101
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Ferreira LK, Rondina JM, Kubo R, Ono CR, Leite CC, Smid J, Bottino C, Nitrini R, Busatto GF, Duran FL, Buchpiguel CA. Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals. ACTA ACUST UNITED AC 2017; 40:181-191. [PMID: 28977066 PMCID: PMC6900774 DOI: 10.1590/1516-4446-2016-2083] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 05/08/2017] [Indexed: 12/01/2022]
Abstract
Objective: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer’s disease (AD). Method: Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation. Results: The accuracy obtained using PET and SPECT data were similar. PET accuracy was 68∼71% and area under curve (AUC) 0.77∼0.81; SPECT accuracy was 68∼74% and AUC 0.75∼0.79, and both had better performance than analysis with T1-MRI data (accuracy of 58%, AUC 0.67). The addition of PET or SPECT to MRI produced higher accuracy indices (68∼74%; AUC: 0.74∼0.82) than T1-MRI alone, but these were not clearly superior to the isolated neurofunctional modalities. Conclusion: In line with previous evidence, FDG-PET and rCBF-SPECT more accurately identified patients with AD than T1-MRI, and the addition of either PET or SPECT to T1-MRI data yielded increased accuracy. The comparable SPECT and PET performances, directly demonstrated for the first time in the present study, support the view that rCBF-SPECT still has a role to play in AD diagnosis.
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Affiliation(s)
- Luiz K Ferreira
- Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), USP, São Paulo, SP, Brazil
| | - Jane M Rondina
- Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, United Kingdom
| | - Rodrigo Kubo
- Laboratório de Medicina Nuclear (LIM43), Departamento de Radiologia e Oncologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Carla R Ono
- Laboratório de Medicina Nuclear (LIM43), Departamento de Radiologia e Oncologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil.,Serviço de Medicina Nuclear, Hospital do Coração da Associação Sanatório Sírio, São Paulo, SP, Brazil
| | - Claudia C Leite
- Departamento de Radiologia e Oncologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Jerusa Smid
- Departamento de Neurologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Cassio Bottino
- Departamento de Psiquiatria, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Ricardo Nitrini
- Departamento de Neurologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Geraldo F Busatto
- Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), USP, São Paulo, SP, Brazil.,Departamento de Psiquiatria, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Fabio L Duran
- Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), USP, São Paulo, SP, Brazil
| | - Carlos A Buchpiguel
- Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), USP, São Paulo, SP, Brazil.,Laboratório de Medicina Nuclear (LIM43), Departamento de Radiologia e Oncologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil.,Serviço de Medicina Nuclear, Hospital do Coração da Associação Sanatório Sírio, São Paulo, SP, Brazil
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102
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Liu J, Wang J, Hu B, Wu FX, Pan Y. Alzheimer’s Disease Classification Based on Individual Hierarchical Networks Constructed With 3-D Texture Features. IEEE Trans Nanobioscience 2017; 16:428-437. [DOI: 10.1109/tnb.2017.2707139] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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103
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Cosa A, Moreno A, Pacheco-Torres J, Ciccocioppo R, Hyytiä P, Sommer WH, Moratal D, Canals S. Multi-modal MRI classifiers identify excessive alcohol consumption and treatment effects in the brain. Addict Biol 2017; 22:1459-1472. [PMID: 27273582 DOI: 10.1111/adb.12418] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 04/22/2016] [Accepted: 05/13/2016] [Indexed: 12/14/2022]
Abstract
Robust neuroimaging markers of neuropsychiatric disorders have proven difficult to obtain. In alcohol use disorders, profound brain structural deficits can be found in severe alcoholic patients, but the heterogeneity of unimodal MRI measurements has so far precluded the identification of selective biomarkers, especially for early diagnosis. In the present work we used a combination of multiple MRI modalities to provide comprehensive and insightful descriptions of brain tissue microstructure. We performed a longitudinal experiment using Marchigian-Sardinian (msP) rats, an established model of chronic excessive alcohol consumption, and acquired multi-modal images before and after 1 month of alcohol consumption (6.8 ± 1.4 g/kg/day, mean ± SD), as well as after 1 week of abstinence with or without concomitant treatment with the antirelapse opioid antagonist naltrexone (2.5 mg/kg/day). We found remarkable sensitivity and selectivity to accurately classify brains affected by alcohol even after the relative short exposure period. One month drinking was enough to imprint a highly specific signature of alcohol consumption. Brain alterations were regionally specific and affected both gray and white matter and persisted into the early abstinence state without any detectable recovery. Interestingly, naltrexone treatment during early abstinence resulted in subtle brain changes that could be distinguished from non-treated abstinent brains, suggesting the existence of an intermediate state associated with brain recovery from alcohol exposure induced by medication. The presented framework is a promising tool for the development of biomarkers for clinical diagnosis of alcohol use disorders, with capacity to further inform about its progression and response to treatment.
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Affiliation(s)
- Alejandro Cosa
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
- Center for Biomaterials and Tissue Engineering; Universitat Politècnica de València; Valencia Spain
| | - Andrea Moreno
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
| | - Jesús Pacheco-Torres
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
| | | | - Petri Hyytiä
- Department of Pharmacology, Faculty of Medicine; University of Helsinki; Helsinki Finland
| | - Wolfgang H. Sommer
- Department of Psychopharmacology, Central Institute of Mental Health; University of Heidelberg; Mannheim Germany
| | - David Moratal
- Center for Biomaterials and Tissue Engineering; Universitat Politècnica de València; Valencia Spain
| | - Santiago Canals
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
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104
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Zhang Y, Zhang H, Chen X, Lee SW, Shen D. Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis. Sci Rep 2017; 7:6530. [PMID: 28747782 PMCID: PMC5529469 DOI: 10.1038/s41598-017-06509-0] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 06/13/2017] [Indexed: 11/21/2022] Open
Abstract
Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on “correlation’s correlation” has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low- and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely “hybrid high-order FC networks” by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the low-order FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome.
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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
| | - 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
| | - 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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105
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Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. Neuroimage 2017; 155:530-548. [PMID: 28414186 PMCID: PMC5511557 DOI: 10.1016/j.neuroimage.2017.03.057] [Citation(s) in RCA: 298] [Impact Index Per Article: 42.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 03/25/2017] [Accepted: 03/28/2017] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
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Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Muhammad Aksam Iftikhar
- Department of Computer Science, Comsats Institute of Information technology, Lahore, Pakistan
| | - Amanda Shacklett
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA.
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106
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Mumtaz W, Ali SSA, Yasin MAM, Malik AS. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput 2017; 56:233-246. [DOI: 10.1007/s11517-017-1685-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 07/03/2017] [Indexed: 12/20/2022]
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107
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Wallert J, Tomasoni M, Madison G, Held C. Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data. BMC Med Inform Decis Mak 2017; 17:99. [PMID: 28679442 PMCID: PMC5499032 DOI: 10.1186/s12911-017-0500-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 06/28/2017] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learning algorithms trained on unselected, nation-wide population data from Sweden to solve the binary classification problem of predicting survival versus non-survival 2 years after first myocardial infarction (MI). METHODS This prospective national registry study for prognostic accuracy validation of predictive models used data from 51,943 complete first MI cases as registered during 6 years (2006-2011) in the national quality register SWEDEHEART/RIKS-HIA (90% coverage of all MIs in Sweden) with follow-up in the Cause of Death register (> 99% coverage). Primary outcome was AUROC (C-statistic) performance of each model on the untouched test set (40% of cases) after model development on the training set (60% of cases) with the full (39) predictor set. Model AUROCs were bootstrapped and compared, correcting the P-values for multiple comparisons with the Bonferroni method. Secondary outcomes were derived when varying sample size (1-100% of total) and predictor sets (39, 10, and 5) for each model. Analyses were repeated on 79,869 completed cases after multivariable imputation of predictors. RESULTS A Support Vector Machine with a radial basis kernel developed on 39 predictors had the highest complete cases performance on the test set (AUROC = 0.845, PPV = 0.280, NPV = 0.966) outperforming Boosted C5.0 (0.845 vs. 0.841, P = 0.028) but not significantly higher than Logistic Regression or Random Forest. Models converged to the point of algorithm indifference with increased sample size and predictors. Using the top five predictors also produced good classifiers. Imputed analyses had slightly higher performance. CONCLUSIONS Improved mortality prediction at hospital discharge after first MI is important for identifying high-risk individuals eligible for intensified treatment and care. All models performed accurately and similarly and because of the superior national coverage, the best model can potentially be used to better differentiate new patients, allowing for improved targeting of limited resources. Future research should focus on further model development and investigate possibilities for implementation.
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Affiliation(s)
- John Wallert
- Department of Public Health and Caring Sciences, Uppsala University, Box 564, Husargatan 3, SE - 75122 Uppsala, Sweden
- Department of Women’s and Children’s Health, Uppsala University, Box 572, Husargatan 3, SE - 75123 Uppsala, Sweden
| | - Mattia Tomasoni
- Department of Public Health and Caring Sciences, Uppsala University, Box 564, Husargatan 3, SE - 75122 Uppsala, Sweden
| | - Guy Madison
- Department of Psychology, Umeå University, Hus Y, Behavioral Sciences Building, Vindarnas Torg, Mediagränd 14 B-115, 901 87 Umeå, Sweden
| | - Claes Held
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Dag Hammarskölds väg 50 A, Uppsala Science Park, 751 83 Uppsala, Sweden
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108
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Tsao S, Gajawelli N, Zhou J, Shi J, Ye J, Wang Y, Leporé N. Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry. Brain Behav 2017; 7:e00733. [PMID: 28729939 PMCID: PMC5516607 DOI: 10.1002/brb3.733] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 04/10/2017] [Accepted: 04/14/2017] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients. METHODS Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor-based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces. RESULTS We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted. CONCLUSIONS By combining the power of the cFSGL multi-task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.
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Affiliation(s)
- Sinchai Tsao
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
| | - Niharika Gajawelli
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering Michigan State University East Lansing MI USA
| | - Jie Shi
- School of Computing, Informatics and Decision Systems Engineering Arizona State University Phoenix AZ USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics & Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering Arizona State University Phoenix AZ USA
| | - Natasha Leporé
- CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA
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109
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Schnyer DM, Clasen PC, Gonzalez C, Beevers CG. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res Neuroimaging 2017; 264:1-9. [PMID: 28388468 PMCID: PMC5486995 DOI: 10.1016/j.pscychresns.2017.03.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Revised: 11/02/2016] [Accepted: 03/08/2017] [Indexed: 02/07/2023]
Abstract
Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n =25) and healthy controls (n =25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.
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Affiliation(s)
- David M Schnyer
- Department of Psychology, University of Texas at Austin, Austin, TX, USA.
| | - Peter C Clasen
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Christopher Gonzalez
- Department of Psychology, University of California, San Diego, San Diego, CA, USA
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110
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Lama RK, Gwak J, Park JS, Lee SW. Diagnosis of Alzheimer's Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:5485080. [PMID: 29065619 PMCID: PMC5494120 DOI: 10.1155/2017/5485080] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2016] [Revised: 03/30/2017] [Accepted: 04/13/2017] [Indexed: 01/13/2023]
Abstract
Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.
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Affiliation(s)
- Ramesh Kumar Lama
- National Research Center for Dementia, Gwangju, Republic of Korea
- Department of Software, Gachon University, 1342 Seongnamdaero, Sujeonggu, Seongnam, Gyeonggido 13120, Republic of Korea
| | - Jeonghwan Gwak
- National Research Center for Dementia, Gwangju, Republic of Korea
- School of Electrical Engineering and Computer Science (EECS), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
| | - Jeong-Seon Park
- Department of Multimedia, Chonnam National University, 50 Daehakro, Yeosu, Jeollanamdo 59626, Republic of Korea
| | - Sang-Woong Lee
- National Research Center for Dementia, Gwangju, Republic of Korea
- Department of Software, Gachon University, 1342 Seongnamdaero, Sujeonggu, Seongnam, Gyeonggido 13120, Republic of Korea
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111
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Zhang J, Liu M, Gao Y, Shen D. Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images. IEEE J Biomed Health Inform 2017; 21:1607-1616. [PMID: 28534798 DOI: 10.1109/jbhi.2017.2704614] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer's disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires time-consuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs. Moreover, the inconsistent longitudinal scans (i.e., different scanning time points and also the total number of scans) hinder extraction of unified feature representations in longitudinal studies. In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which does not require nonlinear registration or tissue segmentation in the application stage and is also robust to inconsistencies among longitudinal scans. Specifically, first, the discriminative landmarks are automatically discovered from the whole brain using training images, and then efficiently localized using a fast landmark detection method for testing images, without the involvement of any nonlinear registration and tissue segmentation; and second, high-level statistical spatial features and contextual longitudinal features are further extracted based on those detected landmarks, which can characterize spatial structural abnormalities and longitudinal landmark variations. Using these spatial and longitudinal features, a linear support vector machine is finally adopted to distinguish AD subjects or mild cognitive impairment (MCI) subjects from healthy controls (HCs). Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate the superior performance and efficiency of the proposed method, with classification accuracies of 88.30% for AD versus HC and 79.02% for MCI versus HC, respectively.
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112
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Gupta T, Gandhi TK, Panigrahi B. Multi-sequential MR brain image classification for tumor detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-169293] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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113
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Long X, Chen L, Jiang C, Zhang L. Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One 2017; 12:e0173372. [PMID: 28264071 PMCID: PMC5338815 DOI: 10.1371/journal.pone.0173372] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 02/20/2017] [Indexed: 12/17/2022] Open
Abstract
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.
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Affiliation(s)
- Xiaojing Long
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Lifang Chen
- Department of Neurology, Shenzhen University 1st Affiliated Hospital, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Chunxiang Jiang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Lijuan Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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Li T, Li W, Yang Y, Zhang W. Classification of brain disease in magnetic resonance images using two-stage local feature fusion. PLoS One 2017; 12:e0171749. [PMID: 28207873 PMCID: PMC5313178 DOI: 10.1371/journal.pone.0171749] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 01/25/2017] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. METHODS Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. RESULTS We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. DISCUSSION This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.
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Affiliation(s)
- Tao Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wu Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yehui Yang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wensheng Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 518] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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116
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Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease. Sci Rep 2017; 7:39880. [PMID: 28079104 PMCID: PMC5227696 DOI: 10.1038/srep39880] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 11/29/2016] [Indexed: 01/18/2023] Open
Abstract
Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.
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117
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A Novel Deep Learning Based Multi-class Classification Method for Alzheimer’s Disease Detection Using Brain MRI Data. Brain Inform 2017. [DOI: 10.1007/978-3-319-70772-3_20] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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118
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Mumtaz W, Xia L, Ali SSA, Yasin MAM, Hussain M, Malik AS. Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.07.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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119
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Pluta R, Kocki J, Ułamek-Kozioł M, Petniak A, Gil-Kulik P, Januszewski S, Bogucki J, Jabłoński M, Brzozowska J, Furmaga-Jabłońska W, Bogucka-Kocka A, Czuczwar SJ. Discrepancy in Expression of β-Secretase and Amyloid-β Protein Precursor in Alzheimer-Related Genes in the Rat Medial Temporal Lobe Cortex Following Transient Global Brain Ischemia. J Alzheimers Dis 2016; 51:1023-31. [PMID: 26890784 DOI: 10.3233/jad-151102] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Brain ischemia may be causally related with Alzheimer's disease. Presumably, β-secretase and amyloid-β protein precursor gene expression changes may be associated with Alzheimer's disease neuropathology. Consequently, we have examined quantitative changes in both β-secretase and amyloid-β protein precursor genes in the medial temporal lobe cortex with the use of quantitative rtPCR analysis following 10-min global brain ischemia in rats with survival of 2, 7, and 30 days. The greatest significant overexpression of β-secretase gene was noted on the 2nd day, while on days 7-30 the expression of this gene was only modestly downregulated. Amyloid-β protein precursor gene was downregulated on the 2nd day, but on days 7-30 postischemia, there was a significant reverse tendency. Thus, the demonstrated alterations indicate that the considerable changes of expression of β-secretase and amyloid-β protein precursor genes may be connected with a response of neurons in medial temporal lobe cortex to transient global brain ischemia. Finally, the ischemia-induced gene changes may play a key role in a late and slow onset of Alzheimer-type pathology.
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Affiliation(s)
- Ryszard Pluta
- Laboratory of Ischemic and Neurodegenerative Brain Research, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| | - Janusz Kocki
- Department of Clinical 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
| | | | - Anna Bogucka-Kocka
- Department of Pharmaceutical Botany, Medical University of Lublin, Lublin, Poland
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Moradi E, Hallikainen I, Hänninen T, Tohka J. Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease. Neuroimage Clin 2016; 13:415-427. [PMID: 28116234 PMCID: PMC5233798 DOI: 10.1016/j.nicl.2016.12.011] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 11/25/2016] [Accepted: 12/11/2016] [Indexed: 12/18/2022]
Abstract
Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in RAVLT scores reflect well the underlying pathology caused by Alzheimer's disease (AD), thus making RAVLT an effective early marker to detect AD in persons with memory complaints. We investigated the association between RAVLT scores (RAVLT Immediate and RAVLT Percent Forgetting) and the structural brain atrophy caused by AD. The aim was to comprehensively study to what extent the RAVLT scores are predictable based on structural magnetic resonance imaging (MRI) data using machine learning approaches as well as to find the most important brain regions for the estimation of RAVLT scores. For this, we built a predictive model to estimate RAVLT scores from gray matter density via elastic net penalized linear regression model. The proposed approach provided highly significant cross-validated correlation between the estimated and observed RAVLT Immediate (R = 0.50) and RAVLT Percent Forgetting (R = 0.43) in a dataset consisting of 806 AD, mild cognitive impairment (MCI) or healthy subjects. In addition, the selected machine learning method provided more accurate estimates of RAVLT scores than the relevance vector regression used earlier for the estimation of RAVLT based on MRI data. The top predictors were medial temporal lobe structures and amygdala for the estimation of RAVLT Immediate and angular gyrus, hippocampus and amygdala for the estimation of RAVLT Percent Forgetting. Further, the conversion of MCI subjects to AD in 3-years could be predicted based on either observed or estimated RAVLT scores with an accuracy comparable to MRI-based biomarkers.
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Affiliation(s)
- Elaheh Moradi
- Institute of Biosciences and Medical Technology, University of Tampere, Tampere, Finland
| | - Ilona Hallikainen
- University of Eastern Finland, Institute of Clinical Medicine, Department of Neurology, Kuopio, Finland
| | - Tuomo Hänninen
- Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Jussi Tohka
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Leganes, Spain
- Instituto de Investigación Sanitaria Gregorio Marañon, Madrid, Spain
- University of Eastern Finland, AI Virtanen Institute for Molecular Sciences, Kuopio, Finland
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Zu C, Jie B, Liu M, Chen S, Shen D, Zhang D. Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment. Brain Imaging Behav 2016; 10:1148-1159. [PMID: 26572145 PMCID: PMC4868803 DOI: 10.1007/s11682-015-9480-7] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.
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Affiliation(s)
- Chen Zu
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ()
| | - Biao Jie
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China, and also with the School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241000, China
| | - Mingxia Liu
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Songcan Chen
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Dinggang Shen
- Department of Radiology and BRIC, the University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA, and also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 136-701, Korea ()
| | - Daoqiang Zhang
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ()
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Mete M, Sakoglu U, Spence JS, Devous MD, Harris TS, Adinoff B. Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach. BMC Bioinformatics 2016; 17:357. [PMID: 27766943 PMCID: PMC5073995 DOI: 10.1186/s12859-016-1218-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Background Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. Results The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively. Sensitivity and specificity were 0.90 and 0.89 for LOO; 0.83 and 0.83 for 10xCV. Many of the 30 selected clusters are highly relevant to the addictive process, including regions relevant to cognitive control, default mode network related self-referential thought, behavioral inhibition, and contextual memories. Relative hyperactivity and hypoactivity of regional cerebral blood flow in brain regions in cocaine-dependent participants are presented with corresponding level of significance. Conclusions The SVM-based approach successfully classified cocaine-dependent and healthy control participants using voxels selected with information theoretic-based and statistical methods from participants’ SPECT data. The regions found in this study align with brain regions reported in the literature. These findings support the future use of brain imaging and SVM-based classifier in the diagnosis of substance use disorders and furthering an understanding of their underlying pathology. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1218-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mutlu Mete
- Department of Computer Science and Information Systems, Texas A&M University-Commerce, Commerce, TX, USA.
| | - Unal Sakoglu
- Computer Engineering, University of Houston - Clear Lake, Houston, TX, USA
| | - Jeffrey S Spence
- Center for Brain Health, University of Texas at Dallas, Richardson, TX, USA
| | - Michael D Devous
- Department of Neurology, UT Southwestern Medical Center, Dallas, TX, USA.,Avid Radiopharmaceuticals, Philadelphia, PA, USA
| | | | - Bryon Adinoff
- Veterans Affairs North Texas Health Care System, Dallas, TX, USA.,Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, 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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124
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Discriminating schizophrenia and schizo-obsessive disorder: a structural MRI study combining VBM and machine learning methods. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2451-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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125
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Magisetty O, Dowlathabad MR, Raichurkar KP, Mannar SN. First magenetic resonance imaging studies on aluminium maltolate-treated aged New Zealand rabbits: an Alzheimer's animal model. Psychogeriatrics 2016; 16:263-7. [PMID: 26419490 DOI: 10.1111/psyg.12158] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2012] [Revised: 02/23/2015] [Accepted: 08/12/2015] [Indexed: 11/29/2022]
Abstract
BACKGROUND Alzheimer's disease is a devastative neurodegenerative disorder. To date, there has been no animal model that could unravel the complete disease pathology. Magnetic resonance imaging has played a pivotal role in the quantitative assessment of brain tissue atrophy for a few decades. In particular, temporal lobe atrophy and ventricular dilatation have been found to be sensitive in Alzheimer's disease. METHODS The present study focused on the replication of these crucial pathological events to enable disease progression to be diagnosed at an early stage and stopped through the use of potential therapeutic strategies. RESULT The objective of this study was to show temporal lobe atrophy and ventricular dilatation in aluminium maltolate-treated aged New Zealand rabbit, and our study was able to demonstrate this for the first time. CONCLUSION The present study makes this animal model a substantial one for further molecular level studies and opens up new targets for potential therapeutic strategies.
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Affiliation(s)
- Obulesu Magisetty
- Department of Materials Science, Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki-305-8573, Japan
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126
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Liu M, Zhang D, Shen D. Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1463-74. [PMID: 26742127 PMCID: PMC5572669 DOI: 10.1109/tmi.2016.2515021] [Citation(s) in RCA: 113] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
As shown in the literature, methods based on multiple templates usually achieve better performance, compared with those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignores important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method on 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification.
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127
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Weakley A, Williams JA, Schmitter-Edgecombe M, Cook DJ. Neuropsychological test selection for cognitive impairment classification: A machine learning approach. J Clin Exp Neuropsychol 2016; 37:899-916. [PMID: 26332171 DOI: 10.1080/13803395.2015.1067290] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
INTRODUCTION Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI), or dementia using a suite of classification techniques. METHOD Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis; Clinical Dementia Rating, CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals with CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. A total of 27 demographic, psychological, and neuropsychological variables were available for variable selection. RESULTS No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0-99.1%), geometric mean (60.9-98.1%), sensitivity (44.2-100%), and specificity (52.7-100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2-9 variables were required for classification and varied between datasets in a clinically meaningful way. CONCLUSIONS The current study results reveal that machine learning techniques can accurately classify cognitive impairment and reduce the number of measures required for diagnosis.
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Affiliation(s)
- Alyssa Weakley
- a Department of Psychology , Washington State University , Pullman , WA , USA
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Tang X, Qin Y, Wu J, Zhang M, Zhu W, Miller MI. Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease. Magn Reson Imaging 2016; 34:1087-99. [PMID: 27211255 DOI: 10.1016/j.mri.2016.05.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 05/11/2016] [Indexed: 01/18/2023]
Abstract
We analyzed, in an integrative fashion, the morphometry and structural integrity of the bilateral hippocampi and amygdalas in Alzheimer's disease (AD) using T1-weighted images and diffusion tensor images (DTIs). We detected significant hippocampal and amygdalar volumetric atrophies in AD relative to healthy controls (HCs). Shape analysis revealed significant region-specific atrophies with the hippocampal atrophy mainly being concentrated on the CA1 and CA2 while the amygdalar atrophy was concentrated on the basolateral and basomedial. In all structures, the structural integrity displayed a significantly decreased mean fractional anisotropy (FA) value and an increased mean trace value in AD. In addition to the inter-group comparisons, we systematically evaluated the discriminative power of our three types of features (volume, shape, and DTI), both individually and in their possible combinations, when differentiating between AD and HCs. We found the volume features to be redundant when the more sophisticated shape features were available. A combination of the shape and DTI features of the right hippocampus, with classification automatically performed by support vector machine, yielded the strongest classification result (overall accuracy, 94.6%; sensitivity, 95.5%; specificity, 93.3%).
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Affiliation(s)
- Xiaoying Tang
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Shunde International Joint Research Institute, Shunde, Guangdong, China.
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiong Wu
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Min Zhang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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Smyser CD, Dosenbach NUF, Smyser TA, Snyder AZ, Rogers CE, Inder TE, Schlaggar BL, Neil JJ. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 2016; 136:1-9. [PMID: 27179605 DOI: 10.1016/j.neuroimage.2016.05.029] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 05/05/2016] [Accepted: 05/08/2016] [Indexed: 12/24/2022] Open
Abstract
Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.
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Affiliation(s)
- Christopher D Smyser
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Tara A Smyser
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Terrie E Inder
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
| | - Bradley L Schlaggar
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Neurobiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Jeffrey J Neil
- Department of Neurology, Boston Children's Hospital, 333 Longwood Avenue, Boston, MA 02115, USA.
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131
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Jongkreangkrai C, Vichianin Y, Tocharoenchai C, Arimura H. Computer-aided classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI. ACTA ACUST UNITED AC 2016. [DOI: 10.1088/1742-6596/694/1/012036] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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132
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Zheng C, Xia Y, Pan Y, Chen J. Automated identification of dementia using medical imaging: a survey from a pattern classification perspective. Brain Inform 2015; 3:17-27. [PMID: 27747596 PMCID: PMC4883162 DOI: 10.1007/s40708-015-0027-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Accepted: 11/26/2015] [Indexed: 11/28/2022] Open
Abstract
In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective. Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex- and ROI-based ones, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks. We also compare the reported performance of many recently published dementia identification algorithms. Our comparison shows that many algorithms can differentiate the Alzheimer’s disease (AD) from elderly normal with a largely satisfying accuracy, whereas distinguishing the mild cognitive impairment from AD or elderly normal still remains a major challenge.
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Affiliation(s)
- Chuanchuan Zheng
- Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
- Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
| | - Yong Xia
- Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China.
- Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China.
| | - Yongsheng Pan
- Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
- Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, People's Republic of China
| | - Jinhu Chen
- Department of Radiation Oncology, Shandong Tumor Hospital, Jinan, 250117, People's Republic of China.
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133
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Anandh KR, Sujatha CM, Ramakrishnan S. A Method to Differentiate Mild Cognitive Impairment and Alzheimer in MR Images using Eigen Value Descriptors. J Med Syst 2015; 40:25. [PMID: 26547845 DOI: 10.1007/s10916-015-0396-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 10/29/2015] [Indexed: 01/19/2023]
Abstract
Automated analysis and differentiation of mild cognitive impairment and Alzheimer's condition using MR images is clinically significant in dementic disorder. Alzheimer's Disease (AD) is a fatal and common form of dementia that progressively affects the patients. Shape descriptors could better differentiate the morphological alterations of brain structures and aid in the development of prospective disease modifying therapies. Ventricle enlargement is considered as a significant biomarker in the AD diagnosis. In this work, a method has been proposed to differentiate MCI from the healthy normal and AD subjects using Laplace-Beltrami (LB) eigen value shape descriptors. Prior to this, Reaction Diffusion (RD) level set is used to segment the ventricles in MR images and the results are validated against the Ground Truth (GT). LB eigen values are infinite series of spectrum that describes the intrinsic geometry of objects. Most significant LB shape descriptors are identified and their performance is analysed using linear Support Vector Machine (SVM) classifier. Results show that, the RD level set is able to segment the ventricles. The segmented ventricles are found to have high correlation with GT. The eigen values in the LB spectrum could show distinction in the feature space better than the geometric features. High accuracy is observed in the classification results of linear SVM. The proposed automated system is able to distinctly separate the MCI from normal and AD subjects. Thus this pipeline of work seems to be clinically significant in the automated analysis of dementic subjects.
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Affiliation(s)
- K R Anandh
- Indian Institute of Technology Madras, Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Chennai, India. .,CEG Campus, Department of Electronics and Communication Engineering, Anna University, Chennai, India.
| | - C M Sujatha
- CEG Campus, Department of Electronics and Communication Engineering, Anna University, Chennai, India
| | - S Ramakrishnan
- Indian Institute of Technology Madras, Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Chennai, India
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Printy BP, Verma N, Cowperthwaite MC, Markey MK. Effects of genetic variation on the dynamics of neurodegeneration in Alzheimer's disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:2464-7. [PMID: 25570489 DOI: 10.1109/embc.2014.6944121] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Although many genetic markers are identified as being associated with Alzheimer's disease (AD), not much is known about their association with the structural changes that happen as the disease progresses. In this study, we investigate the genetic etiology of neurodegeneration in AD by associating genetic markers with atrophy profiles obtained using patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The atrophy profiles were quantified using a linear least-squares regression model over the span of patient enrollment, and used as imaging features throughout the analysis. A subset of the imaging features were selected for genetic association based on their ability to discriminate between healthy individuals and AD patients in a Support Vector Machines (SVM) classifier. Each imaging feature was associated with single-nucleotide polymorphisms (SNPs) using a linear model that included age and cognitive impairment scores as covariates to correct for normal disease progression. After false discovery rate correction, we observed 53 significant associations between SNPs and our imaging features, including associations of ventricular enlargement with SNPs on estrogen receptor 1 (ESR1) and sortilin-related VPS10 domain containing receptor 1 (SORCS1), hippocampal atrophy with SNPs on ESR1, and cerebral atrophy with SNPs on transferrin (TF) and amyloid beta precursor protein (APP). This study provides important insights into genetic predictors of specific types of neurodegeneration that could potentially be used to improve the efficacy of treatment strategies for the disease and allow the development of personalized treatment plans based on each patient's unique genetic profile.
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135
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Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer's Dementia. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:676129. [PMID: 26576199 PMCID: PMC4632180 DOI: 10.1155/2015/676129] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2015] [Revised: 05/08/2015] [Accepted: 05/18/2015] [Indexed: 01/13/2023]
Abstract
The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures. A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques. Ensemble approach which is trained with features selected from multiple biomarkers facilitated accurate classification when compared with conventional classification techniques. Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5. Feature search is done with Particle Swarm Optimisation to retrieve the subset of features for further selection with the ensemble classifier. Features selected by the proposed C4.5 ensemble classifier with Particle Swarm Optimisation search, coupled with Merit Merge technique (CPEMM), outperformed bagging feature selection of SVM, NB, and Random forest classifiers. The proposed CPEMM feature selection found the best subset of features that efficiently discriminated normal individuals and patients affected with Mild Cognitive Impairment and Alzheimer's Dementia with 98.7% accuracy.
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136
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Ben Ahmed O, Mizotin M, Benois-Pineau J, Allard M, Catheline G, Ben Amar C. Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex. Comput Med Imaging Graph 2015; 44:13-25. [DOI: 10.1016/j.compmedimag.2015.04.007] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 02/21/2015] [Accepted: 04/27/2015] [Indexed: 01/18/2023]
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137
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Gorji HT, Haddadnia J. A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI. Neuroscience 2015; 305:361-71. [PMID: 26265552 DOI: 10.1016/j.neuroscience.2015.08.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Revised: 08/04/2015] [Accepted: 08/05/2015] [Indexed: 12/31/2022]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia among older people. The number of patients with AD will grow rapidly each year and AD is the fifth leading cause of death for those aged 65 and older. In recent years, one of the main challenges for medical investigators has been the early diagnosis of patients with AD because an early diagnosis can provide greater opportunities for patients to be eligible for more clinical trials and they will have enough time to plan for future, medical and financial decisions. An established risk factor for AD is mild cognitive impairment (MCI) which is described as a transitional state between normal aging and AD patients. Hence an accurate and reliable diagnosis of MCI can be very effective and helpful for early diagnosis of AD. Therefore in this paper we present a novel and efficient method based on pseudo Zernike moments (PZMs) for the diagnosis of MCI individuals from AD and healthy control (HC) groups using structural MRI. The proposed method uses PZMs to extract discriminative information from the MR images of the AD, MCI, and HC groups. Two types of artificial neural networks, which are based on pattern recognition and learning vector quantization (LVQ) networks, were used to classify the information extracted from the MRIs. We worked with 500 MRIs from the database of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1 1.5T). The 1 slice of 500 MRIs used in this study included 180 AD patients, 172 MCI patients, and 148 HC individuals. We selected 50 percent of the MRIs randomly for use in training the classifiers, 25 percent for validation and we used 25 percent for the testing phase. The technique proposed here yielded the best overall classification results between AD and MCI (accuracy 94.88%, sensitivity 94.18%, and specificity 95.55%), and for pairs of the MCI and HC (accuracy 95.59%, sensitivity 95.89% and specificity 95.34%). These results were achieved using maximum order 30 of PZM and the pattern recognition network with the scaled conjugate gradient (SCG) back-propagation training algorithm as a classifier.
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Affiliation(s)
- H T Gorji
- Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran.
| | - J Haddadnia
- Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran
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138
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Zhan L, Liu Y, Wang Y, Zhou J, Jahanshad N, Ye J, Thompson PM. Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Front Neurosci 2015; 9:257. [PMID: 26257601 PMCID: PMC4513242 DOI: 10.3389/fnins.2015.00257] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 07/10/2015] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.
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Affiliation(s)
- Liang Zhan
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
| | - Yashu Liu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Jiayu Zhou
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, MI, USA ; Department of Electrical Engineering and Computer Science, University of Michigan Ann Arbor, MI, USA
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
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140
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Yun HJ, Kwak K, Lee JM. Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism. PLoS One 2015; 10:e0129250. [PMID: 26061669 PMCID: PMC4463854 DOI: 10.1371/journal.pone.0129250] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 05/06/2015] [Indexed: 01/18/2023] Open
Abstract
Structural MR image (MRI) and 18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer’s disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET.
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Affiliation(s)
- Hyuk Jin Yun
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Kichang Kwak
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
- * E-mail:
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141
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Dickie DA, Job DE, Gonzalez DR, Shenkin SD, Wardlaw JM. Use of brain MRI atlases to determine boundaries of age-related pathology: the importance of statistical method. PLoS One 2015; 10:e0127939. [PMID: 26023913 PMCID: PMC4449178 DOI: 10.1371/journal.pone.0127939] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Accepted: 04/20/2015] [Indexed: 12/03/2022] Open
Abstract
Introduction Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients. Methods Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients. Results The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes. Discussion To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease.
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Affiliation(s)
- David Alexander Dickie
- Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
- * E-mail:
| | - Dominic E. Job
- Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - David Rodriguez Gonzalez
- Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Susan D. Shenkin
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Glasgow, United Kingdom
| | - Joanna M. Wardlaw
- Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
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142
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Goryawala M, Zhou Q, Barker W, Loewenstein DA, Duara R, Adjouadi M. Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015; 2015:865265. [PMID: 26101520 PMCID: PMC4458535 DOI: 10.1155/2015/865265] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Revised: 04/28/2015] [Accepted: 04/29/2015] [Indexed: 11/18/2022]
Abstract
Brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.
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Affiliation(s)
- Mohammed Goryawala
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Qi Zhou
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Warren Barker
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - David A. Loewenstein
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- Department of Psychiatry, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- Department of Psychiatry, Miller School of Medicine, University of Miami, Miami, FL, USA
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
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143
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Demirhan A, Nir TM, Zavaliangos-Petropulu A, Jack CR, Weiner MW, Bernstein MA, Thompson PM, Jahanshad N. FEATURE SELECTION IMPROVES THE ACCURACY OF CLASSIFYING ALZHEIMER DISEASE USING DIFFUSION TENSOR IMAGES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2015; 2015:126-130. [PMID: 26413201 DOI: 10.1109/isbi.2015.7163832] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.
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Affiliation(s)
- Ayşe Demirhan
- Electronics & Computer Technology, Faculty of Technology, Gazi University, Ankara, Turkey ; Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael W Weiner
- Department of Radiology, Medicine, and Psychiatry, University of California San Francisco, CA, USA ; Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | | | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, USA
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144
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Identifying informative imaging biomarkers via tree structured sparse learning for AD diagnosis. Neuroinformatics 2015; 12:381-94. [PMID: 24338729 DOI: 10.1007/s12021-013-9218-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Neuroimaging provides a powerful tool to characterize neurodegenerative progression and therapeutic efficacy in Alzheimer's disease (AD) and its prodromal stage-mild cognitive impairment (MCI). However, since the disease pathology might cause different patterns of structural degeneration, which is not pre-known, it is still a challenging problem to identify the relevant imaging markers for facilitating disease interpretation and classification. Recently, sparse learning methods have been investigated in neuroimaging studies for selecting the relevant imaging biomarkers and have achieved very promising results on disease classification. However, in the standard sparse learning method, the spatial structure is often ignored, although it is important for identifying the informative biomarkers. In this paper, a sparse learning method with tree-structured regularization is proposed to capture patterns of pathological degeneration from fine to coarse scale, for helping identify the informative imaging biomarkers to guide the disease classification and interpretation. Specifically, we first develop a new tree construction method based on the hierarchical agglomerative clustering of voxel-wise imaging features in the whole brain, by taking into account their spatial adjacency, feature similarity and discriminability. In this way, the complexity of all possible multi-scale spatial configurations of imaging features can be reduced to a single tree of nested regions. Second, we impose the tree-structured regularization on the sparse learning to capture the imaging structures, and then use them for selecting the most relevant biomarkers. Finally, we train a support vector machine (SVM) classifier with the selected features to make the classification. We have evaluated our proposed method by using the baseline MR images of 830 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, which includes 198 AD patients, 167 progressive MCI (pMCI), 236 stable MCI (sMCI), and 229 normal controls (NC). Our experimental results show that our method can achieve accuracies of 90.2 %, 87.2 %, and 70.7 % for classifications of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, demonstrating promising performance compared with other state-of-the-art methods.
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Abstract
Advances in methodology have led to expanded application of resting-state functional MRI (rs-fMRI) to the study of term and prematurely born infants during the first years of life, providing fresh insight into the earliest forms of functional cerebral development. In this review, we detail our evolving understanding of the use of rs-fMRI for studying neonates. We initially focus on the biological processes of cortical development related to resting-state network development. We then review technical issues principally affecting neonatal investigations, including the effects of subject motion during acquisition and image distortions related to magnetic susceptibility effects. We next summarize the literature in which rs-fMRI is used to study normal brain development during the early postnatal period, the effects of prematurity, and the effects of cerebral injury. Finally, we review potential future directions for the field, such as the use of complementary imaging modalities and advanced analysis techniques.
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Affiliation(s)
- Christopher D. Smyser
- Division of Pediatric Neurology, Department of Neurology, Washington University School of Medicine, St. Louis, MO
| | - Jeffrey J. Neil
- Department of Neurology, Boston Children’s Hospital, Boston, MA,Corresponding author. Jeff Neil, MD, PhD, Neurology, Boston Children's Hospital, 333 Longwood Avenue, LO 450, Boston, MA 02115, phone (617) 355-6388, fax (617) 730-0284,
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Bron EE, Smits M, van der Flier WM, Vrenken H, Barkhof F, Scheltens P, Papma JM, Steketee RME, Méndez Orellana C, Meijboom R, Pinto M, Meireles JR, Garrett C, Bastos-Leite AJ, Abdulkadir A, Ronneberger O, Amoroso N, Bellotti R, Cárdenas-Peña D, Álvarez-Meza AM, Dolph CV, Iftekharuddin KM, Eskildsen SF, Coupé P, Fonov VS, Franke K, Gaser C, Ledig C, Guerrero R, Tong T, Gray KR, Moradi E, Tohka J, Routier A, Durrleman S, Sarica A, Di Fatta G, Sensi F, Chincarini A, Smith GM, Stoyanov ZV, Sørensen L, Nielsen M, Tangaro S, Inglese P, Wachinger C, Reuter M, van Swieten JC, Niessen WJ, Klein S. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 2015; 111:562-79. [PMID: 25652394 DOI: 10.1016/j.neuroimage.2015.01.048] [Citation(s) in RCA: 165] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 01/21/2015] [Accepted: 01/24/2015] [Indexed: 12/31/2022] Open
Abstract
Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.
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Affiliation(s)
- Esther E Bron
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands.
| | - Marion Smits
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands; Department of Epidemiology & Biostatistics, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands; Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Carolina Méndez Orellana
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Madalena Pinto
- Department of Neurology, Hospital de São João, Porto, Portugal
| | | | - Carolina Garrett
- Department of Neurology, Hospital de São João, Porto, Portugal; Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - António J Bastos-Leite
- Department of Medical Imaging, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Ahmed Abdulkadir
- Department of Psychiatry & Psychotherapy, University Medical Centre Freiburg, Germany; Department of Neurology, University Medical Centre Freiburg, Germany; Department of Computer Science, University of Freiburg, Germany
| | - Olaf Ronneberger
- BIOSS Centre for Biological Signaling Studies, University of Freiburg, Germany; Department of Computer Science, University of Freiburg, Germany
| | - Nicola Amoroso
- National Institute of Nuclear Physics, Branch of Bari, Italy; Department of Physics, University of Bari, Italy
| | - Roberto Bellotti
- National Institute of Nuclear Physics, Branch of Bari, Italy; Department of Physics, University of Bari, Italy
| | - David Cárdenas-Peña
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Colombia
| | | | | | | | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University, Aarhus, Denmark
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, Unit Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, Bordeaux, France
| | - Vladimir S Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Katja Franke
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Germany; Structural Brain Mapping Group, Department of Psychiatry, Jena University Hospital, Germany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Germany; Structural Brain Mapping Group, Department of Psychiatry, Jena University Hospital, Germany
| | - Christian Ledig
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Ricardo Guerrero
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Tong Tong
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Katherine R Gray
- Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK
| | - Elaheh Moradi
- Department of Signal Processing, Tampere University of Technology, Finland
| | - Jussi Tohka
- Department of Signal Processing, Tampere University of Technology, Finland
| | - Alexandre Routier
- Inserm U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, Inria Paris-Rocquencourt, F-75013 Paris, France; Centre d'Acquisition et de Traitement des Images (CATI), Paris, France
| | - Stanley Durrleman
- Inserm U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, Inria Paris-Rocquencourt, F-75013 Paris, France; Centre d'Acquisition et de Traitement des Images (CATI), Paris, France
| | - Alessia Sarica
- Bioinformatics Laboratory, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Giuseppe Di Fatta
- School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Francesco Sensi
- National Institute of Nuclear Physics, Branch of Genoa, Italy
| | | | - Garry M Smith
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, RG6 6AH, UK; School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Zhivko V Stoyanov
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, RG6 6AH, UK; School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
| | - Lauge Sørensen
- Department of Computer Science, University of Copenhagen, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Denmark
| | - Sabina Tangaro
- National Institute of Nuclear Physics, Branch of Bari, Italy
| | - Paolo Inglese
- National Institute of Nuclear Physics, Branch of Bari, Italy
| | - Christian Wachinger
- Computer Science and Artificial Intelligence Lab, MA Institute of Technology, Cambridge, USA; Massachusetts General Hospital, Harvard Medical School, Cambridge, USA
| | - Martin Reuter
- Computer Science and Artificial Intelligence Lab, MA Institute of Technology, Cambridge, USA; Massachusetts General Hospital, Harvard Medical School, Cambridge, USA
| | | | - Wiro J Niessen
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Imaging Physics, Applied Sciences, Delft University of Technology, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
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147
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Liu M, Zhang D, Shen D. View-centralized multi-atlas classification for Alzheimer's disease diagnosis. Hum Brain Mapp 2015; 36:1847-65. [PMID: 25624081 DOI: 10.1002/hbm.22741] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Revised: 12/23/2014] [Accepted: 01/12/2015] [Indexed: 01/29/2023] Open
Abstract
Multi-atlas based methods have been recently used for classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Compared with traditional single-atlas based methods, multiatlas based methods adopt multiple predefined atlases and thus are less biased by a certain atlas. However, most existing multiatlas based methods simply average or concatenate the features from multiple atlases, which may ignore the potentially important diagnosis information related to the anatomical differences among different atlases. In this paper, we propose a novel view (i.e., atlas) centralized multi-atlas classification method, which can better exploit useful information in multiple feature representations from different atlases. Specifically, all brain images are registered onto multiple atlases individually, to extract feature representations in each atlas space. Then, the proposed view-centralized multi-atlas feature selection method is used to select the most discriminative features from each atlas with extra guidance from other atlases. Next, we design a support vector machine (SVM) classifier using the selected features in each atlas space. Finally, we combine multiple SVM classifiers for multiple atlases through a classifier ensemble strategy for making a final decision. We have evaluated our method on 459 subjects [including 97 AD, 117 progressive MCI (p-MCI), 117 stable MCI (s-MCI), and 128 normal controls (NC)] from the Alzheimer's Disease Neuroimaging Initiative database, and achieved an accuracy of 92.51% for AD versus NC classification and an accuracy of 78.88% for p-MCI versus s-MCI classification. These results demonstrate that the proposed method can significantly outperform the previous multi-atlas based classification methods.
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Affiliation(s)
- Mingxia Liu
- School of Computer Science and Technology, Nanjing University of Aeronautics & Astronautics, Nanjing, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, USA; School of Information Science and Technology, Taishan University, Taian, China
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148
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Rosa MJ, Portugal L, Hahn T, Fallgatter AJ, Garrido MI, Shawe-Taylor J, Mourao-Miranda J. Sparse network-based models for patient classification using fMRI. Neuroimage 2015; 105:493-506. [PMID: 25463459 PMCID: PMC4275574 DOI: 10.1016/j.neuroimage.2014.11.021] [Citation(s) in RCA: 103] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Revised: 10/21/2014] [Accepted: 11/08/2014] [Indexed: 12/20/2022] Open
Abstract
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.
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Affiliation(s)
- Maria J Rosa
- Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK.
| | - Liana Portugal
- Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK; LABNEC, Universidade Federal Fluminense, Rio de Janeiro, Brazil
| | - Tim Hahn
- Department of Cognitive Psychology II, Johann Wolfgang Goethe University Frankfurt am Main, Germany
| | - Andreas J Fallgatter
- University of Tuebingen, Department of Psychiatry and Psychotherapy, Tuebingen, Germany
| | - Marta I Garrido
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Australia
| | - John Shawe-Taylor
- Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK
| | - Janaina Mourao-Miranda
- Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK
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149
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Jung WB, Lee YM, Kim YH, Mun CW. Automated Classification to Predict the Progression of Alzheimer's Disease Using Whole-Brain Volumetry and DTI. Psychiatry Investig 2015; 12:92-102. [PMID: 25670951 PMCID: PMC4310927 DOI: 10.4306/pi.2015.12.1.92] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Revised: 12/29/2013] [Accepted: 12/30/2013] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE This study proposes an automated diagnostic method to classify patients with Alzheimer's disease (AD) of degenerative etiology using magnetic resonance imaging (MRI) markers. METHODS Twenty-seven patients with subjective memory impairment (SMI), 18 patients with mild cognitive impairment (MCI), and 27 patients with AD participated. MRI protocols included three dimensional brain structural imaging and diffusion tensor imaging to assess the cortical thickness, subcortical volume and white matter integrity. Recursive feature elimination based on support vector machine (SVM) was conducted to determine the most relevant features for classifying abnormal regions and imaging parameters, and then a factor analysis for the top-ranked factors was performed. Subjects were classified using nonlinear SVM. RESULTS Medial temporal regions in AD patients were dominantly detected with cortical thinning and volume atrophy compared with SMI and MCI patients. Damage to white matter integrity was also accredited with decreased fractional anisotropy and increased mean diffusivity (MD) across the three groups. The microscopic damage in the subcortical gray matter was reflected in increased MD. Classification accuracy between pairs of groups (SMI vs. MCI, MCI vs. AD, SMI vs. AD) and among all three groups were 84.4% (±13.8), 86.9% (±10.5), 96.3% (±4.6), and 70.5% (±11.5), respectively. CONCLUSION This proposed method may be a potential tool to diagnose AD pathology with the current clinical criteria.
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Affiliation(s)
- Won Beom Jung
- Department of Biomedical Engineering/u-HARC, Inje University, Gimhae, Republic of Korea
| | - Young Min Lee
- Department of Psychiatry, Pusan National University Hospital, Busan, Republic of Korea
- Medical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Young Hoon Kim
- Department of Psychiatry, Medical School, Inje University, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Chi-Woong Mun
- Department of Biomedical Engineering/u-HARC, Inje University, Gimhae, Republic of Korea
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150
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Nir TM, Villalon-Reina JE, Prasad G, Jahanshad N, Joshi SH, Toga AW, Bernstein MA, Jack CR, Weiner MW, Thompson PM. Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease. Neurobiol Aging 2015; 36 Suppl 1:S132-40. [PMID: 25444597 PMCID: PMC4283487 DOI: 10.1016/j.neurobiolaging.2014.05.037] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 05/13/2014] [Accepted: 05/13/2014] [Indexed: 10/24/2022]
Abstract
Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.
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Affiliation(s)
- Talia M Nir
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Gautam Prasad
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Shantanu H Joshi
- Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of Southern California, Los Angeles, CA, USA; Department of Psychiatry, University of Southern California, Los Angeles, CA, USA; Department of Radiology, University of Southern California, Los Angeles, CA, USA; Department of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, University of Southern California, Los Angeles, CA, USA; Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA.
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