Evidence map›Paper›PMID 36468414›Full record

ArticleCNS neuroscience & therapeutics2023

Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease.

Song'an Shang, Siying Zhu, Jingtao Wu, Yao Xu, Lanlan Chen, Weiqiang Dou, Xindao Yin, Yu-Chen Chen, Dejuan Shen, Jing Ye

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Article in CNS neuroscience & therapeutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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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

10 authors.

Song'an ShangDepartment of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, China.
Siying ZhuDepartment of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, China.
Jingtao WuDepartment of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, China.ORCID 0000-0002-5308-0329
Yao XuDepartment of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, China.
Lanlan ChenDepartment of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, China.
Weiqiang DouMR Research China, GE Healthcare, Beijing, China.
Xindao YinDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Yu-Chen ChenDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.ORCID 0000-0002-8539-7224
Dejuan ShenDepartment of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, China.
Jing YeDepartment of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsThis study aimed to characterize the topological alterations and classification performance of high-order functional connectivity (HOFC) networks in cognitively preserved patients with Parkinson's disease (PD), relative to low-order FC (LOFC) networks.

methodsThe topological metrics of the constructed networks (LOFC and HOFC) obtained from fifty-one cognitively normal patients with PD and 60 matched healthy control subjects were analyzed. The discriminative abilities were evaluated using machine learning approach.

resultsThe HOFC networks in the PD group showed decreased segregation and integration. The normalized clustering coefficient and small-worldness in the HOFC networks were correlated to motor performance. The altered nodal centralities (distributed in the precuneus, putamen, lingual gyrus, supramarginal gyrus, motor area, postcentral gyrus and inferior occipital gyrus) and intermodular FC (frontoparietal and visual networks, sensorimotor and subcortical networks) were specific to HOFC networks. Several highly connected nodes (thalamus, paracentral lobule, calcarine fissure and precuneus) and improved classification performance were found based on HOFC profiles.

conclusionThis study identified disrupted topology of functional interactions at a high level with extensive alterations in topological properties and improved differentiation ability in patients with PD prior to clinical symptoms of cognitive impairment, providing complementary insights into complex neurodegeneration in PD.

Indexed as

Cognitive DysfunctionParkinson DiseaseBrainCase-Control StudiesHumansMachine LearningMagnetic Resonance Imagingfunctional connectivityfunctional magnetic resonance imaginggraph theorymachine learningParkinson's disease

Identifiers

PMID36468414
PMCPMC9873517

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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.