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Shang S, Wang L, Yao J, Lv X, Xu Y, Dou W, Zhang H, Ye J, Chen YC. Characterizing microstructural patterns within the cortico-striato-thalamo-cortical circuit in Parkinson's disease. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111116. [PMID: 39116929 DOI: 10.1016/j.pnpbp.2024.111116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 08/04/2024] [Accepted: 08/05/2024] [Indexed: 08/10/2024]
Abstract
PURPOSE Parkinson's disease (PD) involves pathological alterations that include cortical impairments at levels of region and network. However, its microstructural abnormalities remain to be further elucidated via an appropriate diffusion neuroimaging approach. This study aimed to comprehensively demonstrate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI). METHODS The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity. RESULTS Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus. CONCLUSION These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.
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Affiliation(s)
- Song''an Shang
- Department of Medical imaging center, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China
| | - Lijuan Wang
- Department of Radiology, Jintang First People's Hospital, Sichuan University, Chengdu, China
| | - Jun Yao
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiang Lv
- Department of Neurology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China
| | - Yao Xu
- Department of Neurology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China
| | - Weiqiang Dou
- MR Research China, GE Healthcare, Beijing, China
| | - Hongying Zhang
- Department of Medical imaging center, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China
| | - Jing Ye
- Department of Medical imaging center, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
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Cohen JS, Radhakrishnan H, Olm CA, Das SR, Cook PA, Wolk DA, Weintraub D, Irwin DJ, McMillan CT. Microstructural changes in the inferior tuberal hypothalamus correlate with daytime sleepiness in Lewy body disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.16.24312102. [PMID: 39185524 PMCID: PMC11343243 DOI: 10.1101/2024.08.16.24312102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Background Excessive daytime sleepiness (EDS) is a disabling symptom of Lewy body disorders (LBD). The hypothalamus is a key sleep-wake regulator, but its contribution to EDS in LBD remains unclear. Objectives Use diffusion MRI to evaluate the relationship of hypothalamic microstructure to EDS symptoms in LBD. Methods We studied 102 patients with clinically-defined LBD (Parkinson's disease, n=93; Parkinson's disease dementia, n=4; and dementia with Lewy bodies, n=5) and Epworth Sleepiness Scale (ESS) within 2 years of MRI. Mean diffusivity (MD) was compared between EDS+ (ESS≥10, n=37) and EDS- (ESS<10, n=65) groups in the whole hypothalamus and three subregions, covarying for age and sex. Secondary analyses tested correlations between subregion MD and continuous ESS, global cognition, and motor scores; and between subregion volume and continuous ESS. Results MD was increased in EDS+ compared to EDS- only in the inferior tuberal subregion (Cohen's d=0.43, p=0.043, β=0.117±0.057), with trend level differences in the whole hypothalamus (Cohen's d=0.39, p=0.064, β=0.070±0.037) and superior tuberal subregion (Cohen's d=0.38, p=0.073, β=0.063±0.035). No difference was seen in the posterior subregion (Cohen's d=0.1, p=0.628, β=0.019±0.038). Significant correlations with continuous ESS were seen in MD of whole hypothalamus (r2=0.074, p=0.0057), superior tuberal (r2=0.081, p=0.0038), and inferior tuberal (r2=0.073, p=0.0059) subregions. There was no correlation of hypothalamic MD with global cognition or motor scores, and no correlation of whole/subregional hypothalamic volumes with ESS. Conclusions Daytime sleepiness associates with increased MD in the inferior tuberal hypothalamus in an LBD cohort. This suggests degeneration within this region could contribute to EDS symptoms.
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Zhang N, Zhao W, Shang S, Zhang H, Lv X, Chen L, Dou W, Ye J. Diffusion Kurtosis Imaging in Diagnosing Parkinson's Disease: A Preliminary Comparison Study Between Kurtosis Metric and Radiomic Features. Acad Radiol 2024:S1076-6332(24)00435-5. [PMID: 39122585 DOI: 10.1016/j.acra.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/25/2024] [Accepted: 07/01/2024] [Indexed: 08/12/2024]
Abstract
RATIONALE AND OBJECTIVES Parkinson's disease (PD) shows small structural changes in nigrostriatal pathways, which can be sensitively captured through diffusion kurtosis imaging (DKI). However, the value of DKI and its radiomic features in the classification performance of PD still need confirmation. This study aimed to compare the diagnostic efficiency of DKI-derived kurtosis metric and its radiomic features with different machine learning models for PD classification. MATERIALS AND METHODS 75 people with PD and 80 healthy individuals had their brains scanned using DKI. These images were pre-processed and the standard atlas were non-linearly registered to them. With the labels in atlas, different brain regions in nigrostriatal pathways, including the caudate nucleus, putamen, pallidum, thalamus, and substantia nigra, were chosen as the region of interests (ROIs) to warped to the native space to measure the mean kurtosis (MK). Additionally, new radiomic features were developed for comparison. To handle the large amount of data, a statistical method called Z-score normalization and another method called LASSO regression were used to simplify the information. From this, a few most important features were chosen, and a combined score called Radscore was calculated using LASSO regression. For the comprehensive analyses, three different conventional machine learning models were then created: logistic regression (LR), support vector machine (SVM), and random forest (RF). To ensure the models were accurate, a process called 10-fold cross-validation was used, where the data were split into 10 parts for training and testing. RESULTS Using MK alone, the models achieved good results in correctly identifying PD in the validation set, with LR at 0.90, RF at 0.93, and SVM at 0.90. When the radiomic features were added, the models performed even better, with LR at 0.92, RF at 0.95, and SVM at 0.91. Additionally, a nomogram combining all the information was created to predict the likelihood of someone having PD, which had an AUC of 0.91. CONCLUSION These findings suggest that the combination of DKI measurements and radiomic features can effectively diagnose PD by providing more detailed information about the brain's condition and the processes involved in the disease.
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Affiliation(s)
- Ninggui Zhang
- Department of Radiology, Clinical Medical College, Yangzhou University, Yangzhou, China (N.Z., S.S., H.Z., J.Y.)
| | - Wei Zhao
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Yangzhou University, China (W.Z.)
| | - Song'an Shang
- Department of Radiology, Clinical Medical College, Yangzhou University, Yangzhou, China (N.Z., S.S., H.Z., J.Y.)
| | - Hongying Zhang
- Department of Radiology, Clinical Medical College, Yangzhou University, Yangzhou, China (N.Z., S.S., H.Z., J.Y.)
| | - Xiang Lv
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, China (X.I., L.C.)
| | - Lanlan Chen
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, China (X.I., L.C.)
| | - Weiqiang Dou
- MR Research China, GE Healthcare, Beijing, China (W.D.)
| | - Jing Ye
- Department of Radiology, Clinical Medical College, Yangzhou University, Yangzhou, China (N.Z., S.S., H.Z., J.Y.).
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Zhou C, You J, Guan X, Guo T, Wu J, Wu H, Wu C, Chen J, Wen J, Tan S, Duanmu X, Qin J, Huang P, Zhang B, Cheng W, Feng J, Xu X, Wang L, Zhang M. Microstructural alterations of the hypothalamus in Parkinson's disease and probable REM sleep behavior disorder. Neurobiol Dis 2024; 194:106472. [PMID: 38479482 DOI: 10.1016/j.nbd.2024.106472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/24/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Whether there is hypothalamic degeneration in Parkinson's disease (PD) and its association with clinical symptoms and pathophysiological changes remains controversial. OBJECTIVES We aimed to quantify microstructural changes in hypothalamus using a novel deep learning-based tool in patients with PD and those with probable rapid-eye-movement sleep behavior disorder (pRBD). We further assessed whether these microstructural changes associated with clinical symptoms and free thyroxine (FT4) levels. METHODS This study included 186 PD, 67 pRBD, and 179 healthy controls. Multi-shell diffusion MRI were scanned and mean kurtosis (MK) in hypothalamic subunits were calculated. Participants were assessed using Unified Parkinson's Disease Rating Scale (UPDRS), RBD Questionnaire-Hong Kong (RBDQ-HK), Hamilton Depression Rating Scale (HAMD), and Activity of Daily Living (ADL) Scale. Additionally, a subgroup of PD (n = 31) underwent assessment of FT4. RESULTS PD showed significant decreases of MK in anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tubular (supTub), and inferior tubular hypothalamus when compared with healthy controls. Similarly, pRBD exhibited decreases of MK in a-iHyp and supTub. In PD group, MK in above four subunits were significantly correlated with UPDRS-I, HAMD, and ADL. Moreover, MK in a-iHyp and a-sHyp were significantly correlated with FT4 level. In pRBD group, correlations were observed between MK in a-iHyp and UPDRS-I. CONCLUSIONS Our study reveals that microstructural changes in the hypothalamus are already significant at the early neurodegenerative stage. These changes are associated with emotional alterations, daily activity levels, and thyroid hormone levels.
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Affiliation(s)
- Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jia You
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433 Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433 Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, 200433 Shanghai, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Chenqing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Sijia Tan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Xiaojie Duanmu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jianmei Qin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433 Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433 Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, 200433 Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433 Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433 Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, 200433 Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China.
| | - Linbo Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433 Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433 Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, 200433 Shanghai, China.
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China.
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Zhu S, Wang L, Lv X, Xu Y, Dou W, Zhang H, Ye J. Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease. Acta Radiol 2024; 65:233-240. [PMID: 38017711 DOI: 10.1177/02841851231216039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
BACKGROUND Parkinson's disease (PD) has been regarded as a disconnection syndrome with functional and structural disturbances. However, as the anatomic determinants, the structural disconnections in PD have yet to be fully elucidated. PURPOSE To non-invasively construct structural networks based on microstructural complexity and to further investigate their potential topological abnormalities in PD given the technical superiority of diffusion kurtosis imaging (DKI) to the quantification of microstructure. MATERIAL AND METHODS The microstructural data of gray matter in both the PD group and the healthy control (HC) group were acquired using DKI. The structural networks were constructed at the group level by a covariation approach, followed by the calculation of topological properties based on graph theory and statistical comparisons between groups. RESULTS A total of 51 patients with PD and 50 HCs were enrolled. Individuals were matched between groups with respect to demographic characteristics (P >0.05). The constructed structural networks in both the PD and HC groups featured small-world properties. In comparison with the HC group, the PD group exhibited significantly altered global properties, with higher normalized characteristic path lengths, clustering coefficients, local efficiency values, and characteristic path lengths and lower global efficiency values (P <0.05). In terms of nodal centralities, extensive nodal disruptions were observed in patients with PD (P <0.05); these disruptions were mainly distributed in the sensorimotor network, default mode network, frontal-parietal network, visual network, and subcortical network. CONCLUSION These findings contribute to the technical application of DKI and the elucidation of disconnection syndrome in PD.
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Affiliation(s)
- Siying Zhu
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, PR China
| | - Lijuan Wang
- Department of Radiology, Jintang First People's Hospital, Sichuan University, Chengdu, PR China
| | - Xiang Lv
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, PR China
| | - Yao Xu
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, PR China
| | - Weiqiang Dou
- MR Research China, GE Healthcare, Beijing, PR China
| | - Hongying Zhang
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, PR China
| | - Jing Ye
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, PR China
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Shang S, Wang L, Xu Y, Zhang H, Chen L, Dou W, Yin X, Ye J, Chen YC. Optimization of structural connectomes and scaled patterns of structural-functional decoupling in Parkinson's disease. Neuroimage 2023; 284:120450. [PMID: 37949260 DOI: 10.1016/j.neuroimage.2023.120450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023] Open
Abstract
Parkinson's disease (PD) is manifested with disrupted topology of the structural connection network (SCN) and the functional connection network (FCN). However, the SCN and its interactions with the FCN remain to be further investigated. This multimodality study attempted to precisely characterize the SCN using diffusion kurtosis imaging (DKI) and further identify the neuropathological pattern of SCN-FCN decoupling, underscoring the neurodegeneration of PD. Diffusion-weighted imaging and resting-state functional imaging were available for network constructions among sixty-nine patients with PD and seventy demographically matched healthy control (HC) participants. The classification performance and topological prosperities of both the SCN and the FCN were analyzed, followed by quantification of the SCN-FCN couplings across scales. The SCN constructed by kurtosis metrics achieved optimal classification performance (area under the curve 0.89, accuracy 80.55 %, sensitivity 78.40 %, and specificity 80.65 %). Along with diverse alterations of structural and functional network topology, the PD group exhibited decoupling across scales including: reduced global coupling; increased nodal coupling within the sensorimotor network (SMN) and subcortical network (SN); higher intramodular coupling within the SMN and SN and lower intramodular coupling of the default mode network (DMN); decreased coupling between the modules of DMN-fronto-parietal network and DMN-visual network, but increased coupling between the SMN-SN module. Several associations between the coupling coefficient and topological properties of the SCN, as well as between network values and clinical scores, were observed. These findings validated the clinical implementation of DKI for structural network construction with better differentiation ability and characterized the SCN-FCN decoupling as supplementary insight into the pathological process underlying PD.
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Affiliation(s)
- Song'an Shang
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lijuan Wang
- Department of Radiology, Jintang First People's Hospital, Sichuan University, Chengdu, China
| | - Yao Xu
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongying Zhang
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lanlan Chen
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Weiqiang Dou
- MR Research China, GE Healthcare, Beijing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Ye
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
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Wang J, Bi Q, Gong W, Zhang H, Deng M, Chen L, Wang B. Histogram analysis of diffusion kurtosis imaging of deep brain nuclei in Parkinson's disease with different motor subtypes. Clin Radiol 2023; 78:e966-e974. [PMID: 37838544 DOI: 10.1016/j.crad.2023.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 10/16/2023]
Abstract
AIM To evaluate the diagnostic and differential efficacy of diffusion kurtosis imaging (DKI) histogram analysis for different motor subtypes of Parkinson's disease (PD). MATERIALS AND METHODS Seventy PD patients including 40 with postural instability and gait disorder (PIGD) and 30 with tremor-dominant (TD) and 36 healthy controls (HC) were enrolled prospectively and underwent MRI examinations. The regions of interest (ROI) in the deep brain nuclei were delineated and features were extracted on the map of mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr), respectively. The differences in histogram features between PD patients and HC and between patients with PIGD and TD were compared. The areas under the curve (AUCs) were calculated to evaluate the diagnostic efficacy of all histogram features. The correlations between histogram features and clinical indicators were evaluated. RESULTS Some DKI histogram features were significantly different between PD patients and HC, and also different between patients with PIGD and TD (all p<0.05). MK of the substantia nigra pars reticulate (SNprkurtosis), Ka of the substantia nigra pars compacta (SNpc) 50 percentile (SNpcP50), and Kr of SNpc 90th percentile showed the highest AUC for distinguishing patients with PIGD from HC. MK-SNpc 10th percentile, Ka-SNpc 25th percentile, and Kr of the head of the caudate nucleus (CN) 90th percentile had the highest AUC for distinguishing patients with TD from HC. MK of the putamen 10th percentile combined with Ka of the bilateral red nucleus RNkurtosis yielded the highest diagnostic performance with an AUC of 0.762 for distinguishing patients with PIGD from TD. Certain DKI histogram features were correlated with Hoehn-Yahr (H&Y) stage, Mini Mental State Examination (MMSE) score, tremor score, and PIGD score (all p<0.05). CONCLUSION DKI histogram analysis was useful to diagnose and discriminate different motor subtypes of PD. Certain DKI histogram features correlated with clinical indicators.
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Affiliation(s)
- J Wang
- Department of Medical Imaging, Southern Central Hospital of Yunnan Province (The First People's Hospital of Honghe State), Mengzi, Yunan, China
| | - Q Bi
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - W Gong
- Department of Anesthesiology, Southern Central Hospital of Yunnan Province (The First People's Hospital of Honghe State), Mengzi, Yunan, China
| | - H Zhang
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - M Deng
- Department of Medical Imaging, Southern Central Hospital of Yunnan Province (The First People's Hospital of Honghe State), Mengzi, Yunan, China
| | - L Chen
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - B Wang
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
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Bao J, Zhang X, Zhao X. MR imaging and outcome in neonatal HIBD models are correlated with sex: the value of diffusion tensor MR imaging and diffusion kurtosis MR imaging. Front Neurosci 2023; 17:1234049. [PMID: 37790588 PMCID: PMC10543095 DOI: 10.3389/fnins.2023.1234049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/30/2023] [Indexed: 10/05/2023] Open
Abstract
Objective Hypoxic-ischemic encephalopathy can lead to lifelong morbidity and premature death in full-term newborns. Here, we aimed to determine the efficacy of diffusion kurtosis (DK) [mean kurtosis (MK)] and diffusion tensor (DT) [fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD), and radial diffusion (RD)] parameters for the early diagnosis of early brain histopathological changes and the prediction of neurodegenerative events in a full-term neonatal hypoxic-ischemic brain injury (HIBD) rat model. Methods The HIBD model was generated in postnatal day 7 Sprague-Dawley rats to assess the changes in DK and DT parameters in 10 specific brain structural regions involving the gray matter, white matter, and limbic system during acute (12 h) and subacute (3 d and 5 d) phases after hypoxic ischemia (HI), which were validated against histology. Sensory and cognitive parameters were assessed by the open field, novel object recognition, elevated plus maze, and CatWalk tests. Results Repeated-measures ANOVA revealed that specific brain structures showed similar trends to the lesion, and the temporal pattern of MK was substantially more varied than DT parameters, particularly in the deep gray matter. The change rate of MK in the acute phase (12 h) was significantly higher than that of DT parameters. We noted a delayed pseudo-normalization for MK. Additionally, MD, AD, and RD showed more pronounced differences between males and females after HI compared to MK, which was confirmed in behavioral tests. HI females exhibited anxiolytic hyperactivity-like baseline behavior, while the memory ability of HI males was affected in the novel object recognition test. CatWalk assessments revealed chronic deficits in limb gait parameters, particularly the left front paw and right hind paw, as well as poorer performance in HI males than HI females. Conclusions Our results suggested that DK and DT parameters were complementary in the immature brain and provided great value in assessing early tissue microstructural changes and predicting long-term neurobehavioral deficits, highlighting their ability to detect both acute and long-term changes. Thus, the various diffusion coefficient parameters estimated by the DKI model are powerful tools for early HIBD diagnosis and prognosis assessment, thus providing an experimental and theoretical basis for clinical treatment.
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Affiliation(s)
- Jieaoxue Bao
- Department of Imaging, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Neuroimaging, Zhengzhou, China
| | - Xiaoan Zhang
- Department of Imaging, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Neuroimaging, Zhengzhou, China
| | - Xin Zhao
- Department of Imaging, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Neuroimaging, Zhengzhou, China
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Lu J, Zhou C, Pu J, Tian J, Yin X, Lv D, Guan X, Guo T, Zhang M, Zhang B, Yan Y, Zhao G. Brain microstructural changes in essential tremor patients and correlations with clinical characteristics: a diffusion kurtosis imaging study. J Neurol 2023; 270:2106-2116. [PMID: 36609498 DOI: 10.1007/s00415-023-11557-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/31/2022] [Accepted: 01/02/2023] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Essential tremor (ET) is the second most common movement disorder; however, the pathophysiological mechanism of ET is unclear. We aimed to investigate the microstructural degeneration of gray matter (GM) and white matter (WM) and their correlations with cognition and tremor in patients with ET. METHODS The participants were 63 patients with ET and 63 matched healthy controls (HCs) who underwent 3D-T1 weighted and diffusion kurtosis images (DKI). Microstructural degeneration was measured using high-level diffusion parameters derived from DKI. A voxel-wise analysis of the means of the GM-based spatial statistics and tract-based spatial statistics were conducted to assess differences in diffusion parameters between the ET and HC groups. The volume differences between the two groups were also assessed, and tremor severity and multi-domain cognitive performance were evaluated. Finally, the relationship between microstructural degeneration and clinical characteristics were assessed. RESULTS The ET group had significantly lower mean kurtosis of the temporal, parietal, and occipital lobes and the cerebellum and lower radial kurtosis in several tracts. These microstructural changes in GM and WM were correlated with tremor and cognitive scores. However, no significant difference in volume was found between the groups. CONCLUSION Our findings suggest that ET entails extensive GM and WM microstructural alterations, which support the neurodegenerative hypothesis of ET. Our study contributes to a better understanding of the mechanisms underlying tremor and cognitive impairment in ET.
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Affiliation(s)
- Jinyu Lu
- Department of Neurology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, Zhejiang, China
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Jiali Pu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Jun Tian
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Xinzhen Yin
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Dayao Lv
- Department of Neurology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, Zhejiang, China
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Yaping Yan
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Guohua Zhao
- Department of Neurology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, Zhejiang, China.
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
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10
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Wang H, Xu J, Yu M, Zhou G, Ren J, Wang Y, Zheng H, Sun Y, Wu J, Liu W. Functional and structural alterations as diagnostic imaging markers for depression in de novo Parkinson's disease. Front Neurosci 2023; 17:1101623. [PMID: 36908791 PMCID: PMC9992430 DOI: 10.3389/fnins.2023.1101623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023] Open
Abstract
Background Depression in Parkinson's disease (PD) is identified and diagnosed with behavioral observations and neuropsychological measurements. Due to the large overlaps of depression and PD symptoms in clinical manifestations, it is challenging for neurologists to distinguish and diagnose depression in PD (DPD) in the early clinical stage of PD. The advancement in magnetic resonance imaging (MRI) technology provides potential clinical utility in the diagnosis of DPD. This study aimed to explore the alterations of functional and structural MRI in DPD to produce neuroimaging markers in discriminating DPD from non-depressed PD (NDPD) and healthy controls (HC). Methods We recruited 20 DPD, 37 NDPD, and 41 HC matched in age, gender, and education years. The patients' diagnosis with PD was de novo. The differences in regional homogeneity (ReHo), voxel-wise degree centrality (DC), cortical thickness, cortical gray matter (GM) volumes, and subcortical GM volumes among these groups were detected, and the relationship between altered indicators and depression was analyzed. Moreover, the receiver operating characteristic (ROC) analysis was performed to assess the diagnostic efficacy of altered indicators for DPD. Results Compared to NDPD and HC, DPD showed significantly increased ReHo in left dorsolateral superior frontal gyrus (DSFG) and DC in left inferior temporal gyrus (ITG), and decreased GM volumes in left temporal lobe and right Amygdala. Among these altered indicators, ReHo value in left DSFG and DC values in left ITG and left DSFG were significantly correlated with the severity of depression in PD patients. Comparing DPD and NDPD, the ROC analysis revealed a better area under the curve value for the combination of ReHo value in left DSFG and DC value in left ITG, followed by each independent indicator. However, the difference is not statistically significant. Conclusion This study demonstrates that both functional and structural impairments are present in DPD. Among them, ReHo value of left DSFG and DC value of left ITG are equally well suited for the diagnosis and differential diagnosis of DPD, with a combination of them being slightly preferable. The multimodal MRI technique represents a promising approach for the classification of subjects with PD.
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Affiliation(s)
- Hui Wang
- Department of Neurology, Lianyungang Hospital of Traditional Chinese Medicine, Lianyungang Affiliated Hospital of Nanjing University of Chinese Medicine, Lianyungang, China
| | - Jianxia Xu
- Department of Neurology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Miao Yu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Gaiyan Zhou
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jingru Ren
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yajie Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Huifen Zheng
- Department of Neurology, Geriatric Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Sun
- International Laboratory of Children Medical Imaging Research, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Jun Wu
- Department of Clinical Laboratory, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Weiguo Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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Welton T, Hartono S, Shih YC, Lee W, Chai PH, Chong SL, Ng SYE, Chia NSY, Choi X, Heng DL, Tan EK, Tan LC, Chan LL. Microstructure of Brain Nuclei in Early Parkinson's Disease: Longitudinal Diffusion Kurtosis Imaging. JOURNAL OF PARKINSON'S DISEASE 2023; 13:233-242. [PMID: 36744346 PMCID: PMC10041414 DOI: 10.3233/jpd-225095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/15/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND Diffusion kurtosis imaging provides in vivo measurement of microstructural tissue characteristics and could help guide management of Parkinson's disease. OBJECTIVE To investigate longitudinal diffusion kurtosis imaging changes on magnetic resonance imaging in the deep grey nuclei in people with early Parkinson's disease over two years, and whether they correlate with disease progression. METHODS We conducted a longitudinal case-control study of early Parkinson's disease. 262 people (Parkinson's disease: n = 185, aged 67.5±9.1 years; 43% female; healthy controls: n = 77, aged 66.6±8.1 years; 53% female) underwent diffusion kurtosis imaging and clinical assessment at baseline and two-year timepoints. We automatically segmented five nuclei, comparing the mean kurtosis and other diffusion kurtosis imaging indices between groups and over time using repeated-measures analysis of variance, and Pearson correlation with the two-year change in Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III. RESULTS At baseline, mean kurtosis was higher in Parkinson's disease than controls in the substantia nigra, putamen, thalamus and globus pallidus when adjusting for age, sex, and levodopa equivalent daily dose (p < 0.027). These differences grew over two years, with mean kurtosis increasing for the Parkinson's disease group while remaining stable for the control group; evident in significant "group ×time" interaction effects for the putamen, thalamus and globus pallidus (ηp2= 0.08-0.11, p < 0.015). However, we did not detect significant correlations between increasing mean kurtosis and declining motor function in the Parkinson's disease group. CONCLUSION Diffusion kurtosis imaging of specific grey matter structures shows abnormal microstructure in PD at baseline and abnormal progression in PD over two years.
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Affiliation(s)
- Thomas Welton
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
- Neuroscience Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Septian Hartono
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
- Neuroscience Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Yao-Chia Shih
- Neuroscience Academic Clinical Program, Duke-NUS Medical School, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan City, Taiwan
| | - Weiling Lee
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Pik Hsien Chai
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Say Lee Chong
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Samuel Yong Ern Ng
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Nicole Shuang Yu Chia
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Xinyi Choi
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Dede Liana Heng
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Eng-King Tan
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
- Neuroscience Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Louis C.S. Tan
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
- Neuroscience Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Ling-Ling Chan
- Neuroscience Academic Clinical Program, Duke-NUS Medical School, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
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12
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Wen J, Guo T, Wu J, Bai X, Zhou C, Wu H, Liu X, Chen J, Cao Z, Gu L, Pu J, Zhang B, Zhang M, Guan X, Xu X. Nigral Iron Deposition Influences Disease Severity by Modulating the Effect of Parkinson's Disease on Brain Networks. JOURNAL OF PARKINSON'S DISEASE 2022; 12:2479-2492. [PMID: 36336939 PMCID: PMC9837680 DOI: 10.3233/jpd-223372] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND In Parkinson's disease (PD), excessive iron deposition in the substantia nigra may exacerbate α-synuclein aggregation, facilitating the degeneration of dopaminergic neurons and their neural projection. OBJECTIVE To investigate the interaction effect between nigral iron deposition and PD status on brain networks. METHODS Eighty-five PD patients and 140 normal controls (NC) were included. Network function and nigral iron were measured using multi-modality magnetic resonance imaging. According to the median of nigral magnetic susceptibility of NC (0.095 ppm), PD and NC were respectively divided into high and low nigral iron group. The main and interaction effects were investigated by mixed effect analysis. RESULTS The main effect of disease was observed in basal ganglia network (BGN) and visual network (VN). The interaction effect between nigral iron and PD status was observed in left inferior frontal gyrus and left insular lobe in BGN, as well as right middle occipital gyrus, right superior temporal gyrus, and bilateral cuneus in VN. Furthermore, multiple mediation analysis revealed that the functional connectivity of interaction effect clusters in BGN and medial VN partially mediated the relationship between nigral iron and Unified Parkinson's Disease Rating Scale II score. CONCLUSION Our study demonstrates an interaction of nigral iron deposition and PD status on brain networks, that is, nigral iron deposition is associated with the change of brain network configuration exclusively when in PD. We identified a potential causal mediation pathway for iron to affect disease severity that was mediated by both BGN dysfunction and VN hyperfunction in PD.
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Affiliation(s)
- Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China,Correspondence to: Xiaojun Xu, MD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255; E-mail: and Xiaojun Guan, PhD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255;
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China,Correspondence to: Xiaojun Xu, MD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255; E-mail: and Xiaojun Guan, PhD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255;
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13
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Lench DH, Keith K, Wilson S, Padgett L, Benitez A, Ramakrishnan V, Jensen JH, Bonilha L, Revuelta GJ. Neurodegeneration of the Globus Pallidus Internus as a Neural Correlate to Dopa-Response in Freezing of Gait. JOURNAL OF PARKINSON'S DISEASE 2022; 12:1241-1250. [PMID: 35367969 PMCID: PMC10792667 DOI: 10.3233/jpd-213062] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND Background: Parkinson's disease (PD) patients who develop freezing of gait (FOG) have reduced mobility and independence. While some patients experience improvement in their FOG symptoms with dopaminergic therapies, a subset of patients have little to no response. To date, it is unknown what changes in brain structure underlie dopa-response and whether this can be measured using neuroimaging approaches. OBJECTIVE We tested the hypothesis that structural integrity of brain regions (subthalamic nucleus and globus pallidus internus, GPi) which link basal ganglia to the mesencephalic locomotor region (MLR), a region involved in automatic gait, would be associated with FOG response to dopaminergic therapy. METHODS In this observational study, thirty-six participants with PD and definite FOG were recruited to undergo diffusion kurtosis imaging (DKI) and multiple assessments of dopa responsiveness (UPDRS scores, gait times ON versus OFF medication). RESULTS The right GPi in participants with dopa-unresponsive FOG showed reduced fractional anisotropy, mean kurtosis (MK), and increased radial diffusivity relative to those with dopa-responsive FOG. Furthermore, using probabilistic tractography, we observed reduced MK and increased mean diffusivity along the right GPi-MLR tract in dopa-unresponsive FOG. MK in the right GPi was associated with a subjective dopa-response for FOG (r = -0.360, df = 30, p = 0.043) but not overall motor dopa-response. CONCLUSION These results support structural integrity of the GPi as a correlate to dopa-response in FOG. Additionally, this study suggests DKI metrics may be a sensitive biomarker for clinical studies targeting dopaminergic circuitry and improvements in FOG behavior.
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Affiliation(s)
- Daniel H. Lench
- Department of Neurology, Medical University of South Carlina, Charleston, SC, USA
| | - Kathryn Keith
- Department of Public Health Sciences, Medical University of South Carlina, Charleston, SC, USA
| | - Sandra Wilson
- Department of Neurology, Medical University of South Carlina, Charleston, SC, USA
| | - Lucas Padgett
- Department of Neurology, Medical University of South Carlina, Charleston, SC, USA
| | - Andreana Benitez
- Department of Neurology, Medical University of South Carlina, Charleston, SC, USA
- Center for Biomedical Imaging, Medical University of South Carlina, Charleston, SC, USA
| | | | - Jens H. Jensen
- Department of Neuroscience, Medical University of South Carlina, Charleston, SC, USA
- Center for Biomedical Imaging, Medical University of South Carlina, Charleston, SC, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carlina, Charleston, SC, USA
| | - Gonzalo J. Revuelta
- Department of Neurology, Medical University of South Carlina, Charleston, SC, USA
- Ralph H. Johnson VA Medical Center, Charleston, SC, USA
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