1
|
Siva K, Ponnusamy P, Ramanathan M. Disrupted Brain Network Measures in Parkinson's Disease Patients with Severe Hyposmia and Cognitively Normal Ability. Brain Sci 2024; 14:685. [PMID: 39061425 PMCID: PMC11274763 DOI: 10.3390/brainsci14070685] [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: 06/12/2024] [Revised: 06/30/2024] [Accepted: 07/04/2024] [Indexed: 07/28/2024] Open
Abstract
Neuroscience has revolved around brain structural changes, functional activity, and connectivity alteration in Parkinson's Disease (PD); however, how the network topology organization becomes altered is still unclear, specifically in Parkinson's patients with severe hyposmia. In this study, we have examined the functional network topological alteration in patients affected by Parkinson's Disease with normal cognitive ability (ODN), Parkinson's Disease with severe hyposmia (ODP), and healthy controls (HCs) using resting-state functional magnetic resonance imaging (rsfMRI) data. We have analyzed brain topological organization using popular graph measures such as network segregation (clustering coefficient, modularity), network integration (participation coefficient, path length), small-worldness, efficiency, centrality, and assortativity. Then, we used a feature ranking approach based on the diagonal adaptation of neighborhood component analysis, aiming to determine a graph measure that is sensitive enough to distinguish between these three different groups. We noted significantly lower segregation and local efficiency and small-worldness in ODP compared to ODN and HCs. On the contrary, we did not find differences in network integration in ODP compared to ODN and HCs, which indicates that the brain network becomes fragmented in ODP. At the brain network level, a progressive increase in the DMN (Default Mode Network) was observed from healthy controls to ODN to ODP, and a continuous decrease in the cingulo-opercular network was observed from healthy controls to ODN to ODP. Further, the feature ranking approach has shown that the whole-brain clustering coefficient and small-worldness are sensitive measures to classify ODP vs. ODN, as well as HCs. Looking at the brain regional network segregation, we have found that the cerebellum and limbic, fronto-parietal, and occipital lobes have higher ODP reductions than ODN and HCs. Our results suggest network topological measures, specifically whole-brain segregation and small-worldness decreases. At the network level, an increase in DMN and a decrease in the cingulo-opercular network could be used as biomarkers to characterize ODN and ODP.
Collapse
Affiliation(s)
| | | | - Malmathanraj Ramanathan
- Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli 620015, India; (K.S.); (P.P.)
| |
Collapse
|
2
|
Liu Y, Yuan J, Tan C, Wang M, Zhou F, Song C, Tang Y, Li X, Liu Q, Shen Q, Congli H, Liu J, Cai S, Liao H. Exploring brain asymmetry in early-stage Parkinson's disease through functional and structural MRI. CNS Neurosci Ther 2024; 30:e14874. [PMID: 39056398 PMCID: PMC11273215 DOI: 10.1111/cns.14874] [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: 02/19/2024] [Revised: 05/05/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
OBJECTIVE This study explores the correlation between asymmetrical brain functional activity, gray matter asymmetry, and the severity of early-stage Parkinson's disease (PD). METHODS Ninety-three early-stage PD patients (ePD, H-Y stages 1-2.5) were recruited, divided into 47 mild (ePD-mild, H-Y stages 1-1.5) and 46 moderate (ePD-moderate, H-Y stages 2-2.5) cases, alongside 43 matched healthy controls (HCs). The study employed the Hoehn and Yahr (H-Y) staging system for disease severity assessment and utilized voxel-mirrored homotopic connectivity (VMHC) for analyzing brain functional activity asymmetry. Asymmetry voxel-based morphometry analysis (VBM) was applied to evaluate gray matter asymmetry. RESULTS The study found that, relative to HCs, both PD subgroups demonstrated reduced VMHC values in regions including the amygdala, putamen, inferior and middle temporal gyrus, and cerebellum Crus I. The ePD-moderate group also showed decreased VMHC in additional regions such as the postcentral gyrus, lingual gyrus, and superior frontal gyrus, with notably lower VMHC in the superior frontal gyrus compared to the ePD-mild group. A negative correlation was observed between the mean VMHC values in the superior frontal gyrus and H-Y stages, UPDRS, and UPDRS-III scores. No significant asymmetry in gray matter was detected. CONCLUSIONS Asymmetrical brain functional activity is a significant characteristic of PD, which exacerbates as the disease severity increases, resembling the dissemination of Lewy bodies across the PD neurological framework. VMHC emerges as a potent tool for characterizing disease severity in early-stage PD.
Collapse
Affiliation(s)
- Yujing Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Jiaying Yuan
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Changlian Tan
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Min Wang
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Fan Zhou
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Chendie Song
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Yuqing Tang
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Xv Li
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Qinru Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Qin Shen
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Huang Congli
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Jun Liu
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Clinical Research Center for Medical Imaging in Hunan ProvinceChangshaChina
| | - Sainan Cai
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Haiyan Liao
- Department of Radiology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Clinical Research Center for Medical Imaging in Hunan ProvinceChangshaChina
| |
Collapse
|
3
|
Wang X, Dong T, Li X, Yu W, Jia Z, Liu Y, Yang J. Global biomarker trends in Parkinson's disease research: A bibliometric analysis. Heliyon 2024; 10:e27437. [PMID: 38501016 PMCID: PMC10945172 DOI: 10.1016/j.heliyon.2024.e27437] [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: 09/14/2023] [Revised: 12/11/2023] [Accepted: 02/28/2024] [Indexed: 03/20/2024] Open
Abstract
As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.
Collapse
Affiliation(s)
- Xingxin Wang
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Tiantian Dong
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Xuhao Li
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Wenyan Yu
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Zhixia Jia
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yuanxiang Liu
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Jiguo Yang
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| |
Collapse
|
4
|
Wang X, Dong T, Li X, Yu W, Jia Z, Liu Y, Yang J. Global biomarker trends in Parkinson's disease research: A bibliometric analysis. Heliyon 2024; 10:e27437. [PMID: 38501016 DOI: 10.1016/j.heliyon.2024.e27437if:] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/11/2023] [Accepted: 02/28/2024] [Indexed: 07/25/2024] Open
Abstract
As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.
Collapse
Affiliation(s)
- Xingxin Wang
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Tiantian Dong
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Xuhao Li
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Wenyan Yu
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Zhixia Jia
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yuanxiang Liu
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Jiguo Yang
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| |
Collapse
|
5
|
Jiang L, Zhuo J, Furman A, Fishman PS, Gullapalli R. Cerebellar functional connectivity change is associated with motor and neuropsychological function in early stage drug-naïve patients with Parkinson's disease. Front Neurosci 2023; 17:1113889. [PMID: 37425003 PMCID: PMC10324581 DOI: 10.3389/fnins.2023.1113889] [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: 12/01/2022] [Accepted: 06/05/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting both motor and cognitive function. Previous neuroimaging studies have reported altered functional connectivity (FC) in distributed functional networks. However, most neuroimaging studies focused on patients at an advanced stage and with antiparkinsonian medication. This study aims to conduct a cross-sectional study on cerebellar FC changes in early-stage drug-naïve PD patients and its association with motor and cognitive function. Methods Twenty-nine early-stage drug-naïve PD patients and 20 healthy controls (HCs) with resting-state fMRI data and motor UPDRS and neuropsychological cognitive data were extracted from the Parkinson's Progression Markers Initiative (PPMI) archives. We used seed-based resting-state fMRI (rs-fMRI) FC analysis and the cerebellar seeds were defined based on the hierarchical parcellation of the cerebellum (AAL atlas) and its topological function mapping (motor cerebellum and non-motor cerebellum). Results The early stage drug-naïve PD patients had significant differences in cerebellar FC when compared with HCs. Our findings include: (1) Increased intra-cerebellar FC within motor cerebellum, (2) increase motor cerebellar FC in inferior temporal gyrus and lateral occipital gyrus within ventral visual pathway and decreased motor-cerebellar FC in cuneus and dorsal posterior precuneus within dorsal visual pathway, (3) increased non-motor cerebellar FC in attention, language, and visual cortical networks, (4) increased vermal FC in somatomotor cortical network, and (5) decreased non-motor and vermal FC within brainstem, thalamus and hippocampus. Enhanced FC within motor cerebellum is positively associated with the MDS-UPDRS motor score and enhanced non-motor FC and vermal FC is negatively associated with cognitive function test scores of SDM and SFT. Conclusion These findings provide support for the involvement of cerebellum at an early stage and prior to clinical presentation of non-motor features of the disease in PD patients.
Collapse
Affiliation(s)
- Li Jiang
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- Center for Advanced Imaging Research (CAIR), University of Maryland School of Medicine, Baltimore, MD, United States
| | - Jiachen Zhuo
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- Center for Advanced Imaging Research (CAIR), University of Maryland School of Medicine, Baltimore, MD, United States
| | - Andrew Furman
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- Center for Advanced Imaging Research (CAIR), University of Maryland School of Medicine, Baltimore, MD, United States
| | - Paul S. Fishman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Rao Gullapalli
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- Center for Advanced Imaging Research (CAIR), University of Maryland School of Medicine, Baltimore, MD, United States
| |
Collapse
|
6
|
Yuan M, Du N, Song Z. Primary motor area-related injury of anterior central gyrus in Parkinson's disease with dyskinesia: a study based on MRS and Q-Space. Neurosci Lett 2023; 805:137224. [PMID: 37019268 DOI: 10.1016/j.neulet.2023.137224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/30/2023] [Accepted: 04/01/2023] [Indexed: 04/05/2023]
Abstract
INTRODUCTION By using magnetic resonance spectroscopy (MRS) and Q-Space imaging technology, this research analyzes the imaging characteristics of white matter fibers in the primary motor cortex and posterior limbs of the subcortical internal capsule in parkinsonian patients with motor disorders. The correlation among the changes in axonal function and structure in the cerebral cortex and subcortical cortex and motor disorder is further revealed. METHODS First, motor function and clinical condition of 20 patients with Parkinson's disease is assessed the third section of the Unified Parkinson's Scale and H&Y Parkinson's Clinical Staging Scale. Magnetic resonance (MR) scanning is performed with 1H-MRS. Secondly, the range maps of N-acetylaspartic acid (NAA), Choline (Cho), and Creatine (Cr) in the region of interest (the primary motor area of anterior central cortex gyrus, i.e. M1 region) are obtained, and the ratios of NAA/Cr and Cho are calculated. Third, Q-Space MR diffusion imaging technique is used to collect Q-Space images, and a Dsi-studio workstation is used to post-process the images. The fraction anisotropic (FA), generalized fraction anisotropic (GFA), and apparent diffusion coefficient (ADC) parameters of Q-Space in the primary motor cortex and the region of interest in the posterior limb of the internal capsule are obtained. Finally, the parameters of MRS and Q-Space in the experimental group and the control group are further analyzed by SPSS statistical software. RESULTS After assessing with Parkinson's score scale, there is obvious motor dysfunction in the experimental group. The average clinical stage of H&Y is 3.0±0.31. In the analysis of MRS data, the ratio of NAA/Cr in the primary motor area of the anterior central gyrus in the experimental group is significantly lower than that in the control group (P<0.05). In the ADC map obtained by Q-Space imaging technique, the ADC value in the primary motor area of the anterior central gyrus in the experimental group is higher than that in the control group (P<0.05), and the difference is statistically significant (P<0.05). There is no significant difference between the experimental group and the control group (P>0.05) in FA and GFA values of the posterior limb of capsule to characterize the characteristics of white matter fibers. CONCLUSIONS In parkinsonian patients with motor dysfunction, there are apparent functional and structural changes in the primary motor area neurons and peripheral white matter of the anterior central gyrus, and no obvious damage to the axonal structure of the descending fibers in the cortex.
Collapse
|
7
|
Wang XH, Liu XF, Ao M, Wang T, He J, Gu YW, Fan JW, Yang L, Yu R, Guo S. Cerebral Perfusion Patterns of Anxiety State in Patients With Pulmonary Nodules: A Study of Cerebral Blood Flow Based on Arterial Spin Labeling. Front Neurosci 2022; 16:912665. [PMID: 35615271 PMCID: PMC9125149 DOI: 10.3389/fnins.2022.912665] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/21/2022] [Indexed: 12/14/2022] Open
Abstract
Background and Purpose The proportion of patients with somatic diseases associated with anxiety is increasing each year, and pulmonary nodules have become a non-negligible cause of anxiety, the mechanism of which is unclear. The study focus on the cerebral blood flow (CBF) of anxiety in patients with pulmonary nodules to explore the cerebral perfusion pattern of anxiety associated with pulmonary nodules, blood perfusion status and mode of pulmonary nodule induced anxiety state. Materials and Methods Patients with unconfirmed pulmonary nodules were evaluated by Hamilton Anxiety Scale (HAMA). The total score > 14 was defined as anxiety group, and the total score ≤ 14 points was defined as non-anxiety group. A total of 38 patients were enrolled, of which 19 patients were the anxiety group and 19 were the non-anxiety group. All subjects underwent arterial spin labeling imaging using a 3.0 T MRI. A two-sample t-test was performed to compare the CBF between the two groups. The CBF was extracted in brain regions with difference, and Spearman correlation was used to analyze the correlation between CBF and HAMA scores; ROC was used to analyze the performance of CBF to distinguish between the anxiety group and the non-anxiety group. Results The CBF in the right insula/Heschl’s cortex of the anxiety group decreased (cluster = 109, peak t = 4.124, and P < 0.001), and the CBF in the right postcentral gyrus increased (cluster = 53, peak t = −3.912, and P < 0.001) in the anxiety group. But there was no correlation between CBF and HAMA score. The ROC analysis of the CBF of the right insula/Heschl’s cortex showed that the AUC was 0.856 (95%CI, 0.729, 0.983; P < 0.001), the optimal cutoff value of the CBF was 50.899, with the sensitivity of 0.895, and specificity of 0.789. The ROC analysis of CBF in the right postcentral gyrus showed that the AUC was 0.845 (95%CI, 0.718, 0.972; P < 0.001), the optimal cutoff value of CBF was 43.595, with the sensitivity of 0.737, and specificity of 0.842. Conclusion The CBF of the right insula/Heschl’s cortex decreased and the CBF of the right postcentral gyrus increased in patients with pulmonary nodules under anxiety state, and the CBF of the aforementioned brain regions can accurately distinguish the anxiety group from the non-anxiety group.
Collapse
Affiliation(s)
- Xiao-Hui Wang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao-Fan Liu
- School of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China
| | - Min Ao
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ting Wang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jinglan He
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yue-Wen Gu
- Department of Clinical Psychology, Fourth Military Medical University, Xi’an, China
| | - Jing-Wen Fan
- Department of Clinical Psychology, Fourth Military Medical University, Xi’an, China
| | - Li Yang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Li Yang,
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Renqiang Yu,
| | - Shuliang Guo
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Shuliang Guo, , orcid.org/0000-0003-3572-7421
| |
Collapse
|
8
|
Ma B, Zhang F, Ma B. Self-Attention-Guided Recurrent Neural Network and Motion Perception for Intelligent Prediction of Chronic Diseases. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6382619. [PMID: 34745506 PMCID: PMC8566041 DOI: 10.1155/2021/6382619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 09/14/2021] [Accepted: 09/16/2021] [Indexed: 11/18/2022]
Abstract
Parkinson's disease is a common chronic disease that affects a large number of people. In the real world, however, Parkinson's disease can result in a loss of physical performance, which is classified as a movement disorder by clinicians. Parkinson's disease is currently diagnosed primarily through clinical symptoms, which are highly dependent on clinician experience. As a result, there is a need for effective early detection methods. Traditional machine learning algorithms filter out many inherently relevant features in the process of dimensionality reduction and feature classification, lowering the classification model's performance. To solve this problem and ensure high correlation between features while reducing dimensionality to achieve the goal of improving classification performance, this paper proposes a recurrent neural network classification model based on self attention and motion perception. Using a combination of self-attention mechanism and recurrent neural network, as well as wearable inertial sensors, the model classifies and trains the five brain area features extracted from MRI and DTI images (cerebral gray matter, white matter, cerebrospinal fluid density, and so on). Clinical and exercise data can be combined to produce characteristic parameters that can be used to describe movement sluggishness. The experimental results show that the model proposed in this paper improves the recognition performance of Parkinson's disease, which is better than the compared methods by 2.45% to 12.07%.
Collapse
Affiliation(s)
- Baojuan Ma
- Physical Education Department, Shijiazhuang Information Engineering Vocational College, Shijiazhuang 05000, Hebei, China
| | - Fengyan Zhang
- Physical Education Department, Shijiazhuang Information Engineering Vocational College, Shijiazhuang 05000, Hebei, China
| | - Baoling Ma
- Physical Education and Health College, Hebei Normal University of Science and Technology, Qinhuangdao 066004, Hebei, China
| |
Collapse
|
9
|
Varfolomeev SD, Bykov VI, Semenova NA, Tsybenova SB. Dynamics of the Multipathway Regulation of the Vasodilator Bold Effect Induced by a Nerve Impulse: A Kinetic Model of the Neurovascular Coupling Process. ACS Chem Neurosci 2021; 12:2202-2208. [PMID: 34096262 DOI: 10.1021/acschemneuro.1c00214] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
A kinetic model of the dynamics of a multipathway mechanism of neurovascular coupling induced by nerve impulses was constructed. The model calculations were compared with experimental data on the changes in the blood oxygen level dependent signal during sensory-motor and visual excitation before and after the use of the nonsteroidal anti-inflammatory drug indomethacin. The influence of the catalytic activity of key enzymes on the dynamics of the neurovascular response in the proposed model is shown. The multipathway mechanism of the biochemical reactions provides stability of the neurovascular coupling during various possible catalytic activities of the key enzymes in the process.
Collapse
Affiliation(s)
- Sergey D. Varfolomeev
- Institute of Physical and Chemical Grounds of Neuronet Functions and Artificial Intelligence, Lomonosov Moscow State University, Moscow 119991, Russia
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
- Emanuel Institute of Biochemical Physics, Moscow 119334, Russia
| | | | | | | |
Collapse
|
10
|
Griffanti L, Klein JC, Szewczyk-Krolikowski K, Menke RAL, Rolinski M, Barber TR, Lawton M, Evetts SG, Begeti F, Crabbe M, Rumbold J, Wade-Martins R, Hu MT, Mackay C. Cohort profile: the Oxford Parkinson's Disease Centre Discovery Cohort MRI substudy (OPDC-MRI). BMJ Open 2020; 10:e034110. [PMID: 32792423 PMCID: PMC7430482 DOI: 10.1136/bmjopen-2019-034110] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
PURPOSE The Oxford Parkinson's Disease Centre (OPDC) Discovery Cohort MRI substudy (OPDC-MRI) collects high-quality multimodal brain MRI together with deep longitudinal clinical phenotyping in patients with Parkinson's, at-risk individuals and healthy elderly participants. The primary aim is to detect pathological changes in brain structure and function, and develop, together with the clinical data, biomarkers to stratify, predict and chart progression in early-stage Parkinson's and at-risk individuals. PARTICIPANTS Participants are recruited from the OPDC Discovery Cohort, a prospective, longitudinal study. Baseline MRI data are currently available for 290 participants: 119 patients with early idiopathic Parkinson's, 15 Parkinson's patients with pathogenic mutations of the leucine-rich repeat kinase 2 or glucocerebrosidase (GBA) genes, 68 healthy controls and 87 individuals at risk of Parkinson's (asymptomatic carriers of GBA mutation and patients with idiopathic rapid eye movement sleep behaviour disorder-RBD). FINDINGS TO DATE Differences in brain structure in early Parkinson's were found to be subtle, with small changes in the shape of the globus pallidus and evidence of alterations in microstructural integrity in the prefrontal cortex that correlated with performance on executive function tests. Brain function, as assayed with resting fMRI yielded more substantial differences, with basal ganglia connectivity reduced in early Parkinson'sand RBD. Imaging of the substantia nigra with the more recent adoption of sequences sensitive to iron and neuromelanin content shows promising results in identifying early signs of Parkinsonian disease. FUTURE PLANS Ongoing studies include the integration of multimodal MRI measures to improve discrimination power. Follow-up clinical data are now accumulating and will allow us to correlate baseline imaging measures to clinical disease progression. Follow-up MRI scanning started in 2015 and is currently ongoing, providing the opportunity for future longitudinal imaging analyses with parallel clinical phenotyping.
Collapse
Affiliation(s)
- Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Konrad Szewczyk-Krolikowski
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Ricarda A L Menke
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Michal Rolinski
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Institute of Clinical Neurosciences, University of Bristol, Bristol, UK
| | - Thomas R Barber
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Michael Lawton
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Samuel G Evetts
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Faye Begeti
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Marie Crabbe
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Jane Rumbold
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Richard Wade-Martins
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, Oxfordshire, UK
| | - Michele T Hu
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Clare Mackay
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
- Oxford Health, NHS Foundation Trust, Oxford, Oxfordshire, UK
| |
Collapse
|
11
|
Manzanera OM, Meles SK, Leenders KL, Renken RJ, Pagani M, Arnaldi D, Nobili F, Obeso J, Oroz MR, Morbelli S, Maurits NM. Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data. Int J Neural Syst 2019; 29:1950010. [DOI: 10.1142/s0129065719500102] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).
Collapse
Affiliation(s)
- Octavio Martinez Manzanera
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Sanne K. Meles
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Klaus L. Leenders
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Remco J. Renken
- Faculty of Medical Sciences, University Medical Center Groningen, University of Groningen, A. Deusinglaan 1, Groningen, The Netherlands
| | - Marco Pagani
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche, via S. Martino della Battaglia, 44-00185 Rome, Italy
- Department of Nuclear Medicine, Karolinska University Hospital, Huddinge, SE-141 86, Stockholm, Sweden
- Department of Nuclear Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1,9713 GZ Groningen, The Netherlands
| | - Dario Arnaldi
- Department of Neuroscience, Rehabilitation, Opthalmology, Genetics and Maternal and Child Science (DINOGMI), University of Genoa Largo Paolo Daneo 3, 16132 Genoa, Italy
- IRCCS AOU San Martino — IST, Largo R. Benzi 10, 16132 Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Rehabilitation, Opthalmology, Genetics and Maternal and Child Science (DINOGMI), University of Genoa Largo Paolo Daneo 3, 16132 Genoa, Italy
- IRCCS AOU San Martino — IST, Largo R. Benzi 10, 16132 Genoa, Italy
| | - Jose Obeso
- CINAC, HM Puerta del Sur, Avda. de Carlos V 70, 28938 Móstoles (Madrid), Spain
- CEU Universidad San Pablo, C/Julián Romea 18, 28003 Madrid, Spain
- CIBERNED, Instituto Carlos III, C/Valderrebollo 5, 28031 Madrid, Spain
| | - Maria Rodriguez Oroz
- Department of Neurosciences, Biodonostia Health Research Institute, Begiristain Doktorea Pasealekua, 20014 Donostia-San Sebastián, Guipúzcoa, Spain
| | - Silvia Morbelli
- IRCCS AOU San Martino — IST, Largo R. Benzi 10, 16132 Genoa, Italy
- Nuclear Medicine Unit, Department of Health Sciences (DISSAL), University of Genoa via A. Pastore 1, 16132 Genoa, Italy
| | - Natasha M. Maurits
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| |
Collapse
|
12
|
Nackaerts E, D'Cruz N, Dijkstra BW, Gilat M, Kramer T, Nieuwboer A. Towards understanding neural network signatures of motor skill learning in Parkinson's disease and healthy aging. Br J Radiol 2019; 92:20190071. [PMID: 30982328 DOI: 10.1259/bjr.20190071] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
In the past decade, neurorehabilitation has been shown to be an effective therapeutic supplement for patients with Parkinson's disease (PD). However, patients still experience severe problems with the consolidation of learned motor skills. Knowledge on the neural correlates underlying this process is thus essential to optimize rehabilitation for PD. This review investigates the existing studies on neural network connectivity changes in relation to motor learning in healthy aging and PD and critically evaluates the imaging methods used from a methodological point of view. The results indicate that despite neurodegeneration there is still potential to modify connectivity within and between motor and cognitive networks in response to motor training, although these alterations largely bypass the most affected regions in PD. However, so far training-related changes are inferred and possible relationships are not substantiated by brain-behavior correlations. Furthermore, the studies included suffer from many methodological drawbacks. This review also highlights the potential for using neural network measures as predictors for the response to rehabilitation, mainly based on work in young healthy adults. We speculate that future approaches, including graph theory and multimodal neuroimaging, may be more sensitive than brain activation patterns and model-based connectivity maps to capture the effects of motor learning. Overall, this review suggests that methodological developments in neuroimaging will eventually provide more detailed knowledge on how neural networks are modified by training, thereby paving the way for optimized neurorehabilitation for patients.
Collapse
Affiliation(s)
| | - Nicholas D'Cruz
- 1Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Bauke W Dijkstra
- 1Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Moran Gilat
- 1Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Thomas Kramer
- 1Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Alice Nieuwboer
- 1Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| |
Collapse
|
13
|
Automated Subfield Volumetric Analysis of Hippocampus in Patients with Drug-Naïve Nondementia Parkinson's Disease. PARKINSONS DISEASE 2019; 2019:8254263. [PMID: 30854188 PMCID: PMC6378059 DOI: 10.1155/2019/8254263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 12/03/2018] [Accepted: 12/13/2018] [Indexed: 11/17/2022]
Abstract
Several studies used automated segmentation of hippocampal subfield (ASHS) for detailed measurements of anatomic subregions of the hippocampus, especially in the field of neurodegenerative disorders. In this study, we investigated the hippocampal subfield volume of patients with early-stage nondementia PD compared with normal healthy subjects using the ASHS method. A total of 32 subjects were enrolled in this study (sixteen patients with drug naive nondementia PD and sixteen healthy controls). All subjects were scanned with a 1.5 tesla MRI. The volumes of the seven subfields were calculated separately, and then, the whole hippocampal volume was calculated by the summing of CA1, CA2-3, CA4-DG, subiculum, presubiculum, and fimbria, excluding the hippocampal fissure. There were significant diagnosis-by-hemisphere interactive effects on the total hippocampal volume (F = 5.197; p=0.031) and the subfield volume of CA2-3 (F = 7.586; p=0.010) and CA4-DG (F = 7.403; p=0.011). The volumes of CA2-3 (F = 19.911; p < 0.001), CA4-DG (F = 20.273; p < 0.001), and total hippocampus (F = 10.573; p=0.005) in the left hemisphere were reduced compared to the right hemisphere. We suggest that the hippocampal volume asymmetry, especially in CA4-DG and CA2-3, could be observed in drug-naïve PD patients even in the early stage of the disease.
Collapse
|
14
|
Albrecht F, Ballarini T, Neumann J, Schroeter ML. FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis. Neuroimage Clin 2018; 21:101594. [PMID: 30514656 PMCID: PMC6413303 DOI: 10.1016/j.nicl.2018.11.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 11/01/2018] [Accepted: 11/10/2018] [Indexed: 11/25/2022]
Abstract
Recently, revised diagnostic criteria for Parkinson's disease (PD) were introduced (Postuma et al., 2015). Yet, except for well-established dopaminergic imaging, validated imaging biomarkers for PD are still missing, though they could improve diagnostic accuracy. We conducted systematic meta-analyses to identify PD-specific markers in whole-brain structural magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) and diffusion tensor imaging (DTI) studies. Overall, 74 studies were identified including 2323 patients and 1767 healthy controls. Studies were first grouped according to imaging modalities (MRI 50; PET 14; DTI 10) and then into subcohorts based on clinical phenotypes. To ensure reliable results, we combined established meta-analytical algorithms - anatomical likelihood estimation and seed-based D mapping - and cross-validated them in a conjunction analysis. Glucose hypometabolism was found using FDG-PET extensively in bilateral inferior parietal cortex and left caudate nucleus with both meta-analytic methods. This hypometabolism pattern was confirmed in subcohort analyses and related to cognitive deficits (inferior parietal cortex) and motor symptoms (caudate nucleus). Structural MRI showed only small focal gray matter atrophy in the middle occipital gyrus that was not confirmed in subcohort analyses. DTI revealed fractional anisotropy reductions in the cingulate bundle near the orbital and anterior cingulate gyri in PD. Our results suggest that FDG-PET reliably identifies consistent functional brain abnormalities in PD, whereas structural MRI and DTI show only focal alterations and rather inconsistent results. In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.
Collapse
Affiliation(s)
- Franziska Albrecht
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Tommaso Ballarini
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Jane Neumann
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany; Department of Medical Engineering and Biotechnology, University of Applied Science, Jena, Germany.
| | - Matthias L Schroeter
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic of Cognitive Neurology, University of Leipzig & FTLD Consortium Germany, Leipzig, Germany.
| |
Collapse
|
15
|
Trigo-Damas I, del Rey NLG, Blesa J. Novel models for Parkinson’s disease and their impact on future drug discovery. Expert Opin Drug Discov 2018; 13:229-239. [DOI: 10.1080/17460441.2018.1428556] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Ines Trigo-Damas
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Spain
- CIBERNED, Instituto Carlos III, Madrid, Spain
| | | | - Javier Blesa
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Spain
- CIBERNED, Instituto Carlos III, Madrid, Spain
| |
Collapse
|