51
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Qiu Y, Jin T, Mason E, Campbell MCW. Predicting Thioflavin Fluorescence of Retinal Amyloid Deposits Associated With Alzheimer's Disease from Their Polarimetric Properties. Transl Vis Sci Technol 2020; 9:47. [PMID: 32879757 PMCID: PMC7443113 DOI: 10.1167/tvst.9.2.47] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/26/2020] [Indexed: 01/30/2023] Open
Abstract
Purpose To use machine learning in those with brain amyloid to predict thioflavin fluorescence (indicative of amyloid) of retinal deposits from their interactions with polarized light. Methods We imaged 933 retinal deposits in 28 subjects with post mortem evidence of brain amyloid using thioflavin fluorescence and polarization sensitive microscopy. Means and standard deviations of 14 polarimetric properties were input to machine learning algorithms. Two oversampling strategies were applied to overcome data imbalance. Three machine learning algorithms: linear discriminant analysis, supporting vector machine, and random forest (RF) were trained to predict thioflavin positive deposits. For each method; accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were computed. Results For the polarimetric positive deposits, using 1 oversampling method, RF had the highest area under the receiver operating characteristic curve (0.986), which was not different from that with the second oversampling method. RF had 95% accuracy, 94% sensitivity, and 97% specificity. After including deposits with no polarimetric signals, polarimetry correctly predicted 93% of thioflavin positive deposits. Linear retardance and linear anisotropy were the dominant polarimetric properties in RF with 1 oversampling method, and no polarimetric properties were dominant in the second method. Conclusions Thioflavin positivity of retinal amyloid deposits can be predicted from their images in polarized light. Polarimetry is a promising dye-free method of detecting amyloid deposits in ex vivo retinal tissue. Further testing is required for translation to live eye imaging. Translational Relevance This dye-free method distinguishes retinal amyloid deposits, a promising biomarker of Alzheimer's disease, in human retinas imaged with polarimetry.
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Affiliation(s)
- Yunyi Qiu
- Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada
| | - Tao Jin
- Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada
| | - Erik Mason
- Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada
| | - Melanie C W Campbell
- Department of Physics and Astronomy, School of Optometry and Vision Science, Department of Systems Design Engineering, Centre for Bioengineering and Biotechnology, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario, Canada.,Centre for Eye and Vision Research, Hong Kong
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52
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Feng J, Zhang SW, Chen L. Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier. Artif Intell Med 2020; 108:101940. [DOI: 10.1016/j.artmed.2020.101940] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/01/2020] [Accepted: 08/07/2020] [Indexed: 02/07/2023]
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53
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Myszczynska MA, Ojamies PN, Lacoste AMB, Neil D, Saffari A, Mead R, Hautbergue GM, Holbrook JD, Ferraiuolo L. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol 2020; 16:440-456. [DOI: 10.1038/s41582-020-0377-8] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2020] [Indexed: 12/11/2022]
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54
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Jeong B, Cho H, Kim J, Kwon SK, Hong S, Lee C, Kim T, Park MS, Hong S, Heo TY. Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data. Diagnostics (Basel) 2020; 10:E415. [PMID: 32570782 PMCID: PMC7345590 DOI: 10.3390/diagnostics10060415] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/03/2020] [Accepted: 06/16/2020] [Indexed: 11/16/2022] Open
Abstract
This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.
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Affiliation(s)
- Bomi Jeong
- Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Korea; (B.J.); (J.K.)
| | - Hyunjeong Cho
- Department of Internal Medicine, Chungbuk National University College of Medicine, Chungbuk 28644, Korea; (H.C.); (S.K.K.)
- Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk 28644, Korea
| | - Jieun Kim
- Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Korea; (B.J.); (J.K.)
| | - Soon Kil Kwon
- Department of Internal Medicine, Chungbuk National University College of Medicine, Chungbuk 28644, Korea; (H.C.); (S.K.K.)
- Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk 28644, Korea
| | - SeungWoo Hong
- Intelligent Network Research Section, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea; (S.H.); (C.L.); (T.K.)
| | - ChangSik Lee
- Intelligent Network Research Section, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea; (S.H.); (C.L.); (T.K.)
| | - TaeYeon Kim
- Intelligent Network Research Section, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea; (S.H.); (C.L.); (T.K.)
| | - Man Sik Park
- Department of Statistics, Sungshin Women’s University, Seoul 02844, Korea;
| | - Seoksu Hong
- Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Korea; (B.J.); (J.K.)
| | - Tae-Young Heo
- Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Korea; (B.J.); (J.K.)
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55
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Nakagawa T, Ishida M, Naito J, Nagai A, Yamaguchi S, Onoda K. Prediction of conversion to Alzheimer's disease using deep survival analysis of MRI images. Brain Commun 2020; 2:fcaa057. [PMID: 32954307 PMCID: PMC7425528 DOI: 10.1093/braincomms/fcaa057] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/18/2020] [Accepted: 04/15/2020] [Indexed: 12/24/2022] Open
Abstract
The prediction of the conversion of healthy individuals and those with mild cognitive impairment to the status of active Alzheimer’s disease is a challenging task. Recently, a survival analysis based upon deep learning was developed to enable predictions regarding the timing of an event in a dataset containing censored data. Here, we investigated whether a deep survival analysis could similarly predict the conversion to Alzheimer’s disease. We selected individuals with mild cognitive impairment and cognitively normal subjects and used the grey matter volumes of brain regions in these subjects as predictive features. We then compared the prediction performances of the traditional standard Cox proportional-hazard model, the DeepHit model and our deep survival model based on a Weibull distribution. Our model achieved a maximum concordance index of 0.835, which was higher than that yielded by the Cox model and comparable to that of the DeepHit model. To our best knowledge, this is the first report to describe the application of a deep survival model to brain magnetic resonance imaging data. Our results demonstrate that this type of analysis could successfully predict the time of an individual’s conversion to Alzheimer’s disease.
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Affiliation(s)
- Tomonori Nakagawa
- Department of Neurology, Masuda Red Cross Hospital, Masuda 698-8501, Japan
| | - Manabu Ishida
- Department of Neurology, Shimane University, Izumo 693-8501, Japan.,ERISA Corporation, Matsue 690-0816, Japan
| | | | - Atsushi Nagai
- Department of Neurology, Shimane University, Izumo 693-8501, Japan
| | - Shuhei Yamaguchi
- Department of Neurology, Shimane University, Izumo 693-8501, Japan
| | - Keiichi Onoda
- Department of Neurology, Shimane University, Izumo 693-8501, Japan.,Department of Psychology, Otemon Gakuin University, Osaka 567-8502, Japan
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56
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Chen T, Chen Y, Yuan M, Gerstein M, Li T, Liang H, Froehlich T, Lu L. The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study. JMIR Med Inform 2020; 8:e15767. [PMID: 32041690 PMCID: PMC7244998 DOI: 10.2196/15767] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 12/01/2019] [Accepted: 02/09/2020] [Indexed: 01/28/2023] Open
Abstract
Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. Objective This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. Methods We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. Results On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. Conclusions Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.
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Affiliation(s)
- Tao Chen
- School of Information Management, Wuhan University, Wuhan, China.,School of Information Technology, Shangqiu Normal University, Shangqiu, China
| | - Ye Chen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Mengxue Yuan
- School of Information Management, Wuhan University, Wuhan, China
| | - Mark Gerstein
- Program in Neurodevelopment and Regeneration, Yale University, New Haven, CT, United States.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, United States.,Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States.,Department of Computer Science, Yale University, New Haven, CT, United States
| | - Tingyu Li
- Children Nutrition Research Center, Chongqing, China.,Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, China
| | - Huiying Liang
- Guangzhou Women and Children's Medical Center, Guangzhou, China.,Guangzhou Medical University, Guangzhou, China
| | - Tanya Froehlich
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Long Lu
- School of Information Management, Wuhan University, Wuhan, China.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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57
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An N, Ding H, Yang J, Au R, Ang TFA. Deep ensemble learning for Alzheimer's disease classification. J Biomed Inform 2020; 105:103411. [PMID: 32234546 PMCID: PMC9760486 DOI: 10.1016/j.jbi.2020.103411] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 02/29/2020] [Accepted: 03/23/2020] [Indexed: 01/01/2023]
Abstract
Ensemble learning uses multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With the growing popularity of deep learning technologies, researchers have started to ensemble these technologies for various purposes. Few, if any, however, have used the deep learning approach as a means to ensemble Alzheimer's disease classification algorithms. This paper presents a deep ensemble learning framework that aims to harness deep learning algorithms to integrate multisource data and tap the 'wisdom of experts'. At the voting layer, two sparse autoencoders are trained for feature learning to reduce the correlation of attributes and diversify the base classifiers ultimately. At the stacking layer, a nonlinear feature-weighted method based on a deep belief network is proposed to rank the base classifiers, which may violate the conditional independence. The neural network is used as a meta classifier. At the optimizing layer, over-sampling and threshold-moving are used to cope with the cost-sensitive problem. Optimized predictions are obtained based on an ensemble of probabilistic predictions by similarity calculation. The proposed deep ensemble learning framework is used for Alzheimer's disease classification. Experiments with the clinical dataset from National Alzheimer's Coordinating Center demonstrate that the classification accuracy of our proposed framework is 4% better than six well-known ensemble approaches, including the standard stacking algorithm as well. Adequate coverage of more accurate diagnostic services can be provided by utilizing the wisdom of averaged physicians. This paper points out a new way to boost the primary care of Alzheimer's disease from the view of machine learning.
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Affiliation(s)
- Ning An
- Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology, Hefei, China; School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China.
| | - Huitong Ding
- Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology, Hefei, China; School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China; School of Medicine, Boston University, Boston, USA.
| | - Jiaoyun Yang
- Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology, Hefei, China; School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China.
| | - Rhoda Au
- School of Medicine, Boston University, Boston, USA; School of Public Health, Boston University, Boston, USA.
| | - Ting F A Ang
- School of Medicine, Boston University, Boston, USA.
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58
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Lian C, Liu M, Zhang J, Shen D. Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020; 42:880-893. [PMID: 30582529 PMCID: PMC6588512 DOI: 10.1109/tpami.2018.2889096] [Citation(s) in RCA: 169] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.
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Affiliation(s)
- Chunfeng Lian
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jun Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
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59
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A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051894] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.
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60
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Bhasin H, Agrawal RK. A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment. BMC Med Inform Decis Mak 2020; 20:37. [PMID: 32085774 PMCID: PMC7035729 DOI: 10.1186/s12911-020-1055-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 02/14/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data. METHODS This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. RESULTS The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. CONCLUSION The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.
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Affiliation(s)
- Harsh Bhasin
- School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Ramesh Kumar Agrawal
- School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India
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61
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Zhou T, Thung KH, Liu M, Shi F, Zhang C, Shen D. Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Med Image Anal 2020; 60:101630. [PMID: 31927474 PMCID: PMC8260095 DOI: 10.1016/j.media.2019.101630] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 12/15/2019] [Accepted: 12/19/2019] [Indexed: 12/21/2022]
Abstract
Fusing multi-modality data is crucial for accurate identification of brain disorder as different modalities can provide complementary perspectives of complex neurodegenerative disease. However, there are at least four common issues associated with the existing fusion methods. First, many existing fusion methods simply concatenate features from each modality without considering the correlations among different modalities. Second, most existing methods often make prediction based on a single classifier, which might not be able to address the heterogeneity of the Alzheimer's disease (AD) progression. Third, many existing methods often employ feature selection (or reduction) and classifier training in two independent steps, without considering the fact that the two pipelined steps are highly related to each other. Forth, there are missing neuroimaging data for some of the participants (e.g., missing PET data), due to the participants' "no-show" or dropout. In this paper, to address the above issues, we propose an early AD diagnosis framework via novel multi-modality latent space inducing ensemble SVM classifier. Specifically, we first project the neuroimaging data from different modalities into a latent space, and then map the learned latent representations into the label space to learn multiple diversified classifiers. Finally, we obtain the more reliable classification results by using an ensemble strategy. More importantly, we present a Complete Multi-modality Latent Space (CMLS) learning model for complete multi-modality data and also an Incomplete Multi-modality Latent Space (IMLS) learning model for incomplete multi-modality data. Extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our proposed models outperform other state-of-the-art methods.
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Affiliation(s)
- Tao Zhou
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27599, USA; Inception Institute of Artificial Intelligence, Abu Dhabi 51133, United Arab Emirates.
| | - Kim-Han Thung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Feng Shi
- United Imaging Intelligence, Shanghai, China.
| | - Changqing Zhang
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, 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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Tabarestani S, Aghili M, Eslami M, Cabrerizo M, Barreto A, Rishe N, Curiel RE, Loewenstein D, Duara R, Adjouadi M. A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study. Neuroimage 2020; 206:116317. [PMID: 31678502 PMCID: PMC11167621 DOI: 10.1016/j.neuroimage.2019.116317] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 10/24/2019] [Accepted: 10/26/2019] [Indexed: 01/19/2023] Open
Abstract
Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algorithm for predicting the progression of Alzheimer's disease using a distributed multimodal, multitask learning method. More specifically, each individual task is defined as a regression model, which predicts cognitive scores at a single time point. Since the prediction tasks for multiple intervals are related to each other in chronological order, multitask regression models have been developed to track the relationship between subsequent tasks. Furthermore, since subjects have various combinations of recording modalities together with other genetic, neuropsychological and demographic risk factors, special attention is given to the fact that each modality may experience a specific sparsity pattern. The model is hence generalized by exploiting multiple individual multitask regression coefficient matrices for each modality. The outcome for each independent modality-specific learner is then integrated with complementary information, known as risk factor parameters, revealing the most prevalent trends of the multimodal data. This new feature space is then used as input to the gradient boosting kernel in search for a more accurate prediction. This proposed model not only captures the complex relationships between the different feature representations, but it also ignores any unrelated information which might skew the regression coefficients. Comparative assessments are made between the performance of the proposed method with several other well-established methods using different multimodal platforms. The results indicate that by capturing the interrelatedness between the different modalities and extracting only relevant information in the data, even in an incomplete longitudinal dataset, will yield minimized prediction errors.
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Affiliation(s)
- Solale Tabarestani
- Center for Advanced Technology and Education (CATE), Florida International University, Miami, FL, USA; Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
| | - Maryamossadat Aghili
- Center for Advanced Technology and Education (CATE), Florida International University, Miami, FL, USA; School of Computing and Information Sciences, Florida International University, Miami, FL, USA
| | - Mohammad Eslami
- Center for Advanced Technology and Education (CATE), Florida International University, Miami, FL, USA; Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- Center for Advanced Technology and Education (CATE), Florida International University, Miami, FL, USA; Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Armando Barreto
- Center for Advanced Technology and Education (CATE), Florida International University, Miami, FL, USA; Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Naphtali Rishe
- School of Computing and Information Sciences, Florida International University, Miami, FL, USA
| | - Rosie E Curiel
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA
| | - David Loewenstein
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA; Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Ranjan Duara
- 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA; Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Malek Adjouadi
- Center for Advanced Technology and Education (CATE), Florida International University, Miami, FL, USA; Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA
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Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS, AND APPLICATIONS 2020; 16:1-35. [DOI: 10.1145/3344998] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 07/01/2019] [Indexed: 08/30/2023]
Abstract
Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
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Affiliation(s)
- M. Tanveer
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - B. Richhariya
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - R. U. Khan
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - A. H. Rashid
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore 8 School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India
| | - P. Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - M. Prasad
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| | - C. T. Lin
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
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64
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Golriz Khatami S, Robinson C, Birkenbihl C, Domingo-Fernández D, Hoyt CT, Hofmann-Apitius M. Challenges of Integrative Disease Modeling in Alzheimer's Disease. Front Mol Biosci 2020; 6:158. [PMID: 31993440 PMCID: PMC6971060 DOI: 10.3389/fmolb.2019.00158] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Christine Robinson
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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65
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Rallabandi VS, Tulpule K, Gattu M. Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100305] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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66
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Bucholc M, Ding X, Wang H, Glass DH, Wang H, Prasad G, Maguire LP, Bjourson AJ, McClean PL, Todd S, Finn DP, Wong-Lin K. A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. EXPERT SYSTEMS WITH APPLICATIONS 2019; 130:157-171. [PMID: 31402810 PMCID: PMC6688646 DOI: 10.1016/j.eswa.2019.04.022] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.
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Affiliation(s)
- Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Xuemei Ding
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
- Fujian Provincial Engineering Technology Research Centre for Public Service Big Data Mining and Application, College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, 350108, China
| | - Haiying Wang
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - David H. Glass
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - Hui Wang
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Anthony J. Bjourson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, United Kingdom
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Northern Ireland, United Kingdom
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, and NCBES Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
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Kwak K, Yun HJ, Park G, Lee JM. Multi-Modality Sparse Representation for Alzheimer's Disease Classification. J Alzheimers Dis 2019; 65:807-817. [PMID: 29562503 DOI: 10.3233/jad-170338] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) and mild cognitive impairment (MCI) are age-related neurodegenerative diseases characterized by progressive loss of memory and irreversible cognitive functions. The hippocampus, a brain area critical for learning and memory processes, is especially susceptible to damage at early stages of AD. OBJECTIVE We aimed to develop prediction model using a multi-modality sparse representation approach. METHODS We proposed a sparse representation approach to the hippocampus using structural T1-weighted magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose-positron emission tomography (FDG-PET) to distinguish AD/MCI from healthy control subjects (HCs). We considered structural and function information for the hippocampus and applied a sparse patch-based approach to effectively reduce the dimensions of neuroimaging biomarkers. RESULTS In experiments using Alzheimer's Disease Neuroimaging Initiative data, our proposed method demonstrated more reliable than previous classification studies. The effects of different parameters on segmentation accuracy were also evaluated. The mean classification accuracy obtained with our proposed method was 0.94 for AD/HCs, 0.82 for MCI/HCs, and 0.86 for AD/MCI. CONCLUSION We extracted multi-modal features from automatically defined hippocampal regions of training subjects and found this method to be discriminative and robust for AD and MCI classification. The extraction of features in T1 and FDG-PET images is expected to improve classification performance due to the relationship between brain structure and function.
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Affiliation(s)
- Kichang Kwak
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gilsoon Park
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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Seo K, Pan R, Lee D, Thiyyagura P, Chen K. Visualizing Alzheimer's disease progression in low dimensional manifolds. Heliyon 2019; 5:e02216. [PMID: 31406946 PMCID: PMC6684517 DOI: 10.1016/j.heliyon.2019.e02216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 01/05/2019] [Accepted: 07/30/2019] [Indexed: 01/18/2023] Open
Abstract
While tomographic neuroimaging data is information rich, objective, and with high sensitivity in the study of brain diseases such as Alzheimer's disease (AD), its direct use in clinical practice and in regulated clinical trial (CT) still has many challenges. Taking CT as an example, unless the relevant policy and the perception of the primary outcome measures change, the need to construct univariate indices (out of the 3-D imaging data) to serve as CT's primary outcome measures will remain the focus of active research. More relevant to this current study, an overall global index that summarizes multiple complicated features from neuroimages should be developed in order to provide high diagnostic accuracy and sensitivity in tracking AD progression over time in clinical setting. Such index should also be practically intuitive and logically explainable to patients and their families. In this research, we propose a new visualization tool, derived from the manifold-based nonlinear dimension reduction of brain MRI features, to track AD progression over time. In specific, we investigate the locally linear embedding (LLE) method using a dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI), which includes the longitudinal MRIs from 562 subjects. About 20% of them progressed to the next stage of dementia. Using only the baseline data of cognitively unimpaired (CU) and AD subjects, LLE reduces the feature dimension to two and a subject's AD progression path can be plotted in this low dimensional LLE feature space. In addition, the likelihood of being categorized to AD is indicated by color. This LLE map is a new data visualization tool that can assist in tracking AD progression over time.
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Affiliation(s)
- Kangwon Seo
- Department of Industrial and Manufacturing Systems Engineering and Department of Statistics, University of Missouri, USA
| | - Rong Pan
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, USA
| | - Dongjin Lee
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, USA
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69
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A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis. Symmetry (Basel) 2019. [DOI: 10.3390/sym11080979] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Epilepsy is a central nervous system disorder that results in asymmetries of brain regional activation and connectivity patterns. The detection of these abnormalities is oftentimes challenging and requires identification of robust bio-markers that are representative of disease activity. Functional Magnetic Resonance Imaging (fMRI) is one of the several methods that can be used to detect such bio-markers. fMRI has a high spatial resolution which makes it a suitable candidate for designing computational methods for computer-aided biomarker discovery. In this paper, we present a computational framework for analyzing fMRI data consisting of 100 epileptic and 80 healthy patients, with an overall goal to produce a novel bio-marker that is predictive of epilepsy. The proposed method is primarily based on Dissimilarity of Activity (DoA) analysis. We demonstrate that the bio-marker presented in this study can be used to capture asymmetries in activities by detecting any abnormalities in Blood Oxygenated Level Dependent (BOLD) signal. In order to represent all asymmetries (of connectivity and activation patterns), we used functional connectivity analysis (FCA) in conjunction with DoA to find underlying connectivity patterns of the regions. Subsequently, these biomarkers were used to train a Support Vector Machine (SVM) classifier that was able to distinguish between healthy and epileptic patients with 87.8% accuracy. These results demonstrate the applicability of computer-aided methods in complex disease diagnosis by simply utilizing the existing data. With the advent of all modern sensing and imaging techniques, the use of intelligent algorithms and advanced computational methods are increasingly becoming the future of computer-aided diagnosis.
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70
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Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9153156] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer’s Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after Bonferroni’s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%.
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71
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Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment. Magn Reson Imaging 2019; 64:190-199. [PMID: 31319126 DOI: 10.1016/j.mri.2019.07.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 01/12/2023]
Abstract
In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets.
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72
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Zhou RQ, Ji HC, Liu Q, Zhu CY, Liu R. Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers. World J Clin Cases 2019; 7:1611-1622. [PMID: 31367620 PMCID: PMC6658377 DOI: 10.12998/wjcc.v7.i13.1611] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 05/15/2019] [Accepted: 05/16/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The incidence of pancreatic neuroendocrine tumors (PNETs) is now increasing rapidly. The tumor grade of PNETs significantly affects the treatment strategy and prognosis. However, there is still no effective way to non-invasively classify PNET grades. Machine learning (ML) algorithms have shown potential in improving the prediction accuracy using comprehensive data.
AIM To provide a ML approach to predict PNET tumor grade using clinical data.
METHODS The clinical data of histologically confirmed PNET cases between 2012 and 2018 were collected. A method of minimum P for the Chi-square test was used to divide the continuous variables into binary variables. The continuous variables were transformed into binary variables according to the cutoff value, while the P value was minimum. Four classical supervised ML models, including logistic regression, support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) were trained by clinical data, and the models were labeled with the pathological tumor grade of each PNET patient. The performance of each model, including the weight of the different parameters, were evaluated.
RESULTS In total, 91 PNET cases were included in this study, in which 32 were G1, 48 were G2 and 11 were G3. The results showed that there were significant differences among the clinical parameters of patients with different grades. Patients with higher grades tended to have higher values of total bilirubin, alpha fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4. Among the models we used, LDA performed best in predicting the PNET tumor grade. Meanwhile, MLP had the highest recall rate for G3 cases. All of the models stabilized when the sample size was over 70 percent of the total, except for SVM. Different parameters varied in affecting the outcomes of the models. Overall, alanine transaminase, total bilirubin, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4 affected the outcome greater than other parameters.
CONCLUSION ML could be a simple and effective method in non-invasively predicting PNET grades by using the routine data obtained from the results of biochemical and tumor markers.
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Affiliation(s)
- Rui-Quan Zhou
- School of Medicine, Nankai University, Tianjin 300071, China
| | - Hong-Chen Ji
- The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing 100853, China
| | - Qu Liu
- The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing 100853, China
| | - Chun-Yu Zhu
- The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing 100853, China
| | - Rong Liu
- School of Medicine, Nankai University, Tianjin 300071, China
- The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing 100853, China
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73
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Zhang Y, Zhang H, Chen X, Liu M, Zhu X, Lee SW, Shen D. Strength and Similarity Guided Group-level Brain Functional Network Construction for MCI Diagnosis. PATTERN RECOGNITION 2019; 88:421-430. [PMID: 31579344 PMCID: PMC6774624 DOI: 10.1016/j.patcog.2018.12.001] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Sparse representation-based brain functional network modeling often results in large inter-subject variability in the network structure. This could reduce the statistical power in group comparison, or even deteriorate the generalization capability of the individualized diagnosis of brain diseases. Although group sparse representation (GSR) can alleviate such a limitation by increasing network similarity across subjects, it could, in turn, fail in providing satisfactory separability between the subjects from different groups (e.g., patients vs. controls). In this study, we propose to integrate individual functional connectivity (FC) information into the GSR-based network construction framework to achieve higher between-group separability while maintaining the merit of within-group consistency. Our method was based on an observation that the subjects from the same group have generally more similar FC patterns than those from different groups. To this end, we propose our new method, namely "strength and similarity guided GSR (SSGSR)", which exploits both BOLD signal temporal correlation-based "low-order" FC (LOFC) and inter-subject LOFC-profile similarity-based "high-order" FC (HOFC) as two priors to jointly guide the GSR-based network modeling. Extensive experimental comparisons are carried out, with the rs-fMRI data from mild cognitive impairment (MCI) subjects and healthy controls, between the proposed algorithm and other state-of-the-art brain network modeling approaches. Individualized MCI identification results show that our method could achieve a balance between the individually consistent brain functional network construction and the adequately maintained inter-group brain functional network distinctions, thus leading to a more accurate classification result. Our method also provides a promising and generalized solution for the future connectome-based individualized diagnosis of brain disease.
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Affiliation(s)
- Yu Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Psychiatry and Behavior Sciences, Stanford University, Stanford, CA 94305, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiaobo Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiaofeng Zhu
- Guangxi Key Lab of MIMS, Guangxi Normal University, Guilin 541004, Guangxi, P.R. China
- Institute of Natural and Mathematical Sciences, Massey University Albany Campus, Auckland 0745, New Zealand
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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Wang X, Ren P, Mapstone M, Conwell Y, Porsteinsson AP, Foxe JJ, Raizada RDS, Lin F. Identify a shared neural circuit linking multiple neuropsychiatric symptoms with Alzheimer's pathology. Brain Imaging Behav 2019; 13:53-64. [PMID: 28913718 PMCID: PMC5854501 DOI: 10.1007/s11682-017-9767-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Neuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD)-associated neurodegeneration. However, NPS lack a consistent relationship with AD pathology. It is unknown whether any common neural circuits can link these clinically disparate while mechanistically similar features with AD pathology. Here, we explored the neural circuits of NPS in AD-associated neurodegeneration using multivariate pattern analysis (MVPA) of resting-state functional MRI data. Data from 98 subjects (70 amnestic mild cognitive impairment and 28 AD subjects) were obtained. The top 10 regions differentiating symptom presence across NPS were identified, which were mostly the fronto-limbic regions (medial prefrontal cortex, caudate, etc.). These 10 regions' functional connectivity classified symptomatic subjects across individual NPS at 69.46-81.27%, and predicted multiple NPS (indexed by Neuropsychiatric Symptom Questionnaire-Inventory) and AD pathology (indexed by baseline and change of beta-amyloid/pTau ratio) all above 70%. Our findings suggest a fronto-limbic dominated neural circuit that links multiple NPS and AD pathology. With further examination of the structural and pathological changes within the circuit, the circuit may shed light on linking behavioral disturbances with AD-associated neurodegeneration.
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Affiliation(s)
- Xixi Wang
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, 14627, USA
| | - Ping Ren
- School of Nursing, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Mark Mapstone
- Department of Neurology, University of California-Irvine, Irvine, CA, 92697, USA
| | - Yeates Conwell
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Anton P Porsteinsson
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - John J Foxe
- Department of Neuroscience, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, 14642, USA
| | - Rajeev D S Raizada
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, 14627, USA
| | - Feng Lin
- School of Nursing, University of Rochester Medical Center, Rochester, NY, 14642, USA.
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, 14642, USA.
- Department of Neuroscience, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, 14642, USA.
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, 14627, USA.
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75
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Rady EHA, Anwar AS. Prediction of kidney disease stages using data mining algorithms. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100178] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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76
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Qiao J, Lv Y, Cao C, Wang Z, Li A. Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis. Front Aging Neurosci 2018; 10:417. [PMID: 30618723 PMCID: PMC6304436 DOI: 10.3389/fnagi.2018.00417] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 12/03/2018] [Indexed: 12/11/2022] Open
Abstract
Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer’s disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.
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Affiliation(s)
- Jianping Qiao
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Institute of Data Science and Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Yingru Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chongfeng Cao
- Department of Emergency, Jinan Central Hospital Affiliated to Shandong University, Jinan, China
| | - Zhishun Wang
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Anning Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
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77
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Rogers CE, Lean RE, Wheelock MD, Smyser CD. Aberrant structural and functional connectivity and neurodevelopmental impairment in preterm children. J Neurodev Disord 2018; 10:38. [PMID: 30541449 PMCID: PMC6291944 DOI: 10.1186/s11689-018-9253-x] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 11/14/2018] [Indexed: 12/15/2022] Open
Abstract
Background Despite advances in antenatal and neonatal care, preterm birth remains a leading cause of neurological disabilities in children. Infants born prematurely, particularly those delivered at the earliest gestational ages, commonly demonstrate increased rates of impairment across multiple neurodevelopmental domains. Indeed, the current literature establishes that preterm birth is a leading risk factor for cerebral palsy, is associated with executive function deficits, increases risk for impaired receptive and expressive language skills, and is linked with higher rates of co-occurring attention deficit hyperactivity disorder, anxiety, and autism spectrum disorders. These same infants also demonstrate elevated rates of aberrant cerebral structural and functional connectivity, with persistent changes evident across advanced magnetic resonance imaging modalities as early as the neonatal period. Emerging findings from cross-sectional and longitudinal investigations increasingly suggest that aberrant connectivity within key functional networks and white matter tracts may underlie the neurodevelopmental impairments common in this population. Main body This review begins by highlighting the elevated rates of neurodevelopmental disorders across domains in this clinical population, describes the patterns of aberrant structural and functional connectivity common in prematurely-born infants and children, and then reviews the increasingly established body of literature delineating the relationship between these brain abnormalities and adverse neurodevelopmental outcomes. We also detail important, typically understudied, clinical, and social variables that may influence these relationships among preterm children, including heritability and psychosocial risks. Conclusion Future work in this domain should continue to leverage longitudinal evaluations of preterm infants which include both neuroimaging and detailed serial neurodevelopmental assessments to further characterize relationships between imaging measures and impairment, information necessary for advancing our understanding of modifiable risk factors underlying these disorders and best practices for improving neurodevelopmental trajectories in this high-risk clinical population.
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Affiliation(s)
- Cynthia E Rogers
- Departments of Psychiatry and Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8504, St. Louis, MO, 63110, USA.
| | - Rachel E Lean
- Departments of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8504, St. Louis, MO, 63110, USA
| | - Muriah D Wheelock
- Departments of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8504, St. Louis, MO, 63110, USA
| | - Christopher D Smyser
- Departments of Neurology, Pediatrics and Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8111, St. Louis, MO, 63110, USA
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78
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Long Z, Huang J, Li B, Li Z, Li Z, Chen H, Jing B. A Comparative Atlas-Based Recognition of Mild Cognitive Impairment With Voxel-Based Morphometry. Front Neurosci 2018; 12:916. [PMID: 30574064 PMCID: PMC6291519 DOI: 10.3389/fnins.2018.00916] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 11/21/2018] [Indexed: 01/18/2023] Open
Abstract
An accurate and reliable brain partition atlas is vital to quantitatively investigate the structural and functional abnormalities in mild cognitive impairment (MCI), generally considered to be a prodromal phase of Alzheimer's disease. In this paper, we proposed an automated structural classification method to identify MCI from healthy controls (HC) and investigated whether the classification performance was dependent on the brain parcellation schemes, including Automated Anatomical Labeling (AAL-90) atlas, Brainnetome (BN-246) atlas, and AAL-1024 atlas. In detail, structural magnetic resonance imaging (sMRI) data of 69 MCI patients and 63 HC matched well on gender, age, and education level were collected and analyzed with voxel-based morphometry method first, then the volume features of every region of interest (ROI) belonging to the above-mentioned three atlases were calculated and compared between MCI and HC groups, respectively. At last, the abnormal volume features were selected as the classification features for a proposed support vector machine based identification method. After the leave-one-out cross-validation to estimate the classification performance, our results reported accuracies of 83, 92, and 89% with AAL-90, BN-246, and AAL-1024 atlas, respectively, suggesting that future studies should pay more attention to the selection of brain partition schemes in the atlas-based studies. Furthermore, the consistent atrophic brain regions among three atlases were predominately located at bilateral hippocampus, bilateral parahippocampal, bilateral amygdala, bilateral cingulate gyrus, left angular gyrus, right superior frontal gyrus, right middle frontal gyrus, left inferior frontal gyrus, and left precentral gyrus.
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Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Jinchang Huang
- Department of Acupuncture and Minimally Invasive Oncology, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Bo Li
- Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing, China.,Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Zuojia Li
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zihao Li
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hongwen Chen
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
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79
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Li S, Yang N, Li B, Zhou Z, Hao H, Folkert MR, Iyengar P, Westover K, Choy H, Timmerman R, Jiang S, Wang J. A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT. Med Image Anal 2018; 50:106-116. [PMID: 30266009 PMCID: PMC6237633 DOI: 10.1016/j.media.2018.09.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 07/20/2018] [Accepted: 09/07/2018] [Indexed: 12/27/2022]
Abstract
We developed a kernelled support tensor machine (KSTM)-based model with tumor tensors derived from pre-treatment PET and CT imaging as input to predict distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). The patient cohort included 110 early stage NSCLC patients treated with SBRT, 25 of whom experienced failure at distant sites. Three-dimensional tumor tensors were constructed and used as input for the KSTM-based classifier. A KSTM iterative algorithm with a convergent proof was developed to train the weight vectors for every mode of the tensor for the classifier. In contrast to conventional radiomics approaches that rely on handcrafted imaging features, the KSTM-based classifier uses 3D imaging as input, taking full advantage of the imaging information. The KSTM-based classifier preserves the intrinsic 3D geometry structure of the medical images and the correlation in the original images and trains the classification hyper-plane in an adaptive feature tensor space. The KSTM-based predictive algorithm was compared with three conventional machine learning models and three radiomics approaches. For PET and CT, the KSTM-based predictive method achieved the highest prediction results among the seven methods investigated in this study based on 10-fold cross validation and independent testing.
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Affiliation(s)
- Shulong Li
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image, Processing, Southern Medical University, Guangzhou 510515, China
| | - Ning Yang
- Department of Medical Imaging, Guangdong No.2 Provincial People's Hospital, Guangzhou 510317, China
| | - Bin Li
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image, Processing, Southern Medical University, Guangzhou 510515, China
| | - Zhiguo Zhou
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA
| | - Hongxia Hao
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Michael R Folkert
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA
| | - Puneeth Iyengar
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA
| | - Kenneth Westover
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA
| | - Hak Choy
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA
| | - Robert Timmerman
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA.
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80
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Zeng N, Qiu H, Wang Z, Liu W, Zhang H, Li Y. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.09.001] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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81
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Xu L, Liang G, Liao C, Chen GD, Chang CC. An Efficient Classifier for Alzheimer's Disease Genes Identification. Molecules 2018; 23:molecules23123140. [PMID: 30501121 PMCID: PMC6321377 DOI: 10.3390/molecules23123140] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 11/17/2018] [Accepted: 11/19/2018] [Indexed: 11/16/2022] Open
Abstract
Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518055, China.
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518055, China.
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
| | - Gin-Den Chen
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
| | - Chi-Chang Chang
- School of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.
- IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
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82
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Wang Z, Zheng Y, Zhu DC, Bozoki AC, Li T. Classification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2018; 6:1801009. [PMID: 30405975 PMCID: PMC6204925 DOI: 10.1109/jtehm.2018.2874887] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 07/10/2018] [Accepted: 09/28/2018] [Indexed: 11/24/2022]
Abstract
This paper proposes a robust method for the Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control subject classification under size limited fMRI data samples by exploiting the brain network connectivity pattern analysis. First, we select the regions of interest (ROIs) within the default mode network and calculate the correlation coefficients between all possible ROI pairs to form a feature vector for each subject. Second, we propose a regularized linear discriminant analysis (LDA) approach to reduce the noise effect due to the limited sample size. The feature vectors are then projected onto a one-dimensional axis using the proposed regularized LDA. Finally, an AdaBoost classifier is applied to carry out the classification task. The numerical analysis demonstrates that the purposed approach can increase the classification accuracy significantly. Our analysis confirms the previous findings that the hippocampus and the isthmus of the cingulate cortex are closely involved in the development of AD and MCI.
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Affiliation(s)
- Zhe Wang
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMI48823USA
| | - Yu Zheng
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMI48823USA
| | - David C. Zhu
- Departments of Radiology and PsychologyMichigan State UniversityEast LansingMI48823USA
| | - Andrea C. Bozoki
- Department of Neurology and OphthalmologyMichigan State UniversityEast LansingMI48823USA
| | - Tongtong Li
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMI48823USA
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83
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Park E, Chang HJ, Nam HS. A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors. Front Neurol 2018; 9:699. [PMID: 30245663 PMCID: PMC6137617 DOI: 10.3389/fneur.2018.00699] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 08/02/2018] [Indexed: 11/13/2022] Open
Abstract
Bayesian network is an increasingly popular method in modeling uncertain and complex problems, because its interpretability is often more useful than plain prediction. To satisfy the core requirement in medical research to obtain interpretable prediction with high accuracy, we constructed an inference engine for post-stroke outcomes based on Bayesian network classifiers. The prediction system that was trained on data of 3,605 patients with acute stroke forecasts the functional independence at 3 months and the mortality 1 year after stroke. Feature selection methods were applied to eliminate less relevant and redundant features from 76 risk variables. The Bayesian network classifiers were trained with a hill-climbing searching for the qualified network structure and parameters measured by maximum description length. We evaluated and optimized the proposed system to increase the area under the receiver operating characteristic curve (AUC) while ensuring acceptable sensitivity for the class-imbalanced data. The performance evaluation demonstrated that the Bayesian network with selected features by wrapper-type feature selection can predict 3-month functional independence with an AUC of 0.889 using only 19 risk variables and 1-year mortality with an AUC of 0.893 using 24 variables. The Bayesian network with 50 features filtered by information gain can predict 3-month functional independence with an AUC of 0.875 and 1-year mortality with an AUC of 0.895. We also built an online prediction service, Yonsei Stroke Outcome Inference System, to substantialize the proposed solution for patients with stroke.
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Affiliation(s)
- Eunjeong Park
- Cardiovascular Research Institute, College of Medicine, Yonsei University, Seoul, South Korea
| | - Hyuk-Jae Chang
- Department of Cardiology, College of Medicine, Yonsei University, Seoul, South Korea
| | - Hyo Suk Nam
- Department of Neurology, College of Medicine, Yonsei University, Seoul, South Korea
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84
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Kim KW, Choi JD, Lee H, Lee NK, Park S, Chin J, Lee BH, Shin J, Kim Y, Jang H, Choi JH, Na DL. Social Event Memory Test (SEMT): A Video-based Memory Test for Predicting Amyloid Positivity for Alzheimer's Disease. Sci Rep 2018; 8:10421. [PMID: 29991732 PMCID: PMC6039498 DOI: 10.1038/s41598-018-28768-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 06/20/2018] [Indexed: 12/02/2022] Open
Abstract
Recent improvements in neuroimaging and molecular markers of Alzheimer’s disease (AD) have aided diagnosis in the early stage of the disease, which greatly increases the chance for successful prevention and treatment. However, the expanding resources for AD diagnosis are unlikely to benefit all elderly due to economic burden. Here, we aimed to develop an inexpensive and sensitive method to detect early-stage AD. A scenario for real-world social event memory test (SEMT) was created and filmed in 360° video. Participants watched the 7-min video through head-mounted display (HMD) and then answered questionnaire about the video. We categorized the SEMT score into recall, recognition, and place-matching scores and compared them to scores on the Mini-Mental State Examination and Seoul Verbal Learning Test. Using the SEMT scores, we built a logistic regression model that discriminated between amyloid positivity and negativity of the participants, with a cross-validation AUC. Furthermore, a classifier was created using support vector machine, which produced 93.8–95.1% sensitivity in classifying individuals into four groups of normal, mild cognitive impairment with or without amyloid, and AD elderly. The high correlation between the SEMT score and amyloid positivity in individuals who experienced virtual social gathering through an HMD opens a new possibility for early diagnosis of AD.
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Affiliation(s)
- Ko Woon Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Neurology, Chonbuk National University Medical School & Hospital, Jeonju, Korea
| | - Jong Doo Choi
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyejoo Lee
- Convergence Research Center for Diagnosis, Treatment, and Care System of Dementia, Center for Neuroscience, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Na Kyung Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Seongbeom Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Juhee Chin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Byung Hwa Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jiwon Shin
- Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yeshin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jee Hyun Choi
- Convergence Research Center for Diagnosis, Treatment, and Care System of Dementia, Center for Neuroscience, Korea Institute of Science and Technology, Seoul, Republic of Korea.
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Korea. .,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.
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85
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Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study. BJGP Open 2018; 2:bjgpopen18X101589. [PMID: 30564722 PMCID: PMC6184101 DOI: 10.3399/bjgpopen18x101589] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 03/01/2018] [Indexed: 12/22/2022] Open
Abstract
Background Up to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice. Aim The aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed. Design & setting The study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data. Method Routinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia. Results The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively. Conclusion The results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.
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86
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Toro CAO, Gonzalo Martín C, García-Pedrero A, Menasalvas Ruiz E. Supervoxels-Based Histon as a New Alzheimer's Disease Imaging Biomarker. SENSORS 2018; 18:s18061752. [PMID: 29844294 PMCID: PMC6022184 DOI: 10.3390/s18061752] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 05/22/2018] [Accepted: 05/25/2018] [Indexed: 01/31/2023]
Abstract
Alzheimer’s disease (AD) represents the prevalent type of dementia in the elderly, and is characterized by the presence of neurofibrillary tangles and amyloid plaques that eventually leads to the loss of neurons, resulting in atrophy in specific brain areas. Although the process of degeneration can be visualized through various modalities of medical imaging and has proved to be a valuable biomarker, the accurate diagnosis of Alzheimer’s disease remains a challenge, especially in its early stages. In this paper, we propose a novel classification method for Alzheimer’s disease/cognitive normal discrimination in structural magnetic resonance images (MRI), based on the extension of the concept of histons to volumetric images. The proposed method exploits the relationship between grey matter, white matter and cerebrospinal fluid degeneration by means of a segmentation using supervoxels. The calculated histons are then processed for a reduction in dimensionality using principal components analysis (PCA) and the resulting vector is used to train an support vector machine (SVM) classifier. Experimental results using the OASIS-1 database have proven to be a significant improvement compared to a baseline classification made using the pipeline provided by Clinica software.
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Affiliation(s)
- César A Ortiz Toro
- Centro de Tecnología Biomédica, Campus de Montegancedo, Universidad Politécnica de Madrid, 28233 Pozuelo de Alarcón, Spain.
| | - Consuelo Gonzalo Martín
- Centro de Tecnología Biomédica, Campus de Montegancedo, Universidad Politécnica de Madrid, 28233 Pozuelo de Alarcón, Spain.
| | - Angel García-Pedrero
- Centro de Tecnología Biomédica, Campus de Montegancedo, Universidad Politécnica de Madrid, 28233 Pozuelo de Alarcón, Spain.
| | - Ernestina Menasalvas Ruiz
- Centro de Tecnología Biomédica, Campus de Montegancedo, Universidad Politécnica de Madrid, 28233 Pozuelo de Alarcón, Spain.
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87
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Tagaris A, Kollias D, Stafylopatis A, Tagaris G, Kollias S. Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset. INT J ARTIF INTELL T 2018. [DOI: 10.1142/s0218213018500112] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.
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Affiliation(s)
- Athanasios Tagaris
- School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, Athens, 15780, Greece
| | - Dimitrios Kollias
- School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, Athens, 15780, Greece
| | - Andreas Stafylopatis
- School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, Athens, 15780, Greece
| | - Georgios Tagaris
- Department of Neurology, Georgios Gennimatas General Hospital, Athens, Greece
| | - Stefanos Kollias
- School of Computer Science, University of Lincoln, Lincoln, United Kingdom
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88
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Multi-class Alzheimer's disease classification using image and clinical features. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.02.019] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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89
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Salvatore C, Castiglioni I. A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer’s disease. J Neurosci Methods 2018; 302:58-65. [DOI: 10.1016/j.jneumeth.2017.12.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 12/21/2017] [Accepted: 12/21/2017] [Indexed: 11/27/2022]
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90
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Long Z, Jing B, Guo R, Li B, Cui F, Wang T, Chen H. A Brainnetome Atlas Based Mild Cognitive Impairment Identification Using Hurst Exponent. Front Aging Neurosci 2018; 10:103. [PMID: 29692721 PMCID: PMC5902491 DOI: 10.3389/fnagi.2018.00103] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 03/27/2018] [Indexed: 11/15/2022] Open
Abstract
Mild cognitive impairment (MCI), which generally represents the transition state between normal aging and the early changes related to Alzheimer’s disease (AD), has drawn increasing attention from neuroscientists due that efficient AD treatments need early initiation ahead of irreversible brain tissue damage. Thus effective MCI identification methods are desperately needed, which may be of great importance for the clinical intervention of AD. In this article, the range scaled analysis, which could effectively detect the temporal complexity of a time series, was utilized to calculate the Hurst exponent (HE) of functional magnetic resonance imaging (fMRI) data at a voxel level from 64 MCI patients and 60 healthy controls (HCs). Then the average HE values of each region of interest (ROI) in brainnetome atlas were extracted and compared between MCI and HC. At last, the abnormal average HE values were adopted as the classification features for a proposed support vector machine (SVM) based identification algorithm, and the classification performance was estimated with leave-one-out cross-validation (LOOCV). Our results indicated 83.1% accuracy, 82.8% sensitivity and 83.3% specificity, and an area under curve of 0.88, suggesting that the HE index could serve as an effective feature for the MCI identification. Furthermore, the abnormal HE brain regions in MCI were predominately involved in left middle frontal gyrus, right hippocampus, bilateral parahippocampal gyrus, bilateral amygdala, left cingulate gyrus, left insular gyrus, left fusiform gyrus, left superior parietal gyrus, left orbital gyrus and left basal ganglia.
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Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Ru Guo
- Department of Tuberculosis, Beijing Chest Hospital Capital Medical University, Beijing, China
| | - Bo Li
- Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Feiyi Cui
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Tingting Wang
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hongwen Chen
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
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91
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Giraldo DL, García-Arteaga JD, Cárdenas-Robledo S, Romero E. Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease. Brain Behav 2018; 8:e00942. [PMID: 29670824 PMCID: PMC5893348 DOI: 10.1002/brb3.942] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 01/02/2018] [Accepted: 01/12/2018] [Indexed: 11/10/2022] Open
Abstract
PURPOSE This work presents an automatic characterization of the Alzheimer's disease describing the illness as a multidirectional departure from a baseline defining the control state, being these directions determined by a distance between functional-equivalent anatomical regions. METHODS After a brain parcellation, a region is described by its histogram of gray levels, and the Earth mover's distance establishes how close or far these regions are. The medoid of the control group is set as the reference and any brain is characterized by its set of distances to this medoid. EVALUATION This hypothesis was assessed by separating groups of patients with mild Alzheimer's disease and mild cognitive impairment from control subjects, using a subset of the Open Access Series of Imaging Studies (OASIS) database. An additional experiment evaluated the method generalization and consisted in training with the OASIS data and testing with the Minimal Interval Resonance Imaging in Alzheimer's disease (MIRIAD) database. RESULTS Classification between controls and patients with AD resulted in an equal error rate of 0.1 (90% of sensitivity and specificity at the same time). The automatic ranking of regions resulting is in strong agreement with those regions described as important in clinical practice. Classification with different databases results in a sensitivity of 85% and a specificity of 91%. CONCLUSIONS This method automatically finds out a multidimensional expression of the AD, which is directly related to the anatomical changes in specific areas such as the hippocampus, the amygdala, the planum temporale, and thalamus.
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Affiliation(s)
- Diana L Giraldo
- Computer Imaging and Medical Applications Laboratory - CIM@LAB Universidad Nacional de Colombia Bogotá Colombia
| | - Juan D García-Arteaga
- Computer Imaging and Medical Applications Laboratory - CIM@LAB Universidad Nacional de Colombia Bogotá Colombia
| | | | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory - CIM@LAB Universidad Nacional de Colombia Bogotá Colombia
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92
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Liu J, Li M, Lan W, Wu FX, Pan Y, Wang J. Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:624-632. [PMID: 28114031 DOI: 10.1109/tcbb.2016.2635144] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Regions of interest (ROIs) based classification has been widely investigated for analysis of brain magnetic resonance imaging (MRI) images to assist the diagnosis of Alzheimer's disease (AD) including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Since an ROI representation of brain structures is obtained either by pre-definition or by adaptive parcellation, the corresponding ROI in different brains can be measured. However, due to noise and small sample size of MRI images, representations generated from single or multiple ROIs may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and health controls (HC). In this paper, we employ a whole brain hierarchical network (WBHN) to represent each subject. The whole brain of each subject is divided into 90, 54, 14, and 1 regions based on Automated Anatomical Labeling (AAL) atlas. The connectivity between each pair of regions is computed in terms of Pearson's correlation coefficient and used as classification feature. Then, to reduce the dimensionality of features, we select the features with higher scores. Finally, we use multiple kernel boosting (MKBoost) algorithm to perform the classification. Our proposed method is evaluated on MRI images of 710 subjects (200 AD, 120 MCIc, 160 MCInc, and 230 HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed method achieves an accuracy of 94.65 percent and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.954 for AD/HC classification, an accuracy of 89.63 percent and an AUC of 0.907 for AD/MCI classification, an accuracy of 85.79 percent and an AUC of 0.826 for MCI/HC classification, and an accuracy of 72.08 percent and an AUC of 0.716 for MCIc/MCInc classification, respectively. Our results demonstrate that our proposed method is efficient and promising for clinical applications for the diagnosis of AD via MRI images.
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93
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Wang S, Hu Y, Shen Y, Li H. Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning. Int J Neural Syst 2018; 28:1750036. [DOI: 10.1142/s0129065717500368] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.
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Affiliation(s)
- Shuqiang Wang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P. R. China
| | - Yong Hu
- Department of Orthopaedic Surgery, The University of Hong Kong, 12 Sandy Bay Road, Hong Kong, Duchess of Kent Children’s Hospital, Pokfulam, Hong Kong
| | - Yanyan Shen
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P. R. China
| | - Hanxiong Li
- Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong
- School of Mechanical and Electrical Engineering, Central South University, Changsha, P. R. China
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94
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Glozman T, Solomon J, Pestilli F, Guibas L. Shape-Attributes of Brain Structures as Biomarkers for Alzheimer's Disease. J Alzheimers Dis 2018; 56:287-295. [PMID: 27911322 PMCID: PMC5240557 DOI: 10.3233/jad-160900] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures. We consider Alzheimer’s disease (AD), mild cognitive impairment of individuals who converted to AD within 18 months (MCIc), and normal controls (NC). Our approach uses statistical learning and a feature space consisting of projection-based shape descriptors, allowing for canonical representation of brain regions. Our framework automatically identifies the structures most affected by the disease. We evaluate our results by comparing to other methods using a standardized data set of 375 adults available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our framework is sensitive to identifying the onset of Alzheimer’s disease, achieving up to 88.13% accuracy in classifying MCIc versus NC, outperforming previous methods.
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Affiliation(s)
- Tanya Glozman
- Department of Electrical Engineering, Stanford, CA, USA
| | - Justin Solomon
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Franco Pestilli
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
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95
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Duraisamy B, Shanmugam JV, Annamalai J. Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network. Brain Imaging Behav 2018; 13:87-110. [DOI: 10.1007/s11682-018-9831-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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96
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Ułamek-Kozioł M, Kocki J, Bogucka-Kocka A, Petniak A, Gil-Kulik P, Januszewski S, Bogucki J, Jabłoński M, Furmaga-Jabłońska W, Brzozowska J, Czuczwar SJ, Pluta R. Dysregulation of Autophagy, Mitophagy, and Apoptotic Genes in the Medial Temporal Lobe Cortex in an Ischemic Model of Alzheimer's Disease. J Alzheimers Dis 2018; 54:113-21. [PMID: 27472881 PMCID: PMC5008226 DOI: 10.3233/jad-160387] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Ischemic brain damage is a pathological incident that is often linked with medial temporal lobe cortex injury and finally its atrophy. Post-ischemic brain injury associates with poor prognosis since neurons of selectively vulnerable ischemic brain areas are disappearing by apoptotic program of neuronal death. Autophagy has been considered, after brain ischemia, as a guardian against neurodegeneration. Consequently, we have examined changes in autophagy (BECN 1), mitophagy (BNIP 3), and apoptotic (caspase 3) genes in the medial temporal lobe cortex with the use of quantitative reverse-transcriptase PCR following transient 10-min global brain ischemia in rats with survival 2, 7, and 30 days. The intense significant overexpression of BECN 1 gene was noted on the 2nd day, while on days 7-30 the expression of this gene was still upregulated. BNIP 3 gene was downregulated on the 2nd day, but on days 7-30 post-ischemia, there was a significant reverse tendency. Caspase 3 gene, associated with apoptotic neuronal death, was induced in the same way as BNIP 3 gene after brain ischemia. Thus, the demonstrated changes indicate that the considerable dysregulation of expression of BECN 1, BNIP 3, and caspase 3 genes may be connected with a response of neuronal cells in medial temporal lobe cortex to transient complete brain ischemia.
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Affiliation(s)
- Marzena Ułamek-Kozioł
- First Department of Neurology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Janusz Kocki
- Department of Clinical Genetics, Medical University of Lublin, Lublin, Poland
| | - Anna Bogucka-Kocka
- Department of Biology and Genetics, Medical University of Lublin, Lublin, Poland
| | - Alicja Petniak
- Department of Clinical Genetics, Medical University of Lublin, Lublin, Poland
| | - Paulina Gil-Kulik
- Department of Clinical Genetics, Medical University of Lublin, Lublin, Poland
| | - Sławomir Januszewski
- Laboratory of Ischemic and Neurodegenerative Brain Research, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| | | | - Mirosław Jabłoński
- Department of Rehabilitation and Orthopaedics, Medical University of Lublin, Lublin, Poland
| | | | - Judyta Brzozowska
- Department of Clinical Psychology, Medical University of Lublin, Lublin, Poland
| | | | - Ryszard Pluta
- Laboratory of Ischemic and Neurodegenerative Brain Research, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
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97
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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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98
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Belathur Suresh M, Fischl B, Salat DH. Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease. Hum Brain Mapp 2017; 39:1500-1515. [PMID: 29271096 DOI: 10.1002/hbm.23922] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 11/28/2017] [Accepted: 12/07/2017] [Indexed: 02/04/2023] Open
Abstract
There is great value to use of structural neuroimaging in the assessment of Alzheimer's disease (AD). However, to date, predictive value of structural imaging tend to range between 80% and 90% in accuracy and it is unclear why this is the case given that structural imaging should parallel the pathologic processes of AD. There is a possibility that clinical misdiagnosis relative to the gold standard pathologic diagnosis and/or additional brain pathologies are confounding factors contributing to reduced structural imaging classification accuracy. We examined potential factors contributing to misclassification of individuals with clinically diagnosed AD purely from cortical thickness measures. Correctly classified and incorrectly classified groups were compared across a range of demographic, biological, and neuropsychological data including cerebrospinal fluid biomarkers, amyloid imaging, white matter hyperintensity (WMH) volume, cognitive, and genetic factors. Individual subject analyses suggested that at least a portion of the control individuals misclassified as AD from structural imaging additionally harbor substantial AD biomarker pathology and risk, yet are relatively resistant to cognitive symptoms, likely due to "cognitive reserve," and therefore clinically unimpaired. In contrast, certain clinical control individuals misclassified as AD from cortical thickness had increased WMH volume relative to other controls in the sample, suggesting that vascular conditions may contribute to classification accuracy from cortical thickness measures. These results provide examples of factors that contribute to the accuracy of structural imaging in predicting a clinical diagnosis of AD, and provide important information about considerations for future work aimed at optimizing structural based diagnostic classifiers for AD.
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Affiliation(s)
- Mahanand Belathur Suresh
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India
| | - Bruce Fischl
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, Massachusetts
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99
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Rondina JM, Ferreira LK, de Souza Duran FL, Kubo R, Ono CR, Leite CC, Smid J, Nitrini R, Buchpiguel CA, Busatto GF. Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases. Neuroimage Clin 2017; 17:628-641. [PMID: 29234599 PMCID: PMC5716956 DOI: 10.1016/j.nicl.2017.10.026] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 10/12/2017] [Accepted: 10/24/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). METHODS We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. RESULTS Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. CONCLUSION The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.
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Key Words
- 18F-FDG-PET, 18F-Fluorodeoxyglucose-Positron Emission Tomography
- AAL, Automated Anatomical Labeling (atlas)
- AD, Alzheimer's Disease
- Alzheimer's Disease
- BA, Brodmann's Area
- Brain atlas
- GM, Gray Matter
- MKL, Multiple Kernel Learning
- MKL-ROI, MKL based on regions of interest
- ML, Machine Learning
- MRI
- Multiple kernel learning
- NF, number of features
- NSR, Number of Selected Regions
- PET
- PVE, Partial Volume Effects
- ROI, Region of Interest
- SPECT
- SVM, Support Vector Machine
- T1-MRI, T1-weighted Magnetic Resonance Imaging
- TN, True Negative (specificity - proportion of healthy controls correctly classified)
- TP, True Positive (sensitivity - proportion of patients correctly classified)
- rAUC, Ratio between negative and positive Area Under Curve
- rCBF-SPECT, Regional Cerebral Blood Flow
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Affiliation(s)
- Jane Maryam Rondina
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, UK.
| | - Luiz Kobuti Ferreira
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), University of São Paulo, São Paulo, Brazil
| | - Fabio Luis de Souza Duran
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Rodrigo Kubo
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil
| | - Carla Rachel Ono
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil
| | - Claudia Costa Leite
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil; Department of Radiology, University of North Carolina at Chapel Hill, NC, USA
| | - Jerusa Smid
- Department of Neurology and Cognitive Disorders Reference Center (CEREDIC), University of São Paulo, São Paulo, Brazil
| | - Ricardo Nitrini
- Department of Neurology and Cognitive Disorders Reference Center (CEREDIC), University of São Paulo, São Paulo, Brazil
| | | | - Geraldo F Busatto
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), University of São Paulo, São Paulo, Brazil; Department and Institute of Psychiatry, University of São Paulo, São Paulo, Brazil
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100
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Betzel RF, Bassett DS. Generative models for network neuroscience: prospects and promise. J R Soc Interface 2017; 14:20170623. [PMID: 29187640 PMCID: PMC5721166 DOI: 10.1098/rsif.2017.0623] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/06/2017] [Indexed: 12/22/2022] Open
Abstract
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modelling approach for network neuroscience.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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