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Müller HP, Kassubek J. Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing. Front Hum Neurosci 2024; 18:1378896. [PMID: 38628970 PMCID: PMC11018884 DOI: 10.3389/fnhum.2024.1378896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 02/26/2024] [Indexed: 04/19/2024] Open
Abstract
Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.
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Souza R, Winder A, Stanley EAM, Vigneshwaran V, Camacho M, Camicioli R, Monchi O, Wilms M, Forkert ND. Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier? IEEE J Biomed Health Inform 2024; 28:2047-2054. [PMID: 38198251 DOI: 10.1109/jbhi.2024.3352513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.
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Camacho M, Wilms M, Almgren H, Amador K, Camicioli R, Ismail Z, Monchi O, Forkert ND. Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data. NPJ Parkinsons Dis 2024; 10:43. [PMID: 38409244 PMCID: PMC10897162 DOI: 10.1038/s41531-024-00647-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/22/2024] [Indexed: 02/28/2024] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.
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Affiliation(s)
- Milton Camacho
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.
- Department of Radiology, University of Calgary, Calgary, AB, Canada.
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Pediatrics and Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Hannes Almgren
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Kimberly Amador
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Richard Camicioli
- Neuroscience and Mental Health Institute and Department of Medicine (Neurology), University of Alberta, Edmonton, AB, Canada
| | - Zahinoor Ismail
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Oury Monchi
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Pediatrics and Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
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Souza R, Stanley EAM, Camacho M, Camicioli R, Monchi O, Ismail Z, Wilms M, Forkert ND. A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm. Front Artif Intell 2024; 7:1301997. [PMID: 38384277 PMCID: PMC10879577 DOI: 10.3389/frai.2024.1301997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.
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Affiliation(s)
- Raissa Souza
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Emma A. M. Stanley
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Milton Camacho
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Richard Camicioli
- Department of Medicine (Neurology), Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - Oury Monchi
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Zahinoor Ismail
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
- Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Matthias Wilms
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
| | - Nils D. Forkert
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Gore S, Dhole A, Kumbhar S, Jagtap J. Radiomics for Parkinson's disease classification using advanced texture-based biomarkers. MethodsX 2023; 11:102359. [PMID: 37791007 PMCID: PMC10543659 DOI: 10.1016/j.mex.2023.102359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 10/05/2023] Open
Abstract
Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities. 43 subjects from Neurocon and 40 subjects from Tao-Wu dataset were examined, which consisted of 36 scans of healthy controls and 47 scans of Parkinson's patients. Total 360 2D MRI images are selected among around 17000 slices of T1-weighted and resting scans of selected 72 subjects. Local binary pattern (LBP) method was applied with custom variants to acquire advanced textural biomarkers from MRI images. LBP histogram helped to learn discriminative local patterns to detect and classify Parkinson's disease. Using recursive feature elimination, data dimensions of around 150-300 LBP histogram features were reduced to 13-21 most significant features based on score, and important features were analysed using SVM and random forest algorithms. Variant-I of LBP has performed well with highest test accuracy of 83.33%, precision of 84.62%, recall of 91.67%, and f1-score of 88%. Classification accuracies were obtained from 61.11% to 83.33% and AUC-ROC values range from 0.43 to 0.86 using four variants of LBP.•Parkinson's classification is carried out using an advanced biomedical texture feature. Texture extraction using four variants of uniform, rotation invariant LBP method is performed for radiomic analysis of Parkinson's disorder.•Proposed method with support vector machine classifier is experimented and an accuracy of 83.33% is achieved with 10-fold cross validation for detection of Parkinson's patients from MRI-based radiomic analysis.•The proposed predictive model has proved the potential of textures of extended version of LBP, which have demonstrated subtle variations in local appearance for Parkinson's detection.
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Affiliation(s)
- Sonal Gore
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Aniket Dhole
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Shrishail Kumbhar
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Jayant Jagtap
- Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), (SIU), Lavale, Pune, Maharashtra, India
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Souza R, Wilms M, Camacho M, Pike GB, Camicioli R, Monchi O, Forkert ND. Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data. J Am Med Inform Assoc 2023; 30:1925-1933. [PMID: 37669158 PMCID: PMC10654841 DOI: 10.1093/jamia/ocad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/07/2023] [Accepted: 08/15/2023] [Indexed: 09/07/2023] Open
Abstract
OBJECTIVE This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences. MATERIAL AND METHODS A large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson's disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants. After preprocessing the T1-weighted scans, 2 additional data types were generated: intensity-harmonized T1-weighted scans and log-Jacobian deformation maps resulting from nonlinear atlas registration. Corresponding DL models were trained to classify sites for each data type. Additionally, logistic regression models were used to investigate the contribution of biological (age, sex, disease status) and non-biological (scanner type) variables to the models' decision. RESULTS A comparison of the 3 different types of data revealed that DL models trained using T1-weighted and intensity-harmonized T1-weighted scans can classify sites with an accuracy of 85%, while the model using log-Jacobian deformation maps achieved a site classification accuracy of 54%. Disease status and scanner type were found to be significant confounders. DISCUSSION Our results demonstrate that MRI scans encode relevant site-specific information that models could use as shortcuts that cannot be removed using simple intensity harmonization methods. CONCLUSION The ability of DL models to exploit site-specific biases as shortcuts raises concerns about their reliability, generalization, and deployability in clinical settings.
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Affiliation(s)
- Raissa Souza
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Matthias Wilms
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Milton Camacho
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - G Bruce Pike
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Richard Camicioli
- Department of Medicine (Neurology), Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Oury Monchi
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC H3C 3J7, Canada
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, QC H3W 1W4, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Nils D Forkert
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
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Mallik S, Majhi B, Kashyap A, Mohanty S, Dash S, Li A, Zhao Z. An Improved Method for Diagnosis of Parkinson's Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm. Res Sq 2023:rs.3.rs-3387953. [PMID: 37886464 PMCID: PMC10602096 DOI: 10.21203/rs.3.rs-3387953/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Accurate diagnosis of Parkinson's disease (PD) at an early stage is challenging for clinicians as its progression is very slow. Currently many machine learning and deep learning approaches are used for detection of PD and they are popular too. This study proposes four deep learning models and a hybrid model for the early detection of PD. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The simulation study is carried out using two standard datasets, T1,T2-weighted and SPECT DaTscan. The metaherustic enhanced deep learning models used are GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3. Simulation results demonstrated that all the models perform well and obtained near above 99% of accuracy. The AUC-ROC score of 99.99 is achieved by the GWO-VGG16 + InceptionV3 and GWO-DenseNet models for T1, T2-weighted dataset. Similarly, the GWO-DenseNet, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 models result an AUC-ROC score of 100 for SPECT DaTscan dataset.
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Affiliation(s)
| | | | | | | | | | - Aimin Li
- Xi'an University of Science and Technology
| | - Zhongming Zhao
- The University of Texas Health Science Center at Houston
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Seada SA, van der Eerden AW, Boon AJW, Hernandez-Tamames JA. Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design. Neuroimage Clin 2023; 39:103506. [PMID: 37696098 PMCID: PMC10500558 DOI: 10.1016/j.nicl.2023.103506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/21/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.
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Affiliation(s)
- Samy Abo Seada
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Anke W van der Eerden
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Agnita J W Boon
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Imaging Physics, TU Delft, The Netherlands.
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Garcia Santa Cruz B, Husch A, Hertel F. Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Front Aging Neurosci 2023; 15:1216163. [PMID: 37539346 PMCID: PMC10394631 DOI: 10.3389/fnagi.2023.1216163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023] Open
Abstract
Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.
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Affiliation(s)
| | - Andreas Husch
- Imaging AI Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
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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] [What about the content of this article? (0)] [Affiliation(s)] [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] [What about the content of this article? (0)] [Affiliation(s)] [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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Shi D, Zhang H, Wang G, Yao X, Li Y, Wang S, Ren K. Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis. Heliyon 2022; 8:e12276. [PMID: 36582679 DOI: 10.1016/j.heliyon.2022.e12276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/19/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.
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Kocar TD, Behler A, Leinert C, Denkinger M, Ludolph AC, Müller HP, Kassubek J. Artificial neural networks for non-linear age correction of diffusion metrics in the brain. Front Aging Neurosci 2022; 14:999787. [PMID: 36337697 PMCID: PMC9632350 DOI: 10.3389/fnagi.2022.999787] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/04/2022] [Indexed: 09/19/2023] Open
Abstract
Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction. Although multiparametric MRI-based models generally perform best, models solely based on diffusion tensor imaging have achieved similar results, with the benefits of faster data acquisition and better replicability across scanners and field strengths. In the present study, we developed an artificial neural network (ANN) for brain age prediction based upon tract-based fractional anisotropy (FA). Consequently, we investigated if this age-prediction model could also be used for non-linear age correction of white matter diffusion metrics in healthy adults. The brain age prediction accuracy of the ANN (R 2 = 0.47) was similar to established multimodal models. The comparison of the ANN-based age-corrected FA with the tract-wise linear age-corrected FA resulted in an R 2 value of 0.90 [0.82; 0.93] and a mean difference of 0.00 [-0.04; 0.05] for all tract systems combined. In conclusion, this study demonstrated the applicability of complex ANN models to non-linear age correction of tract-based diffusion metrics as a proof of concept.
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Affiliation(s)
- Thomas D. Kocar
- Department of Neurology, University of Ulm, Ulm, Germany
- Geriatric Center Ulm, Agaplesion Bethesda Ulm, University of Ulm, Ulm, Germany
- Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany
| | - Anna Behler
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Christoph Leinert
- Geriatric Center Ulm, Agaplesion Bethesda Ulm, University of Ulm, Ulm, Germany
- Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany
| | - Michael Denkinger
- Geriatric Center Ulm, Agaplesion Bethesda Ulm, University of Ulm, Ulm, Germany
- Institute of Geriatric Research, Ulm University Medical Center, Ulm, Germany
| | - Albert C. Ludolph
- Department of Neurology, University of Ulm, Ulm, Germany
- German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
| | | | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
- German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
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Kaplan E, Altunisik E, Ekmekyapar Firat Y, Datta Barua P, Dogan S, Baygin M, Burak Demir F, Tuncer T, Palmer E, Tan RS, Yu P, Soar J, Fujita H, Rajendra Acharya U. Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images. Comput Methods Programs Biomed 2022; 224:107030. [PMID: 35878484 DOI: 10.1016/j.cmpb.2022.107030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/06/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. METHODS Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV). RESULTS Our presented NP-PHOG-MFSMCIMV model achieved 99.22, 98.70, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively. SIGNIFICANCE The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification.
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Affiliation(s)
- Ela Kaplan
- Department of Radiology, Adıyaman Training and Research Hospital, Turkey
| | - Erman Altunisik
- Department of Neurology, Adiyaman University Medicine Faculty, Adiyaman, Turkey
| | | | - Prabal Datta Barua
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Fahrettin Burak Demir
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Bandirma, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Elizabeth Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick 2031, Australia; Discipline of Paediatrics and Child Health, School of Clinical Medicine Randwick, Faculty of Medicine and Health, UNSW, Randwick, NSW 2031, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Ping Yu
- School of Computing and Information Technology, University of Wollongong, Wollongong NSW 2522, Australia
| | - Jeffrey Soar
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University of Technology, Ho Chi Minh City, Viet Nam; Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain; Regional Research Center, Iwate Prefectural University, Iwate, Japan
| | - U Rajendra Acharya
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia; Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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15
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Estudillo-Romero A, Haegelen C, Jannin P, Baxter JSH. Voxel-based diktiometry: Combining convolutional neural networks with voxel-based analysis and its application in diffusion tensor imaging for Parkinson's disease. Hum Brain Mapp 2022; 43:4835-4851. [PMID: 35841274 PMCID: PMC9582380 DOI: 10.1002/hbm.26009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/10/2022] [Accepted: 06/22/2022] [Indexed: 11/11/2022] Open
Abstract
Extracting population‐wise information from medical images, specifically in the neurological domain, is crucial to better understanding disease processes and progression. This is frequently done in a whole‐brain voxel‐wise manner, in which a population of patients and healthy controls are registered to a common co‐ordinate space and a statistical test is performed on the distribution of image intensities for each location. Although this method has yielded a number of scientific insights, it is further from clinical applicability as the differences are often small and altogether do not permit for a high‐performing classifier. In this article, we take the opposite approach of using a high‐performing classifier, specifically a traditional convolutional neural network, and then extracting insights from it which can be applied in a population‐wise manner, a method we call voxel‐based diktiometry. We have applied this method to diffusion tensor imaging (DTI) analysis for Parkinson's disease (PD), using the Parkinson's Progression Markers Initiative database. By using the network sensitivity information, we can decompose what elements of the DTI contribute the most to the network's performance, drawing conclusions about diffusion biomarkers for PD that are based on metrics which are not readily expressed in the voxel‐wise approach.
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Affiliation(s)
| | - Claire Haegelen
- LTSI-INSERM UMR 1099, Université de Rennes 1, Rennes, France.,Département de Neurochirurgie, CHU Rennes, Rennes, France
| | - Pierre Jannin
- LTSI-INSERM UMR 1099, Université de Rennes 1, Rennes, France
| | - John S H Baxter
- LTSI-INSERM UMR 1099, Université de Rennes 1, Rennes, France
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Tanveer M, Rashid A, Kumar R, Balasubramanian R. Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022; 59:102909. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Rojas-valenzuela I, Valenzuela O, Delgado-marquez E, Rojas F. Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm. Applied Sciences 2022; 12:3048. [DOI: 10.3390/app12063048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Ishikuro K, Hattori N, Imanishi R, Furuya K, Nakata T, Dougu N, Yamamoto M, Konishi H, Nukui T, Hayashi T, Anada R, Matsuda N, Hirosawa H, Tanaka R, Shibata T, Mori K, Noguchi K, Kuroda S, Nakatsuji Y, Nishijo H. A Parkinson's disease patient displaying increased neuromelanin-sensitive areas in the substantia nigra after rehabilitation with tDCS: a case report. Neurocase 2021; 27:407-414. [PMID: 34503372 DOI: 10.1080/13554794.2021.1975768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Previous studies have reported that transcranial direct current stimulation (tDCS) of the frontal polar area (FPA) ameliorated motor disability in patients with Parkinson's disease (PD). Here we report changes in neuromelanin (NM) imaging of dopaminergic neurons before and after rehabilitation combined with anodal tDCS over the FPA for 2 weeks in a PD patient. After the intervention, the patient showed clinically meaningful improvements while the NM-sensitive area in the SN increased by 18.8%. This case study is the first report of NM imaging of the SN in a PD patient who received tDCS.Abbreviations FPA: front polar area; PD: Parkinson's disease; NM: neuromelanin; DCI: DOPA decarboxylase inhibitor; STEF: simple test for evaluating hand function; TUG: timed up and go test; TMT: trail-making test; SN: substantia nigra; NM-MRI: neuromelanin magnetic resonance imaging; MCID: the minimal clinically important difference; SNpc: substantia nigra pars compacta; VTA: ventral tegmental area; LC: locus coeruleus; PFC: prefrontal cortex; M1: primary motor cortex; MDS: Movement Disorder Society; MIBG: 123I-metaiodobenzylguanidine; SBR: specific binding ratio; SPECT: single-photon emission computed tomography; DAT: dopamine transporter; NIBS: noninvasive brain stimulation; tDCS: transcranial direct current stimulation; MAOB: monoamine oxidase B; DCI: decarboxylase inhibitor; repetitive transcranial magnetic stimulation: rTMS; diffusion tensor imaging: DTI; arterial spin labeling: ASL.
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Affiliation(s)
- Koji Ishikuro
- Department of Rehabilitation, Toyama University Hospital, Toyama, Japan
| | - Noriaki Hattori
- Department of Rehabilitation, Toyama University Hospital, Toyama, Japan
| | - Rieko Imanishi
- Department of Rehabilitation, Toyama University Hospital, Toyama, Japan
| | - Kohta Furuya
- Department of Rehabilitation, Toyama University Hospital, Toyama, Japan
| | - Takeshi Nakata
- Department of Rehabilitation, Toyama University Hospital, Toyama, Japan
| | - Nobuhiro Dougu
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Mamoru Yamamoto
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Hirofumi Konishi
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Takamasa Nukui
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Tomohiro Hayashi
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Ryoko Anada
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Noriyuki Matsuda
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Hiroaki Hirosawa
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Ryo Tanaka
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Takashi Shibata
- Department of Neurosurgery, Faculty of Medicine, Toyama, Japan
| | - Koichi Mori
- Department of Radiology, Faculty of Medicine, Toyama, Japan
| | - Kyo Noguchi
- Department of Radiology, Faculty of Medicine, Toyama, Japan
| | - Satoshi Kuroda
- Department of Neurosurgery, Faculty of Medicine, Toyama, Japan
| | - Yuji Nakatsuji
- Department of Neurology, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Hisao Nishijo
- System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
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