Evidence map›Paper›PMID 36661217›Full record

ArticleHuman brain mapping2023

Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages.

Boyu Chen, Wenzhuo Cui, Shanshan Wang, Anlan Sun, Hongmei Yu, Yu Liu, Jiachuan He, Guoguang Fan

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
4.3field-weighted citation impact, top 5% of its field
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Pooled it
  2. The role of neuro-imaging in multiple system atrophy.Journal of neural transmission (Vienna, Austria : 1996) · 2026
    Review
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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

8 authors at 2 institutions in 1 country.

Boyu ChenDepartment of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Wenzhuo CuiDepartment of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Shanshan WangDepartment of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Anlan SunYizhun Medical AI Co. Ltd, Beijing, People's Republic of China.
Hongmei YuDepartment of Neurology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Yu LiuDepartment of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Jiachuan HeDepartment of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
Guoguang FanDepartment of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.ORCID 0000-0001-8114-5727
First Hospital of China Medical University · CNWannan Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.

Indexed as

ConnectomeMultiple System AtrophyParkinson DiseaseBasal GangliaBrainHumansMagnetic Resonance Imagingfunctional connectivitygraph theorymachine learningmultiple system atrophyParkinson's diseaseresting-state functional magnetic resonance imaging

Identifiers

PMID36661217
PMCPMC10028675
OpenAlexW4317534798

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.