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Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia research methods optimization. Alzheimers Dement 2023; 19:5934-5951. [PMID: 37639369 DOI: 10.1002/alz.13441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/31/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Lao H, Zhang X. Regression and Classification of Alzheimers Disease Diagnosis using NMF-TDNet Features from 3D Brain MR Image. IEEE J Biomed Health Inform 2021; 26:1103-1115. [PMID: 34543210 DOI: 10.1109/jbhi.2021.3113668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
With the development of deep learning and medical imaging technology, many researchers use convolutional neural network(CNN) to obtain deep-level features of medical image in order to better classify Alzheimer's disease (AD) and predict clinical scores. The principal component analysis network (PCANet) is a lightweight deep-learning network that mainly uses principal component analysis (PCA) to generate multilevel filter banks for the centralized learning of samples and then performs binarization and generates blockwise histograms to obtain image features. However, the dimensions of the extracted PCANet features reaching tens of thousands or even hundreds of thousands, and the formation of the multilevel filter banks is sample data dependent, reducing the flexibility of PCANet. In this paper, based on the idea of PCANet, we propose a data-independent network called the nonnegative matrix factorization tensor decomposition network (NMF-TDNet), which improves the computational efficiency and solves the data dependence problem of PCANet. In this network, we use nonnegative matrix factorization (NMF) instead of PCA to create multilevel filter banks for sample learning, then uses the learning results to build a higher-order tensor and perform tensor decomposition (TD) to achieve data dimensionality reduction, producing the final image features. Finally, our method use these features as the input of the support vector machine (SVM) for AD classification diagnosis and clinical score prediction. The performance of our method is extensively evaluated on the ADNI-1, ADNI-2 and OASIS datasets. The experimental results show that NMF-TDNet can achieve data dimensionality reduction (the dimensionality of the extracted features numbers only a few hundred dimensions, far less than the hundreds of thousands required by PCANet) and the NMF-TDNet features as input achieved superior performance than using PCANet features as input.
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3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI. Neuroinformatics 2020; 18:71-86. [PMID: 31093956 DOI: 10.1007/s12021-019-09419-w] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R2 value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.
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Crespi C, Santi GC, Dodich A, Lupo F, Greco LC, Piccoli T, Lunetta C, Cerami C. Unraveling Moral Reasoning in Amyotrophic Lateral Sclerosis: How Emotional Detachment Modifies Moral Judgment. Front Psychol 2020; 11:2083. [PMID: 32973626 PMCID: PMC7471658 DOI: 10.3389/fpsyg.2020.02083] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 07/28/2020] [Indexed: 01/28/2023] Open
Abstract
In the last decade, scientific literature provided solid evidence of cognitive deficits in amyotrophic lateral sclerosis (ALS) patients and their effects on end-life choices. However, moral cognition and judgment are still poorly investigated in this population. Here we aimed at evaluating both socio-cognitive and socio-affective components of moral reasoning in a sample of 28 ALS patients. Patients underwent clinical and neuropsychological evaluation including basic cognitive and social cognition measures. Additionally, we administered an experimental task including moral dilemmas, with instrumental and incidental conditions. Patients’ performances were compared with a control group [healthy control (HC)], including 36 age-, gender-, and education-matched healthy subjects. Despite that the judgment pattern was comparable in ALS and HC, patients resulted less prone to carry out a moral transgression compared to HC. Additionally, ALS patients displayed higher levels of moral permissibility and lower emotional arousal, with similar levels of engagement in both instrumental and incidental conditions. Our findings expanded the current literature about cognitive deficits in ALS, showing that in judging moral actions, patients may present non-utilitarian choices and emotion flattening. Such a decision-making profile may have relevant implications in applying moral principles in real-life situations and for the judgment of end-of-life treatments and care in clinical settings.
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Affiliation(s)
- Chiara Crespi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Alessandra Dodich
- CeRiN, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Federica Lupo
- Department of Experimental Biomedicine and Clinical Neurosciences, University of Palermo, Palermo, Italy
| | | | - Tommaso Piccoli
- Department of Experimental Biomedicine and Clinical Neurosciences, University of Palermo, Palermo, Italy
| | - Christian Lunetta
- NEuroMuscular Omnicentre, Niguarda Ca' Granda Hospital, Milan, Italy
| | - Chiara Cerami
- Istituto Universitario di Studi Superiori, Pavia, Italy.,IRCCS Mondino Foundation, Pavia, Italy
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Tsapanou A, Stern Y, Habeck C. Optimized prediction of cognition based on brain morphometry across the adult life span. Neurobiol Aging 2020; 93:16-24. [PMID: 32442809 DOI: 10.1016/j.neurobiolaging.2020.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/25/2020] [Accepted: 04/16/2020] [Indexed: 10/24/2022]
Abstract
We mapped out the combined and unique contributions of 5 different biomarkers for 2 cognitive outcomes in cognitively healthy adults. Beside associations of biomarkers with cognition in the full experimental sample, we focused on how well any such associations would persist in held-out data. Three hundred thirty-five cognitively normal participants, 20-80 years older, were included in the study. Z-scores were computed for fluid reasoning and vocabulary. The following imaging data were included: regional brain volume, regional thickness, fractional anisotropy of white-matter tracts, volumes of select deep gray-matter regions, and global white-matter hyperintensity. Volume accounted for most of the variance in both cognitive domains. In out-of-sample data, fluid reasoning was best predicted by volumes, but vocabulary by the combination of all modalities. Although the predictive utility was better overall for older participants, the information gleaned relative to null models was less for older participants. An optimized set of brain biomarkers can thus predict cognition in out-of-sample data, to various degrees, for both fluid and crystallized intelligence.
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Affiliation(s)
- Angeliki Tsapanou
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA.
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Martinez-Murcia FJ, Ortiz A, Gorriz JM, Ramirez J, Castillo-Barnes D. Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders. IEEE J Biomed Health Inform 2019; 24:17-26. [PMID: 31217131 DOI: 10.1109/jbhi.2019.2914970] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data analysis of AD based on deep convolutional autoencoders. We aim at finding links between cognitive symptoms and the underlying neurodegeneration process by fusing the information of neuropsychological test outcomes, diagnoses, and other clinical data with the imaging features extracted solely via a data-driven decomposition of MRI. The distribution of the extracted features in different combinations is then analyzed and visualized using regression and classification analysis, and the influence of each coordinate of the autoencoder manifold over the brain is estimated. The imaging-derived markers could then predict clinical variables with correlations above 0.6 in the case of neuropsychological evaluation variables such as the MMSE or the ADAS11 scores, achieving a classification accuracy over 80% for the diagnosis of AD.
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7
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Mateos-Pérez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC. Structural neuroimaging as clinical predictor: A review of machine learning applications. NEUROIMAGE-CLINICAL 2018; 20:506-522. [PMID: 30167371 PMCID: PMC6108077 DOI: 10.1016/j.nicl.2018.08.019] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 01/22/2018] [Accepted: 08/09/2018] [Indexed: 11/26/2022]
Abstract
In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.
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Affiliation(s)
| | - Mahsa Dadar
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | | | - Yashar Zeighami
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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Li Z, Suk HI, Shen D, Li L. Sparse Multi-Response Tensor Regression for Alzheimer's Disease Study With Multivariate Clinical Assessments. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1927-1936. [PMID: 26960221 PMCID: PMC5154176 DOI: 10.1109/tmi.2016.2538289] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has recently seen serious increase in the number of affected subjects. In the last decade, neuroimaging has been shown to be a useful tool to understand AD and its prodromal stage, amnestic mild cognitive impairment (MCI). The majority of AD/MCI studies have focused on disease diagnosis, by formulating the problem as classification with a binary outcome of AD/MCI or healthy controls. There have recently emerged studies that associate image scans with continuous clinical scores that are expected to contain richer information than a binary outcome. However, very few studies aim at modeling multiple clinical scores simultaneously, even though it is commonly conceived that multivariate outcomes provide correlated and complementary information about the disease pathology. In this article, we propose a sparse multi-response tensor regression method to model multiple outcomes jointly as well as to model multiple voxels of an image jointly. The proposed method is particularly useful to both infer clinical scores and thus disease diagnosis, and to identify brain subregions that are highly relevant to the disease outcomes. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the proposed method enhances the performance and clearly outperforms the competing solutions.
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Affiliation(s)
- Zhou Li
- Department of Statistics, North Carolina State University, Raleigh, NC 27695 USA
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Dinggang Shen
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, 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 02841, South Korea
| | - Lexin Li
- Division of Biostatistics, University of California at Berkeley, Berkeley, CA 94720 USA
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Yu G, Liu Y, Shen D. Graph-guided joint prediction of class label and clinical scores for the Alzheimer's disease. Brain Struct Funct 2015; 221:3787-801. [PMID: 26476928 DOI: 10.1007/s00429-015-1132-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 10/04/2015] [Indexed: 11/24/2022]
Abstract
Accurate diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, is very important for early treatment. Over the last decade, various machine learning methods have been proposed to predict disease status and clinical scores from brain images. It is worth noting that many features extracted from brain images are correlated significantly. In this case, feature selection combined with the additional correlation information among features can effectively improve classification/regression performance. Typically, the correlation information among features can be modeled by the connectivity of an undirected graph, where each node represents one feature and each edge indicates that the two involved features are correlated significantly. In this paper, we propose a new graph-guided multi-task learning method incorporating this undirected graph information to predict multiple response variables (i.e., class label and clinical scores) jointly. Specifically, based on the sparse undirected feature graph, we utilize a new latent group Lasso penalty to encourage the correlated features to be selected together. Furthermore, this new penalty also encourages the intrinsic correlated tasks to share a common feature subset. To validate our method, we have performed many numerical studies using simulated datasets and the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with the other methods, our proposed method has very promising performance.
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Affiliation(s)
- Guan Yu
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.,Carolina Center for Genome Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
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Ji LL, Zhang YY, Zhang L, He B, Lu GH. Mismatch negativity latency as a biomarker of amnestic mild cognitive impairment in chinese rural elders. Front Aging Neurosci 2015; 7:22. [PMID: 25814949 PMCID: PMC4357324 DOI: 10.3389/fnagi.2015.00022] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 02/20/2015] [Indexed: 02/02/2023] Open
Abstract
The aim was to evaluate the mismatch negativity (MMN) component, a correlate of the automatic detection of changes in the acoustic environment, in healthy adults, and adults with amnestic mild cognitive impairment (aMCI). Forty-three aMCI subjects and 43 healthy Chinese older adults were arranged into experimental group and control group, respectively. Their MMN amplitude and latency were measured at the FZ, FCZ, and CZ electrode sites under a passive auditory oddball task. The results showed that the latencies obtained from the FZ, FCZ, and CZ electrode sites were significantly longer in the aMCI adults than in the control adults (P < 0.01) while there were no significant differences in MMN amplitude between two groups (P > 0.05). The MMN latency was found to be a sensitive and specific biomarker of aMCI.
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Affiliation(s)
- Li-Li Ji
- Department of Medical Nursing, Weifang Medical University , Wei Fang , China
| | - Yuan-Yuan Zhang
- Department of Medical Nursing, Weifang Medical University , Wei Fang , China
| | - Lane Zhang
- Department of Medical Nursing, Weifang Medical University , Wei Fang , China
| | - Bing He
- Department of Medical Nursing, Weifang Medical University , Wei Fang , China
| | - Guo-Hua Lu
- Department of Medical Nursing, Weifang Medical University , Wei Fang , China
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11
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Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. Neuroimage 2011; 59:895-907. [PMID: 21992749 DOI: 10.1016/j.neuroimage.2011.09.069] [Citation(s) in RCA: 349] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2011] [Revised: 09/25/2011] [Accepted: 09/27/2011] [Indexed: 02/04/2023] Open
Abstract
Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely multi-modal multi-task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality, and (2) a multi-modal support vector machine which fuses the above-selected features from all modalities to predict multiple (regression and classification) variables. To validate our method, we perform two sets of experiments on ADNI baseline MRI, FDG-PET, and cerebrospinal fluid (CSF) data from 45 AD patients, 91 MCI patients, and 50 healthy controls (HC). In the first set of experiments, we estimate two clinical variables such as Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), as well as one categorical variable (with value of 'AD', 'MCI' or 'HC'), from the baseline MRI, FDG-PET, and CSF data. In the second set of experiments, we predict the 2-year changes of MMSE and ADAS-Cog scores and also the conversion of MCI to AD from the baseline MRI, FDG-PET, and CSF data. The results on both sets of experiments demonstrate that our proposed M3T learning scheme can achieve better performance on both regression and classification tasks than the conventional learning methods.
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Cohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA. Decoding continuous variables from neuroimaging data: basic and clinical applications. Front Neurosci 2011; 5:75. [PMID: 21720520 PMCID: PMC3118657 DOI: 10.3389/fnins.2011.00075] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2011] [Accepted: 05/16/2011] [Indexed: 11/13/2022] Open
Abstract
The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.
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Affiliation(s)
- Jessica R Cohen
- Helen Wills Neuroscience Institute, University of California Berkeley Berkeley, CA, USA
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13
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Wang Y, Fan Y, Bhatt P, Davatzikos C. High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. Neuroimage 2010; 50:1519-35. [PMID: 20056158 DOI: 10.1016/j.neuroimage.2009.12.092] [Citation(s) in RCA: 113] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2009] [Revised: 12/18/2009] [Accepted: 12/22/2009] [Indexed: 11/15/2022] Open
Abstract
This paper presents a general methodology for high-dimensional pattern regression on medical images via machine learning techniques. Compared with pattern classification studies, pattern regression considers the problem of estimating continuous rather than categorical variables, and can be more challenging. It is also clinically important, since it can be used to estimate disease stage and predict clinical progression from images. In this work, adaptive regional feature extraction approach is used along with other common feature extraction methods, and feature selection technique is adopted to produce a small number of discriminative features for optimal regression performance. Then the Relevance Vector Machine (RVM) is used to build regression models based on selected features. To get stable regression models from limited training samples, a bagging framework is adopted to build ensemble basis regressors derived from multiple bootstrap training samples, and thus to alleviate the effects of outliers as well as facilitate the optimal model parameter selection. Finally, this regression scheme is tested on simulated data and real data via cross-validation. Experimental results demonstrate that this regression scheme achieves higher estimation accuracy and better generalizing ability than Support Vector Regression (SVR).
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Affiliation(s)
- Ying Wang
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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