Unraveling morphological brain network disparities Parkinsonian tremor from essential tremor: an artificial intelligence approach for clinical differentiation

Tremor-dominant Parkinson's disease (TD) and Essential Tremor (ET) are the two most common types of tremors, posing huge challenges in diagnosis. This study was to investigate the pathogenesis of tremors using brain morphology and employ artificial intelligence techniques for distinguishing them. The cortical thickness differences in TD were primarily centered on the right precuneus, while in ET were mainly observed in the right medial orbitofrontal cortex. Subcortical analysis revealed that TD patients primarily exhibited an increase in pallidum, whereas ET patients showed a significant reduction in thalamus. Causal network analysis indicated that in TD, the right temporal lobe exhibited the highest out-degree, and gradually extended to motor control regions. In contrast, ET primarily exhibits initial changes in the prefrontal and occipital visual cortices. Finally, by incorporating these specific characteristics, we developed a machine learning model capable of accurately distinguishing between different tremor types, providing valuable insights for clinical practice.

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Publication Details

Journal
npj Parkinson s Disease
Published
2025-08-22
DOI
https://doi.org/10.1038/s41531-025-01107-8
Citations
1
Primary Topic
Neurological disorders and treatments
Type
article
Field-Weighted Citation Impact
1.39

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article

Unraveling morphological brain network disparities Parkinsonian tremor from essential tremor: an artificial intelligence approach for clinical differentiation

Tao Xu, Zijuan Ma, Guoen Cai, Xuzhu Chen et al.
1 citations
npj Parkinson s Disease
Neurological disorders and treatments
1.39
article

Unraveling morphological brain network disparities Parkinsonian tremor from essential tremor: an artificial intelligence approach for clinical differentiation

Tao Xu, Zijuan Ma, Guoen Cai, Xuzhu Chen, Jianguo Zhang, Jinli Ding, Chaonan Zhang, Tao Feng, Yin Jiang, Lin Shi, Anni Wang, Pengfei Yang, Fangang Meng, Qiang Liu, Xiaobo Wang, Chunlei Han, Yuan Gao, Yueping Li, Siyu Zhou, Huizhi Wang, Yuchen Ji, M. Zhang
article en
1 citations

Abstract

Tremor-dominant Parkinson's disease (TD) and Essential Tremor (ET) are the two most common types of tremors, posing huge challenges in diagnosis. This study was to investigate the pathogenesis of tremors using brain morphology and employ artificial intelligence techniques for distinguishing them. The cortical thickness differences in TD were primarily centered on the right precuneus, while in ET were mainly observed in the right medial orbitofrontal cortex. Subcortical analysis revealed that TD patients primarily exhibited an increase in pallidum, whereas ET patients showed a significant reduction in thalamus. Causal network analysis indicated that in TD, the right temporal lobe exhibited the highest out-degree, and gradually extended to motor control regions. In contrast, ET primarily exhibits initial changes in the prefrontal and occipital visual cortices. Finally, by incorporating these specific characteristics, we developed a machine learning model capable of accurately distinguishing between different tremor types, providing valuable insights for clinical practice.

npj Parkinson s DiseaseVol. 11(1)
Fujian Medical University (CN), Capital Medical University (CN), Beijing Tian Tan Hospital (CN), Beijing Institute of Neurosurgery (CN), First Affiliated Hospital of Zhengzhou University (CN), Union Hospital (CN)
National Natural Science Foundation of China, Foundation for Innovative Research Groups of the National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 16%
Neurological disorders and treatments
1.39
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