Evidence map›Paper›PMID 41959946›Full record

ArticleProceedings of machine learning research2025

NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders.

Jun-En Ding, Dongsheng Luo, Chenwei Wu, Feng Liu

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Proceedings of machine learning research, 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

4 authors.

Jun-En DingDepartment of Systems Engineering, Stevens Institute of Technology, New Jersey, USA.
Dongsheng LuoDepartment of Computing and Information Sciences, Florida International University, USA.
Chenwei WuDepartment of Electrical Engineering and Computer Science, University of Michigan, USA.
Feng LiuDepartment of Systems Engineering, Stevens Institute of Technology, New Jersey, USA.

Funding

Epileptic Electrophysiological Brain Networks Modeling with Partially Observable Brain RegionsR21NS135482 · NINDS · THE TRUSTEES OF THE STEVENS INSTITUTE OF TECHNOLOGY · PI LIU, FENG · 2024 to 2024
$439k
NINDS NIH HHS R21 NS135482
6 · The paper itself

Abstract

Mental disorders are among the most widespread diseases globally. Analyzing functional brain networks through functional magnetic resonance imaging (fMRI) is crucial for understanding mental disorder behaviors. Although existing fMRI-based graph neural networks (GNNs) have demonstrated significant potential in brain network feature extraction, they often fail to characterize complex relationships between brain regions and demographic information in mental disorders. To overcome these limitations, we propose a learnable NeuroTree framework that integrates a

Identifiers

PMID41959946
PMCPMC13061348

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.