Evidence map›Paper›PMID 41934528›Full record

ArticleInterdisciplinary sciences, computational life sciences2026

STFusion: A Spatial-Temporal Dual-Pathway Network with Multi-scale Attention Fusion for Early Diagnosis of Parkinson's Disease.

Hailin Yue, Hulin Kuang, Mengshen He, Junjian Li, Yijin Wang, Jianxin Wang

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In one paragraph

Article in Interdisciplinary sciences, computational life sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Hailin YueHunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Hulin KuangHunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China. hulinkuang@csu.edu.cn.
Mengshen HeHunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Junjian LiHunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Yijin WangHunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Jianxin WangHunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson’s disease (PD) is a progressive degenerative neurological disease in which timely diagnosis is essential for slowing clinical deterioration. Existing multimodal neuroimaging methods based on structural MRI (sMRI) and functional MRI (fMRI) often learn suboptimal modality-specific representations and rely on shallow fusion strategies that underutilize cross-modal dependencies. To solve these challenges, a Spatial-Temporal dual-pathway network with multi-scale attention Fusion is proposed for PD diagnosis (STFusion). In particular, a hybrid CNN-Transformer branch models both local and global structural patterns from sMRI, while a spatial-temporal Transformer branch characterizes dynamic functional connectivity from fMRI. A cross-modality multi-scale attention integration (CMAI) block is further introduced to adaptively integrate complementary information across modalities. Comprehensive evaluations on a public PPMI cohort and an external cohort show that STFusion achieves the best performance, reaching accuracies of 0.926 and 0.858, respectively.

Indexed as

CNN-TransformerEarly diagnosisLocal and globalParkinson’s disease

Identifiers

PMID41934528

What OpenQuestion holds

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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.