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Rudroff T, Rainio O, Klén R. AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field. Neurol Sci 2024; 45:5117-5127. [PMID: 38866971 DOI: 10.1007/s10072-024-07649-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/10/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVES The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management. METHODS We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline. RESULTS Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations. CONCLUSION AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.
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Affiliation(s)
- Thorsten Rudroff
- Department of Health and Human Physiology, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
| | - Oona Rainio
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
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2
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Chu CS, Wang DY, Liang CK, Chou MY, Hsu YH, Wang YC, Liao MC, Chu WT, Lin YT. Automated Video Analysis of Audio-Visual Approaches to Predict and Detect Mild Cognitive Impairment and Dementia in Older Adults. J Alzheimers Dis 2023; 92:875-886. [PMID: 36847001 DOI: 10.3233/jad-220999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
BACKGROUND Early identification of different stages of cognitive impairment is important to provide available intervention and timely care for the elderly. OBJECTIVE This study aimed to examine the ability of the artificial intelligence (AI) technology to distinguish participants with mild cognitive impairment (MCI) from those with mild to moderate dementia based on automated video analysis. METHODS A total of 95 participants were recruited (MCI, 41; mild to moderate dementia, 54). The videos were captured during the Short Portable Mental Status Questionnaire process; the visual and aural features were extracted using these videos. Deep learning models were subsequently constructed for the binary differentiation of MCI and mild to moderate dementia. Correlation analysis of the predicted Mini-Mental State Examination, Cognitive Abilities Screening Instrument scores, and ground truth was also performed. RESULTS Deep learning models combining both the visual and aural features discriminated MCI from mild to moderate dementia with an area under the curve (AUC) of 77.0% and accuracy of 76.0% . The AUC and accuracy increased to 93.0% and 88.0%, respectively, when depression and anxiety were excluded. Significant moderate correlations were observed between the predicted cognitive function and ground truth, and the correlation was strong excluding depression and anxiety. Interestingly, female, but not male, exhibited a correlation. CONCLUSION The study showed that video-based deep learning models can differentiate participants with MCI from those with mild to moderate dementia and can predict cognitive function. This approach may offer a cost-effective and easily applicable method for early detection of cognitive impairment.
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Affiliation(s)
- Che-Sheng Chu
- Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan.,Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Di-Yuan Wang
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Kuang Liang
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Geriatric Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Internal Medicine, Division of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, Kaohsiung City, Taiwan
| | - Ming-Yueh Chou
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Geriatric Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan
| | - Ying-Hsin Hsu
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Department of Internal Medicine, Division of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, Kaohsiung City, Taiwan.,Chia Nan University, Tainan, Taiwan, Tainan City, Taiwan
| | - Yu-Chun Wang
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Mei-Chen Liao
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Wei-Ta Chu
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Yu-Te Lin
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Pharmacy, Tajen University, Pingtung, Taiwan, Yanpu Township, Pingtung County, Taiwan
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Tabarestani S, Eslami M, Cabrerizo M, Curiel RE, Barreto A, Rishe N, Vaillancourt D, DeKosky ST, Loewenstein DA, Duara R, Adjouadi M. A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease. Front Aging Neurosci 2022; 14:810873. [PMID: 35601611 PMCID: PMC9120529 DOI: 10.3389/fnagi.2022.810873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
With the advances in machine learning for the diagnosis of Alzheimer's disease (AD), most studies have focused on either identifying the subject's status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject's label and predict related cognitive scores at future timepoints using baseline data. The main intent in this multitask framework is to consolidate the highest accuracy possible in terms of precision, sensitivity, F1 score, and area under the curve (AUC) in the multiclass classification task while maintaining the highest similarity in the MMSE score as measured through the correlation coefficient and the RMSE for all time points under the prediction task, with both tasks, run simultaneously under the same set of hyperparameters. The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85 ± 3.77. The prediction results show an average RMSE of 2.32 ± 0.52 and a correlation of 0.71 ± 5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification. In other words, there is a breakpoint beyond which enhancing further the results of one process could lead to the downgrading in accuracy for the other.
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Affiliation(s)
- Solale Tabarestani
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab and Harvard Medical School, Schepens Eye Research Institute, Massachusetts Eye and Ear, Boston, MA, United States
| | - Mercedes Cabrerizo
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - Rosie E. Curiel
- Center for Cognitive Neuroscience and Aging, Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
| | - Armando Barreto
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - Naphtali Rishe
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - David Vaillancourt
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Steven T. DeKosky
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
| | - David A. Loewenstein
- Center for Cognitive Neuroscience and Aging, Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, United States
| | - Ranjan Duara
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, United States
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
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Mofrad SA, Lundervold A, Lundervold AS. A predictive framework based on brain volume trajectories enabling early detection of Alzheimer's disease. Comput Med Imaging Graph 2021; 90:101910. [PMID: 33862355 DOI: 10.1016/j.compmedimag.2021.101910] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/12/2021] [Accepted: 03/26/2021] [Indexed: 10/21/2022]
Abstract
We present a framework for constructing predictive models of cognitive decline from longitudinal MRI examinations, based on mixed effects models and machine learning. We apply the framework to detect conversion from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to Alzheimer's disease (AD), using a large collection of subjects sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Aging (AIBL). We extract subcortical segmentation and cortical parcellation from corresponding T1-weighted images using FreeSurfer v.6.0, select bilateral 3D regions of interest relevant to neurodegeneration/dementia, and fit their longitudinal volume trajectories using linear mixed effects models. Features describing these model-based trajectories are then used to train an ensemble of machine learning classifiers to distinguish stable CN from converters to MCI, and stable MCI from converters to AD. On separate test sets the models achieved an average of accuracy/precision/recall score of 69/73/60% for converted to MCI and 75/74/77% for converted to AD, illustrating the framework's ability to extract predictive imaging-based biomarkers from routine T1-weighted MRI acquisitions.
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Affiliation(s)
- Samaneh Abolpour Mofrad
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Postbox 7030, 5020 Bergen, Norway; The Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
| | - Arvid Lundervold
- The Neural Networks and Microcircuits Research Group, Department of Biomedicine, University of Bergen, Bergen, Norway; The Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Alexander Selvikvåg Lundervold
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Postbox 7030, 5020 Bergen, Norway; The Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
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- Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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- Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database. The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at www.aibl.csiro.au
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Lu L, Elbeleidy S, Baker LZ, Wang H, Nie F. Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:891-904. [PMID: 33253116 DOI: 10.1109/tmi.2020.3041227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A critical challenge in using longitudinal neuroimaging data to study the progressions of Alzheimer's Disease (AD) is the varied number of missing records of the patients during the course when AD develops. To tackle this problem, in this paper we propose a novel formulation to learn an enriched representation with fixed length for imaging biomarkers, which aims to simultaneously capture the information conveyed by both baseline neuroimaging record and progressive variations characterized by varied counts of available follow-up records over time. Because the learned biomarker representations are a set of fixed-length vectors, they can be readily used by traditional machine learning models to study AD developments. Take into account that the missing brain scans are not aligned in terms of time in a studied cohort, we develop a new objective that maximizes the ratio of the summations of a number of l1 -norm distances for improved robustness, which, though, is difficult to efficiently solve in general. Thus, we derive a new efficient and non-greedy iterative solution algorithm and rigorously prove its convergence. We have performed extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. A clear performance gain has been achieved in predicting ten different cognitive scores when we compare the original baseline biomarker representations against the learned representations with longitudinal enrichments. We further observe that the top selected biomarkers by our new method are in accordance with known knowledge in AD studies. These promising results have demonstrated improved performances of our new method that validate its effectiveness.
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6
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Yang P, Zhou F, Ni D, Xu Y, Chen S, Wang T, Lei B. Fused Sparse Network Learning for Longitudinal Analysis of Mild Cognitive Impairment. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:233-246. [PMID: 31567112 DOI: 10.1109/tcyb.2019.2940526] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with an irreversible and progressive process. To understand the brain functions and identify the biomarkers of AD and early stages of the disease [also known as, mild cognitive impairment (MCI)], it is crucial to build the brain functional connectivity network (BFCN) using resting-state functional magnetic resonance imaging (rs-fMRI). Existing methods have been mainly developed using only a single time-point rs-fMRI data for classification. In fact, multiple time-point data is more effective than a single time-point data in diagnosing brain diseases by monitoring the disease progression patterns using longitudinal analysis. In this article, we utilize multiple rs-fMRI time-point to identify early MCI (EMCI) and late MCI (LMCI), by integrating the fused sparse network (FSN) model with parameter-free centralized (PFC) learning. Specifically, we first construct the FSN framework by building multiple time-point BFCNs. The multitask learning via PFC is then leveraged for longitudinal analysis of EMCI and LMCI. Accordingly, we can jointly learn the multiple time-point features constructed from the BFCN model. The proposed PFC method can automatically balance the contributions of different time-point information via learned specific and common features. Finally, the selected multiple time-point features are fused by a similarity network fusion (SNF) method. Our proposed method is evaluated on the public AD neuroimaging initiative phase-2 (ADNI-2) database. The experimental results demonstrate that our method can achieve quite promising performance and outperform the state-of-the-art methods.
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7
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Jiang P, Wang X, Li Q, Jin L, Li S. Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer's Disease. IEEE J Biomed Health Inform 2019; 23:1450-1456. [DOI: 10.1109/jbhi.2018.2885331] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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8
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Wang M, Zhang D, Shen D, Liu M. Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data. Med Image Anal 2019; 53:111-122. [PMID: 30763830 PMCID: PMC6397780 DOI: 10.1016/j.media.2019.01.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 01/21/2019] [Accepted: 01/26/2019] [Indexed: 11/23/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the early stage using longitudinal data, with each task corresponding to a particular time point. However, the underlying association among different time points in disease progression is still under-explored in previous studies. To this end, we propose a multi-task exclusive relationship learning model to automatically capture the intrinsic relationship among tasks at different time points for estimating clinical measures based on longitudinal imaging data. The proposed method can select the most discriminative features for different tasks and also model the intrinsic relatedness among different time points, by utilizing an exclusive lasso regularization and a relationship induced regularization. Specifically, the exclusive lasso regularization enables partial group structure feature selection among the longitudinal data, while the relationship induced regularization efficiently introduces the relationship information from data to guide knowledge transfer. We further develop an efficient optimization algorithm to solve the proposed objective function. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness of our proposed method. In comparison with several state-of-the-art methods, our proposed method can achieve promising performance for cognitive status prediction and also can help discover disease-related biomarkers.
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Affiliation(s)
- Mingliang Wang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
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9
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Lei H, Huang Z, Zhou F, Elazab A, Tan EL, Li H, Qin J, Lei B. Parkinson's Disease Diagnosis via Joint Learning From Multiple Modalities and Relations. IEEE J Biomed Health Inform 2018; 23:1437-1449. [PMID: 30183649 DOI: 10.1109/jbhi.2018.2868420] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.
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Yang P, Ni D, Chen S, Wang T, Wu D, Lei B. Multi-task fused sparse learning for mild cognitive impairment identification. Technol Health Care 2018; 26:437-448. [PMID: 29710750 PMCID: PMC6004967 DOI: 10.3233/thc-174587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain functional connectivity network (BFCN) has been widely applied to identify biomarkers for the brain function understanding and brain diseases analysis. OBJECTIVE Building a biologically meaningful brain network is a crucial work in these applications. For this task, sparse learning has been widely applied for the network construction. If multiple time-point data is added to the brain imaging application, the disease progression pattern in the longitudinal analysis can be better revealed. METHODS A novel longitudinal analysis for MCI classification is devised based on resting-state functional magnetic resonating imaging (rs-fMRI). Specifically, this paper proposes a novel multi-task learning method to integrate fused penalty by regularization. In addition, a novel objective function is developed for fused sparse learning via smoothness constraint. RESULTS The proposed method achieves the best classification performance with an accuracy of 95.74% for baseline and 93.64% for year 1 data. CONCLUSIONS The experimental results show that our proposed method achieves quite promising classification performance.
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Affiliation(s)
- Peng Yang
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
| | - Dong Ni
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
| | - Siping Chen
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
| | - Tianfu Wang
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
| | - Donghui Wu
- Department of Geriatric Psychiatry, Shenzhen Kangning Hospital, and Shenzhen Mental Health Center, Shenzhen, Guangdong, China
| | - Baiying Lei
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
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11
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Lei H, Huang Z, Han T, Luo Q, Cai Y, Liu G, Lei B. Joint regression and classification via relational regularization for Parkinson's disease diagnosis. Technol Health Care 2018; 26:19-30. [PMID: 29689760 PMCID: PMC6027902 DOI: 10.3233/thc-174540] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
It is known that the symptoms of Parkinson’s disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classification scheme for PD diagnosis using baseline multi-modal neuroimaging data. Specifically, we devise a new feature selection method via relational learning in a unified multi-task feature selection model. Three kinds of relationships (e.g., relationships among features, responses, and subjects) are integrated to represent the similarities among features, responses, and subjects. Our proposed method exploits five regression variables (depression, sleep, olfaction, cognition scores and a clinical label) to jointly select the most discriminative features for clinical scores prediction and class label identification. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method on the Parkinson’s Progression Markers Initiative (PPMI) dataset. Our experimental results demonstrate that multi-modal data can effectively enhance the performance in class label identification compared with single modal data. Our proposed method can greatly improve the performance in clinical scores prediction and outperforms the state-of-art methods as well. The identified brain regions can be recognized for further medical analysis and diagnosis.
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Affiliation(s)
- Haijun Lei
- Guangdong Province Key Laboratory of Popular High Performance Computers, Key Laboratory of Service Computing and Applications, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Zhongwei Huang
- Guangdong Province Key Laboratory of Popular High Performance Computers, Key Laboratory of Service Computing and Applications, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Tao Han
- Guangdong Province Key Laboratory of Popular High Performance Computers, Key Laboratory of Service Computing and Applications, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Qiuming Luo
- Guangdong Province Key Laboratory of Popular High Performance Computers, Key Laboratory of Service Computing and Applications, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Ye Cai
- Guangdong Province Key Laboratory of Popular High Performance Computers, Key Laboratory of Service Computing and Applications, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Gang Liu
- Guangdong Province Key Laboratory of Popular High Performance Computers, Key Laboratory of Service Computing and Applications, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Baiying Lei
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
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12
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Lei H, Zhao Y, Wen Y, Luo Q, Cai Y, Liu G, Lei B. Sparse feature learning for multi-class Parkinson's disease classification. Technol Health Care 2018; 26:193-203. [PMID: 29710748 PMCID: PMC6004973 DOI: 10.3233/thc-174548] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
This paper solves the multi-class classification problem for Parkinson's disease (PD) analysis by a sparse discriminative feature selection framework. Specifically, we propose a framework to construct a least square regression model based on the Fisher's linear discriminant analysis (LDA) and locality preserving projection (LPP). This framework utilizes the global and local information to select the most relevant and discriminative features to boost classification performance. Differing in previous methods for binary classification, we perform a multi-class classification for PD diagnosis. Our proposed method is evaluated on the public available Parkinson's progression markers initiative (PPMI) datasets. Extensive experimental results indicate that our proposed method identifies highly suitable regions for further PD analysis and diagnosis and outperforms state-of-the-art methods.
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Affiliation(s)
- Haijun Lei
- College of Computer Science and Software Engineering, Shenzhen University, Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen, Guangdong, China
| | - Yujia Zhao
- College of Computer Science and Software Engineering, Shenzhen University, Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen, Guangdong, China
| | - Yuting Wen
- College of Computer Science and Software Engineering, Shenzhen University, Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen, Guangdong, China
| | - Qiuming Luo
- College of Computer Science and Software Engineering, Shenzhen University, Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen, Guangdong, China
| | - Ye Cai
- College of Computer Science and Software Engineering, Shenzhen University, Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen, Guangdong, China
| | - Gang Liu
- College of Computer Science and Software Engineering, Shenzhen University, Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen, Guangdong, China
| | - Baiying Lei
- School of Biomedical Engineering, Health Science Center, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
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Franke K, Gaser C, Roseboom TJ, Schwab M, de Rooij SR. Premature brain aging in humans exposed to maternal nutrient restriction during early gestation. Neuroimage 2017; 173:460-471. [PMID: 29074280 DOI: 10.1016/j.neuroimage.2017.10.047] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 10/16/2017] [Accepted: 10/22/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Prenatal exposure to undernutrition is widespread in both developing and industrialized countries, causing irreversible damage to the developing brain, resulting in altered brain structure and decreased cognitive function during adulthood. The Dutch famine in 1944/45 was a humanitarian disaster, now enabling studies of the effects of prenatal undernutrition during gestation on brain aging in late adulthood. METHODS We hypothesized that study participants prenatally exposed to maternal nutrient restriction (MNR) would demonstrate altered brain structure resembling premature brain aging in late adulthood, expecting the effect being stronger in men. Utilizing the Dutch famine birth cohort (n = 118; mean age: 67.5 ± 0.9 years), this study implements an innovative biomarker for individual brain aging, using structural neuroimaging. BrainAGE was calculated using state-of-the-art pattern recognition methods, trained on an independent healthy reference sample, then applied to the Dutch famine MRI sample, to evaluate the effects of prenatal undernutrition during early gestation on individual brain aging in late adulthood. RESULTS Exposure to famine in early gestation was associated with BrainAGE scores indicative of an older-appearing brain in the male sample (mean difference to subjects born before famine: 4.3 years, p < 0.05). Furthermore, in explaining the observed variance in individual BrainAGE scores in the male sample, maternal age at birth, head circumference at birth, medical treatment of hypertension, history of cerebral incidences, actual heart rate, and current alcohol intake emerged to be the most influential variables (adjusted R2 = 0.63, p < 0.01). INTERPRETATION The findings of our study on exposure to prenatal undernutrition being associated with a status of premature brain aging during late adulthood, as well as individual brain structure being shaped by birth- and late-life health characteristics, are strongly supporting the critical importance of sufficient nutrient supply during pregnancy. Interestingly, the status of premature brain aging in participants exposed to the Dutch famine during early gestation occurred in the absence of fetal growth restriction at birth as well as vascular pathology in late-life. Additionally, the neuroimaging brain aging biomarker presented in this study will further enable tracking effects of environmental influences or (preventive) treatments on individual brain maturation and aging in epidemiological and clinical studies.
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Affiliation(s)
- Katja Franke
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany; Department of Psychiatry, Jena University Hospital, Jena, Germany
| | - Tessa J Roseboom
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands; Department of Obstetrics and Gynaecology, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Matthias Schwab
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Susanne R de Rooij
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
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