate in persistent topological structures (e.g., connected components, cycles or cavities) in brain networks. The H-VIP quantifies the topological features of the network at the ROI (node) level by compressing its higher-order connectivity profile using homological constructs. Methods: Leveraging homological constructs of brain connectomes, we extend two of our previously defined network-level measures-average persistence and persistence entropy-to an ROI-level measure, i.e., the H-VIP. We then applied the H-VIP to two independent datasets: structural connectomes from the Human Connectome Project and functional connectomes from the Alzheimer's Disease Neuroimaging Initiative. Persistent homology was computed for each network, and H-VIP scores were derived to evaluate vertex-level contributions. Finally, H-VIP scores were used for the prediction of multiple cognitive measures. Results: In both anatomical and functional brain networks, H-VIP values demonstrate predictive power for various cognitive measures. Notably, the connectivity of the frontal lobe exhibited stronger correlations with cognitive performance than the whole-brain network. Discussion: H-VIP offers a robust and interpretable means to locate, quantify, and visualize region-specific contributions to network's topological, higher-order landscape. Its ability to detect potentially impaired connectivity at the individual level suggests possible applications in personalized medicine for neurological diseases and disorders. Beyond brain connectomics, the H-VIP can be used for other types of complex networks where topological features are of importance, such as financial, social, or ecological networks.

Indexed as

brain networkcognitionHomological Vertex Importance Profileneuroimagingpersistent homology

Identifiers

PMID41427113
PMCPMC12711701

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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.