A radiomics approach for predicting gait freezing in Parkinson's disease based on resting-state functional magnetic resonance imaging indices:A cross-sectional study

Miaoran Guo; Hu Liu; Long Gao; Hongmei Yu; Yan Ren; Yingmei Li; Huaguang Yang; Chenghao Cao; Guoguang Fan

doi:10.4103/NRR.NRR-D-23-01392

🤖This page has been translated by AI and may contain inaccuracies. Please refer to the original content for clarification.

Home>Articles>A radiomics approach for predicting gait freezing in Parkinson's disease based on resting-state functional magnetic resonance imaging indices:A cross-sectional study

DOI: 10.4103/NRR.NRR-D-23-01392

A radiomics approach for predicting gait freezing in Parkinson's disease based on resting-state functional magnetic resonance imaging indices:A cross-sectional study

Miaoran Guo

Hu Liu

Long Gao

Hongmei Yu

Yan Ren

Yingmei Li

Huaguang Yang

Chenghao Cao

Guoguang Fan

Keywords：amplitude of low-frequency fluctuationdegree centralityfeedforward neural networkfreezing of gaitmachine learningparahippocampal gyrusParkinson's diseasereceiver operating characteristicregional homogeneityresting-state functional magnetic resona

Publication Date：2026-04-30

Online Publishing Date：2026-03-25(First online date of this platform, not the publication date of the document)

Pages：7( 1621-1627 )

Neural Regeneration Research

ISTICSCICSCD

ISSN：1673-5374

Year, Vol.(Issue)：2026,21(4)