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Tang S, Cao P, Huang M, Liu X, Zaiane O. Dual feature correlation guided multi-task learning for Alzheimer's disease prediction. Comput Biol Med 2022; 140:105090. [PMID: 34875406 DOI: 10.1016/j.compbiomed.2021.105090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/22/2021] [Accepted: 11/26/2021] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD) is a gradually progressive neurodegenerative disease affecting cognition functions. Predicting the cognitive scores from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of AD. Despite machine learning algorithms having many successful applications, the prediction model suffers from the so-called curse of dimensionality. Multi-task feature learning (MTFL) has helped tackle this problem incorporating the correlations among multiple clinical cognitive scores. However, MTFL neglects the inherent correlation among brain imaging measures. In order to better predict the cognitive scores and identify stable biomarkers, we first propose a generalized multi-task formulation framework that incorporates the task and feature correlation structures simultaneously. Second, we present a novel feature-aware sparsity-inducing norm (FAS-norm) penalty to incorporate a useful correlation between brain regions by exploiting correlations among features. Three multi-task learning models that incorporate the FAS-norm penalty are proposed following our framework. Finally, the algorithm based on the alternating direction method of multipliers (ADMM) is developed to optimize the non-smooth problems. We comprehensively evaluate the proposed models on the cross-sectional and longitudinal Alzheimer's disease neuroimaging initiative datasets. The inputs are the thickness measures and the volume measures of the cortical regions of interest. Compared with MTFL, our methods achieve an average decrease of 4.28% in overall error in the cross-sectional analysis and an average decrease of 7.97% in the Alzheimer's Disease Assessment Scale cognitive total score longitudinal analysis. Moreover, our methods identify sensitive and stable biomarkers to physicians, such as the hippocampus, lateral ventricle, and corpus callosum.
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Affiliation(s)
- Shanshan Tang
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning, 110819, China
| | - Peng Cao
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Min Huang
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning, 110819, China.
| | - Xiaoli Liu
- Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Osmar Zaiane
- Department of Computing Science, University of Alberta, Edmonton, Canada
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Arco JE, Ortiz A, Ramírez J, Zhang YD, Górriz JM. Tiled Sparse Coding in Eigenspaces for Image Classification. Int J Neural Syst 2021; 32:2250007. [PMID: 34967705 DOI: 10.1142/s0129065722500071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. blackThese alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are blackfirst partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. blackThen, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. blackOur system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.
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Affiliation(s)
- Juan E Arco
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Andrés Ortiz
- Department of Communications Engineering, University of Malaga 29010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester LE1 7RH, UK
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
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Martinez-Murcia FJ, Ortiz A, Ramírez J, Górriz JM, Cruz R. Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.04.148] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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4
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Frye BM, Craft S, Register TC, Andrews RN, Appt SE, Vitolins MZ, Uberseder B, Silverstein‐Metzler MG, Chen H, Whitlow CT, Kim J, Barcus RA, Lockhart SN, Hoscheidt S, Say BM, Corbitt SE, Shively CA. Diet, psychosocial stress, and Alzheimer's disease-related neuroanatomy in female nonhuman primates. Alzheimers Dement 2021; 17:733-744. [PMID: 33270373 PMCID: PMC8119381 DOI: 10.1002/alz.12232] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/12/2020] [Accepted: 10/19/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Associations between diet, psychosocial stress, and neurodegenerative disease, including Alzheimer's disease (AD), have been reported, but causal relationships are difficult to determine in human studies. METHODS We used structural magnetic resonance imaging in a well-validated non-human primate model of AD-like neuropathology to examine the longitudinal effects of diet (Mediterranean vs Western) and social subordination stress on brain anatomy, including global volumes, cortical thicknesses and volumes, and 20 individual regions of interest (ROIs). RESULTS Western diet resulted in greater cortical thicknesses, total brain volumes, and gray matter, and diminished cerebrospinal fluid and white matter volumes. Socially stressed subordinates had smaller whole brain volumes but larger ROIs relevant to AD than dominants. DISCUSSION The observation of increased size of AD-related brain areas is consistent with similar reports of mid-life volume increases predicting increased AD risk later in life. While the biological mechanisms underlying the findings require future investigation, these observations suggest that Western diet and psychosocial stress instigate pathologic changes that increase risk of AD-associated neuropathology, whereas the Mediterranean diet may protect the brain.
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Affiliation(s)
- Brett M. Frye
- Department of Pathology/Comparative MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Suzanne Craft
- Wake Forest Alzheimer's Disease Research CenterWinston‐SalemNorth CarolinaUSA
| | - Thomas C. Register
- Department of Pathology/Comparative MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Rachel N. Andrews
- Department of Pathology/Comparative MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Susan E. Appt
- Department of Pathology/Comparative MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Mara Z. Vitolins
- Department of Epidemiology and PreventionWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Beth Uberseder
- Department of Pathology/Comparative MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | | | - Haiying Chen
- Department of Biostatistics and Data ScienceWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | | | - Jeongchul Kim
- Department of RadiologyWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Richard A. Barcus
- Department of RadiologyWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Samuel N. Lockhart
- Wake Forest Alzheimer's Disease Research CenterWinston‐SalemNorth CarolinaUSA
| | | | - Brandon M. Say
- Department of PathologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Sarah E. Corbitt
- Biomedical SciencesMS programIntegrative Physiology and PharmacologyAdult Behavioral HealthUSA
| | - Carol A. Shively
- Department of Pathology/Comparative MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
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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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Ruiz E, Ramírez J, Górriz JM, Casillas J. Alzheimer's Disease Computer-Aided Diagnosis: Histogram-Based Analysis of Regional MRI Volumes for Feature Selection and Classification. J Alzheimers Dis 2019; 65:819-842. [PMID: 29966190 DOI: 10.3233/jad-170514] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer's disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims to detect differences in brain regions of interest (ROIs) relevant for the recognition of subjects with AD and 2) an original greedy algorithm that predicts the severity of the effects of AD on these regions. This algorithm takes account of the progressive nature of AD that affects the brain structure with different levels of severity, i.e., the loss of gray matter in AD is found first in memory-related areas of the brain such as the hippocampus. Moreover, the proposed feature extraction process generates a reduced set of attributes which allows the use of general-purpose classification machine learning algorithms. In particular, the proposed feature extraction approach assesses the ROI image separability between classes in order to identify the ones with greater discriminant power. These regions will have the highest influence in the classification decision at the final stage. Several experiments were carried out on segmented magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in order to show the benefits of the overall method. The proposed CAD system achieved competitive classification results in a highly efficient and straightforward way.
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Martinez-Murcia FJ, Ortiz A, Gorriz JM, Ramirez J, Castillo-Barnes D. Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders. IEEE J Biomed Health Inform 2019; 24:17-26. [PMID: 31217131 DOI: 10.1109/jbhi.2019.2914970] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data analysis of AD based on deep convolutional autoencoders. We aim at finding links between cognitive symptoms and the underlying neurodegeneration process by fusing the information of neuropsychological test outcomes, diagnoses, and other clinical data with the imaging features extracted solely via a data-driven decomposition of MRI. The distribution of the extracted features in different combinations is then analyzed and visualized using regression and classification analysis, and the influence of each coordinate of the autoencoder manifold over the brain is estimated. The imaging-derived markers could then predict clinical variables with correlations above 0.6 in the case of neuropsychological evaluation variables such as the MMSE or the ADAS11 scores, achieving a classification accuracy over 80% for the diagnosis of AD.
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Ortiz A, Munilla J, Martínez-Murcia FJ, Górriz JM, Ramírez J. Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling. Int J Neural Syst 2019; 29:1850040. [DOI: 10.1142/s0129065718500405] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.
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Affiliation(s)
- Andrés Ortiz
- Communications Engineering Department, University of Málaga, Málaga 29071, Spain
| | - Jorge Munilla
- Communications Engineering Department, University of Málaga, Málaga 29071, Spain
| | | | - Juan M. Górriz
- Department of Signal Theory, Communications and Networking, University of Granada, Granada 18060, Spain
| | - Javier Ramírez
- Department of Signal Theory, Communications and Networking, University of Granada, Granada 18060, Spain
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Deep Convolutional Autoencoders vs PCA in a Highly-Unbalanced Parkinson’s Disease Dataset: A DaTSCAN Study. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/978-3-319-94120-2_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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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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Automatic computation of regions of interest by robust principal component analysis. Application to automatic dementia diagnosis. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.02.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Plocharski M, Østergaard LR. Extraction of sulcal medial surface and classification of Alzheimer's disease using sulcal features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 133:35-44. [PMID: 27393798 DOI: 10.1016/j.cmpb.2016.05.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 04/11/2016] [Accepted: 05/19/2016] [Indexed: 05/18/2023]
Abstract
BACKGROUND Recent advancements in medical imaging have resulted in a significant growth in diagnostic possibilities of neurodegenerative disorders. Neuroanatomical abnormalities of the cerebral cortex in Alzheimer's disease (AD), the most frequent type of dementia in the elderly, can be observed in morphology analysis of cortical sulci, and used to distinguish between cognitively normal (CN) subjects and subjects with AD. OBJECTIVE The purpose of this paper was to extract sulcal features by means of computing a sulcal medial surface for AD/CN classification. METHODS 24 distinct sulci per subject were extracted from 210 subjects from the ADNI database by the BrainVISA sulcal identification pipeline. Sulcal medial surface features (depth, length, mean and Gaussian curvature, surface area) were computed for AD/CN classification with a support vector machine (SVM). RESULTS The obtained 10-fold cross-validated classification accuracy was 87.9%, sensitivity 90.0%, and specificity 86.7%, based on ten features. The area under the receiver operating characteristic curve (AUC) was 0.89. CONCLUSIONS The sulcal medial surface features can be used as biomarkers for cortical neuroanatomical abnormalities in AD. All the features were located in the left hemisphere, which had previously been reported to be more severely affected in AD and to lose grey matter faster than the right hemisphere.
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Affiliation(s)
- Maciej Plocharski
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
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Schwarz CG, Gunter JL, Wiste HJ, Przybelski SA, Weigand SD, Ward CP, Senjem ML, Vemuri P, Murray ME, Dickson DW, Parisi JE, Kantarci K, Weiner MW, Petersen RC, Jack CR. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity. NEUROIMAGE-CLINICAL 2016; 11:802-812. [PMID: 28050342 PMCID: PMC5187496 DOI: 10.1016/j.nicl.2016.05.017] [Citation(s) in RCA: 224] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 04/29/2016] [Accepted: 05/27/2016] [Indexed: 01/07/2023]
Abstract
Alzheimer's disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, “AD signature” measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12. Evaluate thickness- and volume-based quantitative measures of AD severity Volume- and thickness-based measures perform similarly for separating by diagnosis. Volume-based measures are correlated with head size; thickness-based mostly aren't. We recommend an AD signature measure formed from cortical thickness measures. We recommend thicknesses using ANTs software with input segmentations from SPM12.
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Affiliation(s)
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Heather J Wiste
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Stephen D Weigand
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Chadwick P Ward
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Melissa E Murray
- Department of Neuroscience (Neuropathology), Mayo Clinic and Foundation, Jacksonville, FL, USA
| | - Dennis W Dickson
- Department of Neuroscience (Neuropathology), Mayo Clinic and Foundation, Jacksonville, FL, USA
| | - Joseph E Parisi
- Department of Laboratory Medicine, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Michael W Weiner
- Veterans Affairs, University of California, San Francisco, CA, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
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15
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Ortiz A, Munilla J, Górriz JM, Ramírez J. Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease. Int J Neural Syst 2016; 26:1650025. [PMID: 27478060 DOI: 10.1142/s0129065716500258] [Citation(s) in RCA: 160] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer's disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.
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Affiliation(s)
- Andrés Ortiz
- 1 Communications Engineering Department, University of Málaga, 29071/Málaga, Spain
| | - Jorge Munilla
- 1 Communications Engineering Department, University of Málaga, 29071/Málaga, Spain
| | - Juan M Górriz
- 2 Department of Signal Theory, Communications and Networking, University of Granada, 18060/Granada, Spain
| | - Javier Ramírez
- 2 Department of Signal Theory, Communications and Networking, University of Granada, 18060/Granada, Spain
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16
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Sudre CH, Cardoso MJ, Bouvy WH, Biessels GJ, Barnes J, Ourselin S. Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:2079-2102. [PMID: 25850086 DOI: 10.1109/tmi.2015.2419072] [Citation(s) in RCA: 98] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In neuroimaging studies, pathologies can present themselves as abnormal intensity patterns. Thus, solutions for detecting abnormal intensities are currently under investigation. As each patient is unique, an unbiased and biologically plausible model of pathological data would have to be able to adapt to the subject's individual presentation. Such a model would provide the means for a better understanding of the underlying biological processes and improve one's ability to define pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a hierarchical fully unsupervised model selection framework for neuroimaging data which enables the distinction between different types of abnormal image patterns without pathological a priori knowledge. Its application on simulated and clinical data demonstrated the ability to detect abnormal intensity clusters, resulting in a competitive to improved behavior in white matter lesion segmentation when compared to three other freely-available automated methods.
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