Evidence map›Paper›PMID 39371470›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024

PRESERVING HUMAN LARGE-SCALE BRAIN CONNECTIVITY FINGERPRINT IDENTIFIABILITY WITH RANDOM PROJECTIONS.

Duy Duong-Tran, Mark Magsino, Joaquín Goñi, Li Shen

Abstract read
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Gene-Modulated Network Diffusion for Improved Modeling of Amyloid-bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
  3. Article
  4. 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
  5. 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
  6. 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
  7. 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

4 authors.

Duy Duong-TranDepartment of Mathematics, United States Naval Academy, Annapolis, Maryland, USA.
Mark MagsinoDepartment of Mathematics, United States Naval Academy, Annapolis, Maryland, USA.
Joaquín GoñiSchool of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA.
Li ShenDept. of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.

Funding

Novel integrative imaging genetics analysis for Alzheimer's disease riskand progressionRF1AG068191 · NIA · YALE UNIVERSITY · PI ZHAO, YIZE · 2021 to 2021
$1.9M
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 AG068191
6 · The paper itself

Abstract

The complex etiology of various neurodegenerative diseases and psychiatric disorders, especially at the individual level, has posed unmatched challenges to the advancement of personalized medicine. Recent technical advancements in functional magnetic resonance imaging has enabled researchers to map brain large-scale connectivity at an unprecedented level of subject precision. Nonetheless, along with the early dawn of promises in personalized medicine using various neuroimaging modalities rose the challenge of clinical utility of brain connectomics (e.g., functional connectomes). Besides many established challenges of functional connectome utility such as edge reliability, there exists an easily overlooked challenge that does not get the same level of attention: computationality of functional connectome. To improve clinical utility of functional connectomics, we propose a random projection method that would preserve a practically similar level of subject identifiability while sampling and retaining only a proportion of functional edges in subjects' functional connectome. Our work pave a way towards computational improvements, hence clinical utility, of functional connectomes while not compromising the integrity of biomarkers learnt from whole-brain large-scale functional connectivity imaging modality.

Indexed as

brain fingerprintconnectome sub-samplingidentifiabilitypersonalized medicinerandom projection

Identifiers

PMID39371470
PMCPMC11452154

What OpenQuestion holds

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Registered trials

None linked

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.