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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] [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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Muller JJ, Wang R, Milddleton D, Alizadeh M, Kang KC, Hryczyk R, Zabrecky G, Hriso C, Navarreto E, Wintering N, Bazzan AJ, Wu C, Monti DA, Jiao X, Wu Q, Newberg AB, Mohamed FB. Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging. Front Neurosci 2023; 17:1182509. [PMID: 37694125 PMCID: PMC10484001 DOI: 10.3389/fnins.2023.1182509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/30/2023] [Indexed: 09/12/2023] Open
Abstract
Background and purpose Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging. Materials and methods A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models. Results Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7-56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7-73.0% accuracy and NODDI with an accuracy of 64.0-72.3%. Conclusion The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging.
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Affiliation(s)
- Jennifer J. Muller
- College of Engineering, Villanova University, Villanova, PA, United States
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Ruixuan Wang
- College of Engineering, Villanova University, Villanova, PA, United States
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Devon Milddleton
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Mahdi Alizadeh
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Ki Chang Kang
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Ryan Hryczyk
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - George Zabrecky
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Anthony J. Bazzan
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chengyuan Wu
- Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel A. Monti
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Xun Jiao
- College of Engineering, Villanova University, Villanova, PA, United States
| | - Qianhong Wu
- College of Engineering, Villanova University, Villanova, PA, United States
| | - Andrew B. Newberg
- Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
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Pizarro-Galleguillos BM, Kunert L, Brüggemann N, Prasuhn J. Neuroinflammation and Mitochondrial Dysfunction in Parkinson's Disease: Connecting Neuroimaging with Pathophysiology. Antioxidants (Basel) 2023; 12:1411. [PMID: 37507950 PMCID: PMC10375976 DOI: 10.3390/antiox12071411] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
There is a pressing need for disease-modifying therapies in patients suffering from neurodegenerative diseases, including Parkinson's disease (PD). However, these disorders face unique challenges in clinical trial designs to assess the neuroprotective properties of potential drug candidates. One of these challenges relates to the often unknown individual disease mechanisms that would, however, be relevant for targeted treatment strategies. Neuroinflammation and mitochondrial dysfunction are two proposed pathophysiological hallmarks and are considered to be highly interconnected in PD. Innovative neuroimaging methods can potentially help to gain deeper insights into one's predominant disease mechanisms, can facilitate patient stratification in clinical trials, and could potentially map treatment responses. This review aims to highlight the role of neuroinflammation and mitochondrial dysfunction in patients with PD (PwPD). We will specifically introduce different neuroimaging modalities, their respective technical hurdles and challenges, and their implementation into clinical practice. We will gather preliminary evidence for their potential use in PD research and discuss opportunities for future clinical trials.
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Affiliation(s)
- Benjamin Matís Pizarro-Galleguillos
- Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Institute of Neurogenetics, University of Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Liesa Kunert
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Institute of Neurogenetics, University of Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Norbert Brüggemann
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Jannik Prasuhn
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Institute of Neurogenetics, University of Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21287, USA
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4
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MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis. Comput Biol Med 2023; 152:106308. [PMID: 36462371 DOI: 10.1016/j.compbiomed.2022.106308] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/27/2022] [Accepted: 11/13/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE The identification of early-stage Parkinson's disease (PD) is important for the effective management of patients, affecting their treatment and prognosis. Recently, structural brain networks (SBNs) have been used to diagnose PD. However, how to mine abnormal patterns from high-dimensional SBNs has been a challenge due to the complex topology of the brain. Meanwhile, the existing prediction mechanisms of deep learning models are often complicated, and it is difficult to extract effective interpretations. In addition, most works only focus on the classification of imaging and ignore clinical scores in practical applications, which limits the ability of the model. Inspired by the regional modularity of SBNs, we adopted graph learning from the perspective of node clustering to construct an interpretable framework for PD classification. METHODS In this study, a multi-task graph structure learning framework based on node clustering (MNC-Net) is proposed for the early diagnosis of PD. Specifically, we modeled complex SBNs into modular graphs that facilitated the representation learning of abnormal patterns. Traditional graph neural networks are optimized through graph structure learning based on node clustering, which identifies potentially abnormal brain regions and reduces the impact of irrelevant noise. Furthermore, we employed a regression task to link clinical scores to disease classification, and incorporated latent domain information into model training through multi-task learning. RESULTS We validated the proposed approach on the Parkinsons Progression Markers Initiative dataset. Experimental results showed that our MNC-Net effectively separated the early-stage PD from healthy controls(HC) with an accuracy of 95.5%. The t-SNE figures have showed that our graph structure learning method can capture more efficient and discriminatory features. Furthermore, node clustering parameters were used as important weights to extract salient task-related brain regions(ROIs). These ROIs are involved in the development of mood disorders, tremors, imbalances and other symptoms, highlighting the importance of memory, language and mild motor function in early PD. In addition, statistical results from clinical scores confirmed that our model could capture abnormal connectivity that was significantly different between PD and HC. These results are consistent with previous studies, demonstrating the interpretability of our methods.
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5
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Pizarro-Galleguillos BM, Kunert L, Brüggemann N, Prasuhn J. Iron- and Neuromelanin-Weighted Neuroimaging to Study Mitochondrial Dysfunction in Patients with Parkinson's Disease. Int J Mol Sci 2022; 23:ijms232213678. [PMID: 36430157 PMCID: PMC9696602 DOI: 10.3390/ijms232213678] [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: 09/27/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022] Open
Abstract
The underlying causes of Parkinson's disease are complex, and besides recent advances in elucidating relevant disease mechanisms, no disease-modifying treatments are currently available. One proposed pathophysiological hallmark is mitochondrial dysfunction, and a plethora of evidence points toward the interconnected nature of mitochondria in neuronal homeostasis. This also extends to iron and neuromelanin metabolism, two biochemical processes highly relevant to individual disease manifestation and progression. Modern neuroimaging methods help to gain in vivo insights into these intertwined pathways and may pave the road to individualized medicine in this debilitating disorder. In this narrative review, we will highlight the biological rationale for studying these pathways, how distinct neuroimaging methods can be applied in patients, their respective limitations, and which challenges need to be overcome for successful implementation in clinical studies.
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Affiliation(s)
- Benjamin Matis Pizarro-Galleguillos
- Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
- Institute of Neurogenetics, University of Lübeck, 23588 Lübeck, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Liesa Kunert
- Institute of Neurogenetics, University of Lübeck, 23588 Lübeck, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Norbert Brüggemann
- Institute of Neurogenetics, University of Lübeck, 23588 Lübeck, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Correspondence: ; Tel.: +49-451-500-43420; Fax: +49-451-500-43424
| | - Jannik Prasuhn
- Institute of Neurogenetics, University of Lübeck, 23588 Lübeck, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
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6
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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] [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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7
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Tsai CC, Chen YL, Lu CS, Cheng JS, Weng YH, Lin SH, Wu YM, Wang JJ. Diffusion Tensor Imaging for the differential diagnosis of Parkinsonism by machine learning. Biomed J 2022; 46:100541. [DOI: 10.1016/j.bj.2022.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 04/28/2022] [Accepted: 05/27/2022] [Indexed: 11/02/2022] Open
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8
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Chan YH, Wang C, Soh WK, Rajapakse JC. Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks. Front Neurosci 2022; 16:866666. [PMID: 35677355 PMCID: PMC9168232 DOI: 10.3389/fnins.2022.866666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Both neuroimaging and genomics datasets are often gathered for the detection of neurodegenerative diseases. Huge dimensionalities of neuroimaging data as well as omics data pose tremendous challenge for methods integrating multiple modalities. There are few existing solutions that can combine both multi-modal imaging and multi-omics datasets to derive neurological insights. We propose a deep neural network architecture that combines both structural and functional connectome data with multi-omics data for disease classification. A graph convolution layer is used to model functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data simultaneously to learn compact representations of the connectome. A separate set of graph convolution layers are then used to model multi-omics datasets, expressed in the form of population graphs, and combine them with latent representations of the connectome. An attention mechanism is used to fuse these outputs and provide insights on which omics data contributed most to the model's classification decision. We demonstrate our methods for Parkinson's disease (PD) classification by using datasets from the Parkinson's Progression Markers Initiative (PPMI). PD has been shown to be associated with changes in the human connectome and it is also known to be influenced by genetic factors. We combine DTI and fMRI data with multi-omics data from RNA Expression, Single Nucleotide Polymorphism (SNP), DNA Methylation and non-coding RNA experiments. A Matthew Correlation Coefficient of greater than 0.8 over many combinations of multi-modal imaging data and multi-omics data was achieved with our proposed architecture. To address the paucity of paired multi-modal imaging data and the problem of imbalanced data in the PPMI dataset, we compared the use of oversampling against using CycleGAN on structural and functional connectomes to generate missing imaging modalities. Furthermore, we performed ablation studies that offer insights into the importance of each imaging and omics modality for the prediction of PD. Analysis of the generated attention matrices revealed that DNA Methylation and SNP data were the most important omics modalities out of all the omics datasets considered. Our work motivates further research into imaging genetics and the creation of more multi-modal imaging and multi-omics datasets to study PD and other complex neurodegenerative diseases.
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Affiliation(s)
| | | | | | - Jagath C. Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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9
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C Monte-Rubio G, Segura B, P Strafella A, van Eimeren T, Ibarretxe-Bilbao N, Diez-Cirarda M, Eggers C, Lucas-Jiménez O, Ojeda N, Peña J, Ruppert MC, Sala-Llonch R, Theis H, Uribe C, Junque C. Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease dataset. Hum Brain Mapp 2022; 43:3130-3142. [PMID: 35305545 PMCID: PMC9188966 DOI: 10.1002/hbm.25838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 02/10/2022] [Accepted: 03/07/2022] [Indexed: 11/10/2022] Open
Abstract
Multi‐site MRI datasets are crucial for big data research. However, neuroimaging studies must face the batch effect. Here, we propose an approach that uses the predictive probabilities provided by Gaussian processes (GPs) to harmonize clinical‐based studies. A multi‐site dataset of 216 Parkinson's disease (PD) patients and 87 healthy subjects (HS) was used. We performed a site GP classification using MRI data. The outcomes estimated from this classification, redefined like Weighted HARMonization PArameters (WHARMPA), were used as regressors in two different clinical studies: A PD versus HS machine learning classification using GP, and a VBM comparison (FWE‐p < .05, k = 100). Same studies were also conducted using conventional Boolean site covariates, and without information about site belonging. The results from site GP classification provided high scores, balanced accuracy (BAC) was 98.39% for grey matter images. PD versus HS classification performed better when the WHARMPA were used to harmonize (BAC = 78.60%; AUC = 0.90) than when using the Boolean site information (BAC = 56.31%; AUC = 0.71) and without it (BAC = 57.22%; AUC = 0.73). The VBM analysis harmonized using WHARMPA provided larger and more statistically robust clusters in regions previously reported in PD than when the Boolean site covariates or no corrections were added to the model. In conclusion, WHARMPA might encode global site‐effects quantitatively and allow the harmonization of data. This method is user‐friendly and provides a powerful solution, without complex implementations, to clean the analyses by removing variability associated with the differences between sites.
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Affiliation(s)
- Gemma C Monte-Rubio
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain.,Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
| | - Barbara Segura
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain.,Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED: CB06/05/0018-ISCIII) Barcelona, Barcelona, Catalonia, Spain
| | - Antonio P Strafella
- Edmond J. Safra Parkinson Disease Program & Morton and Gloria Shulman Movement Disorder Unit, Neurology Division, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Krembil Brain Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health University of Toronto, Toronto, Ontario, Canada
| | - Thilo van Eimeren
- Department of Nuclear Medicine, University of Cologne, Cologne, Germany.,Department of Neurology, University of Cologne, Cologne, Germany
| | - Naroa Ibarretxe-Bilbao
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Maria Diez-Cirarda
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health University of Toronto, Toronto, Ontario, Canada
| | - Carsten Eggers
- Department of Neurology, University Hospital Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Marburg and Gießen, Germany.,Department of Neurology, Knappschaftskrankenhaus Bottrop, Bottrop, Germany
| | - Olaia Lucas-Jiménez
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Natalia Ojeda
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Javier Peña
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Marina C Ruppert
- Department of Neurology, University Hospital Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Marburg and Gießen, Germany
| | - Roser Sala-Llonch
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain.,Department of Biomedicine, University of Barcelona, Barcelona, Catalonia, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Catalonia, Spain
| | - Hendrik Theis
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Carme Uribe
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain.,Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain.,Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health University of Toronto, Toronto, Ontario, Canada
| | - Carme Junque
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain.,Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED: CB06/05/0018-ISCIII) Barcelona, Barcelona, Catalonia, Spain
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10
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Nakano Y, Hirano S, Kojima K, Li H, Sakurai T, Suzuki M, Tai H, Furukawa S, Sugiyama A, Yamanaka Y, Yamamoto T, Iimori T, Yokota H, Mukai H, Horikoshi T, Uno T, Kuwabara S. Dopaminergic Correlates of Regional Cerebral Blood Flow in Parkinsonian Disorders. Mov Disord 2022; 37:1235-1244. [DOI: 10.1002/mds.28981] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/31/2022] [Accepted: 02/06/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Yoshikazu Nakano
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
- Department of Neurology Chibaken Saiseikai Narashino Hospital Narashino Japan
| | - Shigeki Hirano
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
| | - Kazuho Kojima
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
- Department of Neurology Chiba Rosai Hospital Ichihara Japan
| | - Honglinag Li
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
| | - Toru Sakurai
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
| | - Masahide Suzuki
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
| | - Hong Tai
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
| | - Shogo Furukawa
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
- Department of Neurology Japanese Red Cross Narita Hospital Narita Japan
| | - Atsuhiko Sugiyama
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
| | - Yoshitaka Yamanaka
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
| | - Tatsuya Yamamoto
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
- Division of Occupational Therapy, Department of Rehabilitation Chiba Prefectural University of Health Sciences Chiba Japan
| | - Takashi Iimori
- Department of Radiology Chiba University Hospital Chiba Japan
| | - Hajime Yokota
- Diagnostic Radiology and Radiation Oncology Graduate School of Medicine, Chiba University Chiba Japan
| | - Hiroki Mukai
- Department of Radiology Chiba University Hospital Chiba Japan
| | | | - Takashi Uno
- Diagnostic Radiology and Radiation Oncology Graduate School of Medicine, Chiba University Chiba Japan
| | - Satoshi Kuwabara
- Department of Neurology Graduate School of Medicine, Chiba University Chiba Japan
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11
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Shaban M, Amara AW. Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease. PLoS One 2022; 17:e0263159. [PMID: 35202420 PMCID: PMC8870584 DOI: 10.1371/journal.pone.0263159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/12/2022] [Indexed: 02/02/2023] Open
Abstract
Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG. The proposed approach was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the features based on which the approach provided the predictions. A significantly high accuracy, sensitivity, specificity, AUC, and Weighted Kappa Score up to 99.9% were achieved and the visualization of the regions in the Wavelet images that contributed to the deep-learning approach decisions was provided. The proposed framework can then serve as an effective computer-aided diagnostic tool that will support physicians and scientists in further understanding the nature of PD and providing an objective and confident opinion regarding the clinical diagnosis of the disease.
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Affiliation(s)
- Mohamed Shaban
- Electrical and Computer Engineering, University of South Alabama, Mobile, AL, United States of America
| | - Amy W. Amara
- Neurology, University of Alabama at Birmingham, Birmingham, AL, United States of America
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Butt A, Kamtchum-Tatuene J, Khan K, Shuaib A, Jickling GC, Miyasaki JM, Smith EE, Camicioli R. White matter hyperintensities in patients with Parkinson's disease: A systematic review and meta-analysis. J Neurol Sci 2021; 426:117481. [PMID: 33975191 DOI: 10.1016/j.jns.2021.117481] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/25/2021] [Accepted: 05/02/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Mechanisms driving neurodegeneration in Parkinson's disease (PD) are unclear and neurovascular dysfunction may be a contributing factor. White matter hyperintensities (WMH) are commonly found on brain MRI in patients with PD. It is controversial if they are more prevalent or more severe in PD compared with controls. This systematic review aims to answer this question. METHODS A systematic search of electronic databases was conducted for studies of WMH in patients with PD. A qualitative synthesis was done for studies reporting WMH prevalence or WMH scores on a visual rating scale (VRS). In studies reporting total WMH volume, the difference between patients with PD and controls was pooled using random effects meta-analysis. RESULTS Among 3860 subjects from 24 studies, 2360 were cases and 1500 controls. Fifteen studies reported WMH scores and four studies reported the prevalence of WMH. On VRS, five studies reported no difference in WMH scores, three found higher WMH scores in PD compared to controls, three reported increased WMH scores either in periventricular or deep white matter, and four reported higher scores only in PD with dementia. In studies reporting WMH volume, there was no difference between patients with PD and controls (pooled standardized mean difference = 0.1, 95%CI: -0.1-0.4, I2 = 81%). CONCLUSION WMH are not more prevalent or severe in patients with PD than in age-matched controls. PD dementia may have more severe WMH compared to controls and PD with normal cognition. Prospective studies using standardized methods of WMH assessment are needed.
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Affiliation(s)
- Asif Butt
- Department of Medicine, Division of Neurology, University of Alberta, 116 St & 85 Ave, Edmonton, AB T6G 2R3, Canada.
| | - Joseph Kamtchum-Tatuene
- Neuroscience and Mental Health Institute, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Khurshid Khan
- Department of Medicine, Division of Neurology, University of Alberta, 116 St & 85 Ave, Edmonton, AB T6G 2R3, Canada
| | - Ashfaq Shuaib
- Department of Medicine, Division of Neurology, University of Alberta, 116 St & 85 Ave, Edmonton, AB T6G 2R3, Canada
| | - Glen C Jickling
- Department of Medicine, Division of Neurology, University of Alberta, 116 St & 85 Ave, Edmonton, AB T6G 2R3, Canada
| | - Janis M Miyasaki
- Department of Medicine, Division of Neurology, University of Alberta, 116 St & 85 Ave, Edmonton, AB T6G 2R3, Canada
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
| | - Richard Camicioli
- Department of Medicine, Division of Neurology, University of Alberta, 116 St & 85 Ave, Edmonton, AB T6G 2R3, Canada
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