Altered Functional Connectivity Density in Subtypes of Parkinson’s Disease: a Resting-State fMRI Study
Xiaofei Hu1, Yuchao Jiang2, Xiaoyue Zhou3, Cheng Luo2, and Jian Wang1

1Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, 400038, P.R. China, Chongqing, People's Republic of China, 2Key Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, 610054, P.R. China, People's Republic of China, 3Collaboration NEA, Siemens Healthcare Ltd., Shanghai, P.R. China, People's Republic of China


In the current study, combined functional connectivity density (FCD) and seed-based FC analyses were performed to fully characterize the abnormal brain networks in the two subtypes. Our findings obtained using a combination of FCD and seed-based FC analyses provide consistent evidence for that the network disorganization of the brains in the two PD subtypes were different.We also found that the FCD provided good discrimination between the AR and TD patients. These findings have important implications for understanding the neural substrates that underlie these disparate manifestations of PD.


Parkinson’s disease (PD) can be classified into tremor-dominant (TD) and akinetic-rigid (AR) subtypes, each of which exhibits a unique clinical course and prognosis1,2. However, the neural basis for these disparate manifestations is not well understood. This study comprehensively investigated the altered functional integration of these two subtypes.


25 TD patients, 25 AR patients and 26 normal control subjects were studied. Motor performance of patients was quantitatively and accurately assessed in detail by using the motor section (part III) of the Unified Parkinson’s Disease Rating Scale (UPDRS) and Hoehn and Yahr scale. Afterwards, the mean scores of TD and AR were computed, respectively 3,4. Resting-state functional images were acquired using a 3T MAGNETOM Trio (Siemens). Imaging data were collected transversely by using an echo-planar imaging (EPI) sequence with the following settings: TR = 2000 ms, TE = 30 ms, flip angle = 90°, FOV = 192×192mm2, in-plane matrix = 64×64, thickness=3 mm, voxel size = 3.0 × 3.0 × 3.0mm3. For each subject, a total of 240 volumes were acquired with scan time of 480s. Data preprocessing was carried out using Neuroscience Information Toolbox (NIT, http://www.neuro.uestc.edu.cn/NIT.html). Functional MRI (fMRI) data were analyzed using functional connectivity density (FCD) and seed-based functional connectivity methods. For each subject, both the global FCD map and the local FCD map were computed5,6. The brain sites identified by FCD group comparisons between the two subtypes were used as seeds in subsequent functional connectivity analyses. To determine the effect of group, a voxel-based one-way analysis of covariance (ANCOVA) was used with age, gender, and education level as covariates in both the FCD and FC analyses using REST software. The mean FCD and FC values were extracted for post hoc analyses using the least significant difference (LSD) t-test. The AFNI AlphaSim program (http://afni.nih.gov/afni/docpdf/AlphaSim.pdf) was used to correct for multiple comparisons7. Correlations between neuroimaging measures and clinical variables were also calculated to investigate the relationships between each of the clinical variables and the altered functional brain properties, and receiver operating characteristic (ROC) analysis was used to evaluate the ability of the FCD measures to discriminate the PD subtypes.


Compared with the AR patients, significantly increased global functional connectivity density in the cerebellum and decreased global functional connectivity density in the left inferior frontal gyrus, right middle frontal gyrus and right superior frontal gyrus were found in the TD patients (P <0.05, AlphaSim corrected) (Figure 1). Moreover, the identified functional connectivity density correlated significantly with clinical variables in the patients (spearman rank correlation analyses, P < 0.05), and the results of the ROC analysis indicated that the inclusion of the four FCD indices had the highest power to discriminate the AR patients from the TD patients, with an AUC of 0.96 (95% CI=0.91–1.00) (Figure 2). At a cutoff of 0.23, the sensitivity and specificity were 95.2% and 80.9%, respectively. Furthermore, patients of the different subtypes demonstrated different cerebello-cortical functional connectivity patterns (P <0.05, AlphaSim corrected) (Figure 3).


These findings indicate that the functional integration in the cerebellum and frontal lobe are altered in both subtypes of PD. They also provide a new perspective for the classification of PD subtypes based on a comprehensive view of network disorganization in the two subtypes of PD.


No acknowledgement found.


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Figure 1. Results of the FCD analyses. Figure shows the statistical FCD differences between two subtypes (P <0.05, Alphasim corrected). Color bar represents the t-value. MFG: middle frontal gyrus. IFG: inferior frontal gyrus. SFG:superior frontal gyrus.

Figure 2. A-F show the correlations between regions with altered global FCD and clinical data in PD patients (spearman rank correlation analyses, P < 0.05). A shows the correlations in AR, B-F show the correlations in PD group (TDs and ARs). G shows the receiver operating characteristic curve of the combination of the four FCD indexes for distinguishing PD subtypes patients.

Figure 3. Results of the functional connectivity of the left cerebellum. Figure shows the differences on FC of the left cerebellum between TD and AR (P <0.05, Alphasim corrected). Color bar represents the t-value.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)