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Wang Y, Liu S, Spiteri AG, Huynh ALH, Chu C, Masters CL, Goudey B, Pan Y, Jin L. Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians. Alzheimers Res Ther 2024; 16:175. [PMID: 39085973 PMCID: PMC11293066 DOI: 10.1186/s13195-024-01540-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/21/2024] [Indexed: 08/02/2024]
Abstract
Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.
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Affiliation(s)
- Yihan Wang
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Shu Liu
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
- The ARC Training Centre in Cognitive Computing for Medical Technologies, The University of Melbourne, Carlton, VIC, 3010, Australia
| | - Alanna G Spiteri
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Andrew Liem Hieu Huynh
- Department of Aged Care, Austin Health, Heidelberg, VIC, 3084, Australia
- Department of Medicine, Austin Health, University of Melbourne, Heidelberg, VIC, 3084, Australia
| | - Chenyin Chu
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Benjamin Goudey
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
- The ARC Training Centre in Cognitive Computing for Medical Technologies, The University of Melbourne, Carlton, VIC, 3010, Australia
| | - Yijun Pan
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia.
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia.
| | - Liang Jin
- The Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC, 3052, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
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2
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Javeed A, Anderberg P, Ghazi AN, Noor A, Elmståhl S, Berglund JS. Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia. Front Bioeng Biotechnol 2024; 11:1336255. [PMID: 38260734 PMCID: PMC10801181 DOI: 10.3389/fbioe.2023.1336255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/05/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia. Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew's correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system's efficiency. Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535. Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.
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Affiliation(s)
- Ashir Javeed
- Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden
- School of Health Sciences, University of Skövde, Skövde, Sweden
| | - Ahmad Nauman Ghazi
- Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
| | - Adeeb Noor
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sölve Elmståhl
- EpiHealth: Epidemiology for Health, Lund University, SUS Malmö, Malmö, Sweden
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3
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Lamontagne-Caron R, Desrosiers P, Potvin O, Doyon N, Duchesne S. Predicting cognitive decline in a low-dimensional representation of brain morphology. Sci Rep 2023; 13:16793. [PMID: 37798311 PMCID: PMC10556003 DOI: 10.1038/s41598-023-43063-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 09/19/2023] [Indexed: 10/07/2023] Open
Abstract
Identifying early signs of neurodegeneration due to Alzheimer's disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We trained two embeddings, one on cortical thickness measurements of 6237 cognitively healthy participants aged 18-100 years old and the other on 233 mild cognitively impaired (MCI) and AD participants from the longitudinal database, the Alzheimer's Disease Neuroimaging Initiative database (ADNI). Each participant had multiple visits ([Formula: see text]), one year apart. The first embedding's principal axis was shown to be positively associated ([Formula: see text]) with participants' age. Data from ADNI is projected into these 2D spaces. After clustering the data, average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their trajectory in a 2D space with an AUC of 0.80 with 10-fold cross-validation.
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Affiliation(s)
- Rémi Lamontagne-Caron
- Département de médecine, Université Laval, Quebec, QC, G1V 0A6, Canada.
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada.
| | - Patrick Desrosiers
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de physique, de génie physique et d'optique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | | | - Nicolas Doyon
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de mathématiques et de statistique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | - Simon Duchesne
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Département de radiologie et médecine nucléaire, Université Laval, Quebec, QC, G1V 0A6, Canada
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Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification. Biomedicines 2023; 11:biomedicines11020439. [PMID: 36830975 PMCID: PMC9953011 DOI: 10.3390/biomedicines11020439] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew's correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.
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An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning. LIFE (BASEL, SWITZERLAND) 2022; 12:life12071097. [PMID: 35888188 PMCID: PMC9318926 DOI: 10.3390/life12071097] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/22/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022]
Abstract
Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%.
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6
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Zhang F, Petersen M, Johnson L, Hall J, O'Bryant SE. Recursive Support Vector Machine Biomarker Selection for Alzheimer's Disease. J Alzheimers Dis 2021; 79:1691-1700. [PMID: 33492292 DOI: 10.3233/jad-201254] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND There is a need for more reliable diagnostic tools for the early detection of Alzheimer's disease (AD). This can be a challenge due to a number of factors and logistics making machine learning a viable option. OBJECTIVE In this paper, we present on a Support Vector Machine Leave-One-Out Recursive Feature Elimination and Cross Validation (SVM-RFE-LOO) algorithm for use in the early detection of AD and show how the SVM-RFE-LOO method can be used for both classification and prediction of AD. METHODS Data were analyzed on n = 300 participants (n = 150 AD; n = 150 cognitively normal controls). Serum samples were assayed via a multi-plex biomarker assay platform using electrochemiluminescence (ECL). RESULTS The SVM-RFE-LOO method reduced the number of features in the model from 21 to 16 biomarkers and achieved an area under the curve (AUC) of 0.980 with a sensitivity of 94.0% and a specificity of 93.3%. When the classification and prediction performance of SVM-RFE-LOO was compared to that of SVM and SVM-RFE, we found similar performance across the models; however, the SVM-RFE-LOO method utilized fewer markers. CONCLUSION We found that 1) the SVM-RFE-LOO is suitable for analyzing noisy high-throughput proteomic data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance. Our recursive feature elimination model can serve as a general model for biomarker discovery in other diseases.
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Affiliation(s)
- Fan Zhang
- Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA.,Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Melissa Petersen
- Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA.,Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Leigh Johnson
- Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - James Hall
- Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Sid E O'Bryant
- Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA
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Pemberton HG, Goodkin O, Prados F, Das RK, Vos SB, Moggridge J, Coath W, Gordon E, Barrett R, Schmitt A, Whiteley-Jones H, Burd C, Wattjes MP, Haller S, Vernooij MW, Harper L, Fox NC, Paterson RW, Schott JM, Bisdas S, White M, Ourselin S, Thornton JS, Yousry TA, Cardoso MJ, Barkhof F. Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: a multi-rater, clinical accuracy study. Eur Radiol 2021; 31:5312-5323. [PMID: 33452627 PMCID: PMC8213665 DOI: 10.1007/s00330-020-07455-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 10/01/2020] [Accepted: 11/02/2020] [Indexed: 12/13/2022]
Abstract
Objectives We examined whether providing a quantitative report (QReport) of regional brain volumes improves radiologists’ accuracy and confidence in detecting volume loss, and in differentiating Alzheimer’s disease (AD) and frontotemporal dementia (FTD), compared with visual assessment alone. Methods Our forced-choice multi-rater clinical accuracy study used MRI from 16 AD patients, 14 FTD patients, and 15 healthy controls; age range 52–81. Our QReport was presented to raters with regional grey matter volumes plotted as percentiles against data from a normative population (n = 461). Nine raters with varying radiological experience (3 each: consultants, registrars, ‘non-clinical image analysts’) assessed each case twice (with and without the QReport). Raters were blinded to clinical and demographic information; they classified scans as ‘normal’ or ‘abnormal’ and if ‘abnormal’ as ‘AD’ or ‘FTD’. Results The QReport improved sensitivity for detecting volume loss and AD across all raters combined (p = 0.015* and p = 0.002*, respectively). Only the consultant group’s accuracy increased significantly when using the QReport (p = 0.02*). Overall, raters’ agreement (Cohen’s κ) with the ‘gold standard’ was not significantly affected by the QReport; only the consultant group improved significantly (κs 0.41➔0.55, p = 0.04*). Cronbach’s alpha for interrater agreement improved from 0.886 to 0.925, corresponding to an improvement from ‘good’ to ‘excellent’. Conclusion Our QReport referencing single-subject results to normative data alongside visual assessment improved sensitivity, accuracy, and interrater agreement for detecting volume loss. The QReport was most effective in the consultants, suggesting that experience is needed to fully benefit from the additional information provided by quantitative analyses. Key Points • The use of quantitative report alongside routine visual MRI assessment improves sensitivity and accuracy for detecting volume loss and AD vs visual assessment alone. • Consultant neuroradiologists’ assessment accuracy and agreement (kappa scores) significantly improved with the use of quantitative atrophy reports. • First multi-rater radiological clinical evaluation of visual quantitative MRI atrophy report for use as a diagnostic aid in dementia. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-020-07455-8.
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Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK. .,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK. .,Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ferran Prados
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,Universitat Oberta de Catalunya, Barcelona, Spain
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - James Moggridge
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Elizabeth Gordon
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ryan Barrett
- Department of Neuroradiology, Brighton and Sussex University Hospitals, Brighton, UK
| | - Anne Schmitt
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Hefina Whiteley-Jones
- Department of Neuroradiology, Brighton and Sussex University Hospitals, Brighton, UK
| | | | - Mike P Wattjes
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Sven Haller
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands.,Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Lorna Harper
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sotirios Bisdas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Mark White
- Digital Services, University College London Hospital, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John S Thornton
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Tarek A Yousry
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK.,Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
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Zhu Y, Kim M, Zhu X, Kaufer D, Wu G. Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data. Med Image Anal 2021; 67:101825. [PMID: 33137699 PMCID: PMC10613455 DOI: 10.1016/j.media.2020.101825] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 08/25/2020] [Accepted: 08/25/2020] [Indexed: 01/16/2023]
Abstract
The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy.
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Affiliation(s)
- Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, TX, USA.
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, NC, USA
| | - Xiaofeng Zhu
- Department of Computer Science, University of Electronic Science and Technology of China, Chengdu, China
| | - Daniel Kaufer
- Department of Neurology, University of North Carolina at Chapel Hill, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
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9
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Soleimani M, Vahidi A, Vaseghi B. Two-Dimensional Stockwell Transform and Deep Convolutional Neural Network for Multi-Class Diagnosis of Pathological Brain. IEEE Trans Neural Syst Rehabil Eng 2020; 29:163-172. [PMID: 33237865 DOI: 10.1109/tnsre.2020.3040627] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Since the brain lesion detection and classification is a vital diagnosis task, in this paper, the problem of brain magnetic resonance imaging (MRI) classification is investigated. Recent advantages in machine learning and deep learning allows the researchers to develop the robust computer-aided diagnosis (CAD) tools for classification of brain lesions. Feature extraction is an essential step in any machine learning scheme. Time-frequency analysis methods provide localized information that makes them more attractive for image classification applications. Owing to the advantages of two-dimensional discrete orthonormal Stockwell transform (2D DOST), we propose to use it to extract the efficient features from brain MRIs and obtain the feature matrix. Since there are some irrelevant features, two-directional two-dimensional principal component analysis ((2D)2PCA) is used to reduce the dimension of the feature matrix. Finally, convolution neural networks (CNNs) are designed and trained for MRI classification. Simulation results indicate that the proposed CAD tool outperforms the recently introduced ones and can efficiently diagnose the MRI scans.
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Gupta T, Gandhi TK, Gupta R, Panigrahi B. Classification of patients with tumor using MR FLAIR images. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2017.10.037] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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11
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Raamana PR, Strother SC. Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight. Brain Struct Funct 2020; 225:2475-2493. [DOI: 10.1007/s00429-020-02136-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 08/24/2020] [Indexed: 11/30/2022]
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12
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Frenzel S, Wittfeld K, Habes M, Klinger-König J, Bülow R, Völzke H, Grabe HJ. A Biomarker for Alzheimer's Disease Based on Patterns of Regional Brain Atrophy. Front Psychiatry 2020; 10:953. [PMID: 31992998 PMCID: PMC6970941 DOI: 10.3389/fpsyt.2019.00953] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/03/2019] [Indexed: 11/28/2022] Open
Abstract
Introduction: It has been shown that Alzheimer's disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual's pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD. Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend). Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen's f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen's f2 = 0.009). This association was mainly driven by the immediate recall performance. Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too.
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Affiliation(s)
- Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States
| | - Johanna Klinger-König
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Hans Jörgen Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany
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Yan J, Deng C, Luo L, Wang X, Yao X, Shen L, Huang H. Identifying Imaging Markers for Predicting Cognitive Assessments Using Wasserstein Distances Based Matrix Regression. Front Neurosci 2019; 13:668. [PMID: 31354405 PMCID: PMC6636330 DOI: 10.3389/fnins.2019.00668] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/11/2019] [Indexed: 11/18/2022] Open
Abstract
Alzheimer's disease (AD) is a severe type of neurodegeneration which worsens human memory, thinking and cognition along a temporal continuum. How to identify the informative phenotypic neuroimaging markers and accurately predict cognitive assessment are crucial for early detection and diagnosis Alzheimer's disease. Regression models are widely used to predict the relationship between imaging biomarkers and cognitive assessment, and identify discriminative neuroimaging markers. Most existing methods use different matrix norms as the similarity measures of the empirical loss or regularization to improve the prediction performance, but ignore the inherent geometry of the cognitive data. To tackle this issue, in this paper we propose a novel robust matrix regression model with imposing Wasserstein distances on both loss function and regularization. It successfully integrate Wasserstein distance into the regression model, which can excavate the latent geometry of cognitive data. We introduce an efficient algorithm to solve the proposed new model with convergence analysis. Empirical results on cognitive data of the ADNI cohort demonstrate the great effectiveness of the proposed method for clinical cognitive predication.
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Affiliation(s)
- Jiexi Yan
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Cheng Deng
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Lei Luo
- Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xiaoqian Wang
- Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Heng Huang
- Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
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Singh G, Vadera M, Samavedham L, Lim ECH. Multiclass Diagnosis of Neurodegenerative Diseases: A Neuroimaging Machine-Learning-Based Approach. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b06064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gurpreet Singh
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York 10021, United States
| | - Meet Vadera
- Department of Mechanical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India
- Department of Computer Science, University of Massachusetts, Amherst, Massachusetts 01002, United States
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15
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Tsang G, Xie X, Zhou SM. Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges. IEEE Rev Biomed Eng 2019; 13:113-129. [PMID: 30872241 DOI: 10.1109/rbme.2019.2904488] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records, etc. Making the best use of these diverse and strategic resources will lead to high-quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.
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16
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Adeli E, Thung KH, An L, Wu G, Shi F, Wang T, Shen D. Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019; 41:515-522. [PMID: 29994560 PMCID: PMC6050136 DOI: 10.1109/tpami.2018.2794470] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson's and Alzheimer's diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve.
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Affiliation(s)
- Ehsan Adeli
- Stanford University, Stanford, CA 94305, USA. Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Kim-Han Thung
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Le An
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Guorong Wu
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Feng Shi
- Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Tao Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center and the Alzheimer’s Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Dinggang Shen
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA. Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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Bhagwat N, Viviano JD, Voineskos AN, Chakravarty MM. Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data. PLoS Comput Biol 2018; 14:e1006376. [PMID: 30216352 PMCID: PMC6157905 DOI: 10.1371/journal.pcbi.1006376] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 09/26/2018] [Accepted: 07/18/2018] [Indexed: 01/18/2023] Open
Abstract
Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). Individual prognosis is complicated by many factors including the definition of the prediction objective itself. In this work, we present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories using multimodal and longitudinal data. We perform primary analyses on three cohorts from Alzheimer's Disease Neuroimaging Initiative (ADNI), and a replication analysis using subjects from Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). We model the prototypical symptom trajectory classes using clinical assessment scores from mini-mental state exam (MMSE) and Alzheimer's Disease Assessment Scale (ADAS-13) at nine timepoints spanned over six years based on a hierarchical clustering approach. Subsequently we predict these trajectory classes for a given subject using magnetic resonance (MR) imaging, genetic, and clinical variables from two timepoints (baseline + follow-up). For prediction, we present a longitudinal Siamese neural-network (LSN) with novel architectural modules for combining multimodal data from two timepoints. The trajectory modeling yields two (stable and decline) and three (stable, slow-decline, fast-decline) trajectory classes for MMSE and ADAS-13 assessments, respectively. For the predictive tasks, LSN offers highly accurate performance with 0.900 accuracy and 0.968 AUC for binary MMSE task and 0.760 accuracy for 3-way ADAS-13 task on ADNI datasets, as well as, 0.724 accuracy and 0.883 AUC for binary MMSE task on replication AIBL dataset.
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Affiliation(s)
- Nikhil Bhagwat
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Computational Brain Anatomy Laboratory, Brain Imaging Center, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Joseph D. Viviano
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Aristotle N. Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - M. Mallar Chakravarty
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Computational Brain Anatomy Laboratory, Brain Imaging Center, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
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18
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Singh G, Samavedham L, Lim ECH. Determination of Imaging Biomarkers to Decipher Disease Trajectories and Differential Diagnosis of Neurodegenerative Diseases (DIsease TreND). J Neurosci Methods 2018; 305:105-116. [PMID: 29800593 DOI: 10.1016/j.jneumeth.2018.05.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 01/30/2018] [Accepted: 05/14/2018] [Indexed: 11/30/2022]
Abstract
BACKGROUND Understanding disease progression of neurodegenerative diseases (NDs) is important for better prognosis and decisions on the appropriate course of treatment to slow down the disease progression. NEW METHOD We present here an innovative machine learning framework capable of (1) indicating the trajectory of disease progression by identifying relevant imaging biomarkers and (2) automated disease diagnosis. Self-Organizing Maps (SOM) have been used for data dimensionality reduction and to reveal potentially useful disease-specific biomarkers, regions of interest (ROIs). These ROIs have been used for automated disease diagnosis using Least Square Support Vector Machines (LS-SVM) and to delineate disease progression. RESULTS A multi-site, multi-scanner dataset containing 1316 MRIs was obtained from ADNI3 and PPMI. Identified biomarkers have been used to decipher (1) trajectory of disease progression and (2) identify clinically relevant ROIs. Furthermore, we have obtained a classification accuracy of 94.29 ± 0.08% and 95.37 ± 0.02% for distinguishing AD and PD from HC subjects respectively. COMPARISON WITH OTHER EXISTING METHODS The goal of this study was fundamentally different from other machine learning based studies for automated disease diagnosis. We aimed to develop a method that has two-fold benefits (1) It can be used to understand pathology of neurodegenerative diseases and (2) It also achieves automated disease diagnosis. CONCLUSIONS In the absence of established disease biomarkers, clinical diagnosis is heavily prone to misdiagnosis. Being clinically relevant and readily adaptable in the current clinical settings, the developed framework could be a stepping stone to make machine learning based Clinical Decision Support System (CDSS) for neurodegenerative disease diagnosis a reality.
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Affiliation(s)
- Gurpreet Singh
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore; Department of Radiology, Dalio Institute of Cardiovascular Imaging, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, United States.
| | - Lakshminarayanan Samavedham
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore; Residential College 4, 8 College Avenue West, #02-16W, Education Resource Centre, Singapore 138608, Singapore.
| | - Erle Chuen-Hian Lim
- Department of Neurology, National University Health System, National University of Singapore, Singapore.
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19
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Mirzaei G, Adeli A, Adeli H. Imaging and machine learning techniques for diagnosis of Alzheimer's disease. Rev Neurosci 2018; 27:857-870. [PMID: 27518905 DOI: 10.1515/revneuro-2016-0029] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 06/19/2016] [Indexed: 11/15/2022]
Abstract
Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.
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20
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Schrouff J, Monteiro JM, Portugal L, Rosa MJ, Phillips C, Mourão-Miranda J. Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models. Neuroinformatics 2018; 16:117-143. [PMID: 29297140 PMCID: PMC5797202 DOI: 10.1007/s12021-017-9347-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo).
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Affiliation(s)
- Jessica Schrouff
- Laboratory of Behavioral and Cognitive Neuroscience, Stanford University, Stanford, CA, USA.
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- GIGA Research, University of Liège, Liège, Belgium.
| | - J M Monteiro
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, UK
| | - L Portugal
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Physiology and Pharmacology, Federal Fluminense University, Niterói, RJ, Brazil
| | - M J Rosa
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, UK
| | - C Phillips
- GIGA Research, University of Liège, Liège, Belgium
- Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
| | - J Mourão-Miranda
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, UK
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22
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Gupta T, Gandhi TK, Panigrahi B. Multi-sequential MR brain image classification for tumor detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-169293] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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23
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Anconina R, Zur D, Kesler A, Lublinsky S, Toledano R, Novack V, Benkobich E, Novoa R, Novic EF, Shelef I. Creating normograms of dural sinuses in healthy persons using computer-assisted detection for analysis and comparison of cross-section dural sinuses in the brain. J Clin Neurosci 2017; 40:190-194. [PMID: 28286027 DOI: 10.1016/j.jocn.2017.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Revised: 11/14/2016] [Accepted: 02/07/2017] [Indexed: 11/18/2022]
Abstract
Dural sinuses vary in size and shape in many pathological conditions with abnormal intracranial pressure. Size and shape normograms of dural brain sinuses are not available. The creation of such normograms may enable computer-assisted comparison to pathologic exams and facilitate diagnoses. The purpose of this study was to quantitatively evaluate normal magnetic resonance venography (MRV) studies in order to create normograms of dural sinuses using a computerized algorithm for vessel cross-sectional analysis. This was a retrospective analysis of MRV studies of 30 healthy persons. Data were analyzed using a specially developed Matlab algorithm for vessel cross-sectional analysis. The cross-sectional area and shape measurements were evaluated to create normograms. Mean cross-sectional size was 53.27±13.31 for the right transverse sinus (TS), 46.87+12.57 for the left TS (p=0.089) and 36.65+12.38 for the superior sagittal sinus. Normograms were created. The distribution of cross-sectional areas along the vessels showed distinct patterns and a parallel course for the median, 25th, 50th and 75th percentiles. In conclusion, using a novel computerized method for vessel cross-sectional analysis we were able to quantitatively characterize dural sinuses of healthy persons and create normograms.
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Affiliation(s)
- Reut Anconina
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Dinah Zur
- Ophthalmology Division, Sourasky Medical Center, Tel Aviv University, Tel-Aviv, Israel.
| | - Anat Kesler
- Ophthalmology Division, Sourasky Medical Center, Tel Aviv University, Tel-Aviv, Israel.
| | - Svetlana Lublinsky
- Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Ronen Toledano
- Clinical Research Center, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Victor Novack
- Clinical Research Center, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Elya Benkobich
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Rosa Novoa
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Evelyne Farkash Novic
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Ilan Shelef
- Radiology Institute, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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Long X, Chen L, Jiang C, Zhang L. Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One 2017; 12:e0173372. [PMID: 28264071 PMCID: PMC5338815 DOI: 10.1371/journal.pone.0173372] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 02/20/2017] [Indexed: 12/17/2022] Open
Abstract
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.
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Affiliation(s)
- Xiaojing Long
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Lifang Chen
- Department of Neurology, Shenzhen University 1st Affiliated Hospital, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Chunxiang Jiang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Lijuan Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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25
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Beheshti I, Demirel H, Matsuda H. Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 2017; 83:109-119. [PMID: 28260614 DOI: 10.1016/j.compbiomed.2017.02.011] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 01/26/2017] [Accepted: 02/23/2017] [Indexed: 01/18/2023]
Abstract
We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). Regions with significant GM volume reduction were segmented as volumes of interest (VOIs). Second, these VOIs were used to extract voxel values from the respective atrophy regions in AD, HC, stable MCI (sMCI) and progressive MCI (pMCI) patient groups. The voxel values were then extracted into a feature vector. Third, at the feature-selection stage, all features were ranked according to their respective t-test scores and a genetic algorithm designed to find the optimal feature subset. The Fisher criterion was used as part of the objective function in the genetic algorithm. Finally, the classification was carried out using a support vector machine (SVM) with 10-fold cross validation. We evaluated the proposed automatic CAD system by applying it to baseline values from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (160 AD, 162 HC, 65 sMCI and 71 pMCI subjects). The experimental results indicated that the proposed system is capable of distinguishing between sMCI and pMCI patients, and would be appropriate for practical use in a clinical setting.
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Affiliation(s)
- Iman Beheshti
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan.
| | - Hasan Demirel
- Biomedical Image Processing Group, Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan
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Li T, Li W, Yang Y, Zhang W. Classification of brain disease in magnetic resonance images using two-stage local feature fusion. PLoS One 2017; 12:e0171749. [PMID: 28207873 PMCID: PMC5313178 DOI: 10.1371/journal.pone.0171749] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 01/25/2017] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. METHODS Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. RESULTS We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. DISCUSSION This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.
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Affiliation(s)
- Tao Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wu Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yehui Yang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wensheng Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
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Zur D, Anconina R, Kesler A, Lublinsky S, Toledano R, Shelef I. Quantitative imaging biomarkers for dural sinus patterns in idiopathic intracranial hypertension. Brain Behav 2017; 7:e00613. [PMID: 28239523 PMCID: PMC5318366 DOI: 10.1002/brb3.613] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 09/06/2016] [Accepted: 10/17/2016] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To quantitatively characterize transverse dural sinuses (TS) on magnetic resonance venography (MRV) in patients with idiopathic intracranial hypertension (IIH), compared to healthy controls, using a computer assisted detection (CAD) method. MATERIALS AND METHODS We retrospectively analyzed MRV studies of 38 IIH patients and 30 controls, matched by age and gender. Data analysis was performed using a specially developed Matlab algorithm for vessel cross-sectional analysis. The cross-sectional area and shape measurements were evaluated in patients and controls. RESULTS Mean, minimal, and maximal cross-sectional areas as well as volumetric parameters of the right and left transverse sinuses were significantly smaller in IIH patients than in controls (p < .005 for all). Idiopathic intracranial hypertension patients showed a narrowed segment in both TS, clustering near the junction with the sigmoid sinus. In 36% (right TS) and 43% (left TS), the stenosis extended to >50% of the entire length of the TS, i.e. the TS was hypoplastic. Narrower vessels tended to have a more triangular shape than did wider vessels. CONCLUSION Using CAD we precisely quantified TS stenosis and its severity in IIH patients by cross-sectional and volumetric analysis. This method can be used as an exact tool for investigating mechanisms of IIH development and response to treatment.
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Affiliation(s)
- Dinah Zur
- Division of Ophthalmology Sackler Faculty of Medicine Tel Aviv Sourasky Medical Center Tel Aviv University Tel Aviv Israel
| | - Reut Anconina
- Diagnostic Imaging Department Soroka University Medical Center Ben-Gurion University of the Negev Beer-Sheva Israel
| | - Anat Kesler
- Division of Ophthalmology Sackler Faculty of Medicine Tel Aviv Sourasky Medical Center Tel Aviv University Tel Aviv Israel
| | - Svetlana Lublinsky
- Zolotowsky Neuroscience Center Ben-Gurion University of the Negev Beer-Sheva Israel
| | - Ronen Toledano
- Clinical Research Center Soroka University Medical Center Ben-Gurion University of the Negev Beer-Sheva Israel
| | - Ilan Shelef
- Diagnostic Imaging Department Soroka University Medical Center Ben-Gurion University of the Negev Beer-Sheva Israel
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Beheshti I, Demirel H, Farokhian F, Yang C, Matsuda H. Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:177-193. [PMID: 28110723 DOI: 10.1016/j.cmpb.2016.09.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 09/02/2016] [Accepted: 09/22/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. METHODS The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. RESULTS The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. CONCLUSIONS An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models.
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Affiliation(s)
- Iman Beheshti
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan.
| | - Hasan Demirel
- Biomedical Image Processing Lab, Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
| | - Farnaz Farokhian
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100022, China
| | - Chunlan Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100022, China
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan
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Alberdi A, Aztiria A, Basarab A. On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey. Artif Intell Med 2016; 71:1-29. [PMID: 27506128 DOI: 10.1016/j.artmed.2016.06.003] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 05/23/2016] [Accepted: 06/07/2016] [Indexed: 11/15/2022]
Abstract
INTRODUCTION The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient. OBJECTIVE An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection. Some useful tools and specific issues towards this goal will also have to be reviewed. METHODS An extensive literature review was performed following a specific search strategy, inclusion criteria, data extraction and quality assessment in the Inspec, Compendex and PubMed databases. RESULTS This work reviews the extensive list of psychological, physiological, behavioural and cognitive measurements that could be used for AD detection. The most promising measurements seem to be magnetic resonance imaging (MRI) for AD vs control (CTL) discrimination with an 98.95% accuracy, while electroencephalogram (EEG) shows the best results for mild cognitive impairment (MCI) vs CTL (97.88%) and MCI vs AD distinction (94.05%). Available physiological and behavioural AD datasets are listed, as well as medical imaging analysis steps and neuroimaging processing toolboxes. Some issues such as "label noise" and multi-site data are discussed. CONCLUSIONS The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible. Such a multimodal system might probably be based on physiological monitoring of MRI or EEG, as well as behavioural measurements like the ones proposed along the article. The mentioned AD datasets and image processing toolboxes are available for their use towards this goal. Issues like "label noise" and multi-site neuroimaging incompatibilities may also have to be overcome, but methods for this purpose are already available.
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Affiliation(s)
- Ane Alberdi
- Mondragon University, Electronics and Computing Department, Goiru Kalea, 2, Arrasate 20500, Spain.
| | - Asier Aztiria
- Mondragon University, Electronics and Computing Department, Goiru Kalea, 2, Arrasate 20500, Spain.
| | - Adrian Basarab
- Université de Toulouse, Institut de Recherche en Informatique de Toulouse, Centre National de la Recherche Scientifique, Unité Mixte de Recherche 5505, Université Paul Sabatier, 118 Route de Narbonne, 31062 Toulouse, France.
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Liu M, Zhang D, Shen D. Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1463-74. [PMID: 26742127 PMCID: PMC5572669 DOI: 10.1109/tmi.2016.2515021] [Citation(s) in RCA: 115] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
As shown in the literature, methods based on multiple templates usually achieve better performance, compared with those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignores important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method on 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification.
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HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage 2016; 145:346-364. [PMID: 26923371 DOI: 10.1016/j.neuroimage.2016.02.041] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 02/11/2016] [Accepted: 02/12/2016] [Indexed: 11/23/2022] Open
Abstract
Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients (e.g., progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups (e.g., healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies.
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Möller C, Pijnenburg YAL, van der Flier WM, Versteeg A, Tijms B, de Munck JC, Hafkemeijer A, Rombouts SARB, van der Grond J, van Swieten J, Dopper E, Scheltens P, Barkhof F, Vrenken H, Wink AM. Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis. Radiology 2015; 279:838-48. [PMID: 26653846 DOI: 10.1148/radiol.2015150220] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.
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Affiliation(s)
- Christiane Möller
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Yolande A L Pijnenburg
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Wiesje M van der Flier
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Adriaan Versteeg
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Betty Tijms
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Jan C de Munck
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Anne Hafkemeijer
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Serge A R B Rombouts
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Jeroen van der Grond
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - John van Swieten
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Elise Dopper
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Philip Scheltens
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Frederik Barkhof
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Hugo Vrenken
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
| | - Alle Meije Wink
- From the Alzheimer Center and Department of Neurology (C.M., Y.A.L.P., W.M.v.d.F., B.T., E.D., P.S.), Department of Epidemiology and Biostatistics (W.M.v.d.F.), Department of Radiology and Nuclear Medicine (A.V., F.B., H.V., A.M.W.), Department of Physics and Medical Technology (J.C.d.M., H.V.), and Department of Clinical Genetics (J.v.S.), Neuroscience Campus Amsterdam, VU University Medical Center, APO Box 7057, 1007 MB Amsterdam, the Netherlands; Institute of Psychology (A.H., S.A.R.B.R., E.D.) and Leiden Institute for Brain and Cognition (A.H., S.A.R.B.R.), Leiden University, Leiden, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (A.H., S.A.R.B.R., J.v.d.G.); and Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands (J.v.S., E.D.)
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Tiwari V, Solanki V, Tiwari M. In-vivoandin-vitrotechniques used to investigate Alzheimer's disease. FRONTIERS IN LIFE SCIENCE 2015. [DOI: 10.1080/21553769.2015.1044129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Goryawala M, Zhou Q, Barker W, Loewenstein DA, Duara R, Adjouadi M. Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015; 2015:865265. [PMID: 26101520 PMCID: PMC4458535 DOI: 10.1155/2015/865265] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Revised: 04/28/2015] [Accepted: 04/29/2015] [Indexed: 11/18/2022]
Abstract
Brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.
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Affiliation(s)
- Mohammed Goryawala
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Qi Zhou
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Warren Barker
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - David A. Loewenstein
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- Department of Psychiatry, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- Department of Psychiatry, Miller School of Medicine, University of Miami, Miami, FL, USA
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
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Bron EE, Smits M, Niessen WJ, Klein S. Feature Selection Based on the SVM Weight Vector for Classification of Dementia. IEEE J Biomed Health Inform 2015; 19:1617-1626. [PMID: 25974958 DOI: 10.1109/jbhi.2015.2432832] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Computer-aided diagnosis of dementia using a support vector machine (SVM) can be improved with feature selection. The relevance of individual features can be quantified from the SVM weights as a significance map (p-map). Although these p-maps previously showed clusters of relevant voxels in dementia-related brain regions, they have not yet been used for feature selection. Therefore, we introduce two novel feature selection methods based on p-maps using a direct approach (filter) and an iterative approach (wrapper). To evaluate these p-map feature selection methods, we compared them with methods based on the SVM weight vector directly, t-statistics, and expert knowledge. We used MRI data from the Alzheimer's disease neuroimaging initiative classifying Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients who converted to AD (MCIc), MCI patients who did not convert to AD (MCInc), and cognitively normal controls (CN). Features for each voxel were derived from gray matter morphometry. Feature selection based on the SVM weights gave better results than t-statistics and expert knowledge. The p-map methods performed slightly better than those using the weight vector. The wrapper method scored better than the filter method. Recursive feature elimination based on the p-map improved most for AD-CN: the area under the receiver-operating-characteristic curve (AUC) significantly increased from 90.3% without feature selection to 92.0% when selecting 1.5%-3% of the features. This feature selection method also improved the other classifications: AD-MCI 0.1% improvement in AUC (not significant), MCI-CN 0.7%, and MCIc-MCInc 0.1% (not significant). Although the performance improvement due to feature selection was limited, the methods based on the p-map generally had the best performance, and were therefore better in estimating the relevance of individual features.
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Affiliation(s)
- Esther E Bron
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, CA, The Netherlands
| | - Marion Smits
- Department of Radiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, CA, The Netherlands
| | - Wiro J Niessen
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, CA, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, CA, The Netherlands
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Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput Biol Med 2015; 58:101-9. [DOI: 10.1016/j.compbiomed.2015.01.003] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/30/2014] [Accepted: 01/02/2015] [Indexed: 01/15/2023]
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A hybrid intelligent diagnosis approach for quick screening of Alzheimer's disease based on multiple neuropsychological rating scales. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:258761. [PMID: 25815043 PMCID: PMC4359840 DOI: 10.1155/2015/258761] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 11/20/2014] [Accepted: 11/20/2014] [Indexed: 01/23/2023]
Abstract
Neuropsychological testing is an effective means for the screening of Alzheimer's disease. Multiple neuropsychological rating scales should be used together to get subjects' comprehensive cognitive state due to the limitation of a single scale, but it is difficult to operate in primary clinical settings because of the inadequacy of time and qualified clinicians. Aiming at identifying AD's stages more accurately and conveniently in screening, we proposed a computer-aided diagnosis approach based on critical items extracted from multiple neuropsychological scales. The proposed hybrid intelligent approach combines the strengths of rough sets, genetic algorithm, and Bayesian network. There are two stages: one is attributes reduction technique based on rough sets and genetic algorithm, which can find out the most discriminative items for AD diagnosis in scales; the other is uncertain reasoning technique based on Bayesian network, which can forecast the probability of suffering from AD. The experimental data set consists of 500 cases collected by a top hospital in China and each case is determined by the expert panel. The results showed that the proposed approach could not only reduce items drastically with the same classification precision, but also perform better on identifying different stages of AD comparing with other existing scales.
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Bron EE, Smits M, van der Flier WM, Vrenken H, Barkhof F, Scheltens P, Papma JM, Steketee RME, Méndez Orellana C, Meijboom R, Pinto M, Meireles JR, Garrett C, Bastos-Leite AJ, Abdulkadir A, Ronneberger O, Amoroso N, Bellotti R, Cárdenas-Peña D, Álvarez-Meza AM, Dolph CV, Iftekharuddin KM, Eskildsen SF, Coupé P, Fonov VS, Franke K, Gaser C, Ledig C, Guerrero R, Tong T, Gray KR, Moradi E, Tohka J, Routier A, Durrleman S, Sarica A, Di Fatta G, Sensi F, Chincarini A, Smith GM, Stoyanov ZV, Sørensen L, Nielsen M, Tangaro S, Inglese P, Wachinger C, Reuter M, van Swieten JC, Niessen WJ, Klein S. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 2015; 111:562-79. [PMID: 25652394 DOI: 10.1016/j.neuroimage.2015.01.048] [Citation(s) in RCA: 165] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 01/21/2015] [Accepted: 01/24/2015] [Indexed: 12/31/2022] Open
Abstract
Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.
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Affiliation(s)
- Esther E Bron
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands.
| | - Marion Smits
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands; Department of Epidemiology & Biostatistics, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands; Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Carolina Méndez Orellana
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Madalena Pinto
- Department of Neurology, Hospital de São João, Porto, Portugal
| | | | - Carolina Garrett
- Department of Neurology, Hospital de São João, Porto, Portugal; Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - António J Bastos-Leite
- Department of Medical Imaging, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Ahmed Abdulkadir
- Department of Psychiatry & Psychotherapy, University Medical Centre Freiburg, Germany; Department of Neurology, University Medical Centre Freiburg, Germany; Department of Computer Science, University of Freiburg, Germany
| | - Olaf Ronneberger
- BIOSS Centre for Biological Signaling Studies, University of Freiburg, Germany; Department of Computer Science, University of Freiburg, Germany
| | - Nicola Amoroso
- National Institute of Nuclear Physics, Branch of Bari, Italy; Department of Physics, University of Bari, Italy
| | - Roberto Bellotti
- National Institute of Nuclear Physics, Branch of Bari, Italy; Department of Physics, University of Bari, Italy
| | - David Cárdenas-Peña
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Colombia
| | | | | | | | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University, Aarhus, Denmark
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, Unit Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, Bordeaux, France
| | - Vladimir S Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Katja Franke
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Germany; Structural Brain Mapping Group, Department of Psychiatry, Jena University Hospital, Germany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Germany; Structural Brain Mapping Group, Department of Psychiatry, Jena University Hospital, Germany
| | - Christian Ledig
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Ricardo Guerrero
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Tong Tong
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Katherine R Gray
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Elaheh Moradi
- Department of Signal Processing, Tampere University of Technology, Finland
| | - Jussi Tohka
- Department of Signal Processing, Tampere University of Technology, Finland
| | - Alexandre Routier
- Inserm U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, Inria Paris-Rocquencourt, F-75013 Paris, France; Centre d'Acquisition et de Traitement des Images (CATI), Paris, France
| | - Stanley Durrleman
- Inserm U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, Inria Paris-Rocquencourt, F-75013 Paris, France; Centre d'Acquisition et de Traitement des Images (CATI), Paris, France
| | - Alessia Sarica
- Bioinformatics Laboratory, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Giuseppe Di Fatta
- School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Francesco Sensi
- National Institute of Nuclear Physics, Branch of Genoa, Italy
| | | | - Garry M Smith
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, RG6 6AH, UK; School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Zhivko V Stoyanov
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, RG6 6AH, UK; School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Lauge Sørensen
- Department of Computer Science, University of Copenhagen, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Denmark
| | - Sabina Tangaro
- National Institute of Nuclear Physics, Branch of Bari, Italy
| | - Paolo Inglese
- National Institute of Nuclear Physics, Branch of Bari, Italy
| | - Christian Wachinger
- Computer Science and Artificial Intelligence Lab, MA Institute of Technology, Cambridge, USA; Massachusetts General Hospital, Harvard Medical School, Cambridge, USA
| | - Martin Reuter
- Computer Science and Artificial Intelligence Lab, MA Institute of Technology, Cambridge, USA; Massachusetts General Hospital, Harvard Medical School, Cambridge, USA
| | | | - Wiro J Niessen
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Imaging Physics, Applied Sciences, Delft University of Technology, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
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Zhou Q, Goryawala M, Cabrerizo M, Barker W, Loewenstein D, Duara R, Adjouadi M. Multivariate analysis of structural MRI and PET (FDG and 18F-AV-45) for Alzheimer's disease and its prodromal stages. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:1051-4. [PMID: 25570142 DOI: 10.1109/embc.2014.6943774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A multivariate analysis method, orthogonal partial least squares to latent structures (OPLS), was used to discriminate Alzheimer's disease (AD), early and late mild cognitive impairment (EMCI and LMCI) from cognitively normal control (CN) using MRI and PET measures. FreeSurfer 5.1 generated 271 MRI features including 49 subcortical volumes, 68 cortical volumes, 68 cortical thicknesses, 70 surface areas and 16 hippocampus subfields. Subjects with all aforementioned MRI measures passing quality control and valid Fludeoxyglucose (18F) (FDG) and Florbetapir (18F) PET scans were selected from ADNI database, resulting in a total of 524 participants (137 CN, 214 EMCI, 103 LMCI and 70 AD) for the study. Altogether 286 features including 15 significant PET uptake features (7 for FDG and 8 for AV-45) were utilized for OPLS analysis. Predictive power was evaluated by Q2(Y), a quantifier of the statistical significance for class separation. The results show that MRI features (Q2(Y) =0.645), and PET features (Q2(Y) = 0.636) has comparable predictive power in separating AD from CN, and MRI features are better predictor of LMCI (Q2(Y) = 0.282) than PET (Q2(Y) = 0.294). Combination of PET and MRI has the most predictive power for LMCI and AD with Q2(Y) of 0.294 and 0.721, respectively. While for EMCI, cortical thickness was found to be the best predictor with a Q2(Y) of 0.108, suggesting cortical thickness may be the first structural change ahead of others and should be prioritized in prediction of very mild cognitive impairment.
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Single time point high-dimensional morphometry in Alzheimer's disease: group statistics on longitudinally acquired data. Neurobiol Aging 2015; 36 Suppl 1:S11-22. [DOI: 10.1016/j.neurobiolaging.2014.06.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 06/10/2014] [Accepted: 06/14/2014] [Indexed: 12/21/2022]
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Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. Neuroimage 2015; 104:398-412. [PMID: 25312773 PMCID: PMC5957071 DOI: 10.1016/j.neuroimage.2014.10.002] [Citation(s) in RCA: 344] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 09/16/2014] [Accepted: 10/01/2014] [Indexed: 01/20/2023] Open
Abstract
Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.
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Affiliation(s)
- Elaheh Moradi
- Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101, Tampere, Finland
| | - Antonietta Pepe
- Aix Marseille Université, CNRS, ENSAM, Université de Toulon, LSIS UMR 7296,13397, Marseille, France
| | - Christian Gaser
- Department of Psychiatry, University of Jena, Jahnstr 3, D-07743, Jena, Germany
| | - Heikki Huttunen
- Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101, Tampere, Finland
| | - Jussi Tohka
- Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101, Tampere, Finland.
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Sotiras A, Resnick SM, Davatzikos C. Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization. Neuroimage 2014; 108:1-16. [PMID: 25497684 DOI: 10.1016/j.neuroimage.2014.11.045] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Revised: 11/13/2014] [Accepted: 11/18/2014] [Indexed: 01/12/2023] Open
Abstract
In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA.
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Affiliation(s)
- Aristeidis Sotiras
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Christos Davatzikos
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
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Schmitter D, Roche A, Maréchal B, Ribes D, Abdulkadir A, Bach-Cuadra M, Daducci A, Granziera C, Klöppel S, Maeder P, Meuli R, Krueger G. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease. NEUROIMAGE-CLINICAL 2014; 7:7-17. [PMID: 25429357 PMCID: PMC4238047 DOI: 10.1016/j.nicl.2014.11.001] [Citation(s) in RCA: 127] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 06/17/2014] [Accepted: 11/04/2014] [Indexed: 01/10/2023]
Abstract
Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.
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Affiliation(s)
- Daniel Schmitter
- Advanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, Switzerland ; Centre d'Imagerie BioMédicale (CIBM), CH-1015 Lausanne, Switzerland ; Biomedical Imaging Group, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland
| | - Alexis Roche
- Advanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, Switzerland ; Centre d'Imagerie BioMédicale (CIBM), CH-1015 Lausanne, Switzerland ; Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, Switzerland ; Signal Processing Laboratory 5, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, Switzerland ; Centre d'Imagerie BioMédicale (CIBM), CH-1015 Lausanne, Switzerland ; Signal Processing Laboratory 5, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland
| | - Delphine Ribes
- Advanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, Switzerland ; Centre d'Imagerie BioMédicale (CIBM), CH-1015 Lausanne, Switzerland
| | - Ahmed Abdulkadir
- Group of Pattern Recognition and Image Processing, University of Freiburg, D-79110 Freiburg, Germany
| | - Meritxell Bach-Cuadra
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, Switzerland ; Signal Processing Laboratory 5, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland ; Centre d'Imagerie BioMédicale (CIBM), CH-1015 Lausanne, Switzerland
| | - Alessandro Daducci
- Signal Processing Laboratory 5, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland
| | - Cristina Granziera
- Service of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, Switzerland ; Advanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, Switzerland ; Centre d'Imagerie BioMédicale (CIBM), CH-1015 Lausanne, Switzerland ; Signal Processing Laboratory 5, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland
| | - Stefan Klöppel
- Group of Pattern Recognition and Image Processing, University of Freiburg, D-79110 Freiburg, Germany
| | - Philippe Maeder
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, Switzerland
| | - Reto Meuli
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, Switzerland
| | - Gunnar Krueger
- Advanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, Switzerland ; Centre d'Imagerie BioMédicale (CIBM), CH-1015 Lausanne, Switzerland ; Signal Processing Laboratory 5, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland
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Bron EE, Steketee RME, Houston GC, Oliver RA, Achterberg HC, Loog M, van Swieten JC, Hammers A, Niessen WJ, Smits M, Klein S. Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia. Hum Brain Mapp 2014; 35:4916-31. [PMID: 24700485 PMCID: PMC6869162 DOI: 10.1002/hbm.22522] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Revised: 03/14/2014] [Accepted: 03/24/2014] [Indexed: 11/11/2022] Open
Abstract
Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range = 86 - 91%) than all other approaches (AUC = 57 - 84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself.
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Affiliation(s)
- Esther E Bron
- Departments of Medical Informatics and Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC - University Medical Center Rotterdam, the Netherlands
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High-Dimensional Medial Lobe Morphometry: An Automated MRI Biomarker for the New AD Diagnostic Criteria. Int J Alzheimers Dis 2014; 2014:278096. [PMID: 25254139 PMCID: PMC4164123 DOI: 10.1155/2014/278096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 07/25/2014] [Indexed: 11/21/2022] Open
Abstract
Introduction. Medial temporal lobe atrophy assessment via magnetic resonance imaging (MRI) has been proposed in recent criteria as an in vivo diagnostic biomarker of Alzheimer's disease (AD). However, practical application of these criteria in a clinical setting will require automated MRI analysis techniques. To this end, we wished to validate our automated, high-dimensional morphometry technique to the hypothetical prediction of future clinical status from baseline data in a cohort of subjects in a large, multicentric setting, compared to currently known clinical status for these subjects. Materials and Methods. The study group consisted of 214 controls, 371 mild cognitive impairment (147 having progressed to probable AD and 224 stable), and 181 probable AD from the Alzheimer's Disease Neuroimaging Initiative, with data acquired on 58 different 1.5 T scanners. We measured the sensitivity and specificity of our technique in a hierarchical fashion, first testing the effect of intensity standardization, then between different volumes of interest, and finally its generalizability for a large, multicentric cohort. Results. We obtained 73.2% prediction accuracy with 79.5% sensitivity for the prediction of MCI progression to clinically probable AD. The positive predictive value was 81.6% for MCI progressing on average within 1.5 (0.3 s.d.) year. Conclusion. With high accuracy, the technique's ability to identify discriminant medial temporal lobe atrophy has been demonstrated in a large, multicentric environment. It is suitable as an aid for clinical diagnostic of AD.
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Zhou Q, Goryawala M, Cabrerizo M, Wang J, Barker W, Loewenstein DA, Duara R, Adjouadi M. An Optimal Decisional Space for the Classification of Alzheimer's Disease and Mild Cognitive Impairment. IEEE Trans Biomed Eng 2014; 61:2245-53. [DOI: 10.1109/tbme.2014.2310709] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Lillemark L, Sørensen L, Pai A, Dam EB, Nielsen M. Brain region's relative proximity as marker for Alzheimer's disease based on structural MRI. BMC Med Imaging 2014; 14:21. [PMID: 24889999 PMCID: PMC4048460 DOI: 10.1186/1471-2342-14-21] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Accepted: 05/09/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single brain structures like hippocampus, this paper investigates the relationship and proximity between regions in the brain and uses this information as a novel way of classifying normal control (NC), mild cognitive impaired (MCI), and AD subjects. METHODS A longitudinal cohort of 528 subjects (170 NC, 240 MCI, and 114 AD) from ADNI at baseline and month 12 was studied. We investigated a marker based on Procrustes aligned center of masses and the percentile surface connectivity between regions. These markers were classified using a linear discriminant analysis in a cross validation setting and compared to whole brain and hippocampus volume. RESULTS We found that both our markers was able to significantly classify the subjects. The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain. The surface connectivity marker was able to classify MCI-converters with an AUC of 0.599 (p<0.05) for the 1-year period. CONCLUSION Our results show that our relative proximity markers include more information than whole brain and hippocampus volume. Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD.
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Affiliation(s)
- Lene Lillemark
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark.
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Ribbens A, Hermans J, Maes F, Vandermeulen D, Suetens P. Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:201-224. [PMID: 23797244 DOI: 10.1109/tmi.2013.2270114] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Population analysis of brain morphology from magnetic resonance images contributes to the study and understanding of neurological diseases. Such analysis typically involves segmentation of a large set of images and comparisons of these segmentations between relevant subgroups of images (e.g., "normal" versus "diseased"). The images of each subgroup are usually selected in advance in a supervised way based on clinical knowledge. Their segmentations are typically guided by one or more available atlases, assumed to be suitable for the images at hand. We present a data-driven probabilistic framework that simultaneously performs atlas-guided segmentation of a heterogeneous set of brain MR images and clusters the images in homogeneous subgroups, while constructing separate probabilistic atlases for each cluster to guide the segmentation. The main benefits of integrating segmentation, clustering and atlas construction in a single framework are that: 1) our method can handle images of a heterogeneous group of subjects and automatically identifies homogeneous subgroups in an unsupervised way with minimal prior knowledge, 2) the subgroups are formed by automatical detection of the relevant morphological features based on the segmentation, 3) the atlases used by our method are constructed from the images themselves and optimally adapted for guiding the segmentation of each subgroup, and 4) the probabilistic atlases represent the morphological pattern that is specific for each subgroup and expose the groupwise differences between different subgroups. We demonstrate the feasibility of the proposed framework and evaluate its performance with respect to image segmentation, clustering and atlas construction on simulated and real data sets including the publicly available BrainWeb and ADNI data. It is shown that combined segmentation and atlas construction leads to improved segmentation accuracy. Furthermore, it is demonstrated that the clusters generated by our unsupervised framework largely coincide with the clinically determined subgroups in case of disease-specific differences in brain morphology and that the differences between the cluster-specific atlases are in agreement with the expected disease-specific patterns, indicating that our method is capable of detecting the different modes in a population. Our method can thus be seen as a comprehensive image-driven population analysis framework that can contribute to the detection of novel subgroups and distinctive image features, potentially leading to new insights in the brain development and disease.
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Da X, Toledo JB, Zee J, Wolk DA, Xie SX, Ou Y, Shacklett A, Parmpi P, Shaw L, Trojanowski JQ, Davatzikos C. Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. Neuroimage Clin 2013; 4:164-73. [PMID: 24371799 PMCID: PMC3871290 DOI: 10.1016/j.nicl.2013.11.010] [Citation(s) in RCA: 81] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2013] [Revised: 11/20/2013] [Accepted: 11/22/2013] [Indexed: 01/18/2023]
Abstract
This study evaluates the individual, as well as relative and joint value of indices obtained from magnetic resonance imaging (MRI) patterns of brain atrophy (quantified by the SPARE-AD index), cerebrospinal fluid (CSF) biomarkers, APOE genotype, and cognitive performance (ADAS-Cog) in progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a variable follow-up period up to 6 years, using data from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1). SPARE-AD was first established as a highly sensitive and specific MRI-marker of AD vs. cognitively normal (CN) subjects (AUC = 0.98). Baseline predictive values of all aforementioned indices were then compared using survival analysis on 381 MCI subjects. SPARE-AD and ADAS-Cog were found to have similar predictive value, and their combination was significantly better than their individual performance. APOE genotype did not significantly improve prediction, although the combination of SPARE-AD, ADAS-Cog and APOE ε4 provided the highest hazard ratio estimates of 17.8 (last vs. first quartile). In a subset of 192 MCI patients who also had CSF biomarkers, the addition of Aβ1-42, t-tau, and p-tau181p to the previous model did not improve predictive value significantly over SPARE-AD and ADAS-Cog combined. Importantly, in amyloid-negative patients with MCI, SPARE-AD had high predictive power of clinical progression. Our findings suggest that SPARE-AD and ADAS-Cog in combination offer the highest predictive power of conversion from MCI to AD, which is improved, albeit not significantly, by APOE genotype. The finding that SPARE-AD in amyloid-negative MCI patients was predictive of clinical progression is not expected under the amyloid hypothesis and merits further investigation.
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Affiliation(s)
- Xiao Da
- Section of Biomedical Image Analysis, Department of Radiology, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jon B. Toledo
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Jarcy Zee
- Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - David A. Wolk
- Section of Biomedical Image Analysis, Department of Radiology, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon X. Xie
- Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yangming Ou
- Section of Biomedical Image Analysis, Department of Radiology, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda Shacklett
- Section of Biomedical Image Analysis, Department of Radiology, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Paraskevi Parmpi
- Section of Biomedical Image Analysis, Department of Radiology, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Leslie Shaw
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - John Q. Trojanowski
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Department of Radiology, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
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Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making. J Digit Imaging 2013; 26:97-108. [PMID: 22415112 DOI: 10.1007/s10278-012-9465-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.
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