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Liu J, Chen S, Chen J, Wang B, Zhang Q, Xiao L, Zhang D, Cai X. Structural Brain Connectivity Guided Optimal Contact Selection for Deep Brain Stimulation of the Subthalamic Nucleus. World Neurosurg 2024; 188:e546-e554. [PMID: 38823445 DOI: 10.1016/j.wneu.2024.05.150] [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: 05/12/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy in ameliorating the motor symptoms of Parkinson disease. However, postoperative optimal contact selection is crucial for achieving the best outcome of deep brain stimulation of the subthalamic nucleus surgery, but the process is currently a trial-and-error and time-consuming procedure that relies heavily on surgeons' clinical experience. METHODS In this study, we propose a structural brain connectivity guided optimal contact selection method for deep brain stimulation of the subthalamic nucleus. Firstly, we reconstruct the DBS electrode location and estimate the stimulation range using volume of tissue activated from each DBS contact. Then, we extract the structural connectivity features by concatenating fractional anisotropy and the number of streamlines features of activated regions and the whole brain regions. Finally, we use a convolutional neural network with convolutional block attention module to identify the structural connectivity features for the optimal contact selection. RESULTS We review the data of 800 contacts from 100 patients with Parkinson disease for the experiment. The proposed method achieves promising results, with the average accuracy of 97.63%, average precision of 94.50%, average recall of 94.46%, and average specificity of 98.18%, respectively. Our method can provide the suggestion for optimal contact selection. CONCLUSIONS Our proposed method can improve the efficiency and accuracy of DBS optimal contact selection, reduce the dependence on surgeons' experience, and has the potential to facilitate the development of advanced DBS technology.
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Affiliation(s)
- Jiali Liu
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Shouxuan Chen
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Jianwei Chen
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Bo Wang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Qiusheng Zhang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Linxia Xiao
- Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Doudou Zhang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Xiaodong Cai
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
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Eugenia Caligiuri M, Quattrone A, Giovanna Bianco M, Riccardo Aquila V, Celeste Bonacci M, Calomino C, Camastra C, Buonocore J, Augimeri A, Morelli M, Quattrone A. Corpus callosum damage in PSP and unsteady PD patients: A multimodal MRI study. Neuroimage Clin 2024; 43:103642. [PMID: 39029159 DOI: 10.1016/j.nicl.2024.103642] [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: 03/14/2024] [Revised: 06/24/2024] [Accepted: 07/13/2024] [Indexed: 07/21/2024]
Abstract
INTRODUCTION Postural instability (PI) is a common disabling symptom in Parkinson's disease (PD) patients, but the brain alterations underlying this sign are not fully understood yet. This study aimed to investigate the association between PI and callosal damage in PD and progressive supranuclear palsy (PSP) patients, using multimodal MR imaging. METHODS One-hundred and two PD patients stratified according to the presence/absence of PI (PD-steady N=58; PD-unsteady N=44), 69 PSP patients, and 38 healthy controls (HC) underwent structural and diffusion 3T brain MRI. Thickness, fractional anisotropy (FA) and mean diffusivity (MD) were calculated over 50 equidistant points covering the whole midsagittal profile of the corpus callosum (CC) and compared among groups. Associations between imaging metrics and postural instability score were investigated using linear regression. RESULTS Both PSP and PD-unsteady patient groups showed CC involvement in comparison with HC, while no difference was found between PD-steady patients and controls. The CC damage was more severe and widespread in PSP than in PD patients. The CC genu was the regions most damaged in PD-unsteady patients compared with PD-steady patients, showing significant microstructural alterations of MD and FA metrics. Linear regression analysis pointed at the MD in the CC genu as the main contributor to PI among the considered MRI metrics. CONCLUSION This study identified callosal microstructural alterations associated with PI in unsteady PD and PSP patients, which provide new insights on PI pathophysiology and might serve as imaging biomarkers for assessing postural instability progression and treatment response.
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Affiliation(s)
- Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy
| | - Andrea Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy; Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy.
| | - Maria Giovanna Bianco
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy
| | - Valerio Riccardo Aquila
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy
| | - Maria Celeste Bonacci
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy
| | - Camilla Calomino
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy
| | - Chiara Camastra
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy
| | - Jolanda Buonocore
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | | | - Maurizio Morelli
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy
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Li S, Zhu Y, Lai H, Da X, Liao T, Liu X, Deng F, Chen L. Increased prevalence of vertebrobasilar dolichoectasia in Parkinson's disease and its effect on white matter microstructure and network. Neuroreport 2024; 35:627-637. [PMID: 38813904 DOI: 10.1097/wnr.0000000000002046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
This study aimed to investigate the prevalence of vertebrobasilar dolichoectasia (VBD) in Parkinson's disease (PD) patients and analyze its role in gray matter changes, white matter (WM) microstructure and network alterations in PD. This is a cross-sectional study including 341 PD patients. Prevalence of VBD in these PD patients was compared with general population. Diffusion tensor imaging and T1-weighted imaging analysis were performed among 174 PD patients with or without VBD. Voxel-based morphometry analysis was used to estimate gray matter volume changes. Tract-based spatial statistics and region of interest-based analysis were used to evaluate WM microstructure changes. WM network analysis was also performed. Significantly higher prevalence of VBD in PD patients was identified compared with general population. Lower fractional anisotropy and higher diffusivity, without significant gray matter involvement, were found in PD patients with VBD in widespread areas. Decreased global and local efficiency, increased hierarchy, decreased degree centrality at left Rolandic operculum, increased betweenness centrality at left postcentral gyrus and decreased average connectivity strength between and within several modules were identified in PD patients with VBD. VBD is more prevalent in PD patients than general population. Widespread impairments in WM microstructure and WM network involving various motor and nonmotor PD symptom-related areas are more prominent in PD patients with VBD compared with PD patients without VBD.
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Affiliation(s)
- Sichen Li
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Zhang Q, Wang H, Shi Y, Li W. White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model. Quant Imaging Med Surg 2024; 14:3086-3106. [PMID: 38617147 PMCID: PMC11007501 DOI: 10.21037/qims-23-1478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 03/07/2024] [Indexed: 04/16/2024]
Abstract
Background Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. Results In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.
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Affiliation(s)
- Qi Zhang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Haoran Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Yonghong Shi
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Wensheng Li
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
- Department of Human Anatomy and Histoembryology, School of Basic Medical Science, Fudan University, Shanghai, China
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Simonsson O, Bouso JC, Kurth F, Araújo DB, Gaser C, Riba J, Luders E. Preliminary evidence of links between ayahuasca use and the corpus callosum. Front Psychiatry 2022; 13:1002455. [PMID: 36386967 PMCID: PMC9643584 DOI: 10.3389/fpsyt.2022.1002455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/05/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Recent research suggests that ayahuasca and its alkaloid-containing ingredients may be helpful in the treatment and prevention of certain movement and neurodegenerative disorders. However, such research is still in its infancy and more studies in normative samples seem necessary to explore effects of ayahuasca on clinically relevant brain structures, such as the corpus callosum. AIMS The purpose of the present study was to investigate links between ayahuasca use and callosal structure in a normative sample. METHODS Using structural imaging data from 22 ayahuasca users and 22 matched controls we compared the thickness of the corpus callosum between both groups at 100 equidistant points across the entire midsagittal surface. In addition, we investigated point-wise correlations between callosal thickness and the number of past ayahuasca sessions. RESULTS The corpus callosum was significantly thicker within the isthmus in the ayahuasca group than in the control group. There was also a significant positive correlation between callosal thickness and the number of past ayahuasca sessions within the rostral body, albeit none of these effects survived corrections for multiple comparisons. No region was significantly thicker in the control than in the ayahuasca group, and no callosal region was negatively linked to ayahuasca use, even at uncorrected significance thresholds. CONCLUSION This study provides preliminary evidence of links between ayahuasca use and the corpus callosum. However, future studies need to replicate these findings, preferably using larger sample sizes and ideally also utilizing longitudinal research designs, to draw any practical conclusion and offer implications for follow-up clinical research.
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Affiliation(s)
- Otto Simonsson
- Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.,Department of Sociology, University of Oxford, Oxford, United Kingdom
| | - José Carlos Bouso
- ICEERS-International Center for Ethnobotanical Education, Research and Services, Barcelona, Spain.,Medical Anthropology Research Center (MARC), Universitat Rovira i Virgili, Tarragona, Spain.,Department of Neurosciences and Behavior, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Florian Kurth
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - Dráulio B Araújo
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil.,Onofre Lopes University Hospital, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Department of Neurology, Jena University Hospital, Jena, Germany
| | - Jordi Riba
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
| | - Eileen Luders
- School of Psychology, University of Auckland, Auckland, New Zealand.,Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.,Laboratory of Neuro Imaging, School of Medicine, University of Southern California, Los Angeles, CA, United States
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