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Abnormal functional connectivity density involvement in freezing of gait and its application for subtyping Parkinson’s disease

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Abstract

The pathophysiological mechanisms at work in Parkinson’s disease (PD) patients with freezing of gait (FOG) remain poorly understood. Functional connectivity density (FCD) could provide an unbiased way to analyse connectivity across the brain. In this study, a total of 23 PD patients with FOG (PD FOG + patients), 26 PD patients without FOG (PD FOG- patients), and 22 healthy controls (HCs) were recruited, and their resting-state functional magnetic resonance imaging (rs-fMRI) images were collected. FCD mapping was first performed to identify differences between groups. Pearson correlation analysis was used to explore relationships between FCD values and the severity of FOG. Then, a machine learning model was employed to classify each pair of groups. PD FOG + patients showed significantly increased short-range FCD in the precuneus, cingulate gyrus, and fusiform gyrus and decreased long-range FCD in the frontal gyrus, temporal gyrus, and cingulate gyrus. Short-range FCD values in the middle temporal gyrus and inferior temporal gyrus were positively correlated with FOG questionnaire (FOGQ) scores, and long-range FCD values in the middle frontal gyrus were negatively correlated with FOGQ scores. Using FCD in abnormal regions as input, a support vector machine (SVM) classifier can achieve classification with good performance. The mean accuracy values were 0.895 (PD FOG + vs. HC), 0.966 (PD FOG- vs. HC), and 0.897 (PD FOG + vs. PD FOG-). This study demonstrates that PD FOG + patients showed altered short- and long-range FCD in several brain regions involved in action planning and control, motion processing, emotion, cognition, and object recognition.

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Data Availability

The MRI images will be made available upon reasonable request, subject to approval by the Ethics Committee of Guangzhou First People’s Hospital.

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Funding

This work was partly supported by the National Natural Science Foundation of China, China (Nos. 82072008, 81871846), the Fundamental Research Funds for the Central Universities, China (N2124006-3; N2224001-10), the Natural Science Foundation of Guangdong Province, China (2021A1515011288), and the Science and Technology Project of Guangzhou, China (202102010020).

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Authors and Affiliations

  1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

    Chaoyang Jin, Shouliang Qi, Lei Yang, Yueyang Teng & Chen Li

  2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China

    Shouliang Qi

  3. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Yudong Yao

  4. Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China

    Xiuhang Ruan & Xinhua Wei

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Contributions

CJ performed the experiments and analysed the data along with SQ and LY. SQ, YL, YY, and XW conceived the study, presented the results, and wrote the manuscript along with CJ. XR collected and analysed the data. YT and CL supervised the algorithm development and analysed the data. All authors read and approved the final manuscript.

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Correspondence to Shouliang Qi.

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Jin, C., Qi, S., Yang, L. et al. Abnormal functional connectivity density involvement in freezing of gait and its application for subtyping Parkinson’s disease. Brain Imaging and Behavior 17, 375–385 (2023). https://doi.org/10.1007/s11682-023-00765-7

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