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Shi D, Wu S, Zhuang C, Mao Y, Wang Q, Zhai H, Zhao N, Yan G, Wu R. Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease. Neurobiol Dis 2024; 197:106527. [PMID: 38740347 DOI: 10.1016/j.nbd.2024.106527] [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: 04/16/2024] [Accepted: 05/09/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits. METHODS We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities. RESULTS Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected). CONCLUSIONS Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China.
| | - Shuohua Wu
- Department of Radiology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Caiyu Zhuang
- Department of Radiology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yumeng Mao
- Department of Radiology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Qianqi Wang
- Department of Basic Medical Sciences, School of Medicine, Xiamen University, Xiamen, China
| | - Huige Zhai
- Center of Morphological Experiment, Medical College of Yanbian University, Yanji, China
| | - Nannan Zhao
- Center of Morphological Experiment, Medical College of Yanbian University, Yanji, China
| | - Gen Yan
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China.
| | - Renhua Wu
- Department of Radiology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China.
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Liu M, Yu W, Xu D, Wang M, Peng B, Jiang H, Dai Y. Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics. Front Neurosci 2024; 18:1356241. [PMID: 38694903 PMCID: PMC11061487 DOI: 10.3389/fnins.2024.1356241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/14/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and assessment scales, lacking objective diagnostic indicators. Methods In this study, we introduced a novel approach for diagnosing ASD, leveraging T1-based gray matter and ASL-based cerebral blood flow network metrics. Thirty preschool-aged patients with ASD and twenty-two typically developing (TD) individuals were enrolled. Brain network features, including gray matter and cerebral blood flow metrics, were extracted from both T1-weighted magnetic resonance imaging (MRI) and ASL images. Feature selection was performed using statistical t-tests and Minimum Redundancy Maximum Relevance (mRMR). A machine learning model based on random vector functional link network was constructed for diagnosis. Results The proposed approach demonstrated a classification accuracy of 84.91% in distinguishing ASD from TD. Key discriminating network features were identified in the inferior frontal gyrus and superior occipital gyrus, regions critical for social and executive functions in ASD patients. Discussion Our study presents an objective and effective approach to the clinical diagnosis of ASD, overcoming the limitations of subjective behavioral observations. The identified brain network features provide insights into the neurobiological mechanisms underlying ASD, potentially leading to more targeted interventions.
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Affiliation(s)
- Mingyang Liu
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China
| | - Weibo Yu
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China
| | - Dandan Xu
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi, China
| | - Miaoyan Wang
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi, China
| | - Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China
| | - Haoxiang Jiang
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China
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Alhudhaif A. A novel approach to recognition of Alzheimer's and Parkinson's diseases: random subspace ensemble classifier based on deep hybrid features with a super-resolution image. PeerJ Comput Sci 2024; 10:e1862. [PMID: 38435579 PMCID: PMC10909220 DOI: 10.7717/peerj-cs.1862] [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: 11/10/2023] [Accepted: 01/18/2024] [Indexed: 03/05/2024]
Abstract
Background Artificial intelligence technologies have great potential in classifying neurodegenerative diseases such as Alzheimer's and Parkinson's. These technologies can aid in early diagnosis, enhance classification accuracy, and improve patient access to appropriate treatments. For this purpose, we focused on AI-based auto-diagnosis of Alzheimer's disease, Parkinson's disease, and healthy MRI images. Methods In the current study, a deep hybrid network based on an ensemble classifier and convolutional neural network was designed. First, a very deep super-resolution neural network was adapted to improve the resolution of MRI images. Low and high-level features were extracted from the images processed with the hybrid deep convolutional neural network. Finally, these deep features are given as input to the k-nearest neighbor (KNN)-based random subspace ensemble classifier. Results A 3-class dataset containing publicly available MRI images was utilized to test the proposed architecture. In experimental works, the proposed model produced 99.11% accuracy, 98.75% sensitivity, 99.54% specificity, 98.65% precision, and 98.70% F1-score performance values. The results indicate that our AI system has the potential to provide valuable diagnostic assistance in clinical settings.
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Affiliation(s)
- Adi Alhudhaif
- Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
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Wang Q, Zeng W, Dai X. Gait classification for early detection and severity rating of Parkinson's disease based on hybrid signal processing and machine learning methods. Cogn Neurodyn 2024; 18:109-132. [PMID: 38406205 PMCID: PMC10881932 DOI: 10.1007/s11571-022-09925-9] [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: 01/28/2022] [Revised: 12/03/2022] [Accepted: 12/19/2022] [Indexed: 12/31/2022] Open
Abstract
Parkinson's disease (PD) is one of the cognitive degenerative disorders of the central nervous system that affects the motor system. Gait dysfunction represents the pathology of motor symptom while gait analysis provides clinicians with subclinical information reflecting subtle differences between PD patients and healthy controls (HCs). Currently neurologists usually assess several clinical manifestations of the PD patients and rate the severity level according to some established criteria. This is highly dependent on clinician's expertise which is subjective and ineffective. In the present study we address these issues by proposing a hybrid signal processing and machine learning based gait classification system for gait anomaly detection and severity rating of PD patients. Time series of vertical ground reaction force (VGRF) data are utilized to represent discriminant gait information. First, phase space of the VGRF is reconstructed, in which the properties associated with the nonlinear gait system dynamics are preserved. Then Shannon energy is used to extract the characteristic envelope of the phase space signal. Third, Shannon energy envelope is decomposed into high and low resonance components using dual Q-factor signal decomposition derived from tunable Q-factor wavelet transform. Note that the high Q-factor component consists largely of sustained oscillatory behavior, while the low Q-factor component consists largely of transients and oscillations that are not sustained. Fourth, variational mode decomposition is employed to decompose high and low resonance components into different intrinsic modes and provide representative features. Finally features are fed to five different types of machine learning based classifiers for the anomaly detection and severity rating of PD patients based on Hohen and Yahr (HY) scale. The effectiveness of this strategy is verified using a Physionet gait database consisting of 93 idiopathic PD patients and 73 age-matched asymptomatic HCs. When evaluated with 10-fold cross-validation method for early PD detection and severity rating, the highest classification accuracy is reported to be 98.20 % and 96.69 % , respectively, by using the support vector machine classifier. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.
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Affiliation(s)
- Qinghui Wang
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012 People's Republic of China
| | - Wei Zeng
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012 People's Republic of China
| | - Xiangkun Dai
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012 People's Republic of China
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Bu S, Pang H, Li X, Zhao M, Wang J, Liu Y, Yu H. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy. BMC Med Imaging 2023; 23:204. [PMID: 38066432 PMCID: PMC10709839 DOI: 10.1186/s12880-023-01169-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. RESULTS The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. CONCLUSIONS Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.
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Affiliation(s)
- Shuting Bu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Huize Pang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Xiaolu Li
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Mengwan Zhao
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Juzhou Wang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Yu Liu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hongmei Yu
- Department of Neurology, the First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, Liaoning, 110001, PR China.
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Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease. Ageing Res Rev 2023; 90:102013. [PMID: 37429545 DOI: 10.1016/j.arr.2023.102013] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/12/2023]
Abstract
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
| | - Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | | | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
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Li Z, Li H, Ralescu AL, Dillman JR, Parikh NA, He L. A novel collaborative self-supervised learning method for radiomic data. Neuroimage 2023; 277:120229. [PMID: 37321358 PMCID: PMC10440826 DOI: 10.1016/j.neuroimage.2023.120229] [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: 03/19/2023] [Revised: 05/19/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method will have the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.
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Affiliation(s)
- Zhiyuan Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Anca L Ralescu
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, U niversity of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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8
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Camacho M, Wilms M, Mouches P, Almgren H, Souza R, Camicioli R, Ismail Z, Monchi O, Forkert ND. Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets. Neuroimage Clin 2023; 38:103405. [PMID: 37079936 PMCID: PMC10148079 DOI: 10.1016/j.nicl.2023.103405] [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: 08/31/2022] [Revised: 02/13/2023] [Accepted: 04/11/2023] [Indexed: 04/22/2023]
Abstract
INTRODUCTION Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be challenging, especially in the early disease stages. The aim of this work was to develop and evaluate a robust explainable deep learning model for PD classification trained from one of the largest collections of T1-weighted magnetic resonance imaging datasets. MATERIALS AND METHODS A total of 2,041 T1-weighted MRI datasets from 13 different studies were collected, including 1,024 datasets from PD patients and 1,017 datasets from age- and sex-matched healthy controls (HC). The datasets were skull stripped, resampled to isotropic resolution, bias field corrected, and non-linearly registered to the MNI PD25 atlas. The Jacobian maps derived from the deformation fields together with basic clinical parameters were used to train a state-of-the-art convolutional neural network (CNN) to classify PD and HC subjects. Saliency maps were generated to display the brain regions contributing the most to the classification task as a means of explainable artificial intelligence. RESULTS The CNN model was trained using an 85%/5%/10% train/validation/test split stratified by diagnosis, sex, and study. The model achieved an accuracy of 79.3%, precision of 80.2%, specificity of 81.3%, sensitivity of 77.7%, and AUC-ROC of 0.87 on the test set while performing similarly on an independent test set. Saliency maps computed for the test set data highlighted frontotemporal regions, the orbital-frontal cortex, and multiple deep gray matter structures as most important. CONCLUSION The developed CNN model, trained on a large heterogenous database, was able to differentiate PD patients from HC subjects with high accuracy with clinically feasible classification explanations. Future research should aim to investigate the combination of multiple imaging modalities with deep learning and on validating these results in a prospective trial as a clinical decision support system.
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Affiliation(s)
- Milton Camacho
- Biomedical Engineering Program, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada.
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada
| | - Pauline Mouches
- Biomedical Engineering Program, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada
| | - Hannes Almgren
- Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada
| | - Raissa Souza
- Biomedical Engineering Program, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada
| | - Richard Camicioli
- Neuroscience and Mental Health Institute and Department of Medicine (Neurology), University of Alberta, Edmonton, Alberta, Canada
| | - Zahinoor Ismail
- Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada; Department of Psychiatry, University of Calgary, Canada
| | - Oury Monchi
- Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada; Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal, Quebec, Canada; Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Québec, Canada
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada; Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada; Department of Electrical and Software Engineering, University of Calgary, Canada
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Shi D, Ren Z, Zhang H, Wang G, Guo Q, Wang S, Ding J, Yao X, Li Y, Ren K. Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease. Heliyon 2023; 9:e14325. [PMID: 36950566 PMCID: PMC10025115 DOI: 10.1016/j.heliyon.2023.e14325] [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: 05/19/2022] [Revised: 01/18/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Zhendong Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guangsong Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qiu Guo
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Jie Ding
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Yao
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yanfei Li
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory for Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Corresponding author. Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
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Gerraty RT, Provost A, Li L, Wagner E, Haas M, Lancashire L. Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs. Front Aging Neurosci 2023; 15:1076657. [PMID: 36861121 PMCID: PMC9968811 DOI: 10.3389/fnagi.2023.1076657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/16/2023] [Indexed: 02/17/2023] Open
Abstract
The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.
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Affiliation(s)
| | | | - Lin Li
- PharmaLex, Frederick, MD, United States
| | | | - Magali Haas
- Cohen Veterans Bioscience, New York, NY, United States
| | - Lee Lancashire
- Cohen Veterans Bioscience, New York, NY, United States,*Correspondence: Lee Lancashire, ✉
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Pang C, Zhang Y, Xue Z, Bao J, Keong Li B, Liu Y, Liu Y, Sheng M, Peng B, Dai Y. Improving model robustness via enhanced feature representation and sample distribution based on cascaded classifiers for computer-aided diagnosis of brain disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach. Brain Imaging Behav 2022; 16:2150-2163. [PMID: 35650376 DOI: 10.1007/s11682-022-00685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2022] [Indexed: 11/02/2022]
Abstract
To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.
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Ya Y, Ji L, Jia Y, Zou N, Jiang Z, Yin H, Mao C, Luo W, Wang E, Fan G. Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features. Front Aging Neurosci 2022; 14:808520. [PMID: 35493923 PMCID: PMC9043762 DOI: 10.3389/fnagi.2022.808520] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/08/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance. Methods Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets. Results After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models. Conclusion The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.
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Affiliation(s)
- Yang Ya
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Lirong Ji
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yujing Jia
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Nan Zou
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhen Jiang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Erlei Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
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Anusha B, Geetha P, Kannan A. Parkinson’s disease identification in homo sapiens based on hybrid ResNet-SVM and resnet-fuzzy svm models. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The identification of Parkinson’s Disease (PD) is a necessary concern for reducing the occurrences of nervous disorders and brain death. The prediction of PD based on symptoms is depending on the body conditions of patients as the symptoms differ for every individual. Doctors preferably use ionized radiation-free MRI scans since they offer more precise images of soft tissues in the brain. In the recent years, deep learning is the prominently used method for performing image analysis and classification. However, the systems developed using deep learning are not able to predict the PD accurately. In order to bridge the gaps present in the existing systems, we propose a hybrid model based on neuro-fuzzy classification to detect PD more accurately. For enhancing the accuracy of PD identification, we used the ResNet-18 deep learning architecture for the classification of MRI images. In addition to this, a hybrid framework is also proposed in this paper where the softmax layer of ResNet-18 is modified using non-linear SVM and Fuzzy SVM (fSVM) classifiers. The convolution and max-pooling layers of ResNet-18 are able to learn more objective features for classification. The proposed hybrid model of ResNet-fSVM is evaluated on the neuro-MRI images from the PPMI dataset and achieved 4.4% higher accuracy than the ResNet-18 model and 2.8% higher accuracy than hybrid ResNet-SVM model. The age group based results obtained in this work has proved that the accuracy of the proposed ResNet-fSVM hybrid model is better when it is compared with ResNet-18 and hybrid ResNet-SVM models. This system effectively detects Early-onset PD through its efficiency in classification.
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Affiliation(s)
- B. Anusha
- Department of Information Science and Technology, Anna University, Chennai, Tamil Nadu, India
| | - P. Geetha
- Department of Information Science and Technology, Anna University, Chennai, Tamil Nadu, India
| | - A. Kannan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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15
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Automated methods for diagnosis of Parkinson’s disease and predicting severity level. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06626-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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16
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Shi D, Zhang H, Wang G, Wang S, Yao X, Li Y, Guo Q, Zheng S, Ren K. Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis. Front Aging Neurosci 2022; 14:806828. [PMID: 35309885 PMCID: PMC8928361 DOI: 10.3389/fnagi.2022.806828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/19/2022] [Indexed: 12/03/2022] Open
Abstract
Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guangsong Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Yao
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yanfei Li
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qiu Guo
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuang Zheng
- School of Medicine, Xiamen University, Xiamen, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory for Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Ke Ren,
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Salari N, Kazeminia M, Sagha H, Daneshkhah A, Ahmadi A, Mohammadi M. The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-02949-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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18
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Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases. Cogn Neurodyn 2022; 16:1361-1377. [DOI: 10.1007/s11571-022-09787-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/16/2021] [Accepted: 01/21/2022] [Indexed: 11/03/2022] Open
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Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease. NPJ Parkinsons Dis 2022; 8:13. [PMID: 35064123 PMCID: PMC8783003 DOI: 10.1038/s41531-021-00266-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson’s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts’ visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.
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Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis. J Pers Med 2022; 12:jpm12010089. [PMID: 35055404 PMCID: PMC8779164 DOI: 10.3390/jpm12010089] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 02/08/2023] Open
Abstract
Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.
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21
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Peng B, Gong Z, Zhang Y, Shen B, Pang C, Zhang L, Dai Y. Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease. Neurosci Lett 2022; 766:136312. [PMID: 34757107 DOI: 10.1016/j.neulet.2021.136312] [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: 08/11/2021] [Revised: 10/14/2021] [Accepted: 10/24/2021] [Indexed: 10/20/2022]
Abstract
Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI) based CAD methods can help reveal structural changes in brain. Classifier is a key component in CAD system, which directly affects the classification performance. Privileged information (PI) can assist to train the classifier by providing additional information, which makes test samples have less error and improves the classification accuracy. In this paper, we proposed a PI based kernel ridge regression plus (KRR+) in diagnosis of PD. Specifically, morphometric features and brain network features are extracted from MRI. Then, empirical kernel mapping feature expression method is used to make the data separable in high-dimensional space. Besides, we introduce self-paced learning that can adaptively select the sample in training of the model, which can further improve the classification performance. The experimental results show that the proposed method is effective for PD diagnosis, its performance is superior to existing classification model. This method is helpful to assist clinicians to find out possible neuroimaging biomarkers in the diagnosis of PD.
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Affiliation(s)
- Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China; Jinan Guoke Medical Engineering Technology Development co., LTD, Jinan 250000, China
| | - Zhenjia Gong
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China
| | - Yu Zhang
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China
| | - Bo Shen
- Nanjing Medical University and Nanjing Brain Hospital, Nanjing 210029, China
| | - Chunying Pang
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China
| | - Li Zhang
- Nanjing Medical University and Nanjing Brain Hospital, Nanjing 210029, China.
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China; Jinan Guoke Medical Engineering Technology Development co., LTD, Jinan 250000, China.
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22
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Song C, Zhao W, Jiang H, Liu X, Duan Y, Yu X, Yu X, Zhang J, Kui J, Liu C, Tang Y. Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning. Front Comput Neurosci 2021; 15:735991. [PMID: 34795570 PMCID: PMC8594429 DOI: 10.3389/fncom.2021.735991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 09/24/2021] [Indexed: 02/05/2023] Open
Abstract
Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.
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Affiliation(s)
- Chenggang Song
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, China
- College of Computer, Chengdu University, Chengdu, China
| | - Weidong Zhao
- College of Computer, Chengdu University, Chengdu, China
| | - Hong Jiang
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoju Liu
- Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yumei Duan
- Department of Computer and Software, Chengdu Jincheng College, Chengdu, China
| | - Xiaodong Yu
- College of Computer, Chengdu University, Chengdu, China
| | - Xi Yu
- College of Computer, Chengdu University, Chengdu, China
| | - Jian Zhang
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, China
| | - Jingyue Kui
- Department of Urology, Tonghai County People's Hospital, Yuxi, China
| | - Chang Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, China
- College of Computer, Chengdu University, Chengdu, China
| | - Yiqian Tang
- College of Computer, Chengdu University, Chengdu, China
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Shi D, Li Y, Zhang H, Yao X, Wang S, Wang G, Ren K. Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging. DISEASE MARKERS 2021; 2021:9963824. [PMID: 34211615 PMCID: PMC8208855 DOI: 10.1155/2021/9963824] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/03/2021] [Indexed: 01/10/2023]
Abstract
Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Yanfei Li
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Haoran Zhang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Xiang Yao
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Siyuan Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Guangsong Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Ke Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
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Shi D, Zhang H, Wang S, Wang G, Ren K. Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis. Front Aging Neurosci 2021; 13:624731. [PMID: 34045953 PMCID: PMC8144304 DOI: 10.3389/fnagi.2021.624731] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 03/22/2021] [Indexed: 01/08/2023] Open
Abstract
This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.
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Affiliation(s)
| | | | | | | | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xia Men University, Xiamen, China
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Mei J, Desrosiers C, Frasnelli J. Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature. Front Aging Neurosci 2021; 13:633752. [PMID: 34025389 PMCID: PMC8134676 DOI: 10.3389/fnagi.2021.633752] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/22/2021] [Indexed: 12/26/2022] Open
Abstract
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
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Affiliation(s)
- Jie Mei
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
| | - Christian Desrosiers
- Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle (LIVIA), Department of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Johannes Frasnelli
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
- Centre de Recherche de l'Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal (CIUSSS du Nord-de-l'Île-de-Montréal), Montreal, QC, Canada
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Zhang X, Cao X, Xue C, Zheng J, Zhang S, Huang Q, Liu W. Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis. Brain Behav 2021; 11:e02103. [PMID: 33694328 PMCID: PMC8119873 DOI: 10.1002/brb3.2103] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/08/2021] [Accepted: 02/21/2021] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION The current diagnosis of Parkinson's disease (PD) comorbidity with depression (DPD) largely depends on clinical evaluation. However, the modality may tend to lack precision in detecting PD with depression. A radiomic approach that combines functional connectivity and activity with clinical scores has the potential to achieve accurate and differential diagnosis between PD and DPD. METHODS In this study, we aimed to employ the radiomic approach to extract large-scale features of functional connectivity and activity for differentiating among DPD, PD with no depression (NDPD), and healthy controls (HC). We extracted 6,557 features of five types from all subjects including clinical characteristics, resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and voxel-mirrored homotopic connectivity (VMHC). Lasso, random forest, and support vector machine (SVM) were implemented for feature selection and dimension reduction based on the training sets, and the prediction performance for different methods in the testing sets was compared. RESULTS The results showed that nineteen features were selected for the group of DPD versus HC, 34 features were selected for the group of NDPD versus HC, and 17 features were retained for the group of DPD versus NDPD. In the testing sets, Lasso prediction achieved the accuracies of 0.95, 0.96, and 0.85 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. Random forest achieved the accuracies of 0.90, 0.82, and 0.90 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively, while SVM yielded the accuracies of 1, 0.86 and 0.65 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. CONCLUSIONS By identifying aberrant functional connectivity and activity as potential biomarkers, the radiomic approach facilitates a deeper understanding and provides new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.
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Affiliation(s)
- Xulian Zhang
- Department of Radiology, Nanjing Medical University Affiliated Nanjing Brain Hospital, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, USA
| | - Chen Xue
- Department of Radiology, Nanjing Medical University Affiliated Nanjing Brain Hospital, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jingyi Zheng
- Department of Mathematics and Statistics, Auburn University, Auburn, USA
| | - Shaojun Zhang
- Department of Statistics, University of Florida, Gainesville, USA
| | - Qingling Huang
- Department of Radiology, Nanjing Medical University Affiliated Nanjing Brain Hospital, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Weiguo Liu
- Department of Neurology, Nanjing Medical University Affiliated Nanjing Brain Hospital, Nanjing, China
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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28
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Vitale A, Villa R, Ugga L, Romeo V, Stanzione A, Cuocolo R. Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:1753-1773. [PMID: 33757209 DOI: 10.3934/mbe.2021091] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes.
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Affiliation(s)
- Annalisa Vitale
- Department of Advanced Biomedical Sciences, University of Naples "Federico Ⅱ", Via S. Pansini 5, 80131-Naples, Italy
| | - Rossella Villa
- Department of Advanced Biomedical Sciences, University of Naples "Federico Ⅱ", Via S. Pansini 5, 80131-Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico Ⅱ", Via S. Pansini 5, 80131-Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico Ⅱ", Via S. Pansini 5, 80131-Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico Ⅱ", Via S. Pansini 5, 80131-Naples, Italy
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples "Federico Ⅱ", Via S. Pansini 5, 80131-Naples, Italy
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29
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Projective parameter transfer based sparse multiple empirical kernel learning Machine for diagnosis of brain disease. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.008] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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30
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Shu ZY, Cui SJ, Wu X, Xu Y, Huang P, Pang PP, Zhang M. Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter. Magn Reson Med 2020; 85:1611-1624. [PMID: 33017475 DOI: 10.1002/mrm.28522] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 08/21/2020] [Accepted: 08/26/2020] [Indexed: 01/08/2023]
Abstract
PURPOSE This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD). METHODS PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T1 -weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility. RESULTS Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600. CONCLUSION Our results provide evidence that conventional structural MRI can predict the progression of PD. This work also supports the use of a simple radiomics signature built from whole-brain white matter features as a useful tool for the assessment and monitoring of PD progression.
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Affiliation(s)
- Zhen-Yu Shu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.,Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province, China
| | - Si-Jia Cui
- Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Xiao Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Yuyun Xu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang Province, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | | | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
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31
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Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review. Med Biol Eng Comput 2020; 58:2603-2620. [PMID: 32960410 DOI: 10.1007/s11517-020-02256-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 08/28/2020] [Indexed: 12/22/2022]
Abstract
Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and advantageous methods for brain diseases detection and characterization. Magnetic resonance imaging (MRI), computed tomography (CT), and other various brain imaging scans are used to identify different brain diseases and disorders. Brain imaging scans are the efficient tool to understand the anatomical changes in brain in fast and accurate manner. These different brain imaging scans used with segmentation techniques and along with machine learning and deep learning techniques give maximum accuracy and efficiency. This paper focuses on different conventional approaches, machine learning and deep learning techniques used for the detection, and classification of brain diseases and abnormalities. This paper also summarizes the research gap and problems in the existing techniques used for detection and classification of brain disorders. Comparison and evaluation of different machine learning and deep learning techniques in terms of efficiency and accuracy are also highlighted in this paper. Furthermore, different brain diseases like leukoariaosis, Alzheimer's, Parkinson's, and Wilson's disorder are studied in the scope of machine learning and deep learning techniques.
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32
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Zhang XB, Zhai DH, Yang Y, Zhang YL, Wang CL. A novel semi-supervised multi-view clustering framework for screening Parkinson's disease. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:3395-3411. [PMID: 32987535 DOI: 10.3934/mbe.2020192] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In recent years, there are many research cases for the diagnosis of Parkinson's disease (PD) with the brain magnetic resonance imaging (MRI) by utilizing the traditional unsupervised machine learning methods and the supervised deep learning models. However, unsupervised learning methods are not good at extracting accurate features among MRIs and it is difficult to collect enough data in the field of PD to satisfy the need of training deep learning models. Moreover, most of the existing studies are based on single-view MRI data, of which data characteristics are not sufficient enough. In this paper, therefore, in order to tackle the drawbacks mentioned above, we propose a novel semi-supervised learning framework called Semi-supervised Multi-view learning Clustering architecture technology (SMC). The model firstly introduces the sliding window method to grasp different features, and then uses the dimensionality reduction algorithms of Linear Discriminant Analysis (LDA) to process the data with different features. Finally, the traditional single-view clustering and multi-view clustering methods are employed on multiple feature views to obtain the results. Experiments show that our proposed method is superior to the state-of-art unsupervised learning models on the clustering effect. As a result, it may be noted that, our work could contribute to improving the effectiveness of identifying PD by previous labeled and subsequent unlabeled medical MRI data in the realistic medical environment.
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Affiliation(s)
- Xiao Bo Zhang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
- National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Dong Hai Zhai
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
- National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Yan Yang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
- National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Yi Ling Zhang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
- National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Chun Lin Wang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
- National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
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33
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Chen Y, Zhu G, Liu D, Liu Y, Yuan T, Zhang X, Jiang Y, Du T, Zhang J. The morphology of thalamic subnuclei in Parkinson's disease and the effects of machine learning on disease diagnosis and clinical evaluation. J Neurol Sci 2020; 411:116721. [DOI: 10.1016/j.jns.2020.116721] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/23/2020] [Accepted: 02/01/2020] [Indexed: 12/16/2022]
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35
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An Improved Deep Polynomial Network Algorithm for Transcranial Sonography–Based Diagnosis of Parkinson’s Disease. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09691-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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36
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Shi J, Xue Z, Dai Y, Peng B, Dong Y, Zhang Q, Zhang Y. Cascaded Multi-Column RVFL+ Classifier for Single-Modal Neuroimaging-Based Diagnosis of Parkinson's Disease. IEEE Trans Biomed Eng 2019; 66:2362-2371. [DOI: 10.1109/tbme.2018.2889398] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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38
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Xu J, Zhang M. Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease. ACS Chem Neurosci 2019; 10:2658-2667. [PMID: 31083923 DOI: 10.1021/acschemneuro.9b00207] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disorder. It has a delitescent onset and a slow progress. The clinical manifestations of PD in patients are highly heterogeneous. Thus, PD diagnosis process is complex and mainly depends on the professional knowledge and experience of the physician. Magnetic resonance imaging (MRI) could detect the small changes in the brain of PD patients, and quantitative analysis of brain MRI may improve the clinical diagnosis efficiency. However, due to the complexity of clinical courses in PD and the high dimensionality in multimodal MRI data, traditional mathematical analysis could not effectively extract the huge information in them. Up to now, the accuracy of PD diagnosis in large sample size is still unsatisfying. As artificial intelligence (AI) is becoming more mature, varieties of statistical models and machine learning (ML) algorithms have been used for quantitative imaging data analysis to explore a diagnostic result. This review aims to state an overview of existing research recently that used statistical ML/AI methods to perform quantitative analysis of MR image data for the study of PD diagnosis. First we review the recent research in three subareas: diagnosis, differential diagnosis, and subtyping of PD. Then we described the overall workflow from MR image to classification result. Finally, we summarized a critical assessment of the current research and provide some recommendations for likely future research developments and trends.
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Affiliation(s)
- Jingjing Xu
- Department of Radiology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31000, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31000, China
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Nemmi F, Pavy-Le Traon A, Phillips OR, Galitzky M, Meissner WG, Rascol O, Péran P. A totally data-driven whole-brain multimodal pipeline for the discrimination of Parkinson's disease, multiple system atrophy and healthy control. NEUROIMAGE-CLINICAL 2019; 23:101858. [PMID: 31128523 PMCID: PMC6531871 DOI: 10.1016/j.nicl.2019.101858] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 04/17/2019] [Accepted: 05/11/2019] [Indexed: 01/10/2023]
Abstract
Parkinson's Disease (PD) and Multiple System Atrophy (MSA) are two parkinsonian syndromes that share many symptoms, albeit having very different prognosis. Although previous studies have proposed multimodal MRI protocols combined with multivariate analysis to discriminate between these two populations and healthy controls, studies combining all MRI indexes relevant for these disorders (i.e. grey matter volume, fractional anisotropy, mean diffusivity, iron deposition, brain activity at rest and brain connectivity) with a completely data-driven voxelwise analysis for discrimination are still lacking. In this study, we used such a complete MRI protocol and adapted a fully-data driven analysis pipeline to discriminate between these populations and a healthy controls (HC) group. The pipeline combined several feature selection and reduction steps to obtain interpretable models with a low number of discriminant features that can shed light onto the brain pathology of PD and MSA. Using this pipeline, we could discriminate between PD and HC (best accuracy = 0.78), MSA and HC (best accuracy = 0.94) and PD and MSA (best accuracy = 0.88). Moreover, we showed that indexes derived from resting-state fMRI alone could discriminate between PD and HC, while mean diffusivity in the cerebellum and the putamen alone could discriminate between MSA and HC. On the other hand, a more diverse set of indexes derived by multiple modalities was needed to discriminate between the two disorders. We showed that our pipeline was able to discriminate between distinct pathological populations while delivering sparse model that could be used to better understand the neural underpinning of the pathologies. Structuro-functional MRI can discriminate between parkinsonian syndromes Discriminant MRI modalities vary as a function of the discrimination task fMRI is crucial in discriminating between Parkinson's disease patients and controls Structural MRI discriminate between Multiple System Atrophy patients and controls
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Affiliation(s)
- F Nemmi
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.
| | - A Pavy-Le Traon
- UMR Institut National de la Santé et de la Recherche Médicale 1048, Institut des Maladies Métaboliques et Cardiovasculaires, Toulouse, France; Department of Neurology and Institute for Neurosciences, University Hospital of Toulouse, Toulouse, France
| | - O R Phillips
- Brain Key, Palo Alto, California, USA; NeuroToul COEN Center, INSERM, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
| | - M Galitzky
- Centre d'Investigation Clinique (CIC), CHU de Toulouse, Toulouse, France
| | - W G Meissner
- French Reference Center for MSA, Department of Neurology, University Hospital Bordeaux, Bordeaux and Institute of Neurodegenerative Disorders, University Bordeaux, CNRS UMR 5293, 33000 Bordeaux, France; Dept. Medicine, University of Otago, Christchurch, and New Zealand Brain Research Institute, Christchurch, New Zealand
| | - O Rascol
- Departments of Clinical Pharmacology and Neurosciences, Clinical Investigation Center CIC 1436, NS-Park/FCRIN network and NeuroToul COEN Center, INSERM, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
| | - P Péran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
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Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.029] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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41
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Neuroimaging-based diagnosis of Parkinson's disease with deep neural mapping large margin distribution machine. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.09.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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42
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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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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-CLINICAL 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] [Track Full Text] [Download PDF] [Figures] [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. Neuroimaging biomarkers could increase diagnostic accuracy in Parkinson's disease. Multimodal meta-analysis including whole-brain FDG-PET, MRI, and DTI Meta-analysis included 2323 Parkinson's disease patients and 1767 controls. FDG-PET functional changes are more consistent than structural changes in MRI-VBM /DTI. Glucose hypometabolism is detected in inferior parietal cortex/caudate nucleus.
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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.
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Gao C, Sun H, Wang T, Tang M, Bohnen NI, Müller MLTM, Herman T, Giladi N, Kalinin A, Spino C, Dauer W, Hausdorff JM, Dinov ID. Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease. Sci Rep 2018; 8:7129. [PMID: 29740058 PMCID: PMC5940671 DOI: 10.1038/s41598-018-24783-4] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/10/2018] [Indexed: 01/08/2023] Open
Abstract
In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements. The model-based and model-free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5-fold) cross validation as well as by external out-of-bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi-site Parkinson's disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g., patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD). Our findings suggest that, compared to other approaches, model-free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson's patients, for example, with a classification accuracy of about 70-80%.
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Affiliation(s)
- Chao Gao
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Hanbo Sun
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States
- Department of Statistics, University of Michigan, Ann Arbor, MI, United States
| | - Tuo Wang
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States
- Department of Statistics, University of Michigan, Ann Arbor, MI, United States
| | - Ming Tang
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Nicolaas I Bohnen
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Neurology and Ann Arbor VA Medical Center, University of Michigan, Ann Arbor, MI, United States
- Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, United States
| | - Martijn L T M Müller
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Neurology and Ann Arbor VA Medical Center, University of Michigan, Ann Arbor, MI, United States
- Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, United States
| | - Talia Herman
- The Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nir Giladi
- The Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Neurology and Sieratzki Chair in Neurology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Alexandr Kalinin
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States
- Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Cathie Spino
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
- Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, United States
| | - William Dauer
- Department of Neurology and Ann Arbor VA Medical Center, University of Michigan, Ann Arbor, MI, United States
- Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, United States
| | - Jeffrey M Hausdorff
- The Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience and Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center & Orthopaedic Surgery, Rush University, Chicago, IL, USA
| | - Ivo D Dinov
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States.
- Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, United States.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA.
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Towards the Identification of Parkinson’s Disease Using only T1 MR Images. LECTURE NOTES IN COMPUTER SCIENCE 2018. [DOI: 10.1007/978-3-030-04375-9_13] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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